the cycle times of working capital: financial value …
TRANSCRIPT
Miia Pirttilä
THE CYCLE TIMES OF WORKING CAPITAL:FINANCIAL VALUE CHAIN ANALYSIS METHOD
Acta Universitatis Lappeenrantaensis 609
Thesis for the degree of Doctor of Science (Technology) to be presented with due permission for public examination and criticism in the Auditorium of the Student Union House at Lappeenranta University of Technology, Lappeenranta, Finland on the 5th of December, 2014, at noon.
Supervisor Professor Timo Kärri School of Industrial Engineering and Management Department of Innovation Management Lappeenranta University of Technology Finland
Reviewers Professor Marko Järvenpää Faculty of Jyväskylä University School of Business and Economics University of Jyväskylä Finland
Assistant Professor Paulo Afonso Research Centre for Industrial and Technology Management University of Minho Portugal
Opponent Professor Marko Järvenpää Faculty of Jyväskylä University School of Business and Economics University of Jyväskylä Finland
ISBN 978-952-265-696-4
ISBN 978-952-265-697-1 (PDF)
ISSN-L 1456-4491
ISSN 1456-4491
Lappeenranta University of Technology Yliopistopaino 2014
Abstract
Miia Pirttilä The Cycle Times of Working Capital: Financial Value Chain Analysis Method Lappeenranta 2014 93 p. Acta Universitatis Lappeenrantaensis 609 Diss. Lappeenranta University of Technology ISBN 978-952-265-696-4 ISBN 978-952-265-697-1 (PDF) ISSN-L 1456-4491 ISSN 1456-4491
Interest towards working capital management increased among practitioners and researchers because the financial crisis of 2008 caused the deterioration of the general financial situation. The importance of managing working capital effectively increased dramatically during the financial crisis.
On one hand, companies highlighted the importance of working capital management as part of short-term financial management to overcome funding difficulties. On the other hand, in academia, it has been highlighted the need to analyze working capital management from a wider perspective namely from the value chain perspective. Previously, academic articles mostly discussed working capital management from a company-centered perspective.
The objective of this thesis was to put working capital management in a wider and more academic perspective and present case studies of the value chains of industries as instrumental in theoretical contributions and practical contributions as complementary to theoretical contributions and conclusions. The principal assumption of this thesis is that self-financing of value chains can be established through effective working capital management. Thus, the thesis introduces the financial value chain analysis method which is employed in the empirical studies. The effectiveness of working capital management of the value chains is studied through the cycle time of working capital.
The financial value chain analysis method employed in this study is designed for considering value chain level phenomena. This method provides a holistic picture of the value chain through financial figures. It extends the value chain analysis to the industry level. Working capital management is studied by the cash conversion cycle that measures the length (days) of time a company has funds tied up in working capital, starting from the payment of purchases to the supplier and ending when remittance of sales is received from the customers.
The working capital management practices employed in the automotive, pulp and paper and information and communication technology industries have been studied in this research project. Additionally, the Finnish pharmaceutical industry is studied to obtain a deeper understanding of the working capital management of the value chain. The results indicate that the cycle time of working capital is constant in the value chain context over time. The cash
conversion cycle of automotive, pulp and paper, and ICT industries are on average 70, 60 and 40 days, respectively. The difference is mainly a consequence of the different cycle time of inventories. The financial crisis of 2008 affected the working capital management of the industries similarly. Both the cycle time of accounts receivable and accounts payable increased between 2008 and 2009. The results suggest that the companies of the automotive, pulp and paper and ICT value chains were not able to self-finance. Results do not indicate the improvement of value chains position in regard to working capital management either. The findings suggest that companies operating in the Finnish pharmaceutical industry are interested in developing their own working capital management, but collaboration with the value chain partners is not considered interesting.
Competition no longer occurs between individual companies, but between value chains. Therefore the financial value chain analysis method introduced in this thesis has the potential to support value chains in improving their competitiveness.
Keywords: financial value chain analysis, working capital management, cash conversion cycle, financial supply chain management
UDC 658.14/.17:658.7:65.012.4:330.14
Acknowledgements
I am using this opportunity to express my gratitude to everyone who supported me throughout the doctoral studies. I am thankful for their aspiring guidance, invaluably constructive criticism and friendly advice during the doctoral studies. I am sincerely grateful to them for sharing their fruitful comments on a number of issues related to the articles as well as the doctoral dissertation.
I would like to express my deepest gratitude to Professor Timo Kärri. Without his supervision this dissertation would not have been possible. Furthermore, I would like to thank the reviewers of the dissertation, Professor Marko Järvenpää and Assistant Professor Paulo Afonso, who provided their constructive comments to improve and finalize this thesis.
I give my warm thanks to the members of the C3M research team. Special thanks to Sari and Lotta; you helped me a lot during this project. I wouldn’t say no to co-authoring or excursions in the future; it has been fun to write and travel with you.
I am grateful for the financial support received from the Finnish Doctoral Program in Industrial Engineering and Management and the Research Foundation of Lappeenranta University of Technology. I would also like to thank Jutta Jäntti for revising the language of this thesis.
Finally, I would like to express my warmest thanks to friends-and-relations.
“Some people care too much. I think it's called love.” A.A. Milne, Winnie-the-Pooh
Lund, November 2014
Table of contents
1 Introduction ............................................................................................................. 17
1.1 Background and research environment ............................................................. 17
1.2 Objectives and scope ........................................................................................ 17
1.3 Research methodology ..................................................................................... 19
1.4 Outline of the dissertation ................................................................................. 22
2 Theoretical foundations ........................................................................................... 25
2.1 Background and previous research.................................................................... 25
2.2 Management of working capital ........................................................................ 27
2.3 Measures of working capital management ........................................................ 29
2.3.1 Working capital ratios ........................................................................... 29
2.3.2 Cycle time measure of working capital ................................................. 31
2.3.3 Cash conversion cycle in the literature .................................................. 33
2.3.4 Computation of cash conversion cycle .................................................. 35
2.4 Trends of cash conversion cycle and working capital management research ..... 37
2.4.1 Mainstreams of working capital management research .......................... 37
2.4.2 Trends of working capital management measured by cash conversion cycle ..................................................................................................... 42
3 Financial value chain analysis .................................................................................. 52
3.1 Value chain oriented perspective on working capital management .................... 53
3.2 The method ...................................................................................................... 54
3.2.1 Collecting the data ................................................................................ 56
3.2.2 Newly generated data ........................................................................... 57
4 Industries studied ..................................................................................................... 60
4.1 Automotive industry ......................................................................................... 62
4.2 Pulp and paper industry .................................................................................... 63
4.3 Information and communication technology industry ....................................... 65
5 Research contribution .............................................................................................. 67
5.1 Results of article 1: introducing the financial value chain analysis .................... 68
5.2 Results of article 2: in quest of sense of working capital management ............... 71
5.3 Results of article 3: strive for negative CCC ..................................................... 72
5.4 Results of article 4: the effects of financial crisis on working capital management ..................................................................................................................... 74
5.5 Results of article 5: beyond the financial value chain analysis method .............. 77
5.6 Summary .......................................................................................................... 78
6 Conclusions ............................................................................................................. 80
6.1 Theoretical implications ................................................................................... 80
6.2 Practical implications ....................................................................................... 81
6.3 Validity and reliability ...................................................................................... 82
6.4 Recommendations for further research .............................................................. 84
References .......................................................................................................................... 86
List of figures
Figure 1. The research pyramid ........................................................................................ 19 Figure 2. The contribution of original publications to the dissertation .............................. 22 Figure 3. Structure of the first part of the dissertation ....................................................... 23 Figure 4. The number of articles about working capital management per year .................. 25 Figure 5. Net working capital and working capital on the balance sheet ........................... 27 Figure 6. Cash conversion cycle ....................................................................................... 31 Figure 7. CCC and its components for all US manufacturing firms, 1950-1983 ................ 42 Figure 8. The percentage change of US real gross domestic product ................................ 43 Figure 9. The median of CCC and its components from 1986 to 2001 of US firms ........... 44 Figure 10. The median of CCC and its components of European firms from 1995 to 2004 . 45 Figure 11. The median of CCC and its components of European companies from 2002 to
2012 .................................................................................................................. 46 Figure 12. The median of CCC and its components of US companies from 2002 to 2012 .. 46 Figure 13. The median of CCC and its components of European companies from 2003 to
2009 when the denominator of DIO and DPO is COGS .................................... 48 Figure 14. The median of CCC and its components of US companies from 2003 to 2009
when the denominator of DIO and DPO is COGS ............................................. 48 Figure 15. Financial value chain analysis method............................................................... 55 Figure 16. The histogram of CCCs ..................................................................................... 58 Figure 17. The structure of the value chain of automotive industry and research sample .... 62 Figure 18. The structure of the value chain of pulp and paper industry and research sample63 Figure 19. The structure of the value chain of ICT industry and research sample ............... 65 Figure 20. Cash conversion cycles of the automotive value chain in 2006–2008. ............... 69 Figure 21. Cash conversion cycles of the pulp and paper value chain in 2004–2008. .......... 71 Figure 22. Cash conversion cycles of the ICT value chain in 2006–2010............................ 73 Figure 23. CCC and its components in the automotive, pulp and paper and ICT industries . 74 Figure 24. The cash conversion cycles of the stages of the automotive, pulp and paper and
ICT industries in a descending order ................................................................. 76
List of tables
Table 1. Basic beliefs associated with the postpositivism and pragmatic ......................... 20 Table 2. Main trade credit theories .................................................................................. 28 Table 3. Selected financial data of BMW and CCC and its components .......................... 32 Table 4. Scopus search for ‘cash conversion cycle’ and ‘cash-to-cash’ ........................... 33 Table 5. Computation of the cash conversion cycle ......................................................... 35 Table 6. Selected financial data of Nokia and computation of the cash conversion cycle . 36 Table 7. Selected studies on the effect of working capital management on profitability ... 40 Table 8. The percentage change of EU's real GDP growth rate - volume ......................... 46 Table 9. Trade credits proportion of total assets for firms in the G7 countries ................. 49 Table 10. Cash conversion cycle medians of industry sectors in 2009 ............................... 49 Table 11. The median of CCC of food stores and food and kindred products and yearly
change of CCC .................................................................................................. 53 Table 12. Examples of the inventory valuation methods of companies .............................. 57 Table 13. Average and median of CCC of automotive, pulp and paper and ICT industries 57 Table 14. Test of normality ............................................................................................... 59 Table 15. Data used in the articles of the dissertation ........................................................ 60 Table 16. Descriptive statistics on the value chains, year 2008 .......................................... 61 Table 17. A summary of the articles one to four................................................................ 68 Table 18. Results of regression analysis ............................................................................ 83
List of abbreviations and definitions
CCC cash conversion cycle
COGS cost of goods sold
DIO days inventory outstanding
DSO days accounts receivable outstanding
DPO days accounts payable outstanding
EU European Union
ICT information and communication technology
ROI return on investment
UK United Kingdom
US the United States
List of original publications
The articles included in this dissertation are listed below with a summary of the author’s contribution.
Article 1 Lind, L., Pirttilä, M., Viskari, S., Schupp, F. and Kärri, T. (2012) ‘Working capital management in the value chain of automotive industry: financial value chain analysis’, Journal of Purchasing and Supply Management, Vol. 18, No. 2, pp.92-100.
The author planned the study and analyzed the data with co-authors. The author designed the tool for financial value chain analysis. The author was responsible for writing the revised article.
Article 2 Pirttilä, M., Viskari, S. and Kärri, T. (2010) ‘Working capital in the value chain: cycle times of pulp and paper industry’, Proceedings of 19th International IPSERA conference, May 16-19, 2010, Lappeenranta, Finland.
The author planned the study and analyzed the data with co-authors. The author was responsible for gathering the data, conducting the analysis and writing the article.
Article 3 Lind, L., Pirttilä, M., Viskari, S. Schupp, F and Kärri, T. (2012) ‘Competing with the negative cycle time of working capital in ICT value network’, 21st Annual IPSERA conference, April 1-4, 2012, Naples, Italy.
The author analyzed the data with co-authors. The author’s tool for financial value chain analysis was used. The author was responsible for writing the results.
Article 4 Pirttilä, M., Viskari, S., Lind, L. and Kärri, T. (2014) ‘Benchmarking working capital management in the inter-organisational context’, Int. J. Business Innovation and Research, Vol. 8, No. 2, pp.119–136.
The author was responsible for planning the article, conducting the analysis and writing the article. Author updated the data used in the article.
Article 5 Pirttilä, M., Kivinen, K., Monto, S. and Kärri T. (2013) ‘Working capital management in a Finnish pharmaceutical supply chain’, 22nd Annual IPSERA conference, March 24-27, 2013, Nantes, France.
The author planned the study and analyzed the data with co-authors. The author was responsible for collecting quantitative data and writing the article.
Part I Overview of the dissertation
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1 Introduction
1.1 Background and research environment
Interest towards working capital management among managers and academics increased because the financial crisis of 2008 caused deterioration of the general financial situation. The reasons for financial crisis were a combination of banks’ restraints in lending, worldwide insecure consumers and self-made problems in some industries, such as existing excess capacities in the automotive industry, especially the capacities of car manufactures.
In addition to the financial crisis started in 2008, financing for companies has become increasingly difficult due to the previously enacted Basel II. During a period of restrained granting of credit by banks, demand for capital from the supply chain increased. (Hofmann et al., 2011) Therefore the importance of managing working capital effectively increased dramatically during financial crisis. Companies highlighted the importance of working capital management as part of short-term financial management to overcome funding difficulties.
In this thesis the working capital management is considered from an operational view (Hill et al. 2010) consisting inventories, accounts receivable and accounts payable. Furthermore, the supply chain is understood as a sub-set of the value chain similar to the one in the study of Al-Mudimigh et al. (2004). While the concept of supply chain focuses on operations and logistics, in other words material flows (Tan, 2001), the value chain extends the focus to the information and cash flows. In previous literature, information and cash flows have been considered as part of the supply chain as well (Hofmann and Kotzab, 2010). The term value chain is selected to emphasize the importance of working capital management in the supply chain. The aspects of value chain other than those related to working capital management are out of the scope of this study, so the use of the term financial value chain is in well-justified.
1.2 Objectives and scope
Previous management accounting literature has focused mostly on supplier - customer relationships, and little attention has been paid to managerial issues in value chains (Lind and Thrane, 2010). This thesis looks at the working capital management of inter-organizational value chains as a whole.
A number of previous working capital management studies have focused on company level working capital management (e.g. Baños-Caballero et al. (2014); Marttonen et al. (2013); Tauringana and Afrifa (2013)). Few studies have examined working capital management in inter-organizational value chains (Losbicler et al., 2008; Hofmann and Kotzab, 2010; Hofmann and Belin, 2011; Monto, 2013). Recent studies have highlighted the need to study working capital management at the value chain level (Hoffman and Kotzab, 2010; Grosse-Ruyken et al., 2011; Viskari et al., 2012a).
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The objective of this dissertation is to study working capital management in value chains. Three different industries were studied. The main assumption is that effective working capital management can mitigate the need to borrow from financial institutions. Furthermore, the dissertation introduces a new method for the study of financial value chains, namely the financial value chain analysis. Here, ‘method’ means a structured process that should be followed to reach reliable results. The goal of the dissertation is to describe the working capital management of the value chain by the cycle time of working capital. The research period is from 2006 to 2010 in general.
It is assumed that working capital management should be studied in different types of business environments to get a better understanding of it. The studied value chains represent the automotive industry, pulp and paper industry and information and communication technology (ICT) industry. Automotive and pulp and paper industries are capital-intensive and represent batch production and traditional process industries, respectively. Furthermore, business models in the ICT industry are different from the ones applied in automotive and pulp and paper industries. The ICT industry is characterized by an integrated business environment, fast technology development and service-orientation. The end products of these three value chains differ significantly from each other from daily consumer goods to consumer durables or even luxury items, as well as in terms of the variety of customers. Additionally, the Finnish pharmaceutical industry was studied to increase understanding of the results of quantitative research followed through financial value chain analysis method by interviewing the practitioners working in the industry. Furthermore, the Finnish pharmaceutical industry seemed to be holding up well in the financial crisis of 2008, which increased the interest to study it in addition to the open-mindedness to discuss working capital management.
The study addresses four research questions:
1) How to analyze the financial value chain phenomenon? 2) How many days have the value chains tied up working capital?
a. What are the cycle times of working capital? b. How have the cycle times of working capital and its components changed
during the period of study? 3) How have the value chains performed in regards to working capital management? 4) How has the financial crisis of 2008 affected the cycle time of working capital in the
value chains?
The second question arose from the request to study working capital management in the value chain context. This leads to a need to consider what kind of method should be used in order to succeed in the study of working capital management of value chains (RQ 1). Previous studies have either demonstrated working capital management concepts as supplier – focal company or supplier – focal company – customer value chains, but the cycle time of working capital as days has not been studied (e.g. Hofmann and Kotzab, 2010), or they have studied the cycle time of working capital of single industries that are formed by competitors (e.g. Losbicler et al., 2008; Farris and Hutchison, 2003). The third question benchmarks working
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capital management in the value chains of automotive, pulp and paper and ICT industries in order to study the effect of the industry on the cycle time of working capital as well as the importance of management of working capital. Hill et al. (2010) and Chiou et al. (2006) argue that working capital management is determined by internal factors, and external factors, such as industry, do not have a significant impact. Farris and Hutchison (2003) suggest that it is important to look at the cycle time of working capital by industry to create realistic expectations and objectives for working capital management. Research question four considers the financial crisis 2008 influence to cycle times of working capital in automotive, pulp and paper and ICT industries. The crisis boosted the importance of working capital management in general, and it has inspired researchers to study its effects on companies. Kestens et al. (2012) have indicated that the crisis had a negative impact on the overall availability of accounts payable. Molina and Preve (2009) found out that companies having cash flow problems are reducing the level of accounts receivable. Previous studies have focused on accounts receivable and payable instead of operating working capital, and they are focused on single companies.
1.3 Research methodology
This section concerns the methodology and research design of the dissertation. The section is based on the idea of the existence of a research pyramid (Figure 1) that consists of four levels: research paradigm, research methodology, research method and research techniques (Jonker and Pennink, 2010).
Figure 1. The research pyramid (Jonker and Pennink, 2010)
Researchparadigm
Researchmethodology
Researchmethods
Researchtechniques
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Paradigm is understood as a basic set of beliefs and assumptions which guide inquiries. In this research project, two schools of thought about paradigms were considered important: postpositivism and pragmatism. The author has viewed the reality through these paradigms during the study. The aim of postpositivism is to provide solutions for problems which occur in practice. It reflects the need to examine causes which influence outcomes. The knowledge developed through a postpositivist lens is based on careful and critical observation and measurement of objective reality. Developing numeric measures of observations and studying the behavior of individuals have become paramount for postpositivist researchers. In this context, studies are conducted as follows: a researcher begins with a theory, collects the data which either supports or refutes the theory, and then makes necessary revisions before additional tests are conducted. (Creswell 2003; Jonker and Pennink, 2010) Furthermore, pragmatism arises out of actions, situations and consequences rather than antecedent knowledge, as in postpositivism. Thus, the researcher has freedom to choose the methods, techniques and procedures of research. Pragmatism enables the use of multiple methods, different worldviews, different assumptions and different forms of data collection and analysis (Creswell 2003). Table 1 summarizes the basic beliefs of paradigms considered important in this thesis.
Table 1. Basic beliefs associated with the postpositivism and pragmatic (Mertens, 2014) Basic beliefs Postpositivism Pragmatic Ontology One reality; knowable within a
specified level of probability Asserts that there is single reality and that all individuals have their own unique interpretation of reality
Epistemology Objectivity is important; the researcher manipulates and observes in a dispassionate, objective manner
Relationships in research are determined by what the researcher deems as appropriate to that particular study
Axiology Respect privacy; informed consent; minimize harm (beneficence); justice/equal opportunity
Gain knowledge in pursuit of desired ends as influenced by the researcher’s values and politics
Methodology Quantitative (primarily); interventionist; decontextualized
Match methods to specific questions and purposes of research; mixed methods can be used as researcher works back and forth between various approaches.
Kasanen et al. (1993) claimed that management accounting research has merely utilized the innovations constructed elsewhere. The financial value chain analysis method that is developed in this study can be considered as theoretical construction in management accounting research. It produces a solution to the explicit problem: how to analyze the financial value chain phenomenon. However, this study itself cannot be considered as a constructive study.
The next paragraphs discuss the philosophical foundation which has guided this study in more detail. The phenomenon, financial value chain, already exits out there, and the goal of
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this research is to increase an understanding of it and study causes which have influenced the current situation of working capital management. This is a typical ontological starting point of explanatory research (Holmström et al., 2009). Epistemology covers the question of how we know what we know or what we assume to know (Jonker and Pennink, 2010). The correspondence, coherence, and pragmatic theory of truth are the traditional theories of truth (Haaparanta and Niiniluoto, 1998). This study develops relevant true statements which can explain the working capital management in value chains. The truth is in relation to present business environment. The dynamic nature of business environment will change the true statements over the years, however. In this study, axiology is understood mainly as ethics values (Resnik 2001). The author believes that research must be free of values in order to be valid. Co-operation with companies involved in the studied industries did not generate ethical conflicts between scientific values and business values.
Methodology – in other words, strategies of inquiry – is associated with conducting research. The characteristics of pragmatism are probably best seen in the research methods of this thesis because it adapts strategies associated with quantitative and qualitative approaches. Quantitative strategies invoke the postpositivist perspective in particular. As previous theory of working capital management in value chains is scarce, the results of this thesis will mainly increase understanding of the subject. Quantitative strategies include experiments, non-experimental designs, such as equation models, and surveys (Creswell 2003). This thesis applies non-experimental designs. On the other hand, qualitative strategies have been conducted with a large number of various paradigms, such as the pragmatic. It employs several different strategies of inquiry (e.g. Wolcott (2001) has identified 19 different strategies). This thesis has employed multiple case studies. The advantage of multiple case study research is its deeper understanding of a research phenomenon. The automotive industry, pulp and paper industry and ICT industry can be considered as the cases of this dissertation. It was considered that industrial factors, globalization, and the features of end products may have affected the development of working capital management in these value chains. For this study, the researcher has created her own method called “financial value chain analysis” to analyze working capital management in value chains. The financial value chain analysis and research techniques are discussed in chapter 3.2.
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1.4 Outline of the dissertation
Figure 2 shows how the articles attached to this thesis are related with the purposed research questions.
Figure 2. The contribution of original publications to the dissertation
All five articles discuss the development of the financial value chain analysis method answering the first research question ‘How to analyze financial value chain phenomenon?’. Articles one to three describe the research process carried out through the financial value chain analysis method in automotive, pulp and paper, and ICT industries. Together with article four, they validate the financial value chain analysis method. Article five aims to mitigate the limitations of the method. The second research question ‘How many days have the value chains tied up working capital?’ is addressed in publications one to three. The first article studies the working capital management of automotive industry. The second article examines the pulp and paper industry and the third the ICT industry. Article four combines the three antecedent articles benchmarking the industries answering the third research question ‘How have the value chains performed in regards to working capital management?’. It also examines working capital management in a longer period than the antecedent articles. Article four studies the working capital management of the selected industries as the result of the financial crisis begun in 2008 answering the fourth research question ‘How has the financial crisis of 2008 affected the cycle times of working capital in the value chains?’. Some evidence of the effects of the financial crisis can also be detected in article three. Article five supports some conclusions the authors have done in articles one to four.
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The dissertation consists of two parts. The first part provides an introduction and an overview of the research, theoretical foundations, research contribution and conclusions. The original publications are included in the second part of the thesis following the order presented in the list of original publications. Figure 2 summarizes the structure of the first part and illustrates how the sections lead to the contributions. The contribution of the articles presented in the second part of this thesis is discussed in chapters 3 and 5. To compare the structure to the research questions, the first question is answered in the third chapter, where the financial value chain analysis method developed during the research project is introduced. Research questions 2, 3 and 4 are answered in the fifth chapter.
Figure 3. Structure of the first part of the dissertation
Part I of this thesis proceeds from the literature review (Chapter 2) to the empirical part of the study. Chapter 3 discusses previous literature on financial value chain analysis introducing the method developed to study working capital management in the value chain context. Additionally, the reliability of the working capital management measure is evaluated in the chapter. Chapter 4 introduces the value chains of automotive, pulp and paper and ICT industries, including the names of companies whose value chains were studied in the thesis. It presents the general impression of industries that may have affected the working capital management of industries as well as the reliability of samples. Furthermore, Finnish pharmaceutical industry is described briefly. Chapter 5 summarizes the key findings of
Resultsand main findings of the original publications
Research results
1 Introduction
2 Theoretical foundation
3 Financial value chain analysis
5 Research contribution
6 Conclusions
Backgroundof the research
Previous literature
Research cap
The scope of the thesisResearch questionsResearch process
Measures of working capital managementTrends of working capital managementResearch gap
Introduction of the financial value chain analysis methodJustification of the research techniqueAnswer to the research question 1
The results of articlesAnswers to the research questions two to four
Theoretical implicationsPractical implicationsValidity and reliability Recommendations for further research
Backgroundof the research
Previous literature
Research gap
The scope of the dissertationResearch questionsResearch methodologyResearch process
Management of working capitalMeasures of working capital managementTrends of CCC and working capital management
4 Industries studiedResearch cap
Introduction of the studied value chains- automotive industry- pulp and paper industry- information and communication technology industry- Finnish pharmaceutical industry
Data
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articles included in the second part of the dissertation and answers the research questions. Chapter 6 offers theoretical and practical conclusions and discusses the results of the thesis. The validity and reliability of the conducted research and the results are analyzed. Part I ends with recommendations for further study which arise from the results of this dissertation.
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2 Theoretical foundations
2.1 Background and previous research
The bibliometric study of Viskari et al. (2011) showed that the number of articles on working capital has been growing between 1990 and 2010. Data for the study was retrieved from the databases ISI Web of Science and Scopus. The total number of articles on working capital management was 23. International Research Journal of Finance and Economics has published the most working capital management articles, a total of four papers, related to the issue in 1990-2010. This journal is a so called open access journal similar to two other open access journals, the European Journal of Scientific Research and Research Journal of Business Management, which have both published two papers on working capital management. The remaining 15 articles were spread across different journals. Viskari et al. (2011) found that the number of articles increased during the period. They assumed it to be a consequence of increased interest towards working capital management because of the financial crisis of 2008. However, they stress the fact that growth is a common trend in scientific publications in general. To sum up the recent development, a similar search was made for the years 2011 – 2013. The assumption of Viskari et al. (2011) was confirmed as the number of articles related to working capital management was 39 between 2011 and 2013. The International Research Journal of Finance and Economics has continued publishing articles concerning working capital management, seven articles in total during 2011-2013. Otherwise articles have been published by many journals. The number of articles is shown in Figure 4.
Figure 4. The number of articles about working capital management per year
(Viskari et al., 2011)
Viskari et al. (2011) did not accept articles related to the management of working capital components, i.e. inventories, accounts receivable, and accounts payable, which explains the results of the study. Nearly half of the articles published between the years 1991 and 2013 study the relation between operational working capital management and profitability. The
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most common research frame is to study the effects of working capital management on the profitability of companies listed in one stock exchange. The most cited is the article of Deloof (2003) titled ‘Does working capital management affect profitability of Belgium firms? in the Journal of Business Finance & Accounting. Deloof may have been inspired by the article of Shin and Soenen (1998) titled “Efficiency of Working Capital and Corporate Profitability” in the journal of Financial Practice and Education. The article of Shin and Soenen (1998) does not include the articles Viskari et al. (2011) added to their sample, however. The other two topics are: what kind of practices companies use to manage their working capital (e.g. Belt, 1991; Khoury et al., 1999; Howorth and Westhead, 2003; Viskari et al., 2012b; Viskari and Kärri, 2012) and which characteristics and factors affect working capital management in companies (e.g. Baños-Caballero et al., 2010; Hill et al., 2010;). SMEs have been in focus as well (e.g. Howorth and Westhead, 2003; García-Teruel and Martínez-Solano, 2007; Baños-Caballero et al., 2010; Tauringana and Afrifa, 2013).The research has been concentrating mainly on the working capital management of individual companies.
During the financial crisis, working capital management was a hot topic in articles directed for managers (e.g. Seifert and Seifert, 2008; Greenberg, 2009; Hofler, 2009). Firms like StoraEnso, Kimberly Clark, and the Volkswagen group announced programs which aim to reduce tied up working capital to ensure the cash flow which was assumed to be the key to survival. Academic articles were few during and after the financial crisis, however. Mullins (2009) and Kesten et al. (2012) are some of the exceptions. Mullins (2009) stresses the importance of paying attention to working capital as an important part of cash flow rather than profits. He encourages a company to consider its revenue model; how early customers could pay compared to the delivery of goods or services. Secondly, he guides to analyze the payment terms given by key suppliers and compare those to the industry norms. Thirdly, a company should analyze how much cash is tied up in inventories. The fourth and most revealing recommendation Mullins (2009) presents is that a company should consider the possibilities to operate differ dramatically than competitors in working capital terms. Therefore it can be assumed that he demands new working capital management models for companies. The main message of Mullins is that failure to earn profit would not cause immediate failure, but cash running out is different. To avoid this during the downturn, it is essential to manage working capital. Furthermore, Kesten et al. (2012) investigated whether the 2008 financial crisis had an impact on the trade credit of companies. The results of the study show that both the ratio of accounts receivable and the ratio of accounts payable decreased during the 2008 financial crisis. They conclude that the financial crisis had a negative impact on the overall availability of trade credit.
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2.2 Management of working capital
The common definition of working capital is the capital of a business which is used in its day-to-day trading operations, calculated as current assets less current liabilities. It indicates a company’s efficiency and short-term financial health. The focus of the thesis is on operational issues. Therefore, working capital in this thesis is calculated as:
Working capital = Inventories + Accounts receivable - Accounts payable
(1)
This definition can be drawn from the viewpoint that through the normal course of the business, companies acquire inventory on credit, which in turn they use to create products. These products are then sold, oftentimes on credit. These actions generate accounts payable and accounts receivable, with no cash exchanged until the company collects accounts receivable and settles the accounts payable. The other items of current assets and liabilities do not concern the daily operations of a company as directly as inventories and accounts receivable and payable, referred together as trade credit.
Working capital management entails short-term decisions for which maturity is one year maximum. These decisions are therefore not made on the same basis as capital-investment decisions. Capital investments often launch the need to tied-up working capital, for example the expansion of manufacturing capacity. Figure 5 shows the structure of a balance sheet and the position of net working capital and working capital on it.
BALANCE SHEET ASSETS LIABILITIES AND Fixed assets SHAREHOLDER'S EQUITY
Intangible assets Equity Tangible assets Long-term debts Investments Current liabilities Other non-current assets Current debts
Current assets Trade accounts payable Inventories Other current liabilities Current financial assets
Receivables Trade accounts receivable Other receivables Net working capital Other current assets
Cash and cash equivalents Working capital
Figure 5. Net working capital and working capital on the balance sheet
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Working capital management uses a combination of policies and techniques that aim to affect the current assets and liabilities of a firm. In this thesis, the focus is on the inventories and trade credits of companies. Earlier literature presents a considerable number of methods to manage inventories, and the management of accounts receivable has been significantly studied as well. The following subsections present and briefly discuss ‘trade credit management’ and ‘inventory management’.
Trade credit management
The term trade credit is used in this section to introduce theories developed for the management of accounts receivable and payable. Trade credit is the payment time a supplier gives to its customer for the purchase of goods and services. The amount of trade credit of a single transaction is the same for the supplier and the customer. Researchers have developed theories to explain why companies offer payment time for their customers. Customers may have their own needs for having payment time as well. Burkart and Ellingsen (2004) note that companies simultaneously give and take trade credit. Summers and Wilson (2002) studied 655 randomly selected UK manufacturing companies, and their findings suggest that the availability of trade credit is not a major influence on supplier choice. Table 2 lists trade credit theories in short. It is not meant to be a comprehensive list of theories.
Table 2. Main trade credit theories (Petersen and Rajan, 1997; Niskanen and Niskanen, 2006; Seifert and Seifert, 2008)
Supply-side theories Description Competitive pressure Companies have to offer trade credit because their
competitors do so Financing advantage The supplier may have better information on the credit
worthiness of its customers than traditional lenders. Price discrimination Companies use trade credit when direct price discrimination
is not desirable / is prohibited on the basis of antitrust laws Transaction costs By offering trade credit, a firm may be able to reduce the
costs of warehousing the inventory if its customers have a better ability to stock.
Demand-side theories Control protection Customers prefer trade credit rather than bank credit because
suppliers are less likely to liquidate. Credit rationing Trade credit is a funding source for firms which have
difficulties in obtaining bank financing at lower prices, i.e. small and young firms as well as illiquid firms of low credit worthiness.
Financing advantages Trade credit enables the growth of customer Transaction costs Trade credit may reduce the transaction cost of paying bills.
A customer might want to cumulate obligations and pay them only monthly for example. Trade credit allows customers to hold smaller cash balances and save money accordingly.
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Molina and Preve (2009) found out that companies tend to increase the use of accounts receivable when they start facing profitability problems, but provide less accounts receivable when they have cash flow problems and enter full financial distress. They also found that if a firm significantly reduces its accounts receivable in financial distress, sales will drop at least 13%. The study of Kesten et al (2011) confirms these findings, because the results indicate that the financial crisis had a negative impact on the overall availability of accounts payable. Companies’ suppliers were not able to offer accounts receivable to the same extent as before the financial crisis. Petersen and Rajan (1997) find that firms use trade credit more when credit from financial institutions is not available.
Inventory management
Inventory management deals with a range of different approaches and models that can be used when developing inventory management systems and practices. The scope of inventory control concerns, for example, replenishment lead time, carrying costs of inventory, asset management, inventory forecasting, inventory valuation, inventory visibility, future inventory price forecasting, physical inventory, available physical space for inventory, quality management, reorder points, order quantities, decisions of safety stocks, returns and defective goods, and demand forecasting. (Axsäter, 2006)
Reduction in the cycle time of inventories has a positive effect on the tied-up working capital of a company. If it is put into practice, for example in the form of reasonable batch sizes, it is not harmful from an inter-organizational collaboration point of view either.
2.3 Measures of working capital management
This section presents the two main types of measures of working capital management, which are ratios and cycle times. The ratios are developed mainly to support the analysis of financial statements, while the cycle times are developed to also support the management of operations. The cycle time measures were developed to criticize the working capital ratios, as well.
2.3.1 Working capital ratios
This section presents current ratio, quick ratio, net working capital per net sales, net working capital to total assets, and sales to net working capital. Working capital ratios received criticism, however. This leaded to the appearance of circulating capital ratio which was more dynamic than current and quick ratios.
The current ratio and the quick ratio mainly indicate a company's ability to pay back its short-term liabilities. The higher the ratio, the more capable the company is of paying its obligations. Current ratio is defined as current assets / current liabilities. The reference value of current ratio states that a current ratio of more than 2 is good. In this case, the company can carry on its operations without financial strain. A ratio of less than 1 suggests that the
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company would be unable to pay off its obligations if they came due at that point. The quick ratio is similar, except that it does not include inventory and prepayments in current assets that can be liquidated. Quick ratio measures the euro amount of liquid assets available for each euro of current liabilities. The target value of quick ratio is 1 to ensure that a company would be able to pay off its short-term obligations in one go. Therefore current ratio and quick ratio should be considered primarily as indices of financial strength and stability, not as measures of efficient utilization of funds. Furthermore, the net working capital per net sales ratio measures the amount of net working capital (current assets – current liabilities) that is tied up to the extent of business (net sales). The ratio is indicating the activity of business. The net working capital per net sales ratio of supermarkets is typically lower than that of manufacturing companies. The sales to net working capital ratio (also known as working capital turnover) reveals under- or over-trading by the company in relation to its resources. The net working capital to total assets ratio describes the capital structure of a company and reflects its financial stability. An increasing ratio is usually a positive sign, showing that the liquidity of the company is improving over time. There are no obvious rules of thumb for these ratios. Analysts have used working capital ratios as tools for financial statement analysis of companies and models that aim to predict bankruptcies (e.g. Lee et al. 1996). However, working capital ratios have received criticism. For example Guthmann (1954) criticized the sales to net working capital ratio and Wright (1956) criticized current ratio stating that it is a measure which is not as useful as often claimed. In those days, it was considered that working capital is only appropriate if it is not too low (liquidity risk). Too high a working capital (inefficiency risk) was not considered often. Kirkman (1986) found that current ratio and quick ratio based on net working capital have not been found to be very good predictors of bankruptcy, possibly because they are static indicators. Current and quick ratios are based on the ‘gone-concern approach’ and therefore do not serve managers who manage a firm on a ‘going-concern approach’, trying to improve or maintain this dynamic stability. When a business is operating on going-concern basis, the normal course is that a partition of sales and supplies are never paid and a minimum level of inventories never leaves the company. (Bhattacharya, 2009) Wright (1956) defined the circulating capital ratio as a means to solve the problems of current ratio that may rise through the strengthening of financial position and decrease through shortage of funds or through improved efficiency. He indicates that an upward or downward trend in current ratio can be due to causes which are not related to working capital management. The falling trend of current ratio might indicate, for example, that funds accrued from more effective selling have been invested in fixed assets. Wright differentiated inventories, accounts receivable and accounts payable from the remaining current assets and liabilities because each of these is related to sales. The circulating capital ratio is defined as the ratio of the value of inventories, plus accounts receivable, to the value of accounts payable.
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2.3.2 Cycle time measure of working capital
The cash conversion cycle (CCC) concept presents one possibility for measuring and controlling the effectiveness of working capital management on the basis of relative ratios. Richards and Laughlin (1980) developed the CCC to criticize the use of current ratio and quick ratio as key indicators of a firm’s liquidity position. They state that the usefulness of these static liquidity indicators is limited by their failure to provide adequate information on cash flow attributes of the transformation process within a firm's working capital position. Current ratio and quick ratio emphasize essentially liquidation, rather than a going-concern. Richards and Laughlin stress that the focus of management should be on avoiding default situations, and that cannot be supported by using ratios that indicate a firm’s ability to meet its obligations through asset liquidation in the event of default. Shin and Soenen (1998), Deloof (2003), Hutchison et al. (2007), and Ulbrich et al. (2008) have agreed that CCC is a good proxy for working capital management. The cash conversion cycle presents the length (in days) of time a firm has funds tied up in working capital, starting from the payment of purchases to the supplier and ending when remittance of sales is received from the customers. In other words, the CCC is a collection of three activity ratios: the cycle time of inventories (DIO) plus the cycle time of accounts receivable (DSO) less the cycle time of accounts payable (DPO). The DIO is calculated as inventory×365/sales, the DSO as accounts receivable×365/sales, and the DPO as accounts payable×365/sales. The importance of the CCC from the perspective of value chain management is that it bridges purchasing activities with suppliers, internal supply chain activities and sales activities with the customer (Farris and Hutchison, 2002). The CCC is illustrated in Figure 6 where a positive CCC is visualized.
Figure 6. Cash conversion cycle
(adapted from Richards and Laughlin, 1980)
In this case, the company has to finance accounts receivable and, in part, inventories. There is evidence that a company can operate with a negative CCC (for example Apple Inc.), or the
Time (days)
DPO
DIO
t0 t1 t2 t3Purchase Cash outlay Product sales Cash received
DSO
CCC
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CCC can be zero. A numerical example is provided in Table 3 to illustrate the primary point of CCC with an example.
Table 3. Selected financial data of BMW and CCC and its components (BMW Group, 2009; BMW Group, 2008; BMW Group, 2007)
The results of the study show that the shortening of the cycle time of inventories (DIO) from 2006 to 2007 did not improve the CCC, because during the same period the cycle time of accounts payable (DPO) shortened and offset the impact of the improved DIO. From the value chain point of view, a shortened DPO poses a lower risk for suppliers. The DIO mainly reflects the efficiency of the internal supply chain, and therefore its shortening by three days does not affect the other players of the value chain directly. The increase of the CCC from 2007 to 2008 indicates that the management of working capital was not as efficient in 2008 as it was in the previous years from the BMW Group’s point of view; the CCC lengthened by 24 days. The global economy grew strongly in 2006 and 2007 before the financial crisis of 2008 which caused a serious setback to the real economy. Sales of the BMW Group fell in Europe and North America. The BMW Group follows a sustainable leadership business model (Avery and Bergsteiner, 2011) which can be seen especially in the cycle time of accounts receivable and payable. The current share of receivables of financial services is added to DSO as the financial services are an important part of their business. In 2008, the share of new cars leased or financed increased. The repurchase of a previously off-balance-sheet portfolio of vehicles increased the DSO as well. This might indicate that the customers of BMW Group have difficulties in financing their purchases. The shortening of DPO may indicate that the BMW Group has supported its suppliers during the financial crisis of 2008. Its good credit rating (A) might have helped the group lending at favorable rates. Those companies who could not borrow capital from financial markets financed their operations at the expense of customers and suppliers.
Million EURYear ended 31 December 2006 2007 2008Sales 48 999 56 018 53 197Inventories 6 794 7 349 7 290Accounts receivable 14 761 16 668 18 176Accounts Payable 3 737 3 551 2 562
DIO 51 48 50DSO 110 109 125DPO 28 23 18
CCC 133 133 157
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2.3.3 Cash conversion cycle in the literature
The cash conversion cycle is also known as cash-to-cash (C2C). Where the parallel term originated from, is not clear. C2C is widely used in managerial articles (e.g. Sherida, 2000; Bowman, 2001; Ward, 2004) and supply chain management journal articles (e.g. Farris and Hutchison, 2002), whereas CCC is commonly used in financial journal articles (e.g. Deloof, 2003; Lazaridis and Tryfonidis, 2006). Lately, the streams have come together, possibly because more interest is shown towards the management of financial supply chains (Hofmann and Kotzab, 2010; Blackman and Holland, 2006). Table 4 shows results of the three search for ‘cash conversion cycle’ and ‘cash-to-cash’ (or ‘cash to cash’) from Scopus database in article title, abstract and keywords.
Table 4. Scopus search for ‘cash conversion cycle’ and ‘cash-to-cash’
Time ‘cash conversion cycle’ ‘cash-to-cash’ June 2014 55 22
February 2014 48 21 September 2013 41 21
The results shown in Table 4 do not include duplicates; therefore the number of articles discussing working capital management has increased by 15 articles between September 2013 and June 2014. The number of articles where cash conversation cycle is used has increased much more than the number of articles where cash-to-cash is used. The research group in which the author belongs, for example, solely uses the term cash conversion cycle, and three of the last seven new journal papers were written by members of this group, which could possibly affect search results.
Basically, the ideas of the definitions of CCC are similar even though they range from a general statement to detailed descriptions. The definitions include for example the following aspects.
“The cash conversion cycle, by reflecting the net time interval between actual cash expenditures on a firm's purchase of productive resources and the ultimate recovery of cash receipts from product sales, establishes the period of time required to convert a dollar of cash disbursements back into a dollar of cash inflow from a firm's regular
course of operations.” (Richards and Laughlin, 1980, p. 34)
“Cash-to-cash is a composite metric describing the average days required to turn a dollar invested in raw material into a dollar collected from a customer” (Stewart, 1995, p.43)
“The cash conversion cycle, which mirrors the operating cycle, measures the interval between the time cash expenditures are made to purchase inventory for use in the production process and the time funds are received from the sales of the finished
products. This time internal is measured in days and is equal to the net of the average age of the inventory plus the average collection period minus the average of accounts
payable” (Schilling, 1996, p. 4-5)
“The Cash Conversion Cycle (CCC) is an additive measure of the number of days funds are committed to inventories and receivables less the number of days payments are
deferred to suppliers.” (Shin and Soenen, 1998, p. 38)
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“[D]efined as the time elapsed from the payment of cash for materials or components through to the receipt of cash for sale of the finished product” (Hofmann and Kotzab
2010, p. 308)
The development of CCC was directed to two branches after it was published in 1980. The first branch of development improved the accuracy of measure. Gentry et al. (1990) developed the weighted cash conversion cycle (WCCC), which takes into account the amount of funds committed at each step of the cycle. The weights are calculated by dividing the amount of cash tied up in each component by the final value of the product. The WCCC includes both the number of days and the amount of funds that is tied up at each stage of the cash cycle. Furthermore, Viskari et al (2012b) introduced the advanced cash conversion cycle (ACCC) for controlling the amount and cost of working capital. It refines and extends the concept of WCCC. The ACCC is designed for the operational level, and it observes the capital tied up in the operating cycle of an order from raw material purchases to the remittance of the customer for the delivered product. Both WCCC and ACCC are based on the internal data of a company or the value chain of a product, for example. Evaluation and validation of these models is difficult because data used in these models is not available in a database or in public. The other branch of development criticizes the denominators for the three components of CCC. Shin and Soenen (1998) introduced the net trade cycle (NTC) where all three components of CCC are expressed as a percentage of sales. They stated that the denominators are all different, making the addition not particularly useful. Farris and Hutchison (2003) suggest that inventories and accounts payable should be divided by the cost of goods sold and accounts receivable by net sales. When the interest in the management of financial supply chains increased, a new problem emerged: the company’s cost of goods sold is not shown in the profit and loss account. At the moment, the cost of sales method is only absolutely mandatory according to US GAAP (Generally Accepted Accounting Principles in US). The International Financial Reporting Standards (IFRS) allow the use of the cost of sales method and the nature of expense method which does not reallocate expenses among functions within the company. Hofmann and Kotzab (2010) introduced the calculation of CCC based on the definition of Farris and Hutchison (2003), but actually they use the definition of Shin and Soenen (1998). In the footnotes, Hofmann and Kotzab (2010, p. 308) state: “Many companies use the cost of goods sold instead of net sales when calculating DPO and DIO. The article uses net sales across each working capital component to allow a balanced comparison across each C2C cycle element and provides true comparisons between industries”. Soenen (1993) notes that the net trade cycle increases the uniformity and simplicity of calculation. Losbichler et al. (2008) point out that revenue data is usually more readily available than the cost of goods sold. It is not unambiguous to define the value of COGS for a company that follows the nature of expense method in its financial reporting. When the value of sales is used as the denominator instead of the COGS, the cycle time of inventories and accounts payable is shorter for most companies, because normally the value of sales is higher than the value of the COGS.
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2.3.4 Computation of cash conversion cycle
The exact calculation of CCC is not shown widely in papers. For example Stewart (1995) presents the cash-to-cash as the total inventory days-of-supply + days-sales outstanding (A/R) – Average-payment-period to suppliers (A/P). He merely says that inventories are divided by cost of goods sold, but the calculation of other components is not defined. Richards and Laughlin (1980) do not present the formula in the article but it can be defined on the basis of the numerical example given in the article. Table 5 presents how some authors have calculated the CCC in their studies. Table 5.Computation of the cash conversion cycle
Source Formula
Richards and Laughlin (1980)
InventoriesCOGS
× 360 +Notes and accounts receivable
Net sales× 360
Accounts payable + Salaries, benefits and payroll taxCOGS + Selling, general and administrative expense
× 360 (2)
Shin and Soenen (1998), Monto (2013)
(inventories + accounts receivable - accounts payable) ×365sales
(3)
Farris and Hutchison (2003); Ding et al. (2013)
InventoriesCOGS
× 365 +Accounts receivable
Net sales× 365
Accounts payable
COGS× 365
(4)
Deloof (2003); Kroes and Manikas (1 (2014)
InventoryCOGS
× 365 +Accounts receivable
Sales× 365
Accounts payable
COGS+change in inventory× 365
(5)
American Association of Individual Investors (2011)
average inventoryCOGS
× 365 +average accounts receivable
revenue× 365
average accounts payable
COGS× 365
(6)
COGS = cost of goods sold Average = (beginning value of balance sheet item + ending value of balance sheet item)/2 (1 reflects the calculation when the period is one year There are slight differences in the calculation of CCC, as demonstrated in Table 5. Indeed, what is the orthodox way of calculating the CCC is difficult to say. Richards and Laughlin (1980) demonstrated the CCC of a single corporation and they did not have to consider the availability of data; this is possibly why the formula is the most complex. Shin and Soenen (1998) studied 58,985 firm-year records which forced them to consider the uniformity of
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data. They criticize the formula of Richards and Laughlin (1980), stating that the denominators are all different, not making the addition particularly useful, either. Farris and Hutchison (2003) studied the working capital management of different industries. Their data includes only US companies, and therefore they can partially follow the formula presented by Richards and Laughlin (1980). Deloof (2003) modified the denominator of accounts payable compared to the formula of Farris and Hutchinson (2003). Remarkably, Deloof chose this formula because the sample of study is formed of 1,009 large Belgian non-financial firms for the 1992-1996 period. In those days Belgian firms were possibly obligated to use the cost of sales method. The final example of the formula of CCC is from the American Association of Individual Investors, which is a nonprofit organization for individual investors. It is possible that the formula emphasizes the true view of working capital management. A company can fail or succeed compared to a normal year in terms of CCC. If an investor only follows a limited number of companies, it is not too hard to calculate CCC in this way. Table 6 depicts the data required for cash conversion cycle analysis as reported in the SEC 20-F reports for Nokia Corporation and computation of CCC using the five formulas presented in Table 5. Table 6.Selected financial data of Nokia and computation of the cash conversion cycle
Million EUR Year ended 31 December 2006 2007 2008 2009 2010 Net Sales 41 121 51 058 50 710 40 984 42 446 Cost of sales 27 742 33 754 33 337 27 720 29 629 Selling and marketing expenses 3 314 4 380 4 380 3 933 3 877 Administrative and general expenses 666 1 180 1 284 1 145 1 115 Inventories 1 554 2 876 2 533 1 865 2 523 Accounts receivable 5 888 11 200 9 444 7 981 7 570 Accounts payable 3 732 7 074 5 225 4 950 6 101 Social security, VAT and other taxes 966 2 024 1 700 1 808 1 585 Wages and salaries 250 865 665 474 619 Computation of CCC by formula 2006 2007 2008 2009 2010
2 16 18 24 15 9 3 33 50 49 44 34 4 24 35 39 30 21 5 26 38 38 29 23 6 n/a 27 37 40 26
Year 2010 components of CCC / formula 2 3 4 5 6
DIO 31 22 31 31 27 DSO 64 65 65 65 67 DPO 86 52 75 74 68 CCC 9 34 21 23 26
The results of the formulas presented in Table 6 illustrate that the original formula of Richards and Laughlin (1980) provides the most optimistic view of the cycle time of the working capital of Nokia Corporation. The rest of the formulas lead to more consistent results concerning the cycle time of working capital. Formulas three to six may illustrate the virtual
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state of working capital management better than formula two, because the accounting principles and course of business have changed since the 1970s. Formula three, which is used in this study, leads to the longest CCC. Therefore, the results of this study are rather conservative as opposed to radical. The bottom of Table 6 shows how the values of the components of CCC change depending on the used formula. It can be seen from this numerical example that DIO and DPO are longer when the COGS is the denominator (formulas four to six) instead of net sales (formula three). A company can calculate its CCC whichever way it likes, if it only aims at evaluating it working capital management. The company can break down the cash conversion cycle into its three component activity ratios and study the trends of its own CCC despite the calculation method. When a company aims at benchmarking its working capital management efficiency against other firms, it should check that figures are calculated similarly for each company. Nevertheless, the most important fact that limits the use of formulas is the lack of reliable information on cost of goods sold.
2.4 Trends of cash conversion cycle and working capital management research In this section, the idea is to summarize academic research on working capital management and focus on the trends of working capital management measured by the cash conversion cycle in an industry context. The findings of CCC in this study are compared to the results of studies presented in this section. This kind of academic articles are few, however. Some authors have been analyzing the trends of the cash conversion cycle, namely Belt (1985), Petersen and Rajan (1997), Farris and Hutchison (2003) and Losbichler et al. (2008). Also, REL publishes yearly working capital studies which reveal the trend of working capital management in terms of individual companies and industry sectors. CFO Magazine has published the working capital performance studies of REL since the fiscal year 1997 (Karaian, 2008). 2.4.1 Mainstreams of working capital management research
The academic working capital management research in the literature of finance has mainly addressed three topics: the practices of working capital management, the determinants of working capital and the relationship between working capital management and profitability. Working capital management practices Scholars have used surveys to study working capital management in general and its practices to manage separate components of net working capital in one country, or practices between two or more countries are compared. E.g. Belt and Smith (1991) compared working capital practices in Australia and the US, while Khoury et al. (1999) expand the survey of Belt and Smith to learn how Canadian firms manage their working capital. Ricci and Vito (2000) reported the results of a survey on international working capital management practices of the top 200 companies in the UK, and Howorth and Westhead (2003) studied the working capital management practices of small firms in the UK.
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Belt and Smith (1991) found similarities and differences between the net working capital management practices of Australian and US companies. They reported that Australian companies seem to lag behind the US companies in inventory, credit collections, and marketable securities management. Managers in both countries face similar problems, but authors did not discuss these in more detail, however. Khoury et al. (1999) found that only 7% of Canadian companies have formal net working capital policies, while around 40% of Australian and US companies have that. The surveys of both Belt and Smith and Khoury et al. suggest that companies do not see working capital as an ongoing investment. Ricci and Vito (2000) found out that working capital decisions are typically made at the corporate level. The results of the survey showed that international sales levels had little impact on the use of the working capital management vehicles as open account sales were the most commonly used method in the UK. Howorth and Westhead (2003) conclude that small UK firms are not a homogenous group in regards to working capital management practices. They could identify four distinct types of companies. Members of the cluster Cash focused on cash management. They are larger but younger firms with fewer cash sales, more seasonality, more external finance and possibly more cash flow problems. The cluster Stock contained companies focusing on inventory management routines. These companies were smaller, younger, with less external finance and longer production cycles. The cluster Credit included companies which focus upon trade credit management routines. They had lower profitability, they were more likely to be interested in growth, had more credit purchases and fewer customers paying on time. The largest group was the cluster Low. Companies in this cluster were less likely to utilize working capital management routines. Howorth and Westhead described these kinds of firms as having “less sophisticated financial skills, higher profitability, less interest in growth, less external finance, fewer credit purchases, shorter production cycles, more customers paying on time and fewer cash flow problems”. Determinants of working capital Factors affecting the management of working capital can be divided into internal and external types of factor. Scholars have studied the determinants of working capital using statistical methods. Chiou et al. (2006) studied how both internal and external aspects affect the working capital management of a company. The results of their study showed that debt ratio and operating cash flow evaluated by working capital requirements (inventories + accounts receivable – accounts payable – other payable) and net liquid balance (current assets – current liabilities – working capital requirements) have an impact on working capital management. The study of Chiou et al. does not provide evidence that external factors, such as industry, would affect working capital management. The sample of Chiou et al. (2006) included larger firms (listed companies in Taiwan); hence Baños-Caballero et al. (2010) decided to focus on Spanish small and medium-sized enterprises. To measure the quality of working capital management, they use cash conversion cycle. The findings of the study suggest that older firms and companies with larger cash flows and lower leverage had a longer CCC, whereas firms with larger leverage, growth opportunities, investments in tangible assets and return on assets were connected to aggressive working capital policy. The study of Baños-Caballero et
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al. neither provides evidence that external factors, such as interest rates or gross domestic product (GDP) growth, would have an effect on working capital management. Hill et al. (2010) analyzed the determinants of operating working capital management further by using working capital requirements to sales ratio as their variable. Their results indicate that firms with weaker internal financing ability, limited capital market access, and greater costs of external financing will manage operating working capital more aggressively. Increases in sales growth and sales volatility cause similar effects. Hill et al. also studied internal and external factors, and they suggest considering the financial characteristics of company besides industry affiliation when examining working capital levels for optimality. Effects of working capital management on profitability There has been a lot of research on the topic of the relation between working capital management and profitability in academic literature. Several authors have measured profitability using the relative profitability measures e.g. return on assets and cash conversion cycle is used to measure working capital management. The main analysis methods have been correlation analysis and nonparametric and multiple regression analysis. Jose et al. (1996), Shin and Soenen (1998), Deloof (2003), Lazaridis and Tryfonidis (2006) and García-Teruel and Martínez-Solano (2007) were the first scholars to discuss the issue. A list of studies published in 2011-2013 on this topic is presented in Table 7. It is not a comprehensive list but it aims to describe that the topic is still hot and that those five articles listed above are relevant and cited.
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Table 7. Selected studies on the effect of working capital management on profitability Ref.no Year I II III IV V Author(s) Title
I 1996 Jose M.L, Lancaster C. and Stevens J.L.
Corporate returns and cash conversion cycles
II 1998 Shin H. and Soenen L.
Efficiency of working capital and corporate profitability
III 2003 X Deloof M. Does working capital management affect profitability of Belgian firms?
IV 2006 X X Lazaridis I. and Tryfonidis D.
Relationship between working capital management and profitability of listed companies in the Athens stock exchange
V 2007 X X X García-Teruel P.J. and Martínez-Solano P.
Effects of working capital management on SME profitability
2011
X X X X X Hayajneh O.S. and Yassine F.L.A.
The impact of working capital efficiency on profitability - an empirical analysis on Jordanian manufacturing firms
X X Alam H.M., Ali L., Rehman C.A. and Akram M.
Impact of working capital management on profitability and market valuation of Pakistani firms
X X X Bieniasz A. and Golas Z.
The influence of working capital management on the food industry enterprises profitability
X X Mojtahedzadeh V., Tabari S.H.A. and Mosayebi R.
The relationship between working capital management and profitability of the companies (Case study: Listed companies on TSE)
X X X X X Nobanee H., Abdullatif M. and Alhajjar M.
Cash conversion cycle and firm's performance of Japanese firms
X X X X X Stephen M. and Elvis K.
Influence of working capital management on firms profitability: A case of SMEs in Kenya
X X X X Ebben J.J. and Johnson A.C.
Cash Conversion Cycle Management in Small Firms: Relationships with Liquidity, Invested Capital, and Firm Performance
2012
X X X X Abuzayed B. Working capital management and firms' performance in emerging markets: The case of Jordan
X X Ahmadiand M., Arasi I.S. and Garajafary M.
Studying the relationship between working capital management and profitability at Tehran stock exchange: A case study of food industry
X X X X X Akinlo O.O. Effect of Working Capital on Profitability of Selected Quoted Firms in Nigeria
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Table 7. Selected studies on the effect of working capital management on profitability contd. Year I II III IV V Author(s) Title
2012
X X X Al-Mwalla M.
The Impact of Working Capital Management Policies on Firm's Profitability and Value: The Case of Jordan
X X X X
Baños-Caballero S., Garcia-Teruel P.J. and Martinez-Solano P.
How does working capital management affect the profitability of Spanish SMEs?
X X Hussain A., Farooq S.U. and Khan K.U.
Aggressiveness and conservativeness of working capital: A case of Pakistani manufacturing sector
X X X X Rauscher S.and Wheeler J.R.C.
The importance of working capital management for hospital profitability: Evidence from bond-issuing, not-for-profit U.S. hospitals
X X X X Vahid T.K., Mohsen A.K. and Mohammadreza E.
The impact of working capital management policies on firm's profitability and value: Evidence from Iranian companies
2013
X X X X Aregbeyen O. The effects of working capital management on the profitability of Nigerian manufacturing firms
X Karabay G. Working capital management in Turkish clothing industry
X X X X Karadagli E. Profitability effects of cash conversion cycle: Evidence from Turkish companies
X X X X X Tauringana V. and Afrifa G.A.
The relative importance of working capital management and its components to SMEs' profitability
X X X X Knauer T. and Wöhrmann A.
Working capital management and firm profitability
The relation between working capital management and profitability is studied for a broad range of countries, industries and company sizes. The titles of the articles follow a similar theme which indicates those going together. The articles listed in Table 7 are citing Deloof’s (2003) article, so it can be considered that Deloof’s paper is the key article in the field. The majority of the studies conclude that companies can improve their profitability with aggressive working capital policies, i.e. by shortening the cycle time of working capital using single variable CCC as measure. Knauer and Wöhrmann (2013) infer that analyzing the effects of working capital management on profitability with the help of CCC is not convincing or is even misleading. Knauer and Wöhrmann stressed the importance of separately analyzing the individual effects of the working capital components. The results of Viskari et al (2012a) indicate the same. They suggest that the most efficient way to increase profitability by working capital management is to manage the cycle times of working capital components simultaneously. Viskari et al (2012a) applied a linear regression model like most of the prior articles. However, Baños-Caballero et al. (2012) discovered that there is a
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concave relationship between CCC and profitability. Deloof (2003) already suggested that a reasonable minimum is the target for the reduction of the cycle time of accounts receivable and inventories. This indicated that aggressive working capital management can improve a firm’s profitability to a certain extent (Knauer and Wöhrmann, 2013). Additionally, the question of causality is not clear. Deloof (2003) found a negative correlation between accounts payable and profitability which he explained by stating that “less profitable firms wait longer to pay their bills”. 2.4.2 Trends of working capital management measured by cash conversion cycle
Belt (1985) studied the trends of the cash conversion cycle and its components by lines of business of the US firms. The data was gathered from the “Quarterly Financial Report for Manufacturing, Mining and Trade Corporations” by the US Department of Commerce. Figure 7 shows the results of working capital management of all US manufacturing corporations for the period of 1950 – 1983. The calculation of CCC follows the formula (2) presented by Richards and Laughlin (1980). It can be assumed that the figures in the study of Belt (1985) present annual median values. Figure 7 shows the shortening of the CCC as the prevailing trend.
Figure 7. CCC and its components for all US manufacturing firms, 1950-1983
(Belt, 1985)
During the 1970s, decline has been more rapid than during the previous two decades. Over the span of 30 years, the CCC was reduced by half, reaching an average of about 40 days (Belt, 1985). The decrease of CCC can be explained by the faster increase of cycle time of accounts payable (DPO) compared to the cycle time of accounts receivable (DSO). The suppliers of US manufacturing companies have offered manufacturing companies longer
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payment times than the manufacturing companies have offered their customers. In addition, the cycle time of inventories has decreased by approximately 10 days from 1950, which is the beginning of the period. The shaded areas in Figure 7 indicate US recessions. The gross domestic product (GDP) of US economy decreased during those years, as shown in Figure 8.
Figure 8. The percentage change of US real gross domestic product
(FRED 2013)
The results of Belt (1985) presented in Figure 7 indicate that the CCC increases from two days to five days during recessions, e.g. 1957, 1960, 1970 and 1974. The financial crisis begun in 2008 is considered by many economists, e.g. Roubini, Rogoff, and Behravest, as the worst financial crisis since the Great Depression of the 1930s, however (IHS, 2009). Figure 8 illustrate that during the financial crisis 2008, the decrease of GDP has been the strongest since World War II. Petersen and Rajan (1997) report that companies increase supply of trade credit during economic shocks. They find that, on average, firm whose sales drop by 30% increases its accounts receivable / sales ratio by about 3% of sales. This finding means that DSO lengthens during economic shocks. Petersen and Rajan suggest that firms in trouble may use the extension of credit to prevent sales decrease. In addition, the results of Petersen and Rajan indicate that firms making losses tend to extend more credit, especially firms that have fast positive sales growth and losses. The authors note that some of the increase in accounts receivable may nevertheless be involuntary. The customers of distressed firms may be less willing to pay since the threat of cutting off future supplies is less probable. Also, a distressed firm may be less capable of using legal debt collection procedures. Petersen and Rajan used the detailed database compiled by the National Survey of Small Business Finances (US). The findings of Belt (1985) support this since DSO has shortened less than DPO, or DSO has lengthened more than DPO in years of recession.
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Farris and Hutchison (2003) have compared the median CCCs of each industry where 40 or more companies were represented, totally 31 industries. They used the database “Research Insights 7.6, and the usable set of samples included 1,872 US firms from 1986 through 2001. The calculation of CCC follows formula (4). Figure 9 presents the trend of working capital management. The recession years of US economy, the shaded areas, have been added to the figure.
Figure 9. The median of CCC and its components from 1986 to 2001 of US firms
(Farris and Hutchison, 2003)
The trend of CCC has been decreasing but its level is higher than in the study of Belt (1985). The sample of Belt includes only US manufacturing firms, while the sample of Farris and Hutchison (2003) represents different industries. The shapes of the lines of CCC and DIO seem to follow each other (Figure 9). The median DPO has lengthened more than the median DSO during the period under analysis, which together with the shortening of median DIO resulted in the decrease of the median CCC. The difference of CCC, DIO and DPO is significant at the 95% confidence level between 1986 and 2001. Farris and Hutchison suggest that the reduction in DIO has been advanced in technology, communications and logistics over the 16-year period. The recessions of 1991 and 2001 have not lengthened the CCC on the basis of Farris and Hutchison’s (2003) research. They suggest that CCC performance should only be compared within industries. They justify the suggestion with industry-specific characteristics, such as trade credit payment periods. Farris and Hutchison propose that working capital management should be done both within the company and within immediate customers and suppliers. They emphasize the disadvantages of a supply chain if a firm sub-optimizes its CCC, raising network considerations as a topic for future research.
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Losbichler et al. (2008) studied the working capital management of European companies. They utilized the Amadeus database and used a formula similar to the one used by Farris and Hutchison (formula (4)). Because of the limited availability of COGS, the data was reduced from 25,000 companies to 6,925 companies, allowing the use of a complete set of variables. The period of study is between 1995 and 2004, and the figures reflect the median of cash conversion cycle and its components. Figure 10 shows the results of Losbichler et al. (2008) indicating that the CCC of European companies has been steady for the period of 1995 – 2004.
Figure 10. The median of CCC and its components of European firms from 1995 to 2004
(Losbichler and Rothböck, 2008)
Compared to the study of Farris and Hutchison (2003), the CCC of European companies is shorter than that of US companies. Figure 10 shows that in Europe the cycle time of accounts receivable is longer than the cycle time of accounts payable, whereas in the US the DPO lengthened more than the DSO during the period. In Europe, the CCC depends more on DSO, while in the US the cycle time of inventories affects as well. Furthermore, the findings of Losbichler and Rothböck (2008) indicate that the cycle time of working capital varies between industries. The variation between the medians of industries is from –20.4 days to 271.2 days. The reason for the lengthening of CCC e.g. in year 1999 by 1.3 days cannot be explained by the recession. The countries of European Union have entered recession at different phases before the 2008 financial crisis. Thus, it is difficult to analyze the effects of recession on the cycle time of working capital before the 2008 financial crisis. The results of Belt (1985) and Petersen and Rajan (1997) suggest that the CCC of US firms lengthens during economic shocks. The percentage change of the EU’s GDP reduction was larger than that of the US in 2009. This might indicate that companies operating in the EU have run up against problems of working capital management more likely than US companies. Table 8 shows the development of Europe’s GDP.
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Table 8. The percentage change of EU's real GDP growth rate - volume (Eurostat 2013)
2004 2005 2006 2007 2008 2009 2010 2011 2012 EU (27 countries) 2.6 2.2 3.4 3.2 0.4 -4.5 2 1.7 -0.4 Euro area (17 countries) 2.2 1.7 3.3 3 0.4 -4.4 2 1.6 -0.7
REL, a division of the consulting firm The Hackett Group, Inc., has collected data to analyze the working capital management of European and US companies over a long period. The data includes the 1,000 largest listed non-financial companies in Europe and the United States. The data presents an overview of the region studied and the medians of CCC and its components for industries. This data enables the calculation of CCC similarly to Shin and Soenen (1998). Figures 11 and 12 present the trend of working capital management of European and US companies respectively.
Source of data: REL 2013a
Figure 11. The median of CCC and its components of European companies from 2002 to 2012
Source of data: REL 2013b
Figure 12. The median of CCC and its components of US companies from 2002 to 2012
Figures 11 and 12 show the trend of working capital management of European and US companies from 2002 until 2012. The CCC of both the 1,000 largest European and US companies shortened in 2008. This does not necessarily contradict the findings of Belt (1985), because the CCCs lengthened in 2009. Because of the same reason, it can be considered that Figures 11 and 12 support the findings of Petersen and Rajan (1997) as well. The reason for differences might be explained by samples. The data collected by REL include the largest companies, while samples of Belt and Petersen and Rajan included smaller companies. It might be that the 2008 financial crisis did not affect the biggest companies in
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Europe and US immediately. However, the biggest companies could not avoid the effect completely because the magnitude of the financial crises was globally extensive. When the median CCCs of European and US companies are compared, it can be noticed that the CCC of European companies is longer than that of US companies and the cycle times of the components of the CCC of European firms are longer as well. Figure 12 shows that the CCC of US companies follows mainly the DSO since the DPO offsets the DIO. The shape of the DSO and DPO lines of European companies follow each other, but the cycle time of accounts payable cannot offset the cycle time of accounts receivable. The DIO of European companies has not changed much during the period as it has been 37 days ± 1 day. Overall, the CCC of European companies is on average one week longer than the CCC of US companies. REL has published figures of working capital management that are calculated similarly to the formula (4) used by Farris and Hutchison. Unfortunately, the documentation of REL’s studies only reveals the median of samples; the figures of companies are not available. Figures 13 and 14 present the results of calculations where COGS is used as a denominator of DIO and DPO.
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Source of data: REL 2010a
Figure 13. The median of CCC and its components of European companies from 2003 to 2009 when the denominator of DIO and DPO is COGS
Source of data: REL 2010b
Figure 14. The median of CCC and its components of US companies from 2003 to 2009 when the denominator of DIO and DPO is COGS
Figures 13 and 14 show that the CCCs of European and US companies on average differ only 2.5 days. The cycle time of accounts payable of European companies exceeds the 60-day maximum payment time set by the European Parliament (2010). The standard deadline for paying invoices will be 30 days. The actual payment terms cannot be deduced from this kind of data, but the data indicates that there might be a need for a shorter DPO when the directive will be implemented in EU countries. The goal of the new rules is to ensure that small firms no longer face financial problems due to the late payment of invoices by public authorities or companies (European Parliament, 2010). Previous studies, e.g. Petersen and Rajan (1995), suggest that banks lend money for companies differently in concentrated markets and in competitive markets. As the samples include the 1,000 largest European and US companies, it can be assumed that they all have access to similar credit markets and the variance in trade credits cannot be explained by financial environment. Based on these results, it is difficult to say for sure what the reason for this is; it might have something to do with adopted business traditions, competitive environment and trust toward business partners. Studies focusing on country-specific issues concerning trade credit have been published, but they mainly concentrate on emergent countries (i.e. Van Horen, 2005; Delannay and Weill, 2004). Because of a lack of adequate cross-country data, Fisman and Love (2003), for example, assume that trade credit usage by industries in the US is representative of trade credit usage in other countries. Figures 11, 12, 13, 14 appear to indicate that there is a difference between the 1,000 largest European and US companies.
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On the other hand, Rajan and Singales (1995) compare non-financial companies in the G7 countries. Their findings suggest that the proportional share of accounts receivable and payable of total assets differs between countries. Table 9 suggests that the difference is more remarkable in accounts receivable than accounts payable. Fisman and Love (2003) agree that their assumption is strong.
Table 9. Trade credits proportion of total assets for firms in the G7 countries (Rajan and Singales, 1995)
US Germany France Italy UK Europe
average Accounts receivable 17.8 26.9 28.9 29.0 22.1 26.7 Accounts payable 15.0 11.5 17.0 14.7 13.7 14.2
Furthermore, Table 10 also indicates that there is a difference between the 1,000 largest European and US companies when the different industries are taken into account. Table 10 shows CCC by industry sectors where the 1,000 largest European and US companies operate. The number of industries is sixty when the data of European and US industries is combined.
Table 10. Cash conversion cycle medians of industry sectors in 2009 EU change
US change
Industry n CCC of CCC n CCC of CCC Aerospace and Defense 1 106 -14 % 2 85 5 % Air Freight and Logistics 9 12 -12 % 7 21 44 % Airlines 9 11 -12 % 1 3 3451 % Auto Components 2 61 4 % 1 38 -1 % Automobiles 1 42 -9 % 3 35 53 % Biotechnology 2 138 -1 % 7 94 15 % Building Products 1 68 -2 % 7 56 8 % Chemicals 3 78 6 % 4 76 14 % Commercial Services and Supplies 3 45 -8 % 2 36 -10 % Communications Equipment 6 62 -20 % 1 53 -13 % Computers and Peripherals 5 49 -7 % 1 22 -23 % Construction and Engineering 4 45 -8 % 1 50 -4 % Construction Materials 1 61 -12 % 2 87 19 % Containers and Packaging 9 47 -17 % 1 53 4 % Distributors 7 55 -24 % 3 87 10 % Diversified Consumer Services - - - 1 12 57 % Diversified Telecommunication Services 2 6 -20 % 1 17 10 % Electric Utilities 2 37 26 % 2 35 22 % Electrical Equipment 2 94 7 % 1 70 0 % Electronic Equipment, Instruments and 1 71 5 % 2 52 -9 % Energy Equipment and Services 2 68 -1 % 2 85 0 % Food and Staples Retailing 1 0 87 % 2 17 4 % Food Products 4 39 -9 % 2 42 -7 % Gas Utilities 4 20 13241 % 1 39 20 % Health Care Technology - - - 2 62 -5 % Healthcare Equipment and Supplies 2 100 -5 % 2 97 8 % Healthcare Providers and Services 1 50 46 % 5 23 12 % Hotels, Restaurants and Leisure 3 -7 22 % 3 3 149 % Household Durables 2 85 -2 % 2 82 -10 %
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Table 10. Cash conversion cycle medians of industry sectors in 2009 contd. EU change US change
Industry n CCC of CCC n CCC of CCC Household Products 4 30 -22 % 8 51 -7 % Independent Power Producers and Energy 4 54 12 % 7 33 -1 % Industrial Conglomerates 1 74 7 % 8 71 4 % Internet and Catalog Retail 7 46 3 % 6 10 51 % Internet Software and Services 1 -19 -155 % 8 40 15 % IT Services 1 53 -12 % 2 53 -8 % Leisure Equipment and Products 4 103 0 % 6 72 4 % Life Sciences Tools and Services 5 91 10 % 1 92 3 % Machinery 6 100 -5 % 4 81 -2 % Marine 9 22 14 % 3 41 52 % Media 4 37 16 % 3 34 5 % Metals and Mining 2 78 23 % 1 83 38 % Multiline Retail 7 33 5 % 1 30 -12 % Multi-Utilities 1 34 -14 % 2 43 14 % Office Electronics 2 97 4 % 1 69 18 % Oil, Gas and Consumable Fuels 2 33 22 % 7 32 113 % Paper and Forest Products 1 55 -12 % 8 58 -5 % Personal Products 3 42 -17 % 7 57 -15 % Pharmaceuticals 2 85 3 % 1 73 7 % Professional Services 2 53 4 % 1 48 16 % Road and Rail 1 14 18 % 1 21 3 % Semiconductors and Semiconductor 6 89 0 % 2 61 13 % Software 8 66 -1 % 1 53 -6 % Specialty Retail 3 25 -17 % 5 32 0 % Textiles, Apparel and Luxury Goods 2 81 -13 % 1 72 1 % Tobacco 3 107 -16 % 5 53 10 % Trading Companies and Distributors 2 56 -14 % 1 84 0 % Transportation Infrastructure 1 11 231 % - - - Water Utilities 3 35 -14 % 1 30 2 % Wireless Telecommunication Services 3 11 -17 % 8 8 -11 %
Source: REL 2010a; REL 2010b In 34 industries, the CCC median is longer in Europe than in the US. Table 10 also indicates that some industries have positive and some negative developments in CCC medians, and the development of European and US industries have or have not developed in the same direction between 2008 and 2009. The percentage dramatizes the weakening or improvement of working capital management in those industries where the CCC is relatively short. The samples of previous studies and analyses differ a lot from each other, but evidence on the working capital management of inter-organizational value chains remains scarce. It is necessary to build up and analyze working capital management from the value chain perspective to reveal who drove industry improvements and whether they were beneficial or detrimental to the rest of the value chain (Hofmann and Kotzab 2010).
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The next section will discuss previous research concerning financial supply chain management. As aspects of supply chain other than the issues related to working capital management are out of the scope of this thesis, the term financial value chain is used. The financial value chain analysis method developed during the doctoral studies is also introduced in the next section.
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3 Financial value chain analysis In the last decades, academics and practitioners were directed towards the material flow of a value chain and logistics solutions (Popa, 2013); in other words, scholars and companies seem to have paid more attention to inventory than trade credit (Seifert and Seifert, 2008). The reason for this may be that the scholars who were interested in working capital management did not consider the issues related to supply chain management; instead, the financing of companies was in focus. This led to the time gap between the flows of goods and services and cash. The dissertation of Seifert (2010) titled “Collaborative Working Capital Management in Supply Networks” was among the first studies in the field of financial supply chain management. During the financial crisis of 2008, the willingness of banks to lend decreased, which caused liquidity problems for companies. Larger or otherwise powerful companies might have enforced their payment terms on smaller companies, who in turn might have enforced their payment terms on those smaller yet, i.e. a domino effect might have occurred during the financial crisis of 2008. Already Rafuse (1996) stressed that attempts to improve working capital management by delaying payment to suppliers is an inefficient and damaging practice, both to its practitioners and to the economy as a whole. Firms angering their suppliers by paying late not only risk missing out on innovations and losing capacity, but risk encountering supply chain disruptions, Seifert and Seifert (2008) added. Rafuse (1996) only suggests implementing lean supply-chain techniques to improve material flow, however. The recent studies have presented a solution for lean financial flows (Seifert, 2010; Kristofik et al., 2012; Popa, 2013) since it was realized that lean material processes may halt if the supply chain confronts financial distress. Previous financial value chain studies have mainly focused on supplier – customer payment processes. Recently scholars have discussed buyer-lead financing vs. asset-based financing; mainly the techniques reverse factoring (also known as supplier finance) vs. factoring (Seifert and Seifert, 2011; Kristofik et al., 2012). As Shank and Govindarajan (1993, p.50) say “We are aware of no firm that spans the entire value chain in which it operates”; in other words, a firm is a part of the larger set of activities only in the value delivery system. Therefore, the need to study working capital management at the value chain level is relevant (Hoffman and Kotzab, 2010; Grosse-Ruyken et al., 2011; Viskari et al., 2012a). The financial value chain analysis method developed during this study is designed for considering working capital management as a value chain level phenomenon. The bilateral solutions in working capital management may be too narrow to ensure lean supply-chain techniques because e.g. a supplier’s supplier may also have liquidity problems.
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3.1 Value chain oriented perspective on working capital management
Initially, the value chain described the value added activities and the linkages of functions in an organization (Porter, 1985). According to Porter, the group of organizations which together create value to the end customer by supplying, distributing and buying from each other constitutes the value system. However, many studies consider the term value chain to describe the entire chain of companies through which value is created (Al-Mudimigh, et al., 2004; Shank and Govindarajan, 1992; van Weele and Rozemeijer, 1996). This is reasonable because a company does not typically span over the activities of the entire value chain in which it operates. Shank and Govindarajan (1992) have showed that the concept of value chain is not just a theoretical framework. It can be used as meaningful cost analysis, and it could help in understanding the position of a company in its value chain, the position of a value chain against other value chains, and in analyzing the strategic decisions of make or buy and forward/backward integration.
Losbicler et al. (2008) studied working capital management in the supply chain by linking European industries which typically supply each other. They simplify the situation by considering that e.g. a sample from the food stores industry is in an exclusive relationship with a sample from the food and kindred products industry. These authors suggest that the downstream food stores industry decreased its own CCC at the expense of the upstream food and kindred products industry (see Table 11). In addition, Seifert and Seifert (2011) found that relative accounts receivable and payable increase the further upstream they are in the value chain. Their findings were based on US data.
Table 11. The median of CCC of food stores and food and kindred products and yearly change of CCC
Year Food stores Change from previous year
Food and kindred
products Change from previous year Benefit 1
1995 1.5 51.9 1996 -2.1 -3.6 50.7 -1.2 2.4 1997 -7.7 -5.6 55.2 4.5 10.1 1998 -10.8 -3.1 56.3 1.1 4.2 1999 -11.7 -0.9 54.6 -1.7 -0.8 2000 -14.7 -3 55.3 0.7 3.7 2001 -16.2 -1.5 55.9 0.6 2.1 2002 -8.7 7.5 58.8 2.9 -4.6 2003 -12.5 -3.8 56.9 -1.9 1.9 2004 -11.6 0.9 53.3 -3.6 -4.5
1 Food stores industry has benefit compared to food and kindred products industry in working capital management when the number is positive and vice versa
Source: Losbicler et al 2008
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The comparison of these two industries also reveals that the food stores industry’s access to trade credit has improved and its importance in the short-term financing of food stores industry has increased during the period. Hofmann and Kotzab (2010) suggest that the “leading” and most powerful industry sector of a network is often able to reduce its own CCC on a larger scale compared to its supplying industries. They continue that on the one hand buying firms want to develop long term relationships with suppliers, but on the other hand these suppliers are often seen as a cheap source of cash.
This and Monto’s (2013) dissertations are the result of the working capital management research project. The objective of the research project was to study working capital management in the value chain of industries and develop approaches (measures and models) to optimize tied up working capital. While this dissertation focuses on working capital management in value chains, Monto studied the effects of inter-organizational working capital management on performance in the value chain context and developed models for working capital management. Considering academic research as well as practical management towards inter-organizational working capital management, Monto’s (2013) study indicate that companies can increase relative profitability through managing working capital comprehensively by taking into account all its three components (i.e. cycle time of inventories, accounts receivable and account payable) and holistically through the value chain. She stresses that collaborative working capital management actions would improve profitability and decrease the financing costs of working capital on the value chain level. This means that all companies should not just minimize the cycle time of working capital since it is not beneficial for them. Monto (2013) provides models for companies to control working capital in intra- and inter-organizational value chains on the operational level, while previous research has mostly studied working capital management on corporate level. Her study brings management accounting research and supply chain management research closer together and presents an idea of accounting in networks. Monto suggests that the traditional boundaries of companies might be crossed by observing the value chain of a product.
3.2 The method
For this study, the researcher has created her own method called “financial value chain analysis”. The method differs from the value chains analysis because the financial aspects are in the focus of analysis. Porter’s (1985) value chain analysis describes the activities and those linkages, whereas the financial value chain analysis shows the position of the stages (formed from companies) of the value chain and the entire value chain under study. The performance of the stages of the value chain is comparable to each other as the performance of the entire value chain can be compared to other value chains. A stage of the value chain is compounded of companies that operate in the same business, i.e. companies are more or less competitors or they supply goods and services to the same markets and customers.
The financial value chain analysis consists of seven sequential steps. These phases are presented in Figure 15: (1) identify the industry under study; (2) define the value chain,
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including the stages and companies; (3) define the key figures, (4) collect data for the period under analysis; (5) calculate the values of the defined key figures; (6) analyze the calculated key figures and (7) draw conclusions.
Figure 15. Financial value chain analysis method
The analysis of the value chain from this perspective provides a holistic picture of the value chain with financial figures. The importance of successful identification of industry is crucial. Studying a specific industry should increase understanding of a phenomenon that is working capital management in this dissertation. Once the industry to be studied has been identified, the first phase in forming the value chain is to define its structure. The number of stages may vary, but in general the value chain should include more than three stages for the financial value chain analysis, as the purpose of the method is to give a broad view of the value chain from the raw material suppliers to the end customers. To ensure that the value chain is reasonable and that it describes the real-world situation, discussions with practitioners working in the industry are useful during phase two. Before the data collection it should be considered what the key figures which could increase understanding are. The key figures should be calculated using public data. The data used in this method commonly consists of figures from financial statements, as they are published regularly and follow accepted accounting principles. The financial value chain analysis summarizes the calculated values of the key figures for the stages of the value chain. The results should be analyzed using a suitable method. The research questions set should guide the analysis .The method of financial value chain analysis is designed for analyzing industry-level phenomena, even
1 Identify industry
2 Define value chain take a holistic view to the identified industry
3 Define key figures
4 Collect data
5 Calculate values of key figures
6 Analyze key figures decide what methods are used in the analysis
7 Draw conclusions
consider the features of the phenomenon; should you elaborate a new measure or is a existent measure suitable
prefer longitudinal audited data that is public available collect the demographic data of sample also
consider carefully that your calculation provides the true view of phenomenon
what was learned from the studied phenomenon and what are the limitations of study
consider an industry that can learn us a lesson from a phenomenon under study
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though it is based on the key figures of companies. Conclusions should increase comprehension of the studied phenomenon.
3.2.1 Collecting the data
The input data of the financial value chain analysis model is the available numerical data. Examining the reality outside the company imposes limitations to the availability of data. The most sustainable solution to overcome this problem is to use the financial statements of companies as the source of financial figures. Companies generally publish their financial statements annually. The frequency is enough for those industries where business is non-cyclical during the year, because the figures of the end of the year should reflect a similar situation than the figures of the quarter of the year and so on. In that case, the input data of the model is secondary data. Moers (2007) considers this approach as archival research. The inspection of the quality of secondary data is not relevant because auditing ensures adequate quality of financial statements.
This research approach is more time-consuming compared to the use of commercial databases, such as Compustat, Amadeus and Thomson ONE, but it ensures that the data is gathered in a similar manner from each company included in the sample. The data used in this dissertation is mainly reported by the companies. To ensure the homogeneity of balance sheet items related to the working capital of the sample companies, some modifications have been made to the figures presented by the company. Advance payments to suppliers have been removed from the inventories. The inventories include raw material, work-in-process, finished goods or similar. Furthermore, the accounts receivable and payable reflect the receivable and payable that are overdue within a year and are related to trade (e.g. note payable is not included in the accounts payable).
The use of financial statement data has its limitations and it is not without problems. For example, companies report their results based on differing valuation methods of inventories (Belt, 1985), e.g. the IFRS requires inventories to be measured at the lower of cost and net realizable value (NPV), and outlines acceptable methods which are first-in-first-out (FIFO) and weighted average cost (IAS 2, 2012). Examples of the inventory valuation methods of companies included in this study are presented in Table 12. It is obvious that the applied investment valuation method affects the cycle time of inventories. However, it can be accepted in this study because it can be assumed that companies favor those methods among the methods accepted by auditors which are most appropriate for them and which follow the principle of continuity. Additionally, the different cost structures of companies complicate the use of financial statements. Despite these problems, the use of financial statements is reasonable. The figures reflect the true and fair view of a company’s financial standing.
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Table 12. Examples of the inventory valuation methods of companies
Company (annual report) Method
International Paper (2010)
The LIFO inventory method is used to value most (approximately 69%) of IP’s US total raw materials and finished products inventories.
BASF (2010) Inventories are valued using the weighted average cost method.
Continental (2009-2010) Inventories are recognized at the lower of cost and net realizable value.
BP (2009)
Inventory held for trading is recorded at its fair value using period end spot prices whereas any related derivative commodity instruments are recorded at values based on forward prices consistent with the contract maturity.
Vodafone (2009-2010) Inventory is stated at the lower of cost and net realizable value.
3.2.2 Newly generated data
In this study, the result of the financial value chain analysis method is the arithmetic mean (or average) of the calculated key figure; a cash conversion cycle which is calculated according to the definition of Shin & Soenen (1998) (see Eq.(3)). The average of the CCC of a value chain and the stages of it show the effectiveness of working capital management. The information it offers meet the information needs in the analysis of value chain-level working capital management phenomenon. Table 13, Figure 16 and Table 14 reassure this argument.
Table 13 displays the average and the median of the CCC of the studied industries (automotive, pulp and paper and ICT industries). The average is a sum of the CCCs of companies divided by the number of companies. The median is the middle value in a range of ordered values; in this case this is the middle value of the CCCs of companies.
Table 13. Average and median of CCC of automotive, pulp and paper and ICT industries
Automotive industry2004 2005 2006 2007 2008 2009 2010
Average na 73 70 71 71 71 64Median na 74 71 72 72 72 65Pulp and paper industry
2004 2005 2006 2007 2008 2009 2010Average 62 62 59 59 61 60 55Median 61 64 60 61 59 60 55Information and Communications Technology industry (ICT)
2004 2005 2006 2007 2008 2009 2010Average na 39 42 39 39 39 38Median na 40 47 40 40 39 39
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As shown in Table 13, the values of the average and the median of CCC do not differ remarkably. Three times out of 19, the median of CCC has the same value as the average, and 11 times the difference is just one day. This indicates that the CCC of samples could be distributed normally.
Figure 16 shows the histogram of data series for the ICT industry, where the average and the median differ the least ICT2009 (the left hand side of Figure 16) and the most ICT2006 (the right hand side of Figure 16). The figure also displays the normal curve of both distributions.
Figure 16. The histogram of CCCs
Figure 16 shows that the both peaks of distribution are nearly centered and no skewing can be detected. Similar analysis conducted for other industries and years indicated that the distributions of CCCs are one-peaked. The difference between the automotive, pulp and paper, and ICT industries is the sharpness of the peak. Thus, visual examination of Figure 16 indicates that distributions are normally distributed.
Furthermore, the Kolmogorov-Smirnov and Shapiro-Wilk tests can be used for testing the normality of the distributions. The Shapiro-Wilk test is extremely suitable for small sample sizes but, depending on reference, the definition of small sample size is less than 50 samples or less than 2000 samples. The sample size exceeds 50 observations; therefore the results of the Kolmogorov-Smirnov tests are also reported. The problem of the Kolmogorov-Smirnov test is that it has poorer power to detect non-normality. Table 14 displays the results of both tests for the ICT industry.
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Table 14. Test of normality
Both p-values are greater than 0.05, so it can be assumed that the samples are normally distributed. The test of normality confirms what is observed in Figure 16. Similar tests were made for other years and the automotive and pulp and paper industries. Based on these analysis it can be concluded that the average of CCC is a consistent indicator for the working capital management in the value chains of automotive, pulp and paper and ICT industries.
Statistic df Sig. Statistic df Sig.ICT industry 2009 .075 62 .200* .976 62 .264ICT industry 2006 .090 62 .200* .970 62 .139
Kolmogorov-Smirnov Shapiro-Wilk
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4 Industries studied
The automotive, pulp and paper and ICT industries are formed by using the snowball sampling technique (Bryman and Bell, 2011). A company leads to its customers, suppliers or competitors or all of them. This forms the value chain. In this study, it is meaningful that the companies are not independent as it is the inter-organizational value chain that is in focus. Table 15 presents the data used in the different articles of this dissertation.
Table 15. Data used in the articles of the dissertation
Publication Data type Data source Description of the data Period A1 numerical financial
statements the value chain of automotive industry: 65 international and Central European firms
2006–2008
A2 numerical financial statements
the value chain of pulp and paper industry: 44 international firms
2004-2008
A3 numerical financial statements
the value chain of ICT industry: 61 international firms
2006-2010
A4 numerical financial statements
automotive: 65 firms pulp and paper: 42 firms ICT: 59 firms
2006-2010
A5 numerical linguistic
Voitto + database interviews
the value chain of pharmaceutical industry: 19 Finnish firms, 3 persons, autumn 2012
2006-2010
The value chains of the industries studied in publications A1 to A4 are described briefly in next subsections. The value chain of pharmaceutical industry studied in publication A5 is not described because the value chain of the Finnish pharmaceutical industry is considered straightforward. The stages are: pharmaceutical companies (located in Finland); importers of foreign pharmaceutical companies (Finnish subsidiaries of groups which have plants in some other countries than in Finland and independent Finnish companies); pharmaceutical wholesalers which deliver the products to the pharmacies; and pharmacies which serve the end customers. However, the pharmaceutical business is huge in international scale but it is difficult to study because of the country-specific regulations and laws. Therefore, the focus of publication A5 was to develop the financial value chain analysis method, because the results considering working capital management of pharmaceutical industry could not have been generalized. However, the results of the interviews provide some interesting information on working capital management in value chains, but because of the limited amount of companies included in the sample and interviews conclusions are drawn cautiously.
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Table 16 displays the sum of the sales, working capital and assets of each stage and industry included in the research. The year 2008 is selected because it is considered to best reflect the working capital management of value chains. The financial crisis confused companies’ working capital management routines, and it might still have an impact in 2010. Table 16. Descriptive statistics on the value chains, year 2008
MEUR
Automotive Sales Total
assets Working
capital Raw material suppliers 1 158 159 197 381 52 668 Refined raw material suppliers 269 423 45 960 54 263 Component suppliers 94 888 15 885 16 001 System suppliers 144 763 15 008 23 098 Car manufacturers 605 984 108 198 202 550 Car dealers 4 149 183 521 Total 2 277 366 382 614 349 101
Pulp and Paper Sales Total
assets Working
capital Chemicals 49 116 88 986 6 597 Machinery 14 944 13 746 3 808 Market pulp 9 857 11 325 2 064 Paper and board 96 844 119 333 16 932 Merchants 9 249 5 842 1 353 Printers 19 049 17 054 2 877 Brand owners 162 450 247 902 23 740 Publishers 21 445 37 601 2 567 Total 382 954 541 789 59 938
ICT Sales Total
assets Working
capital Network hardware 48 796 50 534 8 349 Component manufacturers 59 450 86 338 7 259 Contract manufacturers 56 364 26 754 4 054 Computers and computer peripherals 291 390 208 402 23 444 Mobile phones 130 206 136 180 19 999 Network operators 373 460 795 478 11 103 IT Services 39 468 29 331 3 441 Software 93 165 105 614 15 093 Internet software 35 702 42 610 3 601 Total 1 128 000 1 481 240 96 344
Pharmaceutical industry Sales Total
assets Working
capital Pharmaceutical companies 1 108 1 561 211 The importers of the foreign pharmaceutical companies 459 273 149 Pharmaceutical wholesalers 1 643 1 099 -10 Pharmacies (* 284 83 13 Total 3 494 3 016 363
(* if the value of year 2008 is missing the value of the year 2009 or 2007 is used instead
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4.1 Automotive industry
The value chain of the automotive industry contains stages that represent the main elements needed for producing and delivering a car for the end customer (see Figure 17).
Figure 17. The structure of the value chain of automotive industry and research sample
The stages upstream consist of two or three sub-stages, but the system suppliers and the stages downstream towards end customers form their own single stage. The upstream of the value chain begins with the raw materials of steel and plastics: iron ore and oil. This is an innovative definition compared to the previous definition of the automotive value chain (Wheelen and Hunger, 2002; Heneric et al., 2005; Blackman and Holland, 2006). In the automotive industry, the term “raw material” usually refers to steel and other materials that are already refined. In this study, the starting point is one step further away, in the interest of the value chain. The refined raw materials, plastics and rubber and steel and metal are on the second step of the chain. The third stage, component suppliers, is formed by the suppliers of plastic and rubber components, steel and metal components, and electronics. These companies supply smaller parts, like bearings or gaskets, to the system suppliers. The system suppliers make complete systems and parts to deliver to the car manufacturers downstream,
Componentsuppliers
Plastic and rubbercomponents:
DaetwylerRaw material Refined ElringKlingersuppliers Raw material Federal Mogul
suppliers PolytecSaint-Gobain System Car Car dealers
Oil: Plastics and rubber: suppliers manufacturersBP BASF Steel and metalExxonMobil DuPont components: BorgWarner BMW AVAGRoyal Dutch Shell EMS Bekaert Bosch Daimler Autohaus WolfsburgTotal Evonik Georg Fischer Continental Geely Feser Graf
Lanxess GKN Denso Honda LuegIron ore: Miba Magna Hyundai Löhr & Becker
BHP Steel and metal: Neumayer Tekfor Mahle Nissan MAG MetzLKAB ArcelorMittal Rheinmetall Schaeffler Renault WellergruppeRio Tinto Salzgitter RUAG Valeo ToyotaVale Stahl-Metall-Service Seissenschmidt ZF VW
ThyssenKrupp TrimetVoestalpineZAPP Electronics:
Alps ElectricAustria MicrosystemsDraexlmaierHellaLeoniNidecTyco Electronics
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which then take care of the assembly of the final product, the car. In this study, the finished cars reach the end customers via the car dealers.
The automotive industry was hit especially hard by the economic crisis. The structural crisis of the industry caused by high fuel prices in previous years resulted in a reallocation of demand in favor of small cars. This was even further impacted by companies failing to invest in their vehicle fleet, as well as by a lack of demand on the part of nervous private consumers. Therefore, car production dropped worldwide by 0.9% in 2008 and by 10.4% in 2009. This was the first reduction in production volume since 2001 (Hofmann et al., 2011).
4.2 Pulp and paper industry
The pulp and paper industry is characterized as a traditional process industry. However, the production processes of downstream companies vary. The value chain of the pulp and paper industry is explained in Figure 18.
Note: In article 2, the research sample differs as follows: Ciba is included instead of BASF and independent companies Aracruz and Votarantim instead of Fibria.
Figure 18. The structure of the value chain of pulp and paper industry and research sample
The value chain of the pulp and paper industry consists of chemical, machinery, market pulp, paper and board, merchant, printer, brand owner, and publisher stages. In the chemical industry field, mergers and acquisitions (M&A) have been common, and so the number of suppliers, especially suppliers of paper chemicals, has decreased. The driver of M&A has been the need to improve profitability. Even though none of the companies in this study is any more specialized in producing chemicals for pulp and paper and board producers, niche suppliers still have opportunities in the field (Webb, 1999). In the second publication of this thesis, the financial statements of the niche supplier Ciba were publicly available, but at the moment it is a part of BASF. The maintenance and upgrades of pulp and paper and board mills have smoothed over the slight demand for new machinery. The companies of the
Brand ownersPaper and board BATHolmen Beiersdorf
Chemicals IP DanoneBASF Market pulp Kimberly-Clark Merchants Procter & GambleDow Arauco MeadWestvaco Paper Linx RocheImerys Canfor M-Real Sequana UnileverKemira Fibria Myllykoski
Metsä-Botnia Nippon Printers PublishersMachinery Södra Skogsägarna Norske Skog Consolidated Graphics Axel SpringerAndritz Oji Dai Nippon Printing EMAPMetso Sappi RR Donnelley N.Y.Times Comp.Voith SCA Pearson
Stora Enso Reed-ElsevierUPM SanomaWSOY
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machinery stage have turned from manufacturers to service providers who are responsible for minimizing the downtime of mills (Gill, 2010). The number of global machinery suppliers is small. Mainly in this value chain, the chemical and machinery stages are formed by the suppliers of the market pulp and paper and board stages.
The magazine Pulp & Paper International lists the Top 100 pulp and paper companies every year. For this study, the listed companies are separated into market pulp companies and paper and board companies. If a firm manufactures more market pulp than paper and board in tons, it is included in the market pulp stage, otherwise in the paper and board stage. The sample covers 51% of market pulp production and 47% of paper and board production in the PPI Top 100 corporate 2007 table (James, 2008). The global consumption of paper and board has increased annually by about 2.6% in the 2000s. Furthermore, it is expected that market demand from Asia, East Europe and Latin America will increase (Finnish Forest Industries, 2010). Thus market changes have been pushing restructuration in both stages. Companies have strengthened their key business areas and simultaneously cut down activities which are not in line with the strategy or which can be outsourced without jeopardizing business operations. For example, during the period of this research project, the paper and board companies divested from merchant business and shot down paper machines. The most remarkable arrangement was the emergence of Fibria as a result of the merger between Aracruz Celulose and Votorantim Celulose e Papel (VCP) in 2009. After the year 2010, mergers and acquisitions have continued; for example, UPM-Kymmene (UPM) has bought out Myllykoski.
The downstream of this study consists of companies which consume paper and board in their own business. Nevertheless, the role of paper and board varies remarkably. For merchants and printers, paper and board are a part of the main product, but for brand owners and publishers they are a sub-product. In recent years, the European merchant industry has experienced many acquisitions. As a consequence, the top eight companies dominate 86% of the merchant market in Europe. Besides five regional and three global players, there are a number of local players in the field. The situation in other market areas does not differ substantially from the European merchant market.
The value chain constructed for this study does not comprise all real world stages of the pulp and paper value chain, because it is difficult to specify, for example, firms which are international wood suppliers. On the other hand, the unavailability of financial statements from public sources has limited the sample as well. For example Papyrys, one of the biggest European merchants in paper owned by a capital investor, is not included in the sample. The annual report of Papyrys is not publicly available and free of charge.
In this case, the most limited sample of companies is the brand owner stage. The selected companies produce food and drink, healthcare, cosmetics and other consumer goods. Paper and board is used to transport, contain or support the products; for example, a package has both technical and marketing functions. Package and commercial printing are the biggest sectors of the global print market. North America, Asia and Western Europe covered 89% of the global print markets in 2008. Beside the brand owners, the publishers use print as one of
65
the many content outputs, and the firms of each stage use print for transactional and promotional purposes.
Despite the heterogeneity of wood suppliers and the approach of this study, it can be said that the downstream firms outnumber the upstream firms of the pulp and paper industry value chain in the real world.
4.3 Information and communication technology industry
The ICT industry is characterized by an integrated business environment, fast technology development and service-orientation. Figure 19 presents the value chain of the ICT industry as it is viewed in this thesis.
Figure 19. The structure of the value chain of ICT industry and research sample
Network operatorsAT&TBT GroupDeutsche Telekom
Network hardware France TelecomAlcatel-Lucent TeliaSoneraHuawei VerizonJuniper networks VodafoneTellabsZTE Mobile phones IT services
Cisco systems AccentureComponent manufacturers HTC AtosOriginADM LM Ericsson CapgeminiBroadcom Motorola ComputaCenterInfineon Techn. Nokia LogicaIntel Corporation RIM S&TNVIDIA Corp. TietoSTMicroelectronics Computers and Texas Instruments computer peripherals SoftwareTSMC Apple AdobeUMC Dell Autodesk
HP MicrosoftContract manufacturers IBM OracleBenchmark Lenovo RedHatCelestica Lexmark SageElcoteq Logitech SAPFlextronics SanDiskFoxconn Internet softwareJabil eBaySanmina Google
United InternetYahoo
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The companies forming the sample are divided into component manufacturers, contract manufacturers, mobile phones, computers and computer peripherals, network hardware, network operators, IT services, software, and internet services and software stages. Placing companies into stages is not always unambiguous, because many companies offer a large variety of different products and services. Apple, for example, offers personal computing products and media devices as well as develops its own software products (Apple, 2011). Thus, it could be placed in several branches. In this study, Apple was included in the stage of computers and computer peripherals, as until the end of year 2009 computers formed the biggest portion of Apple’s sales over mobile phones. Mergers and acquisitions (M&A) have been common in the ICT industry. For example, during the observation period, Google acquired You Tube, Postini, Double Click, AdMob, On2 Technologies, and ITA Software companies (Google, 2013).
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5 Research contribution
In this thesis, four complementary articles present the study undertaken on working capital management in the automotive, pulp and paper and ICT industry value chains. The state of value chains is investigated through the financial value chain analysis. The key figure of working capital management is the cash conversion cycle.
This thesis had two main objectives.
1) To develop an approach for analyzing the financial value chain. 2) To examine working capital management in value chains.
A first research question, ‘How to analyze the financial value chain phenomenon?’, was derived from the first objective. This research question is addressed especially in articles 1 and 2. Articles 3 and 4 validate the financial value chain analysis method.
Research questions two to four are related with the second objective.
Research question two, ‘How many days have the value chains tied up working capital?, is addressed in articles 1 to 4. The articles describe the cycle time of working capital in the automotive, pulp and paper and ICT industries and the change of the cycle time.
Research question three, ‘How have the value chains performed in regards to working capital management?’, is addressed in article 4, which combines the three antecedent publications benchmarking the working capital performance of industries.
Research question four, ‘How has the financial crisis of 2008 affected the cycle times of working capital in the value chains?’, is addressed in article 4 and briefly in article 3. Table 17 summarizes the original publications.
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Table 17. A summary of the articles one to four
Article 1 Article 2 Article 3 Article 4 Title Working capital
management in the automotive industry: Financial value chain analysis
Working Capital in the Value Chain: Cycle Times of Pulp and Paper Industry
Competing with negative cycle time of working capital in the ICT industry
Benchmarking working capital management in the inter-organisational context
Objective To examine working capital management in the value chain of the automotive industry in the years 2006 –2008
To examine working capital management in the pulp and paper industry in the years 2004 – 2008
To study a negative cycle time of working capital especially in the years 2006 –2010 in the value chain of ICT industry
To investigate how the studied industries have performed in regards to working capital management and the effects of the 2008 financial crisis on working capital management
Research question
RQ1, RQ2 RQ1, RQ2 RQ1, RQ2 RQ1 – RQ4
Main results
The change in CCC mostly followed the change in DIO. The CCC of the value chain is 67 days.
The relation between working capital and sales is a constant. The CCC of the value chain is 63 days.
Short DIO does not secure negative CCC. Well-known brand supports in achieving a negative CCC. The CCC of the value chain is 41 days.
The variance of CCC is mainly a correlative of the different DIO. The financial crisis of 2008 affected the working capital management of the industries similarly. Both DSO and DPO increased between 2008 and 2009.
5.1 Results of article 1: introducing the financial value chain analysis
The article introduces the financial value chain analysis method which shows the position of the value chain. The method reveals the performance of the different stages and its effects on the rest of the value chain. The objective of article 1 was to study working capital management in the value chain of the automotive industry from the operational perspective using the financial value chain analysis method. The cash conversion cycle (CCC) is used to express the effectiveness of working capital management. CCC and its components (DIO, DSO and DPO) were calculated for each year of the 2006 – 2008 period. Figure 20 shows the average values of CCC and its components in days in each stage of the value chain defined on the basis of the figures of companies presented in section 4.1.
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Figu
re 2
0. C
ash
conv
ersi
on c
ycle
s of t
he a
utom
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e va
lue
chai
n in
200
6–20
08.
Stag
e 1:
Stag
e 2:
Stag
e 3:
Raw
mat
eria
l sup
plie
rsR
efin
ed ra
w m
ater
ial s
uppl
iers
Com
pone
nt s
uppl
iers
Oil:
Plas
tics
and
rubb
er:
Plas
tic a
nd ru
bber
com
pone
nts:
AM06
0708
AM
0607
08AM
0607
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IO22
2327
15-7
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5455
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IO55
5256
586
DS
O34
3740
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SO53
5755
46-1
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O34
3640
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3129
25-6
DPO
3331
3534
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CC
2224
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-9C
CC
7980
8076
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7575
7673
-1St
age
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Stag
e 6:
Syst
em s
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iers
Car
man
ufac
ture
rsC
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n or
e:St
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:St
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nd m
etal
com
pone
nts:
AM06
0708
AM06
0708
AM
0607
08AM
0607
08A
M06
0708
AM06
0708
DIO
4038
4141
3D
IO46
4444
495
DIO
4539
4848
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IO40
4342
36-6
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7979
7981
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IO60
5957
634
DSO
5560
6046
-13
DS
O10
210
897
101
-8D
SO21
1922
201
DS
O39
4243
33-9
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4245
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-8D
SO54
5657
51-5
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3940
4334
-6D
PO
4145
4138
-7D
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1721
192
DP
O28
3130
23-8
DPO
3136
3224
-12
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4039
4239
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CC
5658
5853
-4C
CC
106
107
9911
35
CC
C47
4249
497
CC
C52
5455
47-7
CC
C90
8890
935
CC
C74
7672
74-2
Elec
troni
cs:
AM06
0708
DIO
5150
5153
4D
SO61
6762
53-1
4D
PO41
4739
35-1
3C
CC
7269
7472
3
Stag
e 1
Stag
e 2
Stag
e 3
AM06
0708
AM
0607
08AM
0607
08D
IO31
3335
26-7
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6767
6668
1D
IO56
5455
584
DS
O37
4041
29-1
1D
SO47
5149
42-1
0D
SO56
5958
51-8
DP
O31
3435
24-1
0D
PO29
3430
25-9
DPO
3839
3936
-3C
CC
3739
4131
-8C
CC
8584
8585
1C
CC
7373
7473
0
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The results of the analysis showed that the value chain of the automotive industry ties up working capital. The CCC was positive in each company included in the sample. The average CCC in the automotive industry is 67 days; the variation of the period is only 0.5 days. The relation between sales and working capital could be considered a constant. The position of the stages measured by the CCC did not change substantially between 2006 and 2008. Only the stage system suppliers managed to reduce their CCC by 4 days at the expense of their customer’s, the car manufacturers, whose CCC lengthened by 5 days.
Even though the CCC remained roughly the same during the observation period in each stage of the value chain of the automotive industry, the components of the CCC varied a lot. The changes in the CCC of the stages mostly followed the change in the cycle time of inventories (DIO). The cycle time of trade credits (DSO and DPO) varied significantly. Their change offset each other; however, the impact on CCC was slight. In the upstream (stages 2 – 4), the differences in the CCC of the stages were caused by the different DIO. The difference of CCC between system suppliers and component suppliers was 17 days, whereas DIO differed by 16 days. Between the stages of component suppliers and refined raw material suppliers, the difference of CCC was 12 days and DIO differed by 11 days.
The observed reduction in the DSO and DPO of most stages of the value chain indicates that the tightened payment terms required by a supplier have affected the credit terms offered to a customer. In other words, companies have not been willing to carry credit risk increasing their tied up capital when their own credit terms have tightened. Especially if there are difficulties in getting external financing to maintain liquidity, collecting payments form customers faster than before is a reasonable action from the supplier’s point of view. It seems that none of the stages truly gained any benefits from tightening the payment terms offered to customers, as the trend was dominant in the whole value chain. However, this prevented the need for more tied-up working capital in the value chain of the automotive industry.
When trade credit is used for the purpose of ensuring product quality before the customer pays the product, only the system suppliers (excluding the year 2008) have been able to finance their inventories with accounts payable. This might indicate that the forecasting of sales became more difficult during the period, and therefore the DIO of system suppliers increased simultaneously with the shortened DPO. The weakened demand could also be seen in the increase of the DIO of component suppliers. The raw material and refined raw material suppliers also operate in other industries besides the automotive industry, so the crisis, which hit the automotive industry especially hard, did not affect them so much.
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5.2 Results of article 2: in quest of sense of working capital management
The objective of article 2 was to study working capital management in the value chain of the pulp and paper industry. The method used was the first version of the financial value chain analysis method introduced in article 1. The computed cash conversion cycle (CCC) was used to study the working capital management of the value chain. CCC and its components (DIO, DSO and DPO) were calculated for each year of the 2004 – 2008 period. Figure 21 shows the average values of CCC and its components in days at each stage of the value chain defined on the basis of the figures of companies presented in section 4.2.
Figure 21. Cash conversion cycles of the pulp and paper value chain in 2004–2008.
The results of the analysis showed that the value chain of the pulp and paper industry ties up working capital. The CCC was positive in each company included in the sample. The average CCC of pulp and paper industry is 63 days; the difference of CCCs during the period is around 3 days maximum (2004:63 days, 2005:63 days; 2006:60 days; 2007:62 days; 2008:63 days). On the basis of this finding, it could be considered that the relation between sales and working capital is a constant. The position of the stages measured by the CCC did not change substantially from 2004 to 2008 either.
The average cycle time of working capital is longer in the upstream stages when compared to the downstream stages. The companies operating upstream are more capital-intensive, which might explain the longer CCC. The CCC of most stages increased slightly. The lengthened CCC does not necessarily indicate inefficiency in working capital management, because the observation period was a high trade cycle. The strategies and operational targets of companies
Upstream Downstream
Chemicals MerchantsAM 04 05 06 07 08 AM 04 05 06 07 08
DIO 53 48 52 53 54 56 9 DIO 46 52 44 46 48 42 -10DSO 52 50 58 55 53 42 -8 DSO 73 84 70 72 77 62 -23DPO 32 31 34 33 33 30 -1 DPO 54 59 52 56 56 50 -9CCC 72 67 76 75 74 69 2 CCC 65 77 63 62 68 54 -23
Paper and boardMarket pulp AM 04 05 06 07 08 Brand owners
AM 04 05 06 07 08 DIO 45 44 46 43 46 47 3 AM 04 05 06 07 08 DIO 51 44 51 50 48 62 18 DSO 54 51 54 56 55 53 2 DIO 43 42 45 42 42 45 3DSO 44 44 51 45 43 39 -6 DPO 37 34 37 37 39 37 3 DSO 44 39 45 44 46 46 6DPO 24 18 21 28 25 24 6 CCC 62 61 63 62 61 63 2 DPO 30 25 29 30 32 32 6CCC 72 71 81 67 66 76 6 CCC 58 56 61 56 56 59 3
Machinery PrintersAM 04 05 06 07 08 AM 04 05 06 07 08
DIO 58 55 58 57 57 65 11 DIO 20 20 18 20 22 22 3DSO 62 60 63 66 61 60 0 DSO 77 77 77 81 80 69 -8DPO 37 29 35 45 39 38 9 DPO 41 42 43 43 40 36 -6CCC 83 86 86 78 80 88 2 CCC 56 55 52 57 61 56 1
PublishersAM 04 05 06 07 08
DIO 16 21 17 16 13 15 -6DSO 48 46 49 46 46 54 8DPO 30 22 32 31 31 32 10CCC 35 45 34 31 28 37 -8
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may have even supported the prolongation of the CCC. Because the weight of each stage is equal in calculating the CCC of the value chain, the CCC remained the same.
When comparing the components of the CCC to each other, it can be noticed that DSO is longer than DPO in each stage. This might indicate that companies offer more generous terms of payment to their customers when compared to the terms of payment offered by suppliers. The changes of DSO and DPO between 2004 and 2008 have occurred unsystematically. In most stages, trade credit has changed in the same direction; cycle times are either shortened or lengthened. The market pulp stage has succeeded in the management of trade credit from its point of view, since the DSO has shortened and DPO has lengthened. Findings showed that downstream stages (except the brand owner stage) are able to finance their inventories with accounts payable (DPO>DIO). They have managed to forecast the demand and negotiate favorable payment terms. The customers of paper and board have dominated the markets because of the overcapacity and overproduction problems of paper and board companies.
5.3 Results of article 3: strive for negative CCC
The article studies the working capital management of the information and communications technology industry. The study is conducted using the financial value chain analysis method. The measured cash conversion cycle (CCC) was applied to express the effectiveness of working capital management. CCC and its components (DIO, DSO and DPO) were calculated for each year of the 2006 – 2010 period. Figure 22 shows the average values of CCC and its components in days at each stage of the value chain defined on the basis of the figures of companies presented in section 4.3. The ICT industry is studied to obtain a better understanding of working capital management in a service-oriented industry. It was noticed that some companies in the ICT industry managed to operate with a negative CCC, which was not detected in the automotive or pulp and paper industries. This added to the interest towards the ICT industry.
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Figure 22. Cash conversion cycles of the ICT value chain in 2006–2010.
The service-orientation and small inventories provided a reason to expect a relatively short CCC. The findings of the study suggest that the average CCC in the ICT industry is 40 days. For each stage, the cycle time of inventories is shorter than the cycle time of DSO or DPO (exception: the DPO of component manufacturers is shorter than the DIO). Thus, it can be noticed that working capital management represents the management of trade credits in addition to inventories. Particularly, companies which operate in the stages of network operators, IT services, software and internet software do not stock goods. In these cases, working capital management is focused on trade credit management. The stages had managed to shorten their CCC from 2006 to 2010 by 2 and 7 days, except for contract manufacturers and network hardware, the CCC of which had lengthened by 6 days and 1 day, respectively. The shortening was mainly achieved by tightening the payment terms offered to customers more compared to suppliers and their tightened payment terms.
These results suggest that component and contract manufacturers carry inventories on behalf of the stages of mobile phones and computers and computer peripherals by having a DIO which is at least 15 days longer than the DIO of their customer stages. Similarly, the DIO of network hardware manufacturers is longer than the DIO of their customer network operators.
Network hardware Network operatorsAM 06 07 08 09 10 AM 06 07 08 09 10
DIO 40 40 38 45 37 41 1 DIO 5 5 5 6 5 5 0DSO 86 92 86 86 86 80 -12 DSO 43 47 43 45 41 39 -7DPO 66 72 71 72 57 60 -12 Mobile phones DPO 39 39 39 41 37 38 -1CCC 60 60 53 59 66 61 1 AM 06 07 08 09 10 CCC 9 13 9 9 9 6 -7
DIO 25 26 25 25 20 27 1DSO 70 66 71 70 68 72 6 IT services
Component manufacturers DPO 36 33 37 35 34 42 8 AM 06 07 08 09 10 AM 06 07 08 09 10 CCC 58 59 59 60 54 57 -2 DIO 4 5 4 4 3 3 -1
DIO 40 42 39 42 39 38 -3 DSO 70 72 72 71 64 69 -3DSO 43 50 45 30 47 44 -5 DPO 29 31 29 29 28 31 0DPO 31 35 34 23 30 31 -4 CCC 44 46 47 47 39 41 -5CCC 52 57 49 48 55 52 -5 Computers and comp. peripherals
AM 06 07 08 09 10 SoftwareDIO 23 23 23 24 24 21 -2 AM 06 07 08 09 10
Contract manufacturers DSO 47 50 49 40 47 48 -2 DIO 1 2 1 1 1 1 -1AM 06 07 08 09 10 DPO 48 48 50 46 51 46 -3 DSO 71 74 74 69 67 69 -5
DIO 42 43 39 38 41 47 3 CCC 22 25 22 18 20 24 -2 DPO 15 17 17 12 14 15 -2DSO 48 46 47 44 48 55 8 CCC 57 59 59 58 54 55 -4DPO 55 56 55 48 55 62 6CCC 34 34 31 34 34 39 6 Internet software
AM 06 07 08 09 10 DIO 1 1 1 1 1 1 0DSO 37 39 39 36 36 37 -2DPO 16 16 20 14 15 16 0CCC 22 24 20 23 21 22 -2
END
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The reason for this is different, since the inventories of network hardware manufacturers turn the fixed assets of network operators. This cannot be considered a situation where a powerful player reduces its own tied-up capital at the expense of its business partners.
The focus of the study was on the negative cycle time of working capital. In general, it means that the suppliers of a company finance the operational tied-up capital, i.e. inventories and accounts receivable. However, the companies which operate in stages where the DIO is negligible are not in most cases the companies which have the shortest or even a negative CCC. Companies operating with a negative CCC were Dell, Apple, Lenovo, France Telecom, BT Group and United Internet. It is common for companies which achieve a negative CCC to have a short cycle time of inventories and an effective trade credit policy. The cycle time of accounts receivable was on average between 17 to 37 days shorter than the cycle time of accounts payable. The companies which were able to operate with a negative CCC deliver both services and physical goods. What is common for them all is that they own well-known brand names.
5.4 Results of article 4: the effects of financial crisis on working capital management
Figure 23 shows that, when the CCCs of the automotive, pulp and paper and ICT industries are compared, it can be seen that the length of the CCCs decrease respectively.
Figure 23. CCC and its components in the automotive, pulp and paper and ICT industries
The CCC of automotive is 70 days, pulp and paper’s CCC is 60 days, and the CCC of ICT is approximately 40 days. The results do not show remarkable changes in the CCC during the observation period. Therefore, the relationship between sales and working capital can be considered a constant. Figure 23 shows that the length of the cycle time of inventories causes
Days Automotive Pulp and paper ICT
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the main differences in the CCCs of the industries. The DIO of the automotive industry is around 50 days, while in the pulp and paper industry and ICT industry the cycle time of inventories is around 40 days and 20 days, respectively. The variation of the DIO has been modest between the years 2006 and 2010 (a maximum of 5 days). The average payment terms of purchases and sales are more similar among the automotive, pulp and paper and ICT industries, and when they are netted, the difference between the industries is even smaller.
It has been assumed that the effects of the 2008 financial crisis appeared in the 2009 figures. Year 2009 can be considered a recession year because of the change in sales (automotive:-21.2%, P&P:-6.0%, ICT: -1.8%). Therefore, the change between the years 2008 and 2009 has been observed to examine the impact of the financial crisis on working capital management. The change in the CCC had been modest in each industry before the year 2009, from one to three days. Between the years 2008 and 2009, the sales of each industry decreased and, in light of the results of the study, the ICT and pulp and paper industries managed to adapt their tied-up working capital or even improve their CCC. Meanwhile, the automotive industry could not maintain its efficiency in working capital management. The cycle time of accounts receivable increased in each value chain between the years 2008 and 2009. Simultaneously, the cycle time of accounts payable increased, but the increase was not enough to offset the increase of the DSO in the automotive industry. The lengthening of the cycle times of accounts receivable and payable from the year 2008 to 2009 suggests that the companies used more trade financing than before the crisis. The companies may have needed to borrow more from their value chain partners to balance the shortage of bank loans. The companies may have changed their payment policies to prefer longer payment terms to the cash discount term to achieve more financing from the value chain in which they also operated. Regardless of the reason, the prolonged cycle time of accounts receivable and payable had an effect throughout the value chain in which the companies operated. In 2010, the automotive industry managed to restore the CCC to its established level. The pulp and paper industry improved the efficiency of working capital management in 2010 compared to 2009 by simultaneously shortening the cycle time of accounts receivable and lengthening the cycle time of accounts payable.
To study the characteristics of working capital management of each value chain, an analysis was conducted at different stage levels. Figure 24 demonstrates the five-year averages of the CCC of the stages of each industry. They have been calculated to visualize how the length of the CCC varies within each industry.
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Figure 24. The cash conversion cycles of the stages of the automotive, pulp and paper and ICT industries in a descending order
The difference in the CCC between the stages with the shortest and longest CCC is threefold in the automotive industry, twofold in the pulp and paper industry, and sixfold in the ICT industry. In days, the biggest difference was found in the automotive industry (80 days). The difference in the pulp and paper industry is 41 days and in the ICT industry 50 days. As it is shown in Figure 24, the shapes of the curves of industries differ. The middle curve (pulp and paper) is flat, which indicates that the CCC is around 60 days at the various stages.
In the automotive value chain, the cycle time of the working capital of car manufactures differs significantly from the other stages of the industry. The major reason for the long CCC is the long cycle time of accounts receivable. This is because the financing business of car manufacturers requires long credit periods. If the DSO of car manufacturers excluded the receivables of their financing business, their CCC would be significantly lower (approximately 50-75 days). The raw material suppliers have the lowest CCC, on average 33 days, which results from the short cycle time of their inventories. They also collect the payments from their customers with almost the same terms as they pay to their suppliers. In the upstream of the automotive value chain, the differences in the CCC of the stages were caused by the different cycle times of inventories.
The CCC and cycle time of inventories place the stages of pulp and paper industry almost in the same order. Only the printers change place with the brand owners when the ordering criterion is the DIO. The publishers, with the shortest CCC, have a more than twofold advantage in terms of the DIO compared to the machinery producers, who have the longest DIO. The publishers lose the advantage by offering favourable payment terms to their customers. The market pulp producers, who have the second longest CCC, have the shortest
Network hardware
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cycle times of accounts receivable and payable. They favor their customers by 17 days, however.
In the ICT industry, the difference between the stages with the shortest and longest CCC is mainly explained by the cycle time of inventories. The stage of internet software and software carry practically no inventories at all, while the cycle time of inventories of the component manufacturers, contract manufacturers and network hardware producers is around 40 days. The CCC of network hardware manufacturers is the longest in the ICT industry. It also allows more favorable payment terms to its customers than it gets from its suppliers. The difference in the trade credit terms is 19 days. The stage of network operators, which has the shortest CCC, has a very short cycle time of inventories (5 days). Network operators do not favor their customers, as the difference of the DSO and DPO is only 4 days. The stage of computers and computer peripherals producers and contract manufactures has been able to collect payments from their customers faster than they have paid to their suppliers. This means that the payment terms of purchases are much longer than the payment terms for customers, as the value of the product increases during the manufacturing process.
5.5 Results of article 5: beyond the financial value chain analysis method
The aim of this study was to develop the sixth and seventh step of the financial value chain analysis method, i.e. the analysis of the computed key figures and conclusions drawing. The study applies multiple approaches to data collection as it utilizes a quantitative financial statement analysis and a qualitative interview study. Creswell (2003) defines this as the mixed method strategy of inquiry. The authors decided to examine the possibilities of this approach because they had found out in their previous studies (A1 – A4) the limitations of the quantitative approach. The authors aimed at achieving a more elaborated analysis on working capital management with the support of interviewees working in the industry. The strategy followed in article 5 can be described as a sequential procedure. At first, the study follows the steps of the financial value chain analysis. The exception in this study is that the data was collected from a database. The use of a database was possible because the sample only included Finnish pharmaceutical companies. It was considered that the information in the database was sufficiently reliable because the companies included in the sample have prepared their financial statement following the accrual basis of accounting. The interviews were carried out after the analysis of the results of the financial statement analysis. The results of the financial value chain analysis were introduced to the interviewees at stage two.
The first revelation of the interviews was that the CCC was not a familiar measure for the interviewees. However, the interviewees considered working capital management to be important for companies. It was found out that the components of CCC were monitored, and DSO and DPO were set as personal goals for those who negotiate with customers or suppliers. The second revelation was that working capital performance is satisfying for each player in
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the value chain. The companies are essentially interested in developing their own working capital management; collaboration with the supply chain partners is not considered.
Nevertheless, the use of a mixed research method did not lead to substantially better results compared to the ones the authors had achieved in their previous studies. The analysis on working capital management with the support of interviewees did not extend previous findings, which may be explained by 1) the professional position of the interviewees, 2) the fact that the pharmaceutical business is regulated by government, and 3) the fact that liquidity has not been a problem for Finnish pharmaceutical companies.
5.6 Summary
Articles 1 to 4 indicate that the financial value chain analysis method is a potential tool when studying financial value chain phenomena. The key figure of this research was the cash conversion cycle in the context of the study of working capital management of value chains. Other financial aspects of value chains can be analyzed with this method, e.g. asset management, profit management or treasury management of value chains. In such cases, a key figure can be computed to support the analysis. The financial value chain analysis is presented in this thesis as a quantitative method. Furthermore, the study supporting article 5 tested the development of the method through qualitative strategies of inquiry. The results obtained do not support such development. Further research can result in additional contributions to this discussion. Nevertheless, the results of this research project show that the financial value chain analysis method contributes to the understanding of the financial value chain level phenomenon.
The working capital management of value chains was studied in the automotive, pulp and paper, and information and communication technology industries. The findings suggest that the relation between tied-up working capital and sales is a constant over the five year period, since the CCCs of value chains remained almost the same during the research period. The CCCs of the value chains are 70, 60 and 40 days, respectively, and the CCC of value chains differs significantly. The difference in CCCs is mainly a result of the different cycle times of inventories. The variance between the DIOs of automotive and pulp and paper is on average 74%, 91% between automotive and ICT, and 99% between pulp and paper and ICT. The components of CCC, especially the trade credit components, have varied a lot during the research period. The development of components has not been identical with the value chains or within the value chains. This would seem to indicate that the benefits achieved in some components lead to the development of disadvantages in some other components of CCC.
The working capital management strategies of the different stages differ from each other in the studied value chains. The most similar strategies can be seen in the value chain of the pulp and paper industry, since the average CCCs of paper and board, printers and merchants are
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almost the same. The findings suggest that stages having consumers as customers have the ability to achieve a shorter CCC on average compared to other stages in the value chain. Furthermore, well-known brand names may give negotiating power to a company compared to the rest of the companies in the value chain. Nevertheless, car manufacturers own well-known brand names and their CCC is the longest of the value chain of automotive industry. However, if the car manufacturers had not offered financial services for their end customers, they would have one of the shortest CCCs of the value chain of automotive industry.
The financial crisis of 2008 affected the working capital management of the value chains of the automotive, pulp and paper and ICT industries similarly. Both the cycle times of accounts receivable and accounts payable increased between 2008 and 2009. The lengthening of the DSO and DPO suggest that the companies used more trade financing during the crisis than before the crisis.
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6 Conclusions
This chapter summarizes and discusses the main findings of this dissertation. The findings are presented as theoretical and practical implications. The chapter will analyze critically the validity and reliability of this research and lastly connect the results with opportunities for further research. This thesis contributes to several management streams of which the most relevant are inter-organizational cost management, financial supply chain management and lean management.
6.1 Theoretical implications
This dissertation introduces the financial value chain analysis method which was designed for studying industry-level phenomena. Analyzing a value chain with this method gives a holistic picture of the value chain with financial figures. The method may be a solution to the request presented in previous studies which asked to analyze working capital management from the value chain perspective (Hoffman and Kotzab, 2010; Grosse-Ruyken et al., 2011; Viskari et al., 2012a). The validity of the financial value chain analysis method is established in this research project by conducting studies in the value chains of the automotive, pulp and paper and information and communication technology industries.
The working capital management of value chains was measured by the cash conversion cycle. This study supports the opinion that the CCC developed by Richards and Laughlin (1980) is a good indicator of working capital management. Even though it was developed for the company level, it is a proper measure also in the value chain context. The findings of this study suggested that the cycle time of working capital, i.e. the relation between tied-up working capital and sales, is a constant in the value chain context. The finding supports the results of Losbichler et al. (2008). They concluded that, on average, companies were only able to decrease the CCC slightly. Previous research on the cycle time of working capital has detected a decreasing trend of CCC over time (Belt, 1985; Farris and Hutchison, 2003). The decreasing trend of CCC cannot be seen when companies entered the 21st century. This might indicate that advances in technology, communications and logistics leading to the shortening of DIO have been minor in the 21st century. Considering that inventory logistics have achieved high levels of sophistication and efficiency, such status quo cannot lead to remarkable improvements in the cycle time of inventories. On the other hand, Losbichler et al. (2008) concluded that the supply chain management projects companies may have carried out have not affected the cycle time of inventories broader than in a single company context.
The findings suggested that the cycle time of working capital varies between the industries: 70 days in the automotive industry, 60 days in the pulp and paper industry, and 40 days in the ICT industry. The main reason for this variation is the different cycle times of inventories, which support the results of Farris and Hutchison (2003) and Hill et al. (2010). Within the value chains, the different cycle times of inventories explain the difference in the CCCs of the
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stages as well. Variation in the DIO has been modest between the years 2006 and 2010. The variation in the DIO between the value chains could be partially explained by different methods of production and outsourcing strategies. Because of these findings, it can be concluded that the cycle times of trade credits are similar in the different value chains.
Belt (1985) found that economic crises have lengthened the CCCs of US manufacturing companies. The findings of this study showed that the lengthening of CCC is not an immediate consequence of a financial crisis. Only the CCC of the automotive industry lengthened. The structural crisis of the industry may have caused the increase of CCC due to a lack of demand on the part of nervous private consumers. It can be assumed that financial crises hit manufacturing companies’ cycle time of working capital harder than that of service companies. The CCC of the ICT industry, which can be considered a service industry, remained unchanging during the financial crisis. The pulp and paper industry has coped with structural problems before the onset of the 2008 financial crisis. They probably have had to pay attention to working capital management before the crisis to ensure their liquidity, so the mechanisms to fit the operations under new conditions were not so difficult to conduct. Kesten et al. (2012) suggest that trade credits decreased during the 2008 financial crisis. The results of this study showed that the cycle times of both accounts receivable and accounts payable of the value chains increased between 2008 and 2009. The measurements of Kesten et al. (2012) differ from the measurements of this study. Their measurements are based on assets; accounts receivable and payable are divided by the total assets which measure the structural change of the assets, not operational effectiveness which was the focus of this study. The results of Petersen and Rajan (1997) also indicate the lengthening of the DSO during economic shocks.
The cycle times of working capital of US based companies have been shown to be shorter on average than those of Europe-based companies (REL, 2013a; REL, 2013b). The findings of this study are consistent with past research. The value chain of automotive industry mostly consists of Europe-based companies, the value chain of pulp and paper industry represents Europe and US based companies, and the value chain of ICT industry includes the most US based companies. However, the country-specific issues are not the reason for variation in CCCs. The cycle time of inventories is the more important factor.
6.2 Practical implications
By analyzing working capital management with the method of financial value chain analysis, a company receives a holistic view of the value chain in which it operates. On the other hand, the company can benchmark its position against competitors in its own stage and its position in the value chain, but the company can also see the most efficient partners and the chain to which it wants to belong. This could be implemented as a part of the competitor cost analysis, for example. Besides this, it is worthwhile to benchmark other industries as well in order to
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adopt suitable practices for working capital management. In the light of this study, a company should benchmark its working capital management against companies which operate in a similar manner, i.e. which have similar business models; not necessarily against the other companies in the same value chain, but, for example, manufacturing companies if the company is a manufacturer, and so on.
The cycle time of inventories depends more on the policy of production and inventory management than on the terms of purchase and sales, which define the turnover time of accounts receivable and payable. Evidence on the benefits of just-in-time (JIT), lean and similar policies in managing the physical supply chain has been given, but the terms of purchase and sales still follow the traditions adopted after an era of cash payments. The problems of financial flow may hinder staying lean, however. The terms of payment are bargaining issues which could be redefined without jeopardizing the production of the physical product. The author would like to emphasize the meaning of relatively long payment periods for working capital. Long credit terms could be a part of sales promotion or required by the customer, but companies should also consider that the credit periods also tie up capital into the value chain. Availability of trade credit is not a major influence on supplier choice, the result of Summers and Wilson (2002) should encourage companies to develop their trade credit policies. The companies of a value chain should collaborate towards a global financial strategy to optimize financial flows through the value chain in addition to the efficiency and synchronization of physical flows which are directly related to products. The financial value chain analysis study is the extension of the value chain analysis of the strategic cost management presented by Shank and Govindarajan (1993). A value chain could probably apply the techniques of strategic cost management to analyzing its strategic position as well. For example, the methods of total cost of ownership and life cycle costing might be beneficial as well, when a value chain aims at achieving competitive advantage.
Indeed, the amount of working capital tied into the value chain directly affects its relative profitability (ROI). New supply chain financial tools introduced by some authors, e.g. Hofmann and Belin (2011), should be considered as helping in solving problems caused by notable differences in the negotiation power of value chain partners and probably improving the self-financing of the value chain.
6.3 Validity and reliability
This chapter discusses how well the research methodology and used methods are able to study working capital management in the value chains. Additionally, one of the results is studied with regression analysis to confirm the result.
To analyze the content validity, i.e. logical validity of study, the philosophical foundation of this study is critical. The two schools of paradigms, postpositivism and pragmatism have
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outlined the direction of this study. At the beginning of the research project, it was considered that the knowledge on working capital management was not sufficient, for example, to change working capital management practices, which is common for participatory theory. This assumption was right since the working capital management studies in the context of value chain were few. More knowledge is still needed before ultimate guidelines could be set.
To assure construct validity, the cash conversation cycle was selected as measure of working capital management based on prior literature. The results would probably have differed if asset based working capital management measures would have been used to study working capital management. Those do not consider the operational aspects of working capital, which were important in this study. The financial value chain analysis method can be considered as an extension of the prior value chain methods which have been accepted in academia. In general, the ability of financial value chain analysis to measure the working capital management of value chains depends remarkably on the definition of value chain. The results of this study indicate the state of working capital management of relatively big companies in each studied value chain, as small and medium sized companies seldom publish their annual reports in public. The results of the study appear to be logical. As this research is among the first to study working capital management in value chains, comparison to prior literature is narrow. Finding that the CCC of upstream is longer than that of downstream is supported by previous literature. The sample of the study may have affected the cycle time of working capital in each value chain but it is reasonable that the CCC of ICT industry is shorter than the CCC of automotive and pulp and paper industries.
The results of this study suggest that the CCC of value chains has been fairly constant during the research period. To analyze this finding, the linear regression analysis is built. It has been studied whether the CCC of 2006 can predict the CCC of 2007, 2008, 2009 and 2010. Company level data has been utilized in the analysis. Table 18 shows the results of the R-squared analysis and the F-test for automotive, pulp and paper and ICT industries.
Table 18. Results of regression analysis
Automotive Pulp and Paper ICT Dependent variable R Square F R Square F R Square F CCC 2007 0.747 182.98 0.911 407.56 0.901 546.83 CCC 2008 0.768 205.76 0.769 133.15 0.818 270.47 CCC 2009 0.625 103.19 0.561 51.01 0.839 313.01 CCC 2010 0.524 68.27 0.648 73.73 0.724 157.23
Predictor CCC 2006, significance p < 0.001
Based on regression analysis, it can be considered that the claim of constant CCC is acceptable. The linear models fit the set of observations in 2007 and 2008 well. The companies’ CCC in 2006 can be used to forecast their CCC in 2007 and 2008. The more time has passed, the more the validity of the model fails. However, the models can explain over half of the dependent variable’s value in each year. All the models are statistically significant.
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It can be considered that the data used in this research is reliable. The audited financial statements of companies should be free from significant misstatements and those should faithfully represent the financial performance and position of the company. The incompleteness of the studied value chains compared to the real world is the most relevant threat to the reliability of the study. For example, the samples only include a few companies compared to the real world, and those do not cover all the activities needed to deliver an end product. Despite the shortcomings, it seems that there is no leak of working capital into or out of the value chains. The results showed that if a stage of a value chain has gained benefits or encountered disadvantages measured by CCC, it has happened at the expense of the other stages, not because of some inexplicable reason.
The value chains of this study can be considered descriptive. This is the result of the idea of forming stages. It can be assumed that in the real world, downstream companies do place orders at some suppliers and they do not order from every company included in their supplier stage. Because of this, the CCC of a real value chain formed by companies co-operating with each other may differ from the results of this study.
6.4 Recommendations for further research
Firstly, country specific issues should be studied more comprehensively since capital is relatively free to move. In the light of this study, it seems that the cycle time of inventories differs from the CCCs of value chains, but the results of the 1,000 European and US companies’ cycle time of working capital suggests that country-specific issues may cause differences as well. However, the 1,000 largest European and US companies are not completely comparable because the economic structure of Europe and US differs from each other. As previous studies have shown, the banking environment influences the trade credit of companies which do not have access to global financial markets. The reasons for large companies are not known. In the light of this study, it would be better for a small company to have a large US company as customer because, on average, it pays faster than a European one. Furthermore, the EU directive which aims at limiting the payment time to 60 days is interesting, since its enforcement must be difficult. What are the noticeable results of the directive for companies? These aspects deserve attention from both the theoretical and practical perspectives.
Secondly, considering that plenty of solutions for inventory management are being developed, effort should be put into developing the financial supply chain which can be considered a part of inter-organizational management. Particularly, the bilateral and multilateral context should be considered, while inventory management may be studied in a single company context. The development of working capital management should be studied in tandem within a company and within the value chain it operates in. Thus, cooperation between multiple players to improve the working capital management of a value chain can be a key factor in supporting
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the competitive advantage of a value chain. It would be beneficial to study the possibilities of lean management philosophy in the context of financial value chains. Most of the companies having implemented lean have achieved promising results concerning physical supply chain activities.
Thirdly, the effects of the financial crisis on the working capital management of companies should be studied through case studies. It has been suggested that the liquidity problems of upstream companies make it more difficult for downstream companies to follow the lean management philosophy, since the delivering reliability of suppliers may be endangered. For the upstream company, the liquidity problems of a downstream company may cause problems as well when the customer is unable to pay its orders or cannot order because of liquidity problems. Also, it would be interesting to study the behavior of powerful companies, especially during the financial crisis. Has it supported its customers and suppliers, or has it only ensured its own position? A researcher may find the leading company of a value chain with the help of Porter’s five forces analysis, for example.
Fourthly, the studies on working capital have mainly been limited to financial or operational issues. Researchers have not seen working capital as an investment, which has remained in the background in this dissertation as well. Similarly to fixed investment, working capital investments have to be financed with shareholders equity or liabilities.
Lastly, this study applies mostly quantitative methods. The results of quantitative analysis suggest several reasons and impacts which could be deeply understood through case studies and interviews. Before interviewing, it should be resolved who the interviewees are. There is no person whose position is called working capital manager, and the chief executive officer responsible of working capital management as a last resort may answer questions too generally. With qualitative methods it might be possible for example to study different working capital management models which companies follow, and what are the pros and cons of those for a single company and the value chain in which it operates.
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Part II Original publications
Article 1
Lind, L., Pirttilä, M., Viskari, S., Schupp, F. and Kärri, T. (2012) ‘Working capital management in the value chain of automotive industry: financial value chain analysis’, Journal of Purchasing and Supply Management, Vol. 18, No. 2, pp.92-100.
Link: http://dx.doi.org/10.1016/j.pursup.2012.04.003
Copyright © Elsevier B.V. Reprinted with permission.
Working capital management in the automotive industry:Financial value chain analysis
Lotta Lind a,n, Miia Pirttila a,1, Sari Viskari a,2, Florian Schupp b,3, Timo Karri a,4
a Department of Industrial Management, Lappeenranta University of Technology, P.O. Box 20, FIN-53851 Lappeenranta, Finlandb Technische Universitat Berlin and Schaeffler Technologies AG & Co. KG, Industriestraße 1-3, 91074 Herzogenaurach, Germany
a r t i c l e i n f o
Available online 3 May 2012
Keywords:
Working capital management
Value chain
Automotive industry
Cycle times
a b s t r a c t
Financial value chain analysis is used to examine working capital management by cycle times in the
value chain of the automotive industry during 2006–2008. The applied method offers a holistic view of
the value chain from raw materials to the end customers. The average cash conversion cycle of the
value chain of the automotive industry was 67 days. According to the study, the change of cycle times
of working capital followed mainly the change of cycle time of inventories. The position of the stages of
the value chain measured by the cash conversion cycle did not change substantially from 2006 to 2008.
& 2012 Elsevier Ltd. All rights reserved.
1. Introduction
1.1. Background
Working capital management is an essential part of the short-term finance of a firm. With an efficient working capital manage-ment, a company can release capital for more strategic objectives,reduce the financial costs, and improve profitability. Supply chainmanagement has typically concentrated on the physical flow ofgoods and services. Working capital management represents,however, the management of financial flows, which was high-lighted by the recent financial downturn.
The recent financial crisis had major effects on the automotiveindustry, but in fact the industry faced profitability problems evenbefore the crisis, and suffered from raised pressure on costs andcompetition. The situation has aroused interest in improvingworking capital management. At present companies see it as animportant part of the management. This was also stated by theBMW Group (2010) in their annual report of 2009: ‘‘Stringentworking capital management is a further key parameter formanaging the business’’.
1.2. Objectives and research methods
The objective of this study is to examine working capitalmanagement in the value chain of the automotive industry inthe years 2006–2008 by using financial value chain analysis. Thepurpose is to analyze working capital management through thevalue chain from the raw material suppliers to the end customers.The research design is similar to the one applied by Pirttila et al.(2010) in their study of working capital management, where thecycle times of working capital in the value chain of the pulp andpaper industry in the years 2004–2008 were analyzed.
The main research question of this paper is as follows: How wasworking capital managed in the value chain of the automotiveindustry during the observation period? The main question isdivided to the following sub-questions: What were the cycle timesof working capital in the stages of the value chain? How did thecycle times of working capital and its components change duringthe observation period? The results of the study are also comparedto previous studies on the working capital management.
In this study, we introduce a method of financial value chainanalysis that shows the position of the value chain and its stages andcompares the stages of the value chain during the selected observa-tion period. The method reveals the performance of the stages andits effects for the rest of the value chain. This is a systematic methodto analyze value chains. The financial value chain analysis consists ofseven steps that follow each other. The phases are presented inFig. 1: (1) choose the industry under study; (2) define the valuechain, including the stages and companies; (3) define the keyfigures, (4) collect data for the period under analysis; (5) calculatethe values of the defined key figures; (6) analyze the calculated keyfigures and (7) draw conclusions. Analyzing the value chain this waygives a holistic picture of the value chain with financial figures.
Contents lists available at SciVerse ScienceDirect
journal homepage: www.elsevier.com/locate/pursup
Journal of Purchasing & Supply Management
1478-4092/$ - see front matter & 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.pursup.2012.04.003
n Corresponding author. Tel.: þ358 44 597 1358; fax: þ358 5 621 2644.
E-mail addresses: [email protected] (L. Lind),
[email protected] (M. Pirttila), [email protected] (S. Viskari),
[email protected] (F. Schupp), [email protected] (T. Karri).1 Tel.: þ358 5 621 2675; fax: þ358 5 621 2644.2 Tel.: þ358 5 621 2657; fax: þ358 5 621 2644.3 Tel.: þ49 172 841 5370.4 Tel.: þ358 5 621 2636; fax: þ358 5 621 2644.
Journal of Purchasing & Supply Management 18 (2012) 92–100
After the industry to be studied has been defined, the firstphase in forming the value chain is to define its structure. Thenumber of stages may vary, but in general the value chain shouldinclude more than three stages for the financial value chainanalysis, as the purpose of the method is to give a broad viewof the value chain from the raw material suppliers to the endcustomers. To ensure that the value chain is reasonable anddescribes the situation in the real world, a discussion withprofessionals working in the industry could be useful duringphase two. The key figures should be selected so that the datacan be collected from public sources. The data used in the methodis commonly figures of financial statements, as they are publishedregularly and follow accepted accounting principles. The financialvalue chain analysis summarizes the calculated values of the keyfigures for the stages of the value chain. The results are analyzedsimilarly at the stage level. The method of financial value chainanalysis is designed for analyzing industry-level phenomena,even though it is based on the key figures of companies.
The paper is structured as follows: Section 2 presents the keyfigures of the study, introduces the findings of previous literature ofworking capital management, and expresses the relevance of study-ing working capital management in the value chain context. Theresearch process, data and limitations are described in Section 3. Theresults are presented in Section 4, and the conclusions in Section 5.
2. Literature review
In this study, working capital is studied from the operationalperspective. Working capital can be defined from the operational
perspective (inventories plus accounts receivable less accountspayable) or from the financial point of view (current assets lesscurrent liabilities). The latter one is actually net working capital,but commonly referred to as working capital. Working capitalshould be considered as an investment for a firm. Inventories andaccounts receivable represent tied-up capital that could be earn-ing interest if invested for example in financial instruments.Accounts payable decrease the tied-up capital. Firms that allowcustomers to make purchases on credit usually acquire goods andservices on credit as well. In the value chain context, the accountsreceivable of the supplier are equivalent to the accounts payableof its customer.
One measure of working capital management is the CashConversion Cycle (CCC), developed by Richards and Laughlin(1980). It is also known as the cash-to-cash (C2C) cycle (Farris andHutchison, 2002). The CCC presents the length (days) of the time afirm has funds tied up in working capital, starting from the paymentof purchases to the supplier and ending when remittance of sales isreceived from the customers. The CCC consists of the cycle times ofinventories, accounts receivable and accounts payable, and isdefined as days inventory outstanding (DIO)þdays accounts recei-vable outstanding (DSO)�days accounts payable outstanding (DPO).The DIO is calculated as [inventory�365]/sales. The DSO is calcu-lated as [accounts receivable�365]/sales. The DPO is calculated as[accounts payable�365]/sales. The CCC is illustrated in Fig. 2, and anumerical example is provided in Table 1.
Fig. 2 visualizes a positive CCC. In this case the company has tofinance accounts receivable and partially inventories. There isevidence that a company can operate with a negative CCC (forexample Apple Inc.), or the CCC can be null. The CCC is commonlycalculated at a company level, but there is no obstacle to loweringthe calculation level to a business unit, a customer, or even anorder. The CCC is a valid measure for the managers of a company.The importance of the CCC from the perspective of value chainmanagement is that it bridges through purchasing activities with
1 Choose industry
2 Define value chain
3 Define key figures
4 Collect data
5 Calculate values of key figures
6 Analyze key figures
7 Draw conclusions
Fig. 1. Method of financial value chain analysis.
Time (days)
Purchase Cash outlay Product sales Cash received
DPO
DIODSO
CCC
t0 t1 t2 t3
Fig. 2. Cash Conversion Cycle (adapted from Richards and Laughlin, 1980).
Table 1Selected financial data of BMW and Cash Conversion Cycle and its components.
2008 2007 2006
Million EUR
Sales 53,197 56,018 48,999
Inventories 7290 7349 6794
Accounts receivable 18,176 16,668 14,761
Accounts payable 2562 3551 3737
Number of days
DIO 50 48 51
DSO 125 109 110
DPO 18 23 28
CCC 157 133 133
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–100 93
suppliers, internal supply chain activities and sales activities withthe customer (Farris and Hutchison, 2002). Table 1 illustrates theprimary point of the CCC with a numerical example. The short-ening of the cycle time of inventories (DIO) from 2006 to 2007 didnot improve the CCC, because at the same period the cycle time ofaccounts payable (DPO) shortened and offset the impact of theimproved DIO. From the value chain point of view, a shortenedDPO poses a lower risk to the suppliers. The DIO reflects mainlythe efficiency of the internal supply chain, and therefore itschanges do not affect the other actors of the value chain directly.The increase of the CCC from 2007 to 2008 indicates that themanagement of working capital was not as efficient in 2008 as itwas in the previous years.
Traditionally, the cost of goods sold (COGS) has been used as adenominator when calculating the cycle times for inventories andaccounts payable. In this paper, the CCC actually indicates ‘‘thenumber of ‘days sales’ the company has to finance its workingcapital under ceteris paribus conditions’’ (Shin and Soenen, 1998,p. 38). When the value of sales is used instead of the COGS as thedenominator, the turnover time of inventories and accountspayable is shorter for most companies, because the value of salesis normally more than the value of the COGS. Some companiesprovide the value of the COGS in their financial statements, but itis not discussed how it has been defined. Therefore it is notunambiguous to define the value of the COGS on the basis ofpublic sources for those who do not report it. To fulfill theobjectives of this study, the use of sales as a denominator wasreasonable, because the different cost structures of the companieswould have blurred the information of the analysis.
The previous literature of working capital management hasconcluded that companies can increase their profitability byshortening the CCC (e.g. Shin and Soenen, 1998; Deloof, 2003;Lazaridis and Tryfonidis, 2006; Grosse-Ruyken et al., 2011), butthere are also arguments against a short CCC. A long cycle time ofinventories reduces the risk of delivery interruptions, pricefluctuations and business losses due to scarcity of products(Blinder and Maccini, 1991; Wang, 2002), and a company cansometimes achieve higher sales and strengthen its customerrelationships with a generous trade credit policy (Long et al.,1993; Deloof and Jegers, 1996; Shah, 2009). However, in previousacademic literature, working capital has been mostly consideredfrom the perspective of an individual company. The literaturelacks the perspective of the value chain. It is even more difficult toadjust the proper cycle time of working capital and its compo-nents, if we take the perspective of the whole value chain.Attempts to tighten the payment periods of the big actor createliquidity pressures to the other companies of the value chain(Blackman and Holland, 2006). On the other hand, in the valuechain a strong dominant player could finance weak subcontrac-tors and customers by adjusting the payment periods and creditterms (Saranga, 2009).
Losbichler et al. (2008) studied a dataset of 6925 Europeancompanies for the period 1995–2004. Their results show thatcompanies were on average able to decrease the CCC only by 2days between 1995 and 2004. To study whether there areindustries or companies which reduce their CCC at the expenseof other companies in the value chain, Losbichler et al. linkedindustries which typically supply to each other. They found outthat the leading industry of a value chain was able to shorten itsCCC more significantly than its supplying industries. Pirttila et al.(2010) researched the cycle times of working capital in the pulpand paper industry and found also that working capital manage-ment is more efficient in the downstream, nearer to the endcustomer. Moss and Stine (1993) investigated retail firms andshowed that the length of the CCC was inversely related toaverage sales, as the smallest 20% of the companies had a
significantly longer CCC than the largest 20% of the companiesof their dataset. Saranga (2009) found empirical evidence thatefficient working capital management resulted in higher opera-tional efficiency in the value chain of the auto-componentindustry. Ulbrich et al. (2008) studied working capital manage-ment in the automotive industry by comparing the cash conver-sion cycle and its components between car manufacturers andtheir suppliers, but the perspective of a broader value chain wasnot considered. In this study, we examine the state of workingcapital management from the value chain perspective in theautomotive industry.
According to Porter (1985), a sustainable competitive advan-tage can be achieved either by reducing the costs of the valuechain or by reconfiguring the value chain the company operatesat. Shank and Govindarajan (1989), who introduced value chainanalysis, argue that the decisions should be analyzed in the widercontext of the value chain, not just from the perspective of onecompany and its closest suppliers and customers. The performerof the analysis should look beyond the organizational boundariesof the value chain from upstream to downstream. Hofmann andKotzab (2010) emphasize that working capital managementshould be analyzed in the value chain context. The method ofanalysis used in this study, referred to as financial value chainanalysis, extends the analysis to the industry level.
3. Research process, data and limitations
The research process started with defining the structure of thevalue chain of the automotive industry. The value chain wasformed by discussions with managers working in the automotiveindustry, and value chains presented in previous literature(Wheelen and Hunger, 2002; Blackman and Holland, 2006;Heneric et al., 2005) offered a basis for the construction of thevalue chain of the study. Fig. 3 presents the value chain referencesfrom previous literature. The bottom value chain in Fig. 3 describesthe value chain structure of this study, six stages before the endcustomers. The stages raw material suppliers, refined raw materialsuppliers, component suppliers, system suppliers, car manufac-turers and car dealers (see Fig. 4) represent the main elementsneeded for producing and delivering a car for the end customer.The first three stages have been divided further to branches. Itshould be noted that the upstream of the value chain, especiallythe raw material suppliers, are suppliers to other industries as well.As our target was to observe the value chain from raw materials tothe end customer, the stage of raw material suppliers (branches oiland iron ore) were included in the analysis.
Secondary data was used in this study, because it was obtainedfrom financial statements and annual reports. A research imple-mented like this study is time-consuming compared to the use ofdatabases, but it ensures that the data is gathered in a similarmanner from each company included in the sample. There weretwo main requirements for the companies included in thesample: the financial statements had to be publicly provided,and the annual sales of the company had to be more than 100million euros, in order to ensure a higher degree of homogeneityof the stages. The companies of this study are named in Fig. 4 andlisted also in Appendix A1. The financial statements were col-lected from public sources: mainly the firms’ web sites, and somewere found in the German Company Register database, which isfree of charge and provided by the Bundesanzeiger (officialpublication of the Federal Republic of Germany published bythe German Department of Justice). The research sample presentsthe value chain of the automotive industry, and it has beenconstructed from the financial statements of 65 firms for eachyear of the 2006–2008 periods.
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–10094
Table 2 contains descriptive statistics on the sample: thenumber of firms, the range of assets and sales in 2008, the changepercentage of sales from 2006 to 2008, and the proportion ofworking capital of total assets of each stage.
Principally, the values a company has reported have been used.To ensure the homogeneity of the sample, some modifications tothe figures presented by a company have been made. Advancepayments to suppliers have been removed from the inventories.The inventories include raw material, work-in-process, finishedgoods or similar. The accounts receivable and payable reflect thereceivable and payable that are overdue within a year and arerelated to trade, for example note payable is not included in theaccounts payable.
The biggest restriction for the method presented here is theunavailability of data: the figures of annual reports are not
detailed enough to calculate the key figures or the annual reportis missing. In this study both problems occurred. The car manu-facturers Ford and Fiat, for example, had to be left out of the studybecause their long- and short-term liabilities were not presentedseparately in the balance sheets. The system supplier Delphi andthe chemical company Rhodia were excluded from the samplebecause annual reports for each year of the observation periodcould not be found.
The sample of this study has some limitations as well. Firstly,it has a strong regional focus because the research was done inEurope. It was difficult to find financial statements of Americanand Asian companies from public sources. The sample does notcover all the components of an automobile, as for exampletextiles, software and glass are missing. In the downstream side,independent garages and spare part shops, as well as car rentals
Raw materials
Primary manu-
facturingFabrication Product
producer Distributor Retailer
Raw materials
Oil
Ironore
Ref ine raw materials
Tier 3: Steel,
raw materials
Plastics and rubber
Steel and metal
Component manufacturer
Tier 2: Value-adding
parts
Plastic and rubber
components
Steel and metal
components
Electronics
Tier 1: Components
System suppliers
Car assembly
OEM
Car manufacturers
Car dealers
Car dealership
Retail
End custom
ers
Typical Value Chain for a Manufactured Product. (Wheelen and Hunger 2002)
A physical supply chain. (Blackman and Holland 2006)
Automotive value chain. (Heneric et al. 2005)
The value chain of the study.
Fig. 3. Structure of the value chain in the present and previous studies.
Stage 1: Stage 2: Stage 3:Raw material suppliers Refined raw material
suppliersComponent suppliers
Oil:Plastics and rubber:
Plastic and rubberBP
BASFcomponents:
ExxonMobilDuPont
DaetwylerRoyal Dutch Shell
EMSElringKlinger
TotalEvonik
Federal Mogul
LanxessPolytecSaint-Gobain Stage 4: Stage 5: Stage 6:
Iron ore: Steel and metal: System suppliers Car manufacturers Car dealersBHP ArcelorMittal Steel and metal LKAB Salzgitter components: BorgWarner BMW AVAGRio Tinto Stahl-Metall-Service Bekaert Bosch Daimler Autohaus WolfsburgVale ThyssenKrupp Georg Fischer Continental Geely Feser Graf
Voestalpine GKN Denso Honda LuegZAPP Miba Magna Hyundai Löhr & Becker
Neumayer Tekfor Mahle Nissan MAG MetzRheinmetall Schaeffler Renault WellergruppeRUAG Valeo ToyotaSeissenschmidt ZF VWTrimet
Electronics:Alps ElectricAustria MicrosystemsDraexlmaierHellaLeoniNidecTyco Electronics
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Fig. 4. Value chain of the automotive industry with the companies of the sample.
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–100 95
and leasing companies were left out of the analysis because of alack of public data detailed enough. However, it can be consideredthat including these branches in the chain would not have had asignificant impact on the main findings of the study. To test thevalidity of our results, we compared them to the results of Ulbrichet al. (2008) and the annual studies of working capital perfor-mance of the REL consultancy.
The inventories, accounts receivable and accounts payabledemonstrate a day’s value. During the fiscal year, the need forworking capital fluctuates depending on the company’s businesscycle. In the automotive industry, seasonal fluctuation is slightlymodest, and so the working capital levels of the end of the fiscalyear represent the need of working capital well. Public financialstatements present the fiscal year of a group of companies, and agroup with a broad product program may be seen for instance asa system supplier with one of their products, while anotherproduct would locate them in the stage of component suppliers.Similarly, a group may operate with other industries as well.
4. Results and analysis
To analyze working capital management in the value chain of theautomotive industry in the years 2006–2008, the CCC and itscomponents were calculated for each year of the observation period.Fig. 5 shows the average values of the CCC and its components indays in every stage of the value chain. Difference D has beencalculated between the years 2008 and 2006. Appendix A1 showsthe CCC and its components for each company of this study.
4.1. Cycle times of working capital
The results of the analysis showed that the value chain of theautomotive industry ties up working capital: the CCC was positivein each stage of the value chain. The average CCC of the average ofthe value chain stages was 67 days, while the average of thesample was 70 days. There were only slight differences in theaverages of the CCC between the years 2006 and 2008, whichindicates that the relation between sales and working capital couldbe considered constant (the CCC was defined as working capital/sales). This was seen also when studying the CCC in the value chainof the pulp and paper industry (Pirttila et al., 2010). The resultsindicate that working capital can be forecasted by the sales in bothvalue chains. The equation of the CCC enables forecasting when theCCC is known and the sales forecast is available. How the working
capital will be divided between inventories, accounts receivableand accounts payable is not important in the forecasting phase,because it is the increase of working capital that should befinanced or decrease of working capital that should be reinvested.
Pirttila et al. (2010) and Losbichler et al. (2008) found that thestages closer to the end customer had shorter turnover times thanthe stages in the upstream. In the value chain of the automotiveindustry, the situation was different—the phenomenon did notexist. The reason for this was a high CCC of the car manufacturersin the downstream. The car manufacturers’ long CCCs were due toa long cycle time of accounts receivable. This is because thefinancing business of car manufacturers requires long creditperiods. If the DSO of the car manufacturers did not include thereceivables of their financing business, their CCC would besignificantly lower, only 32 days, and the same conclusion of ashorter CCC in the stages closer to the end customers could bedrawn. The car manufacturers can be seen to work as a banktowards the end customers by paying their own bills relativelyfast compared to the cycle times of their accounts receivable.
The position of the stages measured by the CCC did not changesubstantially from 2006 to 2008. The single change in the position ofthe stages was that the stage of the system suppliers managed toreduce its CCC by 4 days, while the CCC of their customer stage, thecar manufacturers lengthened by 5 days. Comparing the stages ofthe automotive value chain to each other revealed that there was anotable difference between the maximum and minimum CCC: 69days. The car manufacturers had the longest CCC, 106 days, whichwas due to their accounts receivable that consisted mainly ofreceivables from their financing business, which seemed to beprofitable for the car manufacturers. For example the EBIT marginof Volkswagen Financial Services was 8.7%, while the margin of theGroup was 5.6% in the year 2008. The raw material suppliers had theshortest CCC, 37 days, which reflected the cycle time of accountsreceivable, as the accounts payable offset the need of financinginventories. The first stage of the value chain differed from the otherstages by having the biggest changes in each component of the CCC:its components shortened by 7–11 days from the years 2006 to2008. The raw material suppliers operate also in many otherindustries, and therefore the development of the automotive indus-try is not the only one that affects its cycle times.
4.2. Cycle times of the components of working capital
Even though the CCC remained roughly the same during theobservation period in each stage of the value chain of the automotive
Table 2Descriptive statistics on the sample.
Number of
firms
Total assets 2008
(Mh) max
Total assets 008
Mh) min
Sales 2008 (Mh)
max
Sales 2008
(Mh) min
Change of sales
2006–08 (%)
Working capital % of
total assetsa
Car dealers 7 327 86 1 217 278 5 41
Car manufacturers 9 190 628 993 134 661 420 �8 20
System suppliers 9 46 761 3 173 45 127 3 579 3 18
Component suppliers – – – – – – 22
Plastic and rubber
components
5 43 395 765 43 800 658 5 24
Steel and metal
components
9 5 107 115 5 496 175 14 21
Electronics 7 14 686 307 10 086 185 �3 21
Refined raw material
suppliers
– – – – – – 30
Plastics and rubber 5 50 860 1 058 62 304 947 10 37
Steel and metal 6 90 742 34 84 947 143 48 21
Raw material suppliers – – – – – – 8
Oil 4 192 011 118 310 312 478 179 976 15 8
Iron ore 4 60 932 3 778 40 437 2 405 70 8
a Average of the years 2006–2008.
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–10096
industry, the components of the CCC varied a lot. This was not shownin the CCC, because usually the variations of the DSO and DPO offseteach other. The change that occurred resulted from a change ininventories. For example in the stage of component suppliers branchof electronics, the DSO shortened by 14 days, but at the same timealso the DPO shortened by 13 days. Therefore the change of the CCC,3 days, was mainly affected by the lengthened cycle time of theinventories. In the supplying industry (stages 2–4), the differences inthe CCC were caused by the cycle times of inventories. The differencein the CCC between the system suppliers and component supplierswas 17 days, whereas the difference in the DIO was 16 days. Betweenthe stages component suppliers and refined raw material suppliers,the difference of the CCC was 12 days, which is the same as thedifference in the DIO. The average figures of the DSO and DPO werealmost the same on stage 3 as on stage 4. It seems that the paymentterms are relatively well established on these supplier levels.
In most stages the changes of both the DSO and DPO werenegative, which means that in these stages the cycle times ofaccounts receivable and accounts payable shortened during theobservation period. The changes of the DSO and DPO in the stageof the car dealers were positive. Only in the branches of plasticand rubber components and steel and metal components in thestage of component suppliers the development of the DSO andDPO led to different directions: both branches were able to reducetheir DSO by 5 days while the DPO prolonged by 1–3 days. Thefinding of a reduction of the DSO and DPO in most stages of thechain indicates that the tightened payment terms required by asupplier affect the credit terms given to a customer: in otherwords, when a company is required to pay its suppliers faster, italso wants to get faster payments from its customers becausethey are not willing to invest more capital. Especially if there aredifficulties in getting external financing, collecting payments fromcustomers faster is reasonable from the supplier’s point of view.The negative direction of the change may have also been aconsequence of profitability problems that the automotive indus-try has been facing in recent years: in all stages, except for the cardealers, the DSO was shortened by 5–14 days. The systemsuppliers, for example, were able to reduce their DSO by 22%.This indicates that in the value chain of the automotive industry,the companies have paid attention to the management ofaccounts receivable as they have not been willing to carry creditrisk. This has been done partially by using more factoring services.When selling accounts receivable to a third party, the DSO of afirm looks shorter even if the payment terms given to a customer
are generous. It seems that none of the stages really got benefitsfrom the shortening of the DSO, as the trend was dominating inthe whole value chain, but overall this reduced the need forinvested working capital in the value chain of the automotiveindustry.
The traditional view on working capital management has beenthat inventories can be financed with accounts payable. Whencomparing the components of the CCC to each other, it could beseen that only the system suppliers (excluding the last year of theobservation period) had been able to finance their inventorieswith accounts payable. The cycle times of inventories werelengthened by 2–9 days or remained almost the same in theobservation period, except for the raw material suppliers thatwere able to shorten their DIO by 7 days from the years 2006 to2008. This indicates that making sales forecasts became moredifficult during the period, and therefore the inventories tied upmore working capital in 2008 than in 2006.
The DSO and DPO depend on the payment terms negotiated withthe customers and suppliers. If the firm is willing to shorten the CCC,the component it can best affect by itself is the DIO by developingthe internal value chain. Of course, depending on the contractsbetween the companies, the DIO can also be affected by a customer,if for example a certain level of inventories is required by them. Thesystem suppliers had a relatively short DIO, 40 days. Their suppliersin turn kept their inventories 16 (stage 3) or even 27 (stage 2) dayslonger. This might reflect the information flow in the chain: thesystem suppliers get sales forecasts from their customers, whichmakes it possible for the system suppliers to manage their inven-tories on the basis of that information, but the information is nottransferred to the earlier levels in the chain. The relationshipbetween the car manufacturers and the system suppliers is basedmore on partnership, while there is more traditional purchasingbetween the system suppliers and their suppliers.
The products of the refined raw material suppliers andcomponent suppliers are more standard than the products ofthe following levels, which enables mass production and leads tobigger inventories that also explain the longer cycle times ofinventories. In stage 3, which operates as suppliers for the systemsuppliers, the reliability of delivery may create a competitiveadvantage for the companies that are able to supply goods fortheir customers when needed. The weakened demand in theautomotive industry could also be seen in the value chain, asthe inventories in the stage of system suppliers and componentsuppliers had increased. The raw material suppliers and refined
Stage 1: Stage 2: Stage 3:Raw material suppliers Refined raw material
suppliersComponent suppliers
Oil: Plastics and rubber: Plastic and rubber components:AM 06 07 08 Δ AM 06 07 08 Δ AM 06 07 08 Δ
DIO 22 23 27 15 -7 DIO 54 55 53 55 0 DIO 55 52 56 58 6DSO 34 37 40 24 -13 DSO 53 57 55 46 -11 DSO 52 53 56 48 -5DPO 34 36 40 25 -11 DPO 28 31 29 25 -6 DPO 33 31 35 34 3CCC 22 24 27 14 -9 CCC 79 80 80 76 -4 CCC 75 75 76 73 -1 Stage 4: Stage 5: Stage 6:
System suppliers Car manufacturers Car dealersIron ore: Steel and metal: Steel and metal components: AM 06 07 08 Δ AM 06 07 08 Δ AM 06 07 08 Δ
AM 06 07 08 Δ AM 06 07 08 Δ AM 06 07 08 Δ DIO 40 38 41 41 3 DIO 46 44 44 49 5 DIO 45 39 48 48 9DIO 40 43 42 36 -6 DIO 79 79 79 81 2 DIO 60 59 57 63 4 DSO 55 60 60 46 -13 DSO 102 108 97 101 -8 DSO 21 19 22 20 1DSO 39 42 43 33 -9 DSO 42 45 43 37 -8 DSO 54 56 57 51 -5 DPO 39 40 43 34 -6 DPO 41 45 41 38 -7 DPO 19 17 21 19 2DPO 28 31 30 23 -8 DPO 31 36 32 24 -12 DPO 40 39 42 39 1 CCC 56 58 58 53 -4 CCC 106 107 99 113 5 CCC 47 42 49 49 7CCC 52 54 55 47 -7 CCC 90 88 90 93 5 CCC 74 76 72 74 -2
Electronics:AM 06 07 08 Δ
DIO 51 50 51 53 4DSO 61 67 62 53 -14DPO 41 47 39 35 -13CCC 72 69 74 72 3
Stage 1 Stage 2 Stage 3AM 06 07 08 Δ AM 06 07 08 Δ AM 06 07 08 Δ
DIO 31 33 35 26 -7 DIO 67 67 66 68 1 DIO 56 54 55 58 4DSO 37 40 41 29 -11 DSO 47 51 49 42 -10 DSO 56 59 58 51 -8DPO 31 34 35 24 -10 DPO 29 34 30 25 -9 DPO 38 39 39 36 -3CCC 37 39 41 31 -8 CCC 85 84 85 85 1 CCC 73 73 74 73 0
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Fig. 5. Cash conversion cycles of the automotive value chain in 2006–2008.
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–100 97
raw material suppliers operate in other industries as well, andtheir manufacturing process (processing industry) differs fromthe other stages (batch production).
The best and worst practices in the value chain of theautomotive industry were analyzed by creating two differentchains from the sample. In each stage of the value chain, thecompany with the shortest CCC was picked to the best chain andthe company with the longest CCC to the worst chain. In thestages that included branches, the CCC was picked from eachbranch. The average CCC of the best chain was 37 days, which was30 days shorter than the average of the stages. The average CCC ofthe worst chain was 101 days, 34 days longer than the average ofthe stages. In most stages, the difference between the best andworst company came from the inventories. For example, in thestage of refined raw material suppliers’ branch of steel and metal,the difference in the DIO between the companies with the bestand worst CCC was 55 days.
4.3. Comparisons
The results of this study, compared to the study of the pulpand paper industry (Pirttila et al., 2010) indicate that the averageCCC of the pulp and paper industry in the years 2004–2008 was63 days (62 days in the years 2006–2008), while the average CCCof the automotive value chain in the years 2006–2008 was 4 dayslonger. The difference is surprisingly small, even though the endcommodities of the automotive industry are products bought byend customers, whereas the end commodities of the pulp andpaper industry are used to complement other products as well. Itcan be considered that the difference would be very small if thestructures of the value chains were more uniform. In the valuechain of the pulp and paper industry, two or three of the fourdownstream stages have a relationship to the end customers,while in the value chain of the automotive industry only one stagehas a direct relationship to the end customers. Both studiesindicate that companies acting in the downstream have shortercycle times of working capital than upstream companies.
The results of our financial value chain analysis were alsocompared to the figures of working capital studies by the RELconsultancy (2010, 2009, 2008a, 2008b, 2007a and 2007b). Thecomparison was made by collecting industry level figures for thesame period from the yearly working capital scorecard publica-tions of Europe and the United States. Exactly similar stages asdefined in this study were not available in the studies of REL,where iron ore and steel and metal were combined to metals andmining, and the figures of component suppliers and systemsuppliers were mainly shown in the auto components industries.The stage of car dealers was not defined in the REL studies. Thenumber of firms included in the structured sample varied from259 to 270. The average CCC of the REL studies calculated for theperiod of 2006–2008 was 58 days, while the average cashconversion cycles of this study was 67. The difference of theresults is mainly a consequence of a different way of dealing withthe accounts receivable of the car manufacturers. In our study,short-term accounts receivable of financial services were includedin the analysis. The study of REL did not include accountsreceivable of car manufacturers’ financial services in the figuresof the year 2008, while in the figures of the years 2006 and 2007both long-term and short-term accounts receivable of financialservices were considered. On the other hand, it can be noticedthat more firms of the steel and metal branch were included inREL study, which increased the CCC because of the longerinventory cycle time than in iron ore. A similar effect can be seenin the auto-components industries. More component suppliersthan system suppliers were included in the industry. The oil andchemical companies’ cash conversion cycles were similar in both
studies. The similarities of the results confirm that our studyreflects the real world value chain of the automotive industry.
In a study of Ulbrich et al. (2008), the CCC of five carmanufacturers and 12 first-tier automotive suppliers, referred toas system suppliers in this study, was examined in the periods of2001–2004. The results of their study showed that in this period,the car manufacturers managed their working capital more effi-ciently in each component of the CCC than the suppliers. Whencomparing the results of the study of Ulbrich et al. to the results ofour study, it seems that the suppliers had been able to improvetheir working capital management from the year 2004. In ourstudy, the DIO of the system suppliers was even shorter than theDIO of the car manufacturers. On the other hand, the results of thesuppliers may not be comparable with the stage of the systemsuppliers, as many of the companies Ulbrich et al. (2008) con-sidered as first-tier suppliers were in this study of the automotivevalue chain placed on the stage of component suppliers.
5. Conclusions
In this study, financial value chain analysis was used toexamine working capital management in the value chain of theautomotive industry during 2006–2008. The companies operatingin the value chain are dependent on their relations with othercompanies. A company that seeks to reduce its working capital atthe expense of its value chain partners does not become morecompetitive, because competition is rather a value chain against avalue chain than a company against a company. The financialvalue chain analysis applied in this study reveals the present stateof value chain that can be used as a starting point for managingworking capital through the value chain.
The measure of working capital management used in thisstudy was the cash conversion cycle (CCC). The average CCC of theautomotive industry was 67 days for the period 2006–2008. Theposition of the value chain had not changed, as the difference ofthe CCC between the years 2006 and 2008 was small. Thisindicates that the relation between working capital and sales isnearly constant. Pirttila et al. (2010) made the same conclusion ofthe CCC in their study of the pulp and paper industry, and thefindings of a study by Losbichler et al. (2008) were similar. On theother hand, even though the CCC remained constant in the valuechain of the automotive industry, its components, the daysaccounts receivable outstanding (DSO) and days accounts payableoutstanding (DPO), changed remarkably, while the change in thedays inventory outstanding (DIO) was low. Because the changesof the DSO and DPO usually offset each other, the CCC follows thechanges of the DIO. An interesting finding was that in each stageof the automotive industry, the turnover time of accountsreceivable had shortened. This indicates that the companies hadpaid attention to the management of accounts receivable andfocused on collecting remittance from the customers.
The cycle time of inventories depends more on the policy ofproduction and inventory management than the terms of pur-chase and sales, which define the turnover time of accountsreceivable and payable. Evidence of the benefits of just-in-time(JIT) and similar policies to managing the physical supply chainhas been given, but the terms of purchase and sales still followthe traditions adopted after an era of cash payments. The terms ofpayment are bargaining issues that could be redefined withoutjeopardizing the production of the physical product. The authorswould like to emphasize the meaning of relatively long paymentperiods for working capital. Long credit terms could be a part ofsales promotion or required by the customer, but have thecompanies considered that the credit periods also tie up capitalinto the value chain. The amount of working capital tied into the
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–10098
value chain affects the return on investment (ROI) directly byincreasing the invested capital and decreasing the ROI.
By analyzing working capital management with the method offinancial value chain analysis, a company receives a holistic viewof the value chain it operates at. On the other hand, the companycan benchmark its position against competitors in its own stageand its position in the value chain, but the company can also seethe most efficient partners and the chain which it wants to belongto. Besides this, it is worthwhile to benchmark other industries as
well in order to adopt suitable practices for working capitalmanagement. Therefore more research is needed in this area toincrease the understanding of working capital management in theholistic value chain context.
Appendix A
The dataset of study is presented in Table A1.
Table A1Dataset of study.
CCC DIO DSO DPO
06 07 08 D AM 06 07 08 D AM 06 07 08 D AM 06 07 08 D AM
Stage 1: Raw material suppliersOil (average) 24 27 14 �9 22 23 27 15 �7 22 37 40 24 �13 34 36 40 25 �11 34
BP p.l.c. 21 27 13 �9 20 26 34 17 �9 26 46 44 25 �21 38 51 51 29 �22 44
Exxon Mobil Corporation 8 8 4 �4 7 11 10 9 �1 10 25 29 15 �10 23 28 31 20 �8 26
Royal Dutch Shell p.l.c. 32 38 19 �12 30 27 32 15 �11 25 35 43 25 �11 34 30 37 20 �10 29
Total S.A. 33 34 20 �13 29 28 32 20 �8 26 41 44 31 �10 39 36 42 30 �6 36
Iron ore (average) 54 55 47 �7 52 43 42 36 �6 40 42 43 33 �9 39 31 30 23 �8 28
BHP Billiton 31 33 51 20 39 31 30 31 �1 31 32 31 49 18 37 31 28 28 �3 29
LKAB 52 48 51 �1 50 41 36 43 2 40 42 43 31 �12 39 31 31 23 �8 28
Rio Tinto 49 78 41 �8 56 36 59 35 �1 43 31 54 23 �7 36 19 34 18 0 24
Vale S.A. 85 59 46 �39 63 63 43 37 �26 47 65 44 30 �34 46 43 27 21 �21 30
STAGE AVERAGE 39 41 31 �8 37 33 35 26 �7 31 40 41 29 �11 37 34 35 24 �10 31
Stage 2: Refined raw material suppliersPlastics and rubber (average) 80 80 76 �4 79 55 53 55 0 54 57 55 46 �11 53 31 29 25 �6 28
BASF 70 71 69 �1 70 46 41 39 �7 42 57 54 45 �12 52 33 24 16 �17 24
DuPont 95 91 83 �12 89 66 66 68 2 66 58 58 46 �12 54 29 32 31 2 31
EMS Group 91 98 83 �8 91 59 65 59 0 61 61 60 41 �20 54 29 27 17 �12 25
Evonik Industries 75 73 76 1 75 47 46 50 4 48 61 60 59 �2 60 33 33 34 1 33
Lanxess 72 67 72 0 70 55 49 58 3 54 49 45 40 �8 44 32 27 27 15 28
Steel and metal (average) 88 90 93 5 90 79 79 81 2 79 45 43 37 �8 42 36 32 24 �12 31
ArcelorMittal 106 61 62 �44 77 119 75 72 �47 89 57 35 21 �36 37 70 49 31 �40 50
Salzgitter 91 100 96 5 96 70 73 73 4 72 48 54 48 0 50 26 27 25 �1 26
Stahl-Metall-Service Holding AG 57 60 65 9 61 53 61 50 �3 55 25 22 22 �3 23 22 23 7 �15 17
ThyssenKrupp 77 81 80 3 79 57 63 65 8 61 55 53 54 �1 54 35 35 39 4 36
Voestalpine 79 120 106 27 102 73 104 91 18 89 50 61 41 �9 51 45 45 26 �19 39
ZAPP 118 120 148 31 128 100 98 132 32 110 36 32 34 �2 34 18 10 17 �1 15
STAGE AVERAGE 84 85 85 1 85 67 66 68 1 67 51 49 42 �10 47 34 30 25 �9 29
Stage 3: Component suppliersPlastic and rubber components (average) 75 76 73 �1 75 52 56 58 6 55 53 56 48 �5 52 31 35 34 3 33
Datwyler 94 86 86 �7 89 67 59 62 �5 62 49 48 42 �7 46 22 21 17 �5 20
Elring Klinger 98 99 108 9 102 62 66 72 10 67 56 56 54 �2 56 19 23 18 �1 20
Federal Mogul 81 76 64 �16 74 51 57 48 �4 52 57 58 50 �7 55 28 38 33 5 33
Polytec 45 68 56 12 56 33 48 59 27 47 47 65 49 2 54 36 45 53 17 44
Saint�Gobain 56 53 51 �5 53 49 49 51 2 50 55 52 47 �8 52 48 48 47 �2 48
Steel and metal components (average) 76 76 73 �3 74 59 57 63 4 60 56 56 48 �7 53 39 35 34 �5 36
Bekaert 98 99 101 3 100 67 65 70 3 67 72 74 66 �6 71 41 39 35 �7 38
Georg Fischer 87 80 81 16 83 58 57 67 9 61 65 59 46 �20 57 36 36 31 �5 35
GKN 52 43 48 �3 48 47 52 60 13 53 46 48 46 0 47 41 57 57 16 52
Miba 84 85 89 5 86 49 50 57 8 52 66 64 54 �11 61 31 29 23 �9 28
Neumayer Tekfor 43 33 25 �17 34 54 47 43 �10 48 36 42 37 0 38 47 56 54 7 52
Rheinmetall 66 84 90 24 80 63 64 71 8 66 50 71 67 17 63 47 50 48 1 48
RUAG 103 96 102 �2 100 97 97 94 �3 96 68 74 84 17 75 61 74 76 15 71
Seissenschmidt 84 81 90 6 85 48 54 70 23 57 53 48 33 �20 45 17 21 13 �3 17
Trimet 67 44 41 �26 51 51 30 34 �17 38 44 30 24 �21 33 27 16 16 �11 20
Electronics (average) 69 74 72 3 72 50 51 53 4 51 67 62 53 �14 61 47 39 35 �13 41
Alps Electric 69 62 59 �10 63 40 38 35 �5 38 67 58 46 �21 57 38 34 22 �16 31
Austria Microsystems 80 157 162 82 133 60 92 125 65 92 98 105 73 �25 92 78 40 36 �43 51
Draexlmaier 61 50 46 �15 52 50 45 39 �11 45 42 30 32 �10 35 31 25 25 16 27
Hella 56 49 47 �9 50 47 39 41 �6 43 51 50 41 �10 47 43 40 36 �7 40
Leoni 74 60 44 �30 59 57 53 44 �14 51 54 45 47 �7 49 37 39 47 9 41
Nidec 54 48 59 4 54 37 34 34 �3 35 85 73 66 �19 75 68 60 42 �26 57
Tyco Electronics 90 91 88 �2 90 55 56 57 2 56 72 73 67 �5 71 37 37 36 �1 37
STAGE AVERAGE 73 74 73 0 73 54 55 58 4 56 59 58 51 �8 56 39 39 36 �3 38
Stage 4: System suppliersBorgWarner 49 44 43 �6 45 32 32 32 1 32 60 55 42 �17 53 43 43 32 �10 39
Bosch 85 82 84 �1 83 47 49 54 6 50 65 62 56 �8 61 27 29 26 �1 27
Continental 61 82 51 �9 65 39 56 39 0 45 57 87 50 �8 65 36 61 37 1 45
Denso 49 39 40 �9 42 32 28 30 �2 30 67 59 43 �24 56 50 48 33 �17 44
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–100 99
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Table A1 (continued )
CCC DIO DSO DPO
06 07 08 D AM 06 07 08 D AM 06 07 08 D AM 06 07 08 D AM
Magna 22 30 27 5 26 22 24 25 4 24 55 56 43 �11 51 54 49 42 �12 49
Mahle 70 67 70 0 69 41 48 55 14 48 62 56 47 �14 55 33 36 32 0 34
Schaeffler Group 104 104 98 �6 102 69 69 69 1 69 55 54 43 �13 51 20 19 14 �5 18
Valeo 19 19 11 �8 16 24 24 23 �1 23 67 65 49 �18 60 72 70 61 �10 68
ZF Group 59 57 55 �4 57 38 38 41 3 39 50 49 42 �8 47 29 30 28 0 29
STAGE AVERAGE 58 58 53 �4 56 38 41 41 3 40 60 60 46 �13 55 40 43 34 �6 39
Stage 5: Car manufacturersBayerische Motoren Werke 133 133 157 24 141 51 48 50 �1 50 110 109 125 15 114 28 23 18 �10 23
Daimler 181 111 135 �46 143 68 51 63 �4 61 164 86 96 �67 115 50 25 25 �26 34
Geely 31 5 �3 �34 11 28 39 41 13 36 59 49 56 �3 55 56 83 100 44 79
Honda Motor Company 83 77 95 11 85 39 36 45 6 40 82 72 74 �8 76 37 31 25 �13 31
Hyundai 39 59 70 31 56 57 59 77 20 64 29 33 31 2 31 47 33 38 �9 39
Nissan Motor Company 120 126 138 17 128 35 34 33 �2 34 144 130 132 �13 135 59 38 27 �32 41
Renault 179 181 192 13 184 47 53 51 4 50 197 202 194 �4 198 65 74 52 �13 64
Toyota Motor Corporation 86 83 97 11 89 27 25 26 �2 26 92 88 94 2 91 34 31 23 �11 29
Volkswagen 114 119 132 18 122 43 47 57 14 49 99 103 106 7 103 29 30 31 3 30
STAGE AVERAGE 107 99 113 5 106 44 44 49 5 46 108 97 101 �8 102 45 41 38 �7 41
Stage 6: Car dealersAutohaus Wolfsburg 42 46 48 6 46 37 47 49 13 44 22 27 23 1 24 17 28 24 8 23
AVAG 38 39 42 4 39 30 31 36 6 32 14 14 12 �2 13 6 6 7 1 6
Feser Graf 22 26 24 2 24 27 34 32 5 31 21 31 26 4 26 26 40 33 7 33
Lohr & Becker 38 48 56 18 47 35 47 44 9 42 16 18 20 4 18 13 17 9 �5 13
Lueg 43 52 45 2 47 38 44 40 2 41 28 28 21 �6 26 22 20 16 �6 19
MAG Metz 60 76 86 26 74 50 63 76 26 63 20 25 22 2 22 10 13 12 2 12
Wellergruppe 51 60 44 �7 52 58 69 58 0 62 14 14 16 2 15 21 23 30 8 25
STAGE AVERAGE 42 49 49 7 47 44 44 49 5 46 19 22 20 1 21 17 21 19 2 19
D change from 2006 to 2008 in days (CCC, DIO, DSO, DPO), AM average of the years 2006–2008.
L. Lind et al. / Journal of Purchasing & Supply Management 18 (2012) 92–100100
Article 2
Pirttilä, M., Viskari, S. and Kärri, T. (2010) ‘Working capital in the value chain: cycle times of pulp and paper industry’, Proceedings of 19th International IPSERA conference, May 16-19, 2010, Lappeenranta, Finland.
Working Capital in the Value Chain: Cycle Times of Pulp and Paper Industry
Miia Pirttilä*, Sari Viskari and Timo Kärri Department of Industrial Management Lappeenranta University of Technology
P.O.Box 20 FIN-53851 Lappeenranta
Finland *Corresponding author: Miia Pirttilä
e-mail: [email protected], phone: +358 5 621 2675, fax: +358 5 621 2644 e-mail: [email protected], phone: +358 5 621 2657, fax: +358 5 621 2644 e-mail: [email protected], phone: +358 5 621 2636, fax: +358 5 621 2644
Abstract
In the current financial situation, working capital has been highlighted by many companies. In this paper, working capital is studied by cycle times in the context of pulp and paper industry value chain. Working capital and value chain have been studied slightly in the same context previously. The study was conducted by analysing the financial statements of 44 international firms in the years 2004-2008. The findings suggest that the relation between sales and working capital is constant. Process-related working capital can be forecasted by sales, whereas net working capital seems to fluctuate irregularly. Key words: Working capital, value chain, pulp and paper industry
Introduction
Due to the financial crisis and deterioration of the general financial situation, most companies now set sufficient securing of cash-flow as a high priority in their key financial targets, even higher than profitability. Hence, working capital as a part of short-term financial management has been highlighted by companies in the pulp and paper industry as well. For example the following statement can be read in Stora Enso’s annual report 2008: “Cost and working capital management will also be more important than ever before. We will reinvent our processes to adapt to uneven demand, and reduce both finished goods and raw material inventories to safeguard our cash flow.” (StoraEnso Oyj, 2009, p.21). A similar statement is also given by Kimberly-Clark: “As we enter a new year, our success will require a shift in priorities. The effects of the global economic weakness we experienced in 2008 will likely persist throughout 2009. Consequently, we will be solidly focused on managing and generating cash flow, and on margin improvement.” (Kimberly-Clark Corporation, 2009, p.7). In this paper, value chain analysis is used to study tied-up working capital. The value chain framework developed by Michael Porter (1985) provides a systematic way to examine activities and interactions within a company and within a larger stream of activities. It highlights the concept of value added within the value chain of a company and between the
value chains of companies i.e. the entire chain of companies through which the value of a product is created (Lynch, 2006). Porter (1985) argues that sustainable competitive advantage could be found from the unique linkages of a value chain. Furthermore the management has to focus not only on activities internal to the company, but also consider the value creation and costs within the value chain of a product (Shank and Govindarajan, 1992). There are several definitions for the value chain in the literature. According to Porter (1985), the value chain describes the value added activities and the linkages of functions in an organization. The group of organisations that together create value to the end customer by supplying, distributing and buying from each other constitutes the value system by Porter. The concept of value network is also used as a synonym to the value system (Johnson, Scholes and Whittington, 2008), especially in industries such as telecommunication, banking, insurance, music and entertainment, where value is co-created by combinations of companies rather than in a chain (Li and Whalley, 2002; Peppard and Rylander, 2006). However, many studies consider the term value chain to describe the entire chain of companies through which the value is created (Al-Mudimigh, Zairi and Ahmed, 2004; Shank and Govindarajan, 1992; van Weele and Rozemeijer, 1996). This is reasonable because a company does not typically span the activities of the entire value chain in which it operates. Due to the traditional product nature of the pulp and paper industry the term value chain is used in this paper to describe the overall chain of value-creating activities where a company has a certain position; upstream suppliers provide inputs and pass them downstream to the next stage in the chain (Lamming, Johnsen, Zheng and Harland, 2000; Peppard and Rylander, 2006). In this paper the supply chain is considered a sub-set of the value chain (Al-Mudimigh et al., 2004). While the concept of supply chain focuses on operations, materials and logistics (Tan, 2001) the value chain concept extends the focus to the information flow including financial aspects. Hergert and Morris (1989) and Shank and Govindarajan (1992) suggest that value chain analysis is a more effective tool to find the competitive advantage than traditional costing systems. In their study on the value chain of paper product industry, Shank and Govindarajan (1992) show that the concept of value chain is not just a theoretical framework. It can be used as meaningful cost analysis from the perspective of strategic cost management, and it could help to understand the position of a company in its value chain, the value chain position against other value chains and analyse the strategic decisions of make or buy and forward/backward integration. Also Purnomo, Guizol and Muhtaman (2009) discuss the value chain analysis in furniture business research. CFO Magazine has published the working capital performance studies of REL consultancy since fiscal year 1997. Studies include annually the largest 1000 European and 1000 United States public companies. REL classifies companies according to the Standard & Poor’s global industry classification standard (GICS). (Karaian, 2008) Some earlier managerial articles have addressed the importance of working capital management through the value chain for minimizing the liquidity risk and simultaneously reducing working capital and maximizing cash inflow (Hofler, 2009; Hutchison, Farris and Anders, 2007). However, previous academic research about working capital lacks the value chain approach. It concerns mostly working capital issues within a company by describing the practices of working capital management in individual companies (Belt and Smith, 1991; Howorth and Westhead, 2003; Ricci and Morrison, 1996; Smith and Sell, 1980) or by studying the correlation between a company’s working capital management and profitability (Deloof, 2003; Lazaridis and Tryfonidis, 2006; Shin and Soenen, 1998). Only a few studies have taken into account the holistic view of value chain from the perspective of working capital. Blackman and Holland (2006) extend the just-
in-time (JIT) approach for production systems to financial services in the value chain to achieve savings by reducing the time delays of financial supply chains and cooperating. Saranga (2009) has found empirical evidence that efficient working capital management results in higher operational efficiencies in the value chain of the auto component industry. In this paper, a holistic approach to the cycle times of working capital in the value chain is discussed. The interest of the paper is on processes, while most previous studies of working capital are related to finance and liquidity rather than operational issues (Back, 2001; Chiou, Cheng and Wu, 2006; Fazzari and Petersen, 1993). The objective of the paper is to examine the cycle times of working capital in different stages of the value chain of the pulp and paper industry, how the cycle times changed during the observation period, and whether there are changes if comprehensive net working capital is used instead of narrow process-related working capital.
Research design
The process-related working capital is studied with the cash conversation cycle (CCC) developed by Richards and Laughlin (1980), and also called the cash-to-cash cycle (C2C), which is the length of the time a firm has funds tied up in the working capital. It is defined as follows:
DPODSODIOCCC −+= (1) where DIO = number of days inventory outstanding
DSO = number of days sales (accounts receivable) outstanding DPO = number of days payables (accounts payable) outstanding
The three components of the cash conversation cycle are expressed as a proportion of sales. Traditionally, the cost of goods sold (COGS) has been used as a denominator when calculating the cycle times for inventory and accounts payables. In this paper, CCC actually indicates “the number of “days sales” the company has to finance its working capital under ceteris paribus conditions” (Shin and Soenen, 1998). When the value of sales is used instead of COGS as denominator turnover time is shorter for the most part of companies because the value of sales is normally more than the value of COGS. The components of CCC are defined as follows:
Sales365Inventory ⋅
=DIO (2)
Sales365Receivable Accounts ⋅
=DSO (3)
Sales365Payable Accounts ⋅
=DPO (4)
The net working capital is studied with the net cash conversation cycle (NCCC), which refers to the number of days current assets less the number of days current liabilities. Further,
current asset consists of inventories, receivables, cash and cash equivalents. NCCC is defined as follows:
DLODRODIONCCC −+= (5) where DRO = number of days current assets less inventory outstanding
DLO = number of days current liabilities outstanding. The components of NCCC are defined as follows:
Sales365Inventory)Assets(Current ⋅−
=DRO (6)
Sales365sLiabilitieCurrent ⋅
=DLO (7)
The difference between CCC and NCCC are the accounts of current assets and current liabilities which are not included in the process-related working capital. In this paper they are defined as residual (r). By using residual, NCCC can be presented as follows:
rCCCNCCC += (8) where r is the residual. r can be defined as follows:
)()()( DPODLODSODRODIODIOr −−−+−= (9) simplified:
)()( DPODLODSODROr −−−= (10) where the first part describes the residual of current assets and the last part describes the residual of current liabilities. In the analysis, CCC, NCCC and r are studied with two measures: five-year average of the observation period and change from the year 2004 to the year 2008. The average of the period in a stage is defined as follows:
( ) { }2008,2007,2006,2005,2004 , 1111
== ∑∑==
yyanm
AM i
n
ij
m
j
(11)
where aj(yi) is firm j’s value y at the year i, and
m is the number of firms in the stage.
The absolute change of value y from the year 2004 to the year 2008 is studied with Δ, which is defined as follows:
∑∑==
−=Δm
jj
m
jj ya
mya
m 120042008
1)(1)(1 (12)
The change percentage of the sales from the year 2004 to the year 2008 is studied with δ. This approach describes the change percentage of the value of stage rather than the change percentage of the value of individual firms.
%100)(
)()(
12004
1 120042008
⋅−
=
∑
∑ ∑
=
= =m
jj
m
j
m
jjj
ya
yayaδ (13)
The study is conducted by the analysis of financial statements. The sample presents the value chain of pulp and paper industry from machinery and chemicals to brand owners and publishers, and it has been constructed from the financial statements of 44 international firms for each year of the 2004-2008 period (n=5). The firms of each stage of the value chain have been defined with the help of different sources, such as the PPI Top 100 table (James, 2008), Ernst & Young’s value chain analysis of the pulp and paper industry and clusters in 2007 (Ernst & Young, 2007), Thomson One Banker database, and persons working in a firm of the pulp and paper industry value chain. The data has been gathered from consolidated financial statements that mainly follow the U.S. GAAP or IFRS standards. The financial statements have been collected from firms’ web sites, and some have been found in the U.S. Securities and Exchange Commission’s EDGAR System. Figure 1 illustrates the value chain configuration of the pulp and paper industry used in the analysis and lists the names of companies included in it. In the analysis, the center is the stage of pulp and paper. The stages before the pulp and paper at the supplier side are referred to as upstream, and the stages after pulp and paper at the end customers’ side are called downstream.
Figure 1. The value chain of the pulp and paper industry In Table 1 is presented the descriptive statistics on sample: the number of firms, assets range in 2008, sales range in 2008 and the change percentage of sales (δsales) from the year 2004 to 2008 of each stage. The number of firms (m) in each stage varies from 2 to 13. The change of sales varies from 71% growth to a reduction of 7%. For the value chain of the pulp and paper industry the change is 11%, which is equivalent to yearly growth rate of 3%. The total assets
and sales of each firm were converted to euros by the yearly average rate course released by the European Central Bank. Table 1. Descriptive statistics on sample
the number of firms (m)
Total Assets 2008 (M€) Sales 2008 (M€)
max min max min δsales 04->08
Chemicals 4 30 919 2 860 39 105 2 833 16 %Machinery 3 5 511 3 086 6 400 3 610 71 %Market pulp 6 5 168 1 677 2 512 929 4 %Pulp and paper 13 18 299 1 693 16 882 1 471 -7 %Merchants 2 3 328 2 514 4 951 4 298 21 %Printers 3 10 079 520 10 396 779 12 %Brand owners 6 97 903 4 459 56 776 5 971 20 %Publishers 7 16 158 612 6 699 354 -5 %
The value chain (Figure 1) does not comprise all real world stages of the pulp and paper industry, because it is difficult to specify for example the firms that are international wood suppliers. In addition, in downstream, for example converters are not included in the study, because it has been difficulties to name international firms. Despite the heterogeneity of wood supplies and the approach of this study, it can be said that the downstream firms outnumber the upstream firms of the pulp and paper industry value chain in the real world. After the observation period, mergers and acquisitions (M&A) have accrued for example Aracruz Celulose and Votorantim Celulose e Papel have merged, and Ciba is now a part of BASF. These transactions do not affect the data used in this study or the research frame.
Analysis and results
Cycle times of working capital
To analyze the cycle times of working capital in the different stages of the value chain of the pulp and paper industry and changes during the observation period, CCC (eq.1) and its components DIO (eq. 2), DSO (eq. 3) and DPO (eq. 4) were calculated. Figure 2 shows the average (eq. 11) of cash conversion cycle in days and its components, the values of year 2004 and 2008 and their difference Δ (eq. 12) in each stage.
Figure 2. Cash conversion cycle of each stage of the pulp and paper value chain The Figure 2 demonstrates that the pulp and paper value chain ties working capital up. The CCC is positive in each stage of the value chain. Hence the pulp and paper value chain fails to finance the process-related working capital totally with accounts payable. The average turn over time of working capital is longer in upstream stages than downstream stages, and the CCC of the pulp and paper stage is nearly equal to the average 63 days of the averages of stages (the average of the sample is 61 days). Only publishers have a substantially shorter CCC than the average of the value chain. Overall, the companies in the upstream are more capital-intensive than the companies in the downstream, which might explain the longer cycle times of upstream stages. There are only slight differences in the averages of CCC 2004 and CCC 2008 within each stage; Δ is between 1-6 days; except for merchants and publishers. This indicates that the working capital can be forecasted by the sales in the pulp and paper value chain. While in other stages of the pulp and paper industry the CCC has lengthened marginally from 2004 to 2008 the merchants and publishers have shortened their cash conversion cycle. The lengthened CCC does not necessary indicate inefficiency, because the analyzed period was a high trade cycle, and the targets may have even supported the prolongation of the CCC. As mentioned above, the merchants’ CCC has shortened substantially. It seems that the values of the year 2004 do not reflect a typical year. If the change of the CCC were calculate from 2005 to 2008, the difference would be -8 days, which is 15 days less than the difference between the 2004 and 2008 CCC values. Besides the out of line values in 2004 the shortened CCC may be related to the limited amount of companies in the stage, and so a firm’s value contributes to the average more than in those stages where the numbered of firms is greater. When comparing the components of the CCC to each other, it can be seen in Figure 2 that in each stage the DSO is longer than the DPO i.e. each stage accounts receivables from
customers are more than accounts payables to suppliers. This might indicate that the terms of payment are more generous to customers than the suppliers’ terms of payment to a company. When the DPO and the DIO are compared, it can be seen that merchants, printers and publishers can finance their inventories with accounts payable (DPO>DIO). Merchants and publishers have released tied up capital from inventories from 2004 to 2008 and the cycle time of the publisher’s inventory was the shortest of value chain in 2008. Only the cycle time of the inventory of printers is similar. In other stages, the DIO of 2008 has been lengthened compared to the average and the year 2004 value. The biggest difference is in the market pulp stage. The monetary value of finished products inventory has increased among the firms of the stage from 2004 to 2008 by at least 11% to 366% and the monetary value of raw material inventory, except for one company, has also increased.
Cycle times of net working capital and residual
Figure 3 shows the average (eq. 11) of net cash conversion cycle times (eq. 5) and its components (eq. 6 and eq. 7), the values of year 2004 and 2008 NCCC and their difference Δ (eq. 12) in each stage of the pulp and paper value chain. As can be noticed in the definitions of the CCC and the NCCC (eq. 1 and eq. 5), the DIO is defined similarly in both equations, and therefore it is not reanalyzed.
Figure 3. Net Cash Conversion Cycle of each stage of the pulp and paper value chain Figure 3 illustrates that except for the pulp and paper stage, the net cash conversion cycle has shortened substantially from 2004 to 2008 through the value chain and the NCCC of publishers is even negative. The negative NCCC indicates that the current liabilities are higher than the current assets. In other words, capital investments i.e. fixed assets are financed with short-term debt. The change has been dramatic in all upstream stages, where the NCCC has shortened from 42 to 55 days. When this is converted to percentages, the change has been from 42% to 75%. The average of the averages of stages is 46 days (the average of the sample is 37 days), and the difference between the CCC and NCCC is 17 days. The turnover time of current financial assets has been shortened, while the turnover time of current liabilities and inventories has been lengthened in the upstream stages. The change has been caused by other current financial assets than accounts receivable and other current liabilities than accounts payable, because the DSO and DPO (Figure 2) have not changed as much as the DLO and DRO (Figure 3). The NCCC of the pulp and paper stage has lengthened from 2004 to 2008 as said above, and Figure 3 shows that each component of it has lengthened. The time cycle of the current liabilities can be considered stable, while the current financial assets fluctuate more in the pulp and paper stage.
In order to analyze the time cycle rate of components not included in process-related working capital, residual values (r) are calculated (eq. 10). Figure 4 illustrates the residuals that can be calculated also with the help of Figure 2 and Figure 3 as well. Because DIO - DIO is zero it is not included in Figure 4.
Figure 4. Residual of each stage of the pulp and paper value chain As an exception from the other stages of the value chain, the average residual of market pulp is positive (23 days) while the other stages have negative average residual. In other words, current assets less inventories and accounts receivable are more than current liabilities less accounts payable on average in the market pulp stage. The residual has changed from 2004 to 2008 from positive to negative or more negative except stages pulp and paper and merchants. In general the residual seems to fluctuate irregularly. Although the CCC is predictable by sales in the pulp and paper value chain, the predictability of NCCC is weak because of items that are not related to the process.
Conclusions
Due to the current financial crisis, companies are paying more attention to the cash flow i.e. liquidity. In some companies, it has become an even more essential financial target than profitability. Working capital as a part of short term financing and an important element of cash flow was studied in the pulp and paper industry value chain. The study gives a holistic view on the cycle times of working capital in pulp and paper industry. Instead of observing a single company, the study examines different stages of the value chain and compares upstream, the pulp and paper stage and downstream. Its contribution is in by studying the differences between process-related working capital and comprehensive net working capital and increasing the understanding of the behavior of these two concepts as well in value chain context. The average cash conversation cycle (CCC) of the pulp and paper value chain is 63 days. There is fluctuation between the stages, however. The CCC of the stage of pulp and paper in the middle of the chain is on the average while the upstream stages have longer cash cycles, with the maximum of 83 days in machinery, and the downstream stages have shorter cash cycles, with the minimum of 35 days in publishers. Despite of the differences among the stages, the difference between the years 2004 and 2008 within the stages is small. This indicates that the CCC can be estimated by the sales forecast. The estimate of the CCC is useful when modeling for example the capital investment needs and the assets growth of a company or the valuation of company in M&A situations.
When results of this paper are compared to the REL/CFO Europe working capital scorecard 2008 (fiscal years 2005 – 2007) there is not remarkable differentiations. The average (eq. 11 m=996, n=3, y={2005, 2006, 2007}) of the REL/CFO study’s CCC is 64 days. REL/CFO study includes data of 19 companies which are the same than in this study, covering other stages of this study than market pulp and printers. The average of CCC of these 19 companies is 58 days. If the CCC is calculated from the same period using the data of this study it is 64 days. The small difference is consequence for example the missing data of turnover time of accounts receivable of M-Real Oyj in REL/CFO study. Therefore results of this study can be considered reliable and those are congruent with sample of 1000 largest European companies. The average net cash conversation cycle (NCCC) of the value chain of pulp and paper industry is 46 days, but it differs within the stages even more than the CCC, with the maximum of 95 days in market pulp and the minimum of -8 days in publishers. When comparing the stages, the fluctuation of the NCCC is random from upstream to downstream. In addition, the change of the NCCC from 2004 to 2008 was irregular and in some stages remarkable, over 50 %. The same predictability that was concluded in the CCC cannot be found in the figures of the cycle times of the net working capital. Despite of the random changes of the NCCC, its trend has been decreasing. Only the pulp and paper stage differs from the other. It had a longer NCCC in 2008 than in 2004. In other words, all other stages had more current liabilities compared to current assets in 2008 than in 2004, a finance deficit is compensated for current debt. Although the sample size of the present study is modest compared to the usual sample size of research conducted with financial statement analysis, the sample is representative. The public accessibility of financial statements and unitemized information appeared as a major barrier during the data collection. The observation period from 2004 to 2008 was a growth period in the economy. The results may change slightly if the study is replicated in a different period. The cycle time of the working capital, the averages and the range of cycle time analyzed in this study may be an inadequate approach for the decision makers of a firm. This study has only investigated the working capital in the value chain context, firm specific issues were not considered. The findings suggest that the relation between sales and working capital is constant in the pulp and paper value chain. The process-related working capital can be forecasted by sales, whereas the net working capital fluctuates. A conclusion can be made that the residual r changes irregularly. In other words, the items of current assets and liabilities that are not related to the process cannot be forecasted by sales. In future research, factors that influence the changes of r should be studied. It would be interesting to examine, whether for example the operating profit and residual correlate. Instead of the value chain context it would be worthwhile to study the process-related working capital in the context of an individual firm as well. In addition, in previous academic research on the correlation between profitability and working capital can be continued and studied in the context of the value chain.
Managerial Implications
Typically the working capital management of a company is divided to various managers for example sales, supply and production managers. This paper encourages a company to consider the management of working capital from holistic view and wider the view outside the organizational boundaries to achieve competitive advantage. To increase the value to the
end customers, working capital needs to be coordinated through the entire value chain. A stage can just not transfer the responsibility of working capital to the other stages of the value chain. End customers value the efficient working capital management, it means fresh paper and board. This paper gives two benchmarks to a company, which it can exploit in its management process of (net) working capital: (1) benchmarking of the cycle times of (net) working capital of the stage the firm operates at; and (2) a view of the firm’s position in the entire value chain and benchmark of the cycle times of the (net) working capital of customers and suppliers. Authors would like to emphasize that the working capital is a source of funds also. CEO of StoraEnso highlighted again in fourth quarter and full year 2009 results release significance of working capital management. In his message to shareholders he wrote “In light of the dramatic drop in demand, we promised a year ago that we would finance at least two thirds of our capital expenditure of about EUR 400 million through working capital improvement – the improvement was actually a lot more, EUR 500 million.” (Stora Enso Oyj, 2010) In the light of this study the working capital improvement was mainly result of the drop of sales, not the progress of the efficiency of working capital management. The cash conversion cycle is shortened from 77 to 69 days while the average of CCC was 62 days in the pulp and paper stage in the period 2004-2008.
References
Al-Mudimigh,S.A., Zairi, M., Ahmed, A.M.M., 2004. Extending the concept of supply chain: The effective management of value chains. International Journal of Production Economics 87 (3), 309–320 Back, P. 2001. Testing liquidity measures as bankruptcy prediction variables. The Finnish Journal of Business Economics 50 (3), 309-327 Belt, B and Smith, K.V., 1991. Comparison of working capital practices in Australia and the United States. Global Finance Journal 2 (1-2), 27-54 Blackman, I.D. and Holland, C., 2006. The management of financial supply chains: From adversarial to co-operative strategies. In: Suomi, R., Cabral, R., Hampe, J., Heikkila, A., Järveläinen J., Koskivaara, E. (eds.). Project E-society: Building Bricks. IFIP International Federation for Information Processing 226, Boston Springer. pp. 82-95 Chiou, J., Cheng, L., and Wu, H., 2006. The determinants of working capital. The Journal of American Academy of Business 10 (1), 149-155 Deloof, M., 2003. Does working capital management affect profitability of Belgium firms? Journal of Business Finance & Accounting 30 (3-4), 573-587 Ernst&Young, 2007. At the crossroads: Global pulp and paper report 2007. Fazzari, S.M. and Petersen, B.C., 1993. Working capital and fixed investments: new evidence on financing constrains. RAND Journal of Economics 24 (3), 328-342
Hergert, M. and Morris, D.,1989. Accounting data for value chain analysis. Strategic Management Journal 10 (2), 175-188 Hofler, D., 2009. Field report: Strategies for high-yield working capital in today's economic environment Outsourced logistics 2 (1), 26-29 Howorth, C. and Westhead, P., 2003. The focus of working capital management in UK small firms. Management Accounting Research 14 (2), 94-111 Hutchison, P.D., Farris, M.T and Anders, S.B., 2007. Cash-to-cash analysis and management. The CPA journal 77 (8), 42-47 James, R., 2008. Chinese Producers Creep Up The Top 100 Table. Pulp & Paper International 50 (9), 25-33 Johnson, G., Scholes, K. and Whittington, R., 2008. Exploring corporate strategy: Text & cases. Eight ed. Prentice Hall. Pearson Education Limited. UK. Karaian, J., 2008. Working Capital Scorecard 2008. CFO Europe Magazine. From: http://www.cfo.com/article.cfm/11661239 Kimberly-Clark Corporation, 2009. Annual Report 2008. Lamming, R., Johnsen, T., Zheng, J., Harland, C., 2000. An initial classification of supply networks. International Journal of Operations and Production Management 20 (6), 675-691 Lazaridis, I. and Tryfonidis, D., 2006. Relationship between working capital management and profitability of listed companies in the Athens stock exchange. Journal of Financial Management and Analysis 19 (1), 26-35 Li, F. and Whalley, J., 2002. Deconstruction of the telecommunications industry: from value chains to value networks. Telecommunications Policy 26 (9), 451-472 Lynch, R., 2006. Corporate Strategy. Fourth ed. Prentice Hall. Pearson Education Limited. UK. Peppard, J. and Rylander, A., 2006. From Value chain to value network: Insights for mobile operators. European Management Journal 24 (2-3), 128-141. Porter, M., 1985. Competitive Advantage: Creating and Sustaining Superior Performance. Free Press, New York. Purnomo, H., Guizol, P. and Muhtaman D.R., 2009. Governing the teak furniture business: A global value chain system dynamic modelling approach. Environmental Modelling & Software 24 (12), 1391-1401 Ricci, C and Morrison, G., 1996. International working capital practices of the Fortune 200. Financial Practice and Education 6 (2), 7-20
Richards, V.D. and Laughlin, E.J., 1980. A cash conversation cycle approach to liquidity analysis. Financial Management 9 (1), 32-38 Saranga, H., 2009. The Indian auto component industry – Estimation of operational efficiency and its determinants using DEA. European Journal of Operation Research 196. (2), 707–718 Shank, J.K. and Govindarajan, V., 1992. Strategic cost management: The value chain perspective. Journal of cost management 4 (1), 179-97 Shin, H. and Soenen, L., 1998. Efficiency of working capital management and corporate profitability. Financial Practice and Education 8 (2), 37-45 Smith, K.V and Sell, S.B., 1980. Working capital management in practice. In: Smith, K.V (ed.). Readings on the management of working capital. Second ed. West Publishing: St. Paul. MN. pp. 51-84 StoraEnso Oyj, 2009. Annual Report 2008. StoraEnso Oyj, 2010. Stora Enso Fourth Quarter and Full Year Results 2009. From: http://www.storaenso.com/media-centre/press-releases/2010/02/Pages/stora-enso-fourth-quarter-and.aspx Tan, K.C., 2001. A framework of supply chain management literature. European Journal of Purchasing & Supply Management 7 (1), 39-48 van Weele, A. and Rozemeijer, F.A., 1996. Revolution in purchasing: Building competitive power through proactive. European Journal of Purchasing & Supply Management 2 (4), 153-160
Article 3
Lind, L., Pirttilä, M., Viskari, S. Schupp, F and Kärri, T. (2012) ‘Competing with the negative cycle time of working capital in ICT value network’, 21st Annual IPSERA conference, April 1-4, 2012, Naples, Italy.
1
Competing with negative cycle time of working capital in the ICT
industry
Lotta Linda, Miia Pirttilä
a, Sari Viskari
a, Timo Kärri
a, Florian Schupp
b
Lappeenranta University of Technology, Finland; Schaeffler Technologies GmbH & Co. KG, Germany
Abstract
This paper concerns working capital management by studying cycle times and the
profitability levels of 61 information and communications technology (ICT) companies in
the observation period of 2006-2010. The ICT industry, being different from traditional
manufacturing industries, offers an interesting framework for studying working capital
management. Low inventory levels and the strong negotiation position of powerful
companies enable even negative cycle times of working capital (CCC) in the ICT
industry. This means that a company uses other value chain actors as financiers by paying
its own remittances after receiving payment from the customers. In the study, companies
within the same branch had remarkable differences in their working capital management.
The sample included six companies that were able to operate with a negative CCC. These
companies were also among the most profitable companies in their own branches.
However, a negative or very short CCC is not a requirement for good profitability, as
there are companies that achieve a good level in profitability with a relatively long cycle
time of working capital. Low profitability levels and long cycle time of inventories (DIO)
seem to follow each other in the ICT sector: the branches with the longest DIO had also
the lowest profitability.
Keywords: working capital management, cycle times, negative cash conversion cycle,
profitability, ICT industry
1. Introduction
This paper concerns operational working capital management, including the
management of inventories, accounts receivable and accounts payable, with cycle times in the
information and communications technology (ICT) industry. Efficient management of
operational working capital, which is usually associated with a short cycle time of working
capital, is found to have a positive impact on profitability. It also improves the liquidity of a
company and decreases the financing cost of capital. A company can achieve a negative cycle
a Lotta Lind – Researcher – Department of Industrial Management, Lappeenranta University of Technology,
email: [email protected] a
Miia Pirttilä – Doctoral student – Department of Industrial Management, Lappeenranta University of
Technology, Skinnarilankatu 34, FIN-53850 Lappeenranta, Finland, phone: +358 2944 63198, fax: +358 5
621 2644, email: [email protected] (corresponding author) a
Sari Viskari – Doctoral student – Department of Industrial Management, Lappeenranta University of
Technology; email: [email protected] a
Timo Kärri – Professor of Cost Management – Department of Industrial Management, Lappeenranta
University of Technology; email: [email protected] b Florian Schupp – Dr.-Ing., Vice President Purchasing Automotive – Schaeffler Technologies GmbH & Co.
KG, email: [email protected]
2
time of working capital if it operates with a business model which allows short cycle times of
inventories and accounts receivable, and a long cycle time of accounts payable. The cycle
time of accounts payable should cover the sum of the cycle times of inventories and accounts
receivable. In other words, powerful companies can use their position to dominate suppliers
and customers, or other operators in a network, and use them as financiers, and then benefit
from the situation where working capital is not tied up in the company itself.
Collaboration and the management of networks in the inter-organizational business
landscape have been discussed widely in recent years, but still, only a few studies have
considered working capital management in the wider perspective of an industry network. The
working capital management of a company is affected by the actions of its customers and
suppliers, which makes it reasonable to view the situation also from a wider perspective. In
this paper, the industry of information and communication technology is taken under
examination and the following research questions are addressed:
1. Which companies of the study were able to achieve a negative cycle time of working
capital (CCC) and how did they do it?
2. Does a negative CCC lead to better profitability in the ICT industry?
The ICT industry is characterized by an integrated business environment and fast
technology development. The business models in the ICT industry are different from the ones
applied in the previously studied pulp and paper (Pirttilä, Viskari and Kärri, 2010) and
automotive industries (Lind, Pirttilä, Viskari, Kärri and Schupp, 2011), which are more
capital-intensive and represent a traditional process industry (pulp and paper industry) and
batch production (automotive industry). The ICT industry is service-oriented, and it has a
large variety of end products and customers. Because of the nature of the industry, the
research sample includes many companies that provide only services. In working capital
management, this is seen as low inventory levels: the service providers are able to operate
with negligible inventories, or in some cases even without inventories altogether. Besides
providing services, the ICT industry is known also for the use of contract manufacturers. On
the basis of these characteristics, it could be anticipated that studying working capital
management in the context of the value network of the ICT industry would bring up new
aspects and interesting findings. One of these new aspects is that some ICT companies are
able to operate at a negative cycle time of working capital. This was not found in the previous
studies of working capital management in the pulp and paper and automotive industries.
The paper is structured as follows. The next section is a literature review giving the
theoretical background for the study. It is followed by a section that describes the research
method used in this study. The results of the study are presented in two parts: first, in section
4, a short overview of the results in the ICT industry is given and the working capital
performance of the companies with a negative CCC is analyzed. After this, the connection
between the CCC and profitability in the ICT industry is discussed. Section 6 presents the
results of the study. In the last section of the paper, conclusions and limitations of the study
are presented, and topics for future research are suggested.
2. Literature review
The efficiency of working capital management has been widely studied from the
perspective of individual companies. The studies have concluded that a shorter cycle time of
working capital, which can be achieved by more efficient management of accounts receivable
and inventories, increases the profitability of a company (e.g. Shin and Soenen, 1998,
Lazaridis and Tryfonidis, 2006, García-Teruel and Martínez-Solano, 2007). Most previous
3
studies have also shown that the longer cycle time of accounts payable, which decreases the
cycle time of working capital, is not associated with better profitability (e.g. Deloof, 2003).
Only a few studies have observed the efficiency of working capital management from the
perspective of business network. Pirttilä et al. (2010) have studied the cycle time of working
capital (CCC) and its components in the value chain of the pulp and paper industry. Lind et al.
(2011) have used a similar research frame in the value chain of the automotive industry. Both
these studies found no significant changes in the CCC during the observation period, and
therefore the relationship between sales and working capital could be considered nearly
constant. Lind et al. (2011) conclude that the changes of the CCC follow the changes of the
cycle time of inventories, as the changes of the cycle time of accounts receivable and accounts
payable offset each other. The results of a study of Hoffman and Kotzab (2010) showed that
working capital management should be analyzed in the value chain context in collaboration
with the other value chain actors, and not from the perspective of a single company. Viskari,
Pirttilä and Kärri (2011) have examined the relation between working capital management
and profitability in the value chain context. They conclude that the relation between short
cycle times and increased profitability is not straightforward. Companies can also benefit
from longer cycle times.
The business landscape is evolving to a more integrated direction in every industry,
but this can be seen especially in the ICT sector. The entry of new players, rapid technological
development and changing industry structures in the past years have turned traditional value
chain structures to value networks with several points of entry to markets and industry, and
exit points to do business with the end customer (Li et al., 2002).
The ICT industry has special features that have an effect on working capital
management in the industry. The companies in the ICT industry have perhaps more different
business models to choose from than traditional manufacturing industries. For example Dell
communicates with customers in the internet and offers some customization possibilities
online with prompt delivery and a relatively lower price than competing offerings. Dell
benefits from increased sales volumes, lower inventory levels, and negative working capital
(Walters, 2008; Walters, Bhattacharjya and Chapman., 2011). Traditional business models are
more or less questioned, and emerging business models are discussed in connection with
relationships and reconfiguration of the structure of the ICT industry (Li et al., 2002).
Outsourcing is one issue related to the new business models with partnerships, which are
utilized in the ICT industry. Marshall, McIvor and Lamming (2007) have shown that in the
telecommunication industry also the core functions, in addition to supporting activities, of a
company can be outsourced successfully. Two case companies, a line operator and a network
equipment provider, were able to outsource successfully the areas of business that were
regarded as a core. The first one outsourced the activity consisting manufacturing, final
assembly and testing, and the latter one outsourced the activity of network implementation.
Marshall et al. (2007) conclude that to succeed, the outsourcing company should develop a
confidential partnership with the supplier.
The largest global ICT companies are well known and they have been examined a lot.
Nokia, for example, with the dramatical changes of the business model in 1990s that led to a
huge success, and with the current struggling alongside tightened competition, has been a case
company in several academic studies (e.g. Ali-Yrkkö, Rouvinen, Seppälä and Ylä-Anttila,
2011; Aspara, Lamberg, Laukia and Tikkanen, 2011; McCray, Gonzalez and Darling, 2010).
It has been recognized that companies in the ICT industry can operate with different business
models, and this has inspired researches to perform comparative case studies. For example
Berggren and Bengtsson (2004) have compared the outsourcing procedures in Nokia and
Ericsson.
4
Even though individual companies in the ICT industry have been used in many case
studies, and the supply chains of single products or companies have been examined, the ICT
networks at the industry level have been studied relatively little. Hallikas, Varis, Sissonen and
Virolainen (2008) have studied the network structure and dynamics of the ICT sector. They
examined the position of the companies in the ICT network and compared the position to the
R&D input. The results of their study indicate that companies with large R&D expenditure
also possess a central position in the value network (e.g. Microsoft). An interesting finding in
their study was also that traditional hardware producers of the ICT value network (e.g. Nokia
and Ericsson) had large variations in their network positions and relatively high R&D
investments. In other words, even if hardware producers spend relatively large amounts in
R&D, the networking strategy differs.
The case studies and comparisons of business models have mostly concentrated on
strategy-level issues with only a notion of efficient operations. In this paper, the analysis, as
well as the positioning of the ICT companies, is based on the efficiency of working capital
management.
3. Research design
This study of working capital management in the ICT industry has been conducted by
analyzing the financial statements of 61 companies in different branches of the ICT industry
during the observation period of 2006-2010. The used methodology can be described as
archival research (Moers, 2007). All data for the study has been collected from public
sources, and only official financial statements and annual reports found on the company
websites, have been used. The data was collected manually from the companies’ balance
sheets and income statements by one person during the Fall of 2011, which ensures that all
the data of the companies was gathered and handled in a similar manner. In addition to the
cycle times of working capital and its components, the profitability levels of the companies in
the ICT industry, are studied.
The working capital management in the ICT industry is studied by the cash conversion
cycle (CCC) developed by Richards and Laughlin (1980). The CCC consists of three
components: cycle times of inventories (days inventory outstanding, DIO), accounts
receivable (days sales outstanding, DSO) and accounts payable (days accounts payable
outstanding, DPO). Relative profitability is measured by the return on capital employed
(ROC%). The definitions and calculations for each measure are shown in Table 1.
Table 1. Measurement methods of working capital management and profitability.
Variable Description Definition
DIO Cycle time of inventories DIO = (Inventories/Sales)*365
DSO Cycle time of accounts
receivable DSO = (Accounts receivable/Sales)*365
DPO Cycle time of accounts payable DPO = (Accounts payable/Sales)*365
CCC Cash conversion cycle CCC = DIO + DSO – DPO
ROC% Return on capital employed ROC% =
EBIT/((Equityt+Equityt-1)+ (LT liabilitiest+LT liabilitiest-1))/2
5
The companies forming the sample are divided into nine different branches:
component manufacturers, contract manufacturers, mobile phones, computers and computer
peripherals, network hardware, network operators, IT services, software, and internet services
and software. Some of the selected companies were familiar to the authors beforehand, and
the list of companies was extended with the help of different sources, such as Yahoo
Finance’s Industry Center (Yahoo, 2011), REL/CFO working capital studies, which list 1000
largest US and 1000 largest European public companies (REL 2010a, REL 2010b), ICT
companies’ annual reports, and ICT -related news in the media. The sample companies are
listed in the Appendix. Placing companies into branches is not always unambiguous, because
many companies offer a large variety of different products and services. Apple, for example,
offers personal computing products and media devices, and develops its own software
products (Apple, 2011), and it could be placed in several branches. In our study, Apple is
located in the branch of computers and computer peripherals, as until the end of the year 2009
computers made the biggest portion of Apple’s sales before mobile phones.
Table 2 presents descriptive statistics on the sample by different branches: the number
of firms, maximum and minimum values of total assets, sales and ROC% in the year 2010,
and the change of the average sales of the branch from 2006 to 2010. During the observation
period, the sales of each branch, excluding the contract manufacturers, increased. In the
network hardware branch the growth was enormous, 97 %, from 2006 to 2010. Especially the
sales of Taiwanese companies developed rapidly. Also the sales volume of internet software
and services grew significantly, by 82 %. The branches had very different ROC%. The
variation was also wide within the branches, when observing the minimum and maximum
values (see the Appendix).
Table 2. Descriptive statistics on the sample and case companies.
Number
of
firms
Total
assets 2010
(M€)
max
Total
assets 2010
(M€)
min
Sales
2010
(M€)
max
Sales
2010
(M€)
min
Change
of
sales
2006-2010
ROC%
2010
(max)
ROC%
2010
(min)
Component manufacturers 9 47.662 3.391 32.906 2.673 7% 41% 4%
Contract manufacturers 7 11.633 475 28.680 1.070 -2% 15% -14%
Mobile phones 6 61.198 4.559 42.446 6.675 7% 63% 5%
Computers and computer peripherals 8 93.915 1.862 95.069 2.363 28% 41% 11%
Network hardware 5 24.876 1.963 20.641 1.239 97% 45% -2%
Network operators 8 202.390 2.542 93.747 3.340 16% 15% 4%
IT services 7 9.968 181 16.256 351 11% 47% -81%
Software 7 64.957 1.659 47.133 686 45% 43% 10%
Internet software and services 4 43.638 1.271 22.117 1.907 82% 37% 6%
Sample 61 202.390 181 95.069 351 63% -81%
4. Cycle times related to working capital management: gaining advantage with a
negative cash conversion cycle
As stated above, the ICT industry has some special characteristics that affect the
management of working capital in the industry. The service-orientation and small inventories
give grounds for expecting shorter cycle times of working capital in the ICT industry than
found in previous studies of working capital management. This expectation came true: the
6
average CCC of the study was 40 days in the observation period of 2006-2010, whereas the
average in the value chain of the pulp and paper industry was 63 days during the observation
period of 2004-2008 (Pirttilä et al., 2010), and in the value chain of the automotive industry
67 days in 2006-2008 (Lind et al., 2011). Interestingly, the short cycle time of inventories
does not seem to be a guarantee for a short cash conversion cycle: the companies in the ICT
industry that operate with only negligible inventories or completely without them are not in
the most cases the ones having the shortest, or even negative, CCC. The branch with the
longest CCC, 60 days, is the network hardware one, whereas its customer branch, network
operators, has the shortest CCC, 9 days. Hence, even the longest CCC of the ICT study is
shorter than the value chain averages found in the pulp and paper and automotive studies.
Seven branches of nine had managed to shorten their CCC from 2006 to 2010 (see the
Appendix) by 2-7 days. Figure 1 shows the results of the study: the average CCC, DIO, DSO,
DPO and ROC% of each company in the sample in the observation period and the average of
each branch. The averages shown in the results in this study are unweighted averages.
Network hardware Network operators
CCC DIO DSO DPO ROC% CCC DIO DSO DPO ROC%
Alcatel-Lucent 41 50 91 101 -10% AT&T 33 3 55 25 9%
Huawei 86 59 132 106 41% BT Group -34 2 32 69 12%
Juniper networks 20 0 47 27 4% Deutsche Telekom 9 7 42 40 5%
Tellabs 87 33 74 20 -3% France Telecom -14 6 45 65 12%
ZTE 65 59 85 79 7% Freenet 19 6 47 34 11%
AVERAGE 60 40 86 66 8% TeliaSonera 17 5 45 32 14%
Verizon 34 6 44 16 9%
Component manufacturers Computers and computer peripherals Vodafone 8 4 34 29 5%
CCC DIO DSO DPO ROC% CCC DIO DSO DPO ROC% AVERAGE 9 5 43 39 10%
AMD 38 43 47 53 -6% Apple -32 5 28 65 31%
Broadcom 34 27 38 31 7% Dell -11 6 49 66 30% IT Services
Infineon 56 57 55 56 0% HP 38 24 58 44 17% CCC DIO DSO DPO ROC%
Intel 37 35 23 22 19% IBM 80 10 99 29 24% Accenture 27 0 43 16 55%
NVIDIA 54 41 46 33 12% Lenovo -19 13 20 51 9% AtosOrigin 41 0 77 36 3%
STM 71 57 52 38 -1% Lexmark 28 34 40 46 18% Capgemini 27 0 64 37 8%
Texas Instruments 65 39 40 15 28% Logitech 44 40 45 41 17% ComputaCenter 50 14 69 33 13%
TSMC 50 24 37 10 23% SanDisk 46 54 35 43 2% Logica 52 0 74 21 6%
UMC 67 36 36 36 3% AVERAGE 22 23 47 48 19% S&T 53 13 87 47 -10%
AVERAGE 52 40 42 33 10% Tieto 58 0 73 16 10%
Mobile phones AVERAGE 44 4 70 29 12%
Contract manufacturers CCC DIO DSO DPO ROC%
CCC DIO DSO DPO ROC% Cisco systems 44 13 39 8 20% Software
Benchmark 72 51 64 43 4% HTC 22 22 69 69 60% CCC DIO DSO DPO ROC%
Celestica 36 42 47 54 -3% LM Ericsson 124 46 116 38 14% Adobe 42 0 48 6 16%
Elcoteq 12 28 37 52 -14% Motorola 41 27 56 42 1% Autodesk 46 0 59 13 20%
Flextronics 18 46 36 63 -9% Nokia 42 18 67 44 27% Microsoft 61 7 77 23 43%
Foxconn 39 33 61 55 14% RIM 74 21 69 15 49% Oracle 70 1 76 7 23%
Jabil 20 46 41 67 1% AVERAGE 58 25 70 36 28% RedHat 68 0 77 9 7%
Sanmina 44 45 50 51 -7% Sage 40 2 64 26 16%
AVERAGE 34 42 48 55 -2% SAP 72 0 95 23 33%
AVERAGE 57 1 71 15 23%
Internet software
CCC DIO DSO DPO ROC%
eBay 13 0 20 7 12%
Google 43 0 48 5 25%
United Internet -15 4 26 45 40%
Yahoo 48 0 56 8 5%
AVERAGE 22 1 37 16 20%
Figure 1. Results of the study: average figures of the years 2006-2010.
The results indicate that inventories are overall relatively well managed in the ICT
industry. The DIOs were at most 42 days, whereas in the automotive industry (Lind et al.,
2011) only two branches of nine were able to operate with a DIO shorter than 42 days.
Network operators, IT services, software, and internet software and services do not need to
maintain inventories. These branches had a DIO of 1-5 days. The results suggest that contract
manufacturers carry inventories on behalf of the branches of mobile phones and computers
and computer peripherals by having a DIO which is about 20 days longer than the DIO of
their customer branches. Some of the branches in the study offer quite generous payment
terms to their customers. Network hardware, mobile phones, IT services and software
companies had a DSO of 70-86 days, while the remaining branches required payments in 42-
48 days. The network operators, who were able to shorten their CCC by seven days, made it
7
by collecting the payments from the customers seven days faster in 2010 than in 2006. The
network operators were able to halve the cycle time of working capital during the observation
period. The branches contract manufacturers and computers and computer peripherals had
more generous credit terms from their suppliers than the terms they gave to their customers.
However, the DSO and DPO of the branch computers and computer peripherals were quite
balanced, as the difference was only one day. The contract manufacturers’ DPO was one
week longer than the DSO: they received payments from the customers in a shorter time than
they used for paying their suppliers.
The results of the study show that companies within the same branch may differ
remarkably from each other in their working capital management performance. For example,
in the branch of computers and computer peripherals, the differences in the CCC were
notable. The companies Dell, Apple and Lenovo had negative CCCs (-11, -32 and -19 days,
respectively), whereas their competitors HP and IBM operated with clearly longer CCCs: HP
with a CCC of 38 days and IBM with a CCC of even 80 days. All the mentioned companies
had relatively short DIOs (5-24 days). The difference resulted from the payment terms: the
companies having a negative CCC received fast payments from their customers (DSO 20-49
days) and took advantage of long payment periods to suppliers (DPO 51-66 days). HP and
IBM, in turn, offered more time to pay for their customers with DSOs of 58 and 99 days,
respectively, and paid their purchases to the suppliers faster (DPOs 44 and 29 days). All in all,
this trend was seen overall in the network, when observing the results of the companies with
negative CCCs. The companies had short cycle time of inventories, but they also managed
their accounts receivable effectively. The cycle time of accounts payable was naturally longer
than the cycle time of accounts receivable, and in all cases also higher than the average of the
branch. In other words, their management of each working capital component supports the
achievement of a negative CCC. In addition to above mentioned Dell, Apple and Lenovo, the
network operators France Telecom and BT Group, as well as the internet software and service
company United Internet, were able to achieve negative CCCs during the observation period.
These companies deliver both services and physical goods. Table 3 summarizes the CCC,
DIO, DSO, DPO and ROC% of the six companies of the sample that were able to operate
with a negative CCC.
Table 3. Companies operating with a negative CCC and their results.
CCC DIO DSO DPO ROC%
Computers and computer peripherals (average) 22 23 47 48 19
Dell -11 6 49 66 30
Apple -32 5 28 65 31
Lenovo -19 13 20 51 9
Network operators (average) 9 5 43 39 10
France Telecom -14 6 45 65 12
BT Group -34 2 32 69 12
Internet software and services (average) 22 1 37 16 20
United Internet -15 4 26 45 40Average of the years 2006-2010
The explaining factors for the negative CCC are partially common for all these
companies. The common factor is the cycle time of accounts payable. For these companies it
is longer than the average DPO of the branch where the companies operate. Dell, Apple and
8
Lenovo operate with faster inventory turnover than the companies on average in the branch of
computers and computer peripherals. This can be partially explained by outsourced
production: the inventory of these companies is carried by their outsourcing partners.
According to Apple (2011), outsourcing partners produce substantially all of its assembled
products. An interesting detail is that the cycle time of inventories of these companies
lengthened during the observation period, whereas the companies on average in the branch
succeeded in shortening the cycle time of inventories (see the Appendix). A similar trend is
seen with the CCC and DSO: the cycle times of Dell, Apple and Lenovo lengthened during
the observation period, while the branch on average managed to shorten the cycle times of
working capital and accounts receivable. Despite the noticeable trends, Dell, Apple and
Lenovo had been able to keep their cycle time of accounts payable long enough to cover both
the cycle time of accounts receivable and the cycle time of inventories, and therefore their
cycle time of working capital remained negative. France Telecom, BT Group and United
Internet achieved a negative cycle time of working capital because of a substantially longer
cycle time of accounts payable than the cycle time of accounts receivable. For these
companies, the role of inventories was not big, as they offer mainly services and intangible
goods.
5. Connection between working capital management and profitability: does a negative
CCC lead companies to better profitability in the ICT industry?
Figure 2 shows the relation between working capital management and relative
profitability in the ICT industry when unweighted average figures of the branches are used.
Each branch is represented by its average CCC and return on capital employed (ROC%), and
the size of the circle represents the average sales of the branch. In addition to branches, the
performance of the companies with a negative CCC is shown in Figure 2 as well. In previous
studies, a negative correlation between the CCC and profitability has been found when large
samples of companies from different industries have been used. In other words, companies
that have a short cycle time of working capital have also better profitability. In the ICT
industry, the results of the branches do not clearly support this finding. The branch of mobile
phones, for example, has the best profitability, but also one of the longest CCCs. Their
contract manufacturers, in turn, suffer from very low profitability but operate with a relatively
short CCC. Only two branches of the seven that were able to shorten their CCC were able to
improve their profitability measured by ROC% when comparing the years 2006 and 2010 (see
the Appendix). In this study, a connection between a short CCC and better profitability was
not found when observing the branches of the ICT industry. The analysis of the sample (305
firm-year observations) showed that the CCC and ROC% are independent variables in the
ICT industry.
The comparison within the branches of computers and computer peripherals, network
operators, and internet software and services revealed that five of the six companies with a
negative CCC were among the most profitable companies in their branches. Apple and Dell
were the top two companies in their branch, measured by the ROC%, but Lenovo’s
profitability, in turn, was the second lowest in the branch. Also the profitability of the BT
Group and France Telecom were very good as compared to other network operators. United
Internet was clearly the most profitable company in its branch. On the basis of these findings,
it can be stated that it seems that in the ICT industry, a negative CCC usually leads to better
profitability as well. However, a negative or very short CCC is not a requirement for good
profitability in every case. For example, IBM with the longest CCC of the branch had the
third best profitability within the computers and computer peripherals branch. This supports
the view that a long CCC may as well be a strategic choice for some companies.
9
Component manufacturers
Contract manufacturers
Network hardware
Computers and peripherals
Mobile phones
Network operators
IT services
SoftwareInternet software
and services
Dell
Apple
Lenovo
France TelecomBT Group
United Internet
-5
0
5
10
15
20
25
30
35
40
45
-40 -20 0 20 40 60 80
CCC (days)
RO
C (
%)
B
C
A
Figure 2. Relation between working capital management and relative profitability in the ICT industry.
As shown in Figure 2, we have divided the branches into 3 groups: A, B and C. The
groups have different logics in doing business. Group A, formed by contract manufacturers,
component manufacturers and network hardware, produces physical products and operates as
suppliers mainly in the business-to-business markets, and is dependent on the performance of
their customer branches. Their operations are based on project business. Group B has the
branches network operators, software and IT services. They operate also mainly in the
business markets, but have, especially network operators, some connection to consumer
markets as well. These branches have really small inventories, and the difference in the cycle
times of working capital between these branches results from the payment terms. Network
operators, which clearly have the most B-to-C business of these branches and apply
established payment practices with consumers, have the shortest DSO (43 days), while the
DSO of the other two branches, where the payment terms are more a negotiation issue, is
around 70 days. The network operators have the lowest profitability in this group, but on the
other hand, its cash flow is stable because of the monthly payments from consumers. The
basis of the Group B’s business is on contracts, and changing the supplier would cause
switching the costs to the customer. Group B benefits from a stable income from monthly or
annual invoicing. Group C is formed by the branches mobile phones and computers and
computer peripherals, and manufactures the core products of the industry. It operates on the
customer interface and also has the best profitability. In our analysis, the branch of internet
software and services is not included in any of the groups, as it is a young and increasing
business which has not yet established its position. According to our analysis, it seems that
10
having consumers as customers leads to better profitability in the ICT industry. The analysis
of the different groups also revealed that branches operating with similar logics of doing
business can have very different performance in working capital management.
According to the results of the study (see Figure 1), the performance of the DIO divides
the branches roughly into three groups. The division to groups A, B and C is seen here as
well. Group A, contract manufacturers, component producers and network hardware has on
average clearly the longest cycle time of inventories, around 40 days. The middle group is
formed by the branches of computer and computer peripherals and mobile phones, Group C,
whose DIO remains in about 25 days. The network operators, IT services, software and
internet software, in other words Group B, have really low inventory levels: their DIO is 1-5
days. Low profitability levels and a long DIO seem to follow each other in the ICT sector:
the branches with the longest DIO have also the lowest profitability.
6. Discussion
A short, even negative CCC improves the performance of the company itself. The
company benefits from the situation where no working capital is tied up in its operations. But
how does this affect the other companies in the value chain of the company? A company’s
achievement of a negative cycle time of working capital cannot unambiguously be seen as a
positive thing when viewing the situation from a wider perspective. A negative CCC of one
company may improve the performance of other actors as well, for example when managing
inventories effectively. Payment term adjustments at the expense of suppliers and customers,
in turn, harms the partners.
The ICT industry has a variety of end products and customers, and the companies have
very different targets. The ICT industry does not have one clear end product that would be a
result of collaboration of the different branches in the industry. The findings of the study
suggest that big, powerful companies can achieve a short or even negative cycle time of
working capital at the expense of the other value chain actors. The companies act selfishly by
pursuing self-interest – should the competition in the market take care of the rest, or should
companies try to cooperate in the management of working capital in order to make their value
chain more efficient?
One option for the optimization of working capital management at the value chain
level could be the use of financial service providers. This could shorten the cycle times of
working capital of each actor in the chain, when cycle times of accounts receivable and
accounts payable could be reduced close to zero days by selling accounts receivables to a
third party, as also the current technology with electronic invoicing enables almost real-time
payments. A value chain bank, a joint venture factoring company owned by the value chain
actors, could be a further collaborative way of releasing working capital in the value chain.
7. Conclusions
This study has concerned working capital management by studying the cycle times
and profitability levels of 61 information and communications technology companies (ICT) in
the observation period of 2006-2010. The results showed that the average cash conversion
cycle of the industry was 40 days, which is clearly shorter than the averages in previously
studied pulp and paper (Pirttilä et al., 2010) and automotive (Lind et al., 2011) industries. The
analysis of the results by branches revealed that seven of the nine branches were able to
shorten their cycle time of working capital, but only two of these seven were able to improve
their profitability from 2006 to 2010. In many branches in the ICT industry the inventories are
11
very small, but this does not necessarily lead to a short cycle time of working capital. The
sample of 61 companies included 6 companies that were able to operate with a negative cycle
time of working capital, which means that the cycle time of accounts payable is longer than
the sum of the cycle times of inventories and accounts receivable. In other words, companies
having a negative CCC do not make their remittances until they have received payment from
the customers. Hence, the customers and suppliers are used as financiers and no working
capital is tied up in the company itself.
The companies within the same branch had remarkable differences in their working
capital management. For example in the branch of computers and computer peripherals, many
companies were able to achieve a negative cycle time of working capital but the branch
included also companies whose CCC was relatively long. Were these strategic choices or a
consequence of poor working capital management? We focused on analyzing the results of
six companies that were able to operate with a negative CCC in the observation period:
Apple, Dell and Lenovo in the branch of computers and computer peripherals, the network
operators BT Group and France Telecom, and United Internet in the branch of internet
services and software. The findings suggested that these companies were also profitable: 5 of
the 6 companies belonged to the top companies in their branches from the point of view of
profitability. A negative CCC, however, is not a requirement for good profitability. The
results suggested that having consumers as customers leads to better profitability in the ICT
industry. Low profitability levels and a long DIO seem to follow each other in the ICT sector:
the branches with the longest DIO had also the lowest profitability.
7.1 Limitations
The data used in the study was collected from public sources, which limits the
available information. There are many joint ventures in the ICT industry, such as Nokia
Siemens Networks or Sony-Ericsson, which cannot be analyzed, as their financial figures are
not published separately. To conduct deeper analyses of the companies in the ICT industry,
more detailed information should be gathered for example by interviews, in addition to
collecting material from financial statements. The big differences in the working capital
management performance within the branches revealed one limitation of the applied research
method: the average figures of the branches do not always describe the real character of the
branch, and the use of averages may in some cases be even misleading. In further studies, this
should be observed carefully and for example standard deviation should be used to describe
the variation from the average value.
7.2 Future research
In the future, it would be interesting to study the relationships between the branches in
the ICT industry from the point of view of working capital management. The results of the
study suggested that certain branches and companies manage their working capital and benefit
from the advantages of a negative cash conversion cycle at the expense of the other value
chain partners. The relationships between the actors in the ICT industry should be studied in
order to find the “hosts and servers” of the network.
The impact of the business model on working capital management could be studied
with the case method. The case studies could cover for example the companies that are able to
have a negative cycle time of working capital. This method would enable a deeper
understanding of the differences in working capital management than the analysis of financial
statements.
Working capital management has now been studied in the pulp and paper industry
(Pirttilä et al. 2010), automotive industry (Lind et al. 2011) and ICT industry. The results have
12
been quite different. The differences could be observed in a study that summarizes and
compares the findings of each industry.
7. References
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supply chains: case Nokia N95 Smartphone. Journal of Industry, Competition and Trade 11, 263–
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Aspara, J., Lamberg, J., Laukia, A. and Tikkanen, H., 2011. Strategic management of business model
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Deloof, M., 2003. Does working capital management affect profitability of Belgium firms?
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García-Teruel, P.J. and Martínez-Solano, P., 2007. Effects of working capital management on
SME profitability. International Journal of Managerial Finance 3 (2), 164–177.
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Lazaridis, I. and Tryfonidis, D. (2006) ‘Relationship between working capital management and
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Lind, L., Pirttilä, M., Viskari, S., Schupp, F. and Kärri, T., 2011. Working capital management in the
value chain of automotive industry. The 20th Annual IPSERA Conference, April 10-13, 2011,
Maastricht, The Netherlands.
Marshall, D., McIvor, R. and Lamming, R., 2007. Influences and outcomes of outsourcing: Insights
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13
Ap
pen
dix
: R
esu
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the
stu
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of
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ICT
in
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06
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Article 4
Pirttilä, M., Viskari, S., Lind, L. and Kärri, T. (2014) ‘Benchmarking working capital management in the inter-organisational context’, Int. J. Business Innovation and Research, Vol. 8, No. 2, pp.119–136.
Link: http://dx.doi.org/10.1504/IJBIR.2014.059529
Copyright © Inderscience Enterprises Ltd. Reprinted with permission.
Int. J. Business Innovation and Research, Vol. 8, No. 2, 2014 119
Copyright © 2014 Inderscience Enterprises Ltd.
Benchmarking working capital management in the inter-organisational context
Miia Pirttilä*, Sari Viskari, Lotta Lind and Timo Kärri Department of Industrial Management, Lappeenranta University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author
Abstract: This paper benchmarks working capital management in the inter-organisational context by applying the financial value chain analysis method. The method extends the value chain analysis to industry level. Working capital management is studied in ICT, pulp and paper and automotive industries with the cash conversion cycle (CCC). The CCC of these industries is approximately 40, 60 and 70 days, respectively. The difference is mainly a consequence of the different cycle time of inventories. The 2008 financial crisis affected the working capital management of the industries similarly. Both the cycle time of accounts receivable and accounts payable increased between 2008 and 2009. Inside each industry, the CCC of the branches of the industry varies. The difference of the CCC between the branches that have the shortest and the longest CCC is six-fold in the ICT industry, two-fold in the pulp and paper industry and three-fold in the automotive industry.
Keywords: working capital management; cash conversion cycle; CCC; value chain; financial crisis.
Reference to this paper should be made as follows: Pirttilä, M., Viskari, S., Lind, L. and Kärri, T. (2014) ‘Benchmarking working capital management in the inter-organisational context’, Int. J. Business Innovation and Research, Vol. 8, No. 2, pp.119–136.
Biographical notes: Miia Pirttilä (MSc) is a Doctoral student in the Department of Industrial Management at Lappeenranta University of Technology, Finland. Her research interests include capital, capacity and cost management. Her doctoral studies focus on working capital management in value chain context.
Sari Viskari (MSc) is a Doctoral student in the Department of Industrial Management at Lappeenranta University of Technology, Finland. She received her MSc (Tech) in Industrial Management in 2006 and after that she has worked in the private sector as well as in the academic world. Her research interests include capital, capacity and cost management. Her doctoral studies are related to financial supply chain management and accounting in networks, more specifically in measuring and management of working capital.
120 M. Pirttilä et al.
Lotta Lind (MSc) is a Doctoral student in the Department of Industrial Management at Lappeenranta University of Technology, Finland and working at the moment in the private sector. She received her MSc (Tech) in 2011. Her research interest is on working capital models.
Timo Kärri is a Professor (acting) in the Department of Industrial Management at Lappeenranta University of Technology, Finland. He received his DSc (Tech) in Industrial Management in 2007. His dissertation considered timing of capacity changes in capital intensive industries and research interests include capital, capacity and cost management.
1 Introduction
Working capital management was in the focus of many companies when the financial crisis swept through the economy in 2008. Many companies announced programmes to improve the efficiency of their working capital management. For example, the BMW Group (2010) made the following statement in their annual report of 2009: “Stringent working capital management is a further key parameter for managing the business.” Companies set sufficient securing of cash-flow as a high priority in their key financial targets, even higher than profitability. Companies should show interest in the tied-up working capital of their value chain partners in addition to improving their own working capital management, however. Companies are often so tightly coupled that the domino-effect of suboptimal working capital management can lead to financial glitches at a single actor of the value chain and even bankruptcy (Grosse-Ruyken et al., 2011). During the financial crisis the willingness of banks to lend decreased, which caused liquidity problems to companies. A company’s own business suffers as well, if a supplier cannot deliver raw material or a customer cannot order. Internal financing during the financial crisis helped companies, but it did not solve the financing problems of the entire value chain.
Lean philosophy is adapted to manage physical flow, but the problems of financial flow may hinder being Lean. Manufacturing managers are concerned of inventories, but that is inadequate from the point of view of working capital management. The financial crisis highlighted the importance of managing the financial flow as well. Kestens et al. (2012) studied whether the 2008 financial crisis had an impact on Belgian companies’ accounts receivable and payable i.e., the financial flow. They found that the financial crisis had a negative impact on the overall availability of accounts payable. Companies’ suppliers were not able to offer accounts receivable to the same extent as before the financial crisis. This, together with the degreased willingness of banks to lend was a challenge for liquidity.
Previous literature has focused mostly on supplier-customer relationships, and little attention has been paid to managerial issues in value chains (Lind and Thrane, 2010). Recent studies have highlighted a need to study working capital management at the value chain level (Hofmann and Kotzab, 2010; Grosse-Ruyken et al., 2011; Viskari et al., 2012). The aim of this paper is to study working capital management as a whole in the inter-organisational context. The research questions of the study are the following:
Benchmarking working capital management 121
• How have benchmarked industries performed as regards working capital management?
• How did the 2008 financial crisis affect working capital management in benchmarked industries?
The paper is structured as follows. Section 2 provides the theoretical underpinning of the relevance of studying working capital management in the inter-organisational context. The research design and data are described in Section 3. Section 4 presents the analysis and results, and the final section contains conclusions of the study.
2 Literature review
Working capital management combines the management of the physical flow of goods and services with the financial flows of money between trading partners. Gupta and Dutta (2011) argue that “for an effective supply chain system, the management of upstream flow of money is as important as the management of downstream flow of goods.” That way, working capital management is an important part of operational efficiency and financial management in companies. The findings of Noreen et al. (2009) suggest that working capital management is conducted in corporate level. The cycle time measurer, cash conversion cycle (CCC) is broadly used as an indicator of working capital management (e.g., Farris and Hutchison, 2002; Deloof, 2003), and it is used in this study as well. The CCC developed by Richards and Laughlin (1980) calculates the length (days) of the time a firm has funds tied up in working capital, starting from the payment of purchases to the supplier and ending when remittance of sales is received from the customers. The CCC consists of three cycle times: the cycle time of inventories (DIO), the cycle time of accounts receivable (DSO) and the cycle time of accounts payable (DPO). A short CCC has been connected to better profitability, more efficient processes and lower costs. Therefore, it can be argued that working capital management is first of all the management of operational time, and less the management of the monetary value of working capital. The problem of working capital management according to Richards and Laughlin (1980) is to find the ideal level of working capital so a company has enough cash as well as money to invest. New measurements for studying operational efficiency are developed continuously e.g., Gomes et al. (2007) have introduced the manufacturing operational effectiveness (MOE) indicator.
There are controversial results about whether the type of industry affects working capital management. Shin and Soenen (1998) and Wang (2002) have reported of industry effect on the relation between profitability and the CCC, but Hill et al. (2010) and Chiou et al. (2006) argue that working capital management is determined by internal factors, and external factors, such as industry, do not have a significant impact. There are only a few academic studies which benchmark the working capital management, i.e., the CCC performance of different industries. Farris and Hutchison (2003) analysed the CCC of 5,800 companies by industry and found significant differences between the industries. For example, the communications industry managed to operate with a negative CCC on average due to their low inventories and high level of accounts payable. On the other hand, the paper product industry had a relatively long CCC, mostly due to the high inventory level. Farris and Hutchison (2003) suggest that it is important to look at the
122 M. Pirttilä et al.
CCC and its components by industry to create realistic expectations and objectives for working capital management. Also the study of Hill et al. (2010) indicates that optimal levels of working capital are depending on the industry. Some consultant companies have also published benchmark studies of working capital management in different industries (e.g., REL, 2010a, 2010b).
The traditional research of working capital management has concentrated on managing working capital within a company. Several studies have shown that companies benefit of a short CCC. The results of statistical analysis have shown a negative relationship between the CCC and the return on investment (e.g., Shin and Soenen, 1998; Deloof, 2003; Lazaridis and Tryfonidis, 2006; García-Terual and Martínez-Solano, 2007; Talha et al., 2010). Recent studies of working capital management in the field of supply chain management have highlighted the inter-organisational perspective of working capital management (Grosse-Ruyken et al., 2011; Hofmann and Kotzab, 2010; Viskari et al., 2012). Bayazit (2007) has shown that collaborative planning impacts the performance of the supply chain positively. In the network context, the management of working capital really requires collaborative planning as the financial flows overlap. The accounts receivable of the suppliers is the accounts payable of the customers. In addition, the policies of inventory management affect the other actors in the network.
Strong actors in the supply chain do not regularly follow the balanced working capital approach along the supply chain they operate in. The focus of their working capital management is on reducing their CCC (Grosse-Ruyken et al., 2011). If financial disruption occurs in a single actor of the value chain, it may have an impact on the others, and the influence may propagate across the chain up to the strong actor. The propagated influence accrues inefficiency across the chain, which causes costs and decreases profitability (More and Babu, 2011), and this may be forgotten when the focus is on reducing a company’s own CCC.
Even though the importance of studying working capital management in the inter-organisational context has been discussed and it has been raised into the topics of academic studies, there is still little research about optimisation opportunities in working capital management among trading partners. Losbichler et al. (2008) found out that there was little evidence that the cycle time of working capital had changed in European companies between the years 1995 and 2004. It had decreased from 56 days to 54 days. Losbichler et al. also mention that the cycle time of working capital varied between industries. Also, Farris and Hutchison (2003) have compared single industries, raising the network considerations as a topic of future research. In the present paper, we take the inter-organisational perspective in our benchmark study. The working capital management of three different inter-organisational entities, which have been built for the information and telecommunication technology (ICT) industry, pulp and paper industry and automotive industry, are studied. The working capital management of these industries has been studied previously by Lind et al. (2012a, 2012b) and Pirttilä et al. (2010). This paper deepens the analysis by benchmarking the working capital performance in these entities, which represent service-oriented industry, process industry and batch and series production industry.
The 2008 financial crisis has inspired researchers to study its effects on companies (Kestens et al., 2012; Mullins, 2009; Wonglimpiyarat, 2011; Iskin et al., 2011). Mullins (2009) stresses the importance of paying attention to working capital as an important part of cash flow. The study of Padachi et al. (2008) indicates that working capital is financed mainly with short-term liabilities like accounts payable and other payables. Kestens et al.
Benchmarking working capital management 123
(2012) have studied whether the 2008 financial crisis had an impact on Belgian companies’ accounts receivable and payable, i.e., trade credit. They found that the financial crisis had a negative impact on the overall availability of accounts payable. Companies’ suppliers were not able to offer accounts receivable to the same extent as before the financial crisis. The study of Kestens et al. focused on single companies. Molina and Preve (2009) found out that companies having cash flow problems are reducing the level of accounts receivable. The present study increases the knowledge of the effects of the financial downturn on working capital management in the inter-organisational context.
3 Research design
This study applies the method of financial value chain analysis. The method shows the position of the industry and its branches during the observation period. The method reveals the performance of the industry and its branches. This is a systematic method to analyse an industry that is in the form of a value chain. The financial value chain analysis consists of seven steps that follow each other. The phases are:
1 choose the industry under study
2 define the value chain, including the branches and companies
3 define the key figures
4 collect data for the period under analysis
5 calculate the values of the defined key figures
6 analyse the calculated key figures
7 draw conclusions.
The method of financial value chain analysis has been designed for analysing industry-level phenomena, even though it is based on the key figures of companies. Analysing an industry that is actually a value chain of companies by this method gives a holistic picture of the value chain with financial figures. The financial value chain analysis method extends the value chain analysis to the industry level. Previously the method has been applied in the studies of the ICT industry (Lind et al., 2012b), pulp and paper industry (Pirttilä et al., 2010) and automotive industry (Lind et al., 2012a).
Working capital management is studied by the CCC developed by Richards and Laughlin (1980). The CCC consists of three components: cycle times of inventories (days inventory outstanding, DIO), accounts receivable (days sales outstanding, DSO) and accounts payable (days accounts payable outstanding, DPO). The equation of the CCC is defined as follows:
Inventories Accounts receivable Accounts payable365 365 365Sales Sales Sales
CCC DIO DSO DPO= + −
= × + × − × (1)
In this study the components of the CCC are expressed as a proportion of sales. Alternatively, it would be possible to use the cost of goods sold (COGS) as a
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denominator when calculating the cycle times of inventories and accounts payable. When the value of sales is used instead of the COGS as the denominator, the cycle time of inventories and accounts payable is shorter for most companies, because the value of sales is normally more than the value of the COGS. To fulfil the objectives of this study, the use of sales as a denominator was reasonable, because the different cost structures of the companies would have blurred the information of the analysis. Hofmann and Kotzab (2010) have decided to use sales as the denominator as well, to allow a balanced comparison across each CCC element and to enable true comparison between industries. The CCC is illustrated in Figure 1.
Figure 1 The CCC
Time (days)Purchase Cash outlay Product sales Cash received
DPO
DIODSO
CCC
t0 t1 t2 t3
Source: Adapted from Richards and Laughlin (1980)
Figure 1 visualises a positive CCC. In this case the company has to finance accounts receivable and partially inventories. There is evidence that a company can operate with a negative CCC (for example, Apple Inc.), or the CCC can be null. The CCC is commonly calculated at company level, but there is no obstacle to lowering the calculation level to a business unit, a customer, or even an order. The CCC is a valid measure for the managers of a company. The importance of the CCC from the perspective of value chain management is that it bridges through purchasing activities with suppliers, internal supply chain activities and sales activities with the customer (Farris and Hutchison, 2002).
In the analysis, the CCC is studied with the one-year average and five-year average of the period 2006-2010. The one-year average is calculated at the industry level. The five-year averages are calculated for branches so that at first the five-year average values of each company are summed and this value is divided by the number of companies in a branch.
Industries are formed by using the snowball sampling technique (Bryman and Bell, 2011). A company leads to its customer, supplier or competitor or all of them, which then forms the industry. In this study it is meaningful that the companies are not independent as it is the inter-organisational value chain that is in focus. Secondary data is used in this study, because only official financial statements and annual reports found on company websites, the US Securities and Exchange Commission database and the German Company Register database, have been used. All the data for the study have been collected from public, free of charge, sources. A research implemented like this one is time-consuming, compared to the use of databases, but it ensures that the data is gathered in a similar manner from each company included in the sample. Principally, the values a company has reported have been used. To ensure the homogeneity of the sample, some modifications to the figures presented by a company have been made. Advance payments
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to suppliers have been removed from the inventories. The inventories include raw material, work-in-process, and finished goods or similar. The accounts receivable and payable reflect the receivables and payables that are overdue within a year and are related to trade, for example a note payable is not included in the accounts payable. The observation period of the study is from 2006−2010. The figures of the companies for each year are available on request.
Figure 2 The structure and sample companies of the ICT industry
Network operatorsDeutsche TelekomFrance Telecom
Network hardware BT GroupJuniper networks TeliaSoneraZTEHuawei IT servicesAlcatel‐Lucent Computers and computer peripherals AccentureTellabs Logitech Capgemini
HP AtosOriginIBM Logica
Component manufacturers Dell ComputaCenterSTMicroelectronics Apple TietoInfineon Technologies Lexmark S&TIntel Corporation SanDiskTexas Instruments Lenovo SoftwareNVIDIA Corporation SAPADM Mobile phones SageBroadcom Nokia Adobe
LM Ericsson AutodeskCisco systems Microsoft
Contract manufacturers RIM OracleElcoteq HTC RedHatFoxconn MotorolaJabil Internet softwareBenchmark United InternetSanmina GoogleFlextronics eBayCelestica Yahoo
END CU
STOMERS
The ICT industry data contains the figures of 59 companies. The companies forming the sample of the ICT industry are divided into nine branches: component manufacturers, contract manufacturers, mobile phones, computers and computer peripherals, network hardware, network operators, IT services, software, and internet services and software. Some of the selected companies were familiar to the authors beforehand, and the list of companies was extended with the help of different sources, such as Yahoo Finance’s Industry Center (Yahoo, 2011), REL/CFO working capital studies, which list 1000 largest US and 1,000 largest European public companies (REL, 2010a, 2010b), ICT companies’ annual reports, and ICT-related news in the media. Placing companies into branches is not always unambiguous, because many companies offer a large variety of different products and services. Apple, for example, offers personal computing products and media devices, and develops its own software products (Apple, 2011), and it could be placed in several branches. In our study, Apple is located in the branch of computers and computer peripherals, as until the end of the year 2009 computers made the biggest
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portion of Apple’s sales before mobile phones. Figure 2 lists the companies of the ICT industry in this study.
The value chain of the pulp and paper industry in this study consist of machinery, chemical, market pulp, paper and board, merchant, printer, brand owner and publisher companies. The number of companies included the sample of the pulp and paper industry is 42. The firms of each branch of industry have been defined with the help of different sources, such as the PPI Top 100 table (James, 2008), Ernst & Young’s value chain analysis of the pulp and paper industry and clusters in 2007 (Ernst & Young, 2007), and persons working in firms of the value chain of the pulp and paper industry. Figure 3 illustrates the value chain configuration of the pulp and paper industry used in the analysis, and lists the names of the companies included in it.
Figure 3 The structure and sample companies of the pulp and paper industry
Brand ownersPulp and board BATHolmen Beiersdorf
Chemicals IP DanoneBASF Market pulp Kimberly‐Clark Merchants Procter & GambleDow Arauco MeadWestvaco Paper Linx RocheImerys Canfor M‐Real Sequana UnileverKemira Fibria Myllykoski
Metsä‐Botnia Nippon Printers PublishersMachinery Södra Skogsägarna Norske Skog Consolidated Graphics Axel SpringerAndritz Oji Dai Nippon Printing EMAPMetso Sappi RR Donnelley N.Y.Times Comp.Voith SCA Pearson
Stora Enso Reed‐ElsevierUPM SanomaWSOY
END CU
STOMERS
Figure 4 The structure and sample companies of the automotive industry Raw material suppliers Refined raw material suppliers Component suppliersBHP Bill iton ArcelorMittal AlpsBP BASF Austria MicrosystemsExxonMobil Dupont BekaertLKAB EMS DaetwylerRio Tinto Evonik DraexlmaierRoyal Dutch Shell Lanxess ElringKlinger AGTotal Salzgitter Federal MogulVale Stahl‐Metall‐Service Georg Fischer System suppliers Car manufacturers Car dealers
ThyssenKrupp GKN INA‐Schaeffler BMW AVAGVoestalpine Hella Continental Daimler Autohaus WolfsburgZAPP Leoni Bosch VW Löhr & Becker
Miba Mahle Renault WellergruppeNeumayer Tekfor ZF Sachs Nissan MAG MetzNidec Valeo Honda LuegPolytec BorgWarner Toyota Feser GrafRheinmetall Denso HyundaiRUAG MagnaSaint‐GobainSeissenschmidtTrimetTyco
END CU
STOMERS
The automotive industry forms the third sample, which has been constructed from the financial statements of 65 firms. The companies are divided to six branches that represent the main elements needed for producing and delivering a car for an end customer. The chain begins with the raw materials of steel and plastics: iron ore and oil. The refined raw materials, plastics and rubber and steel and metal, are on the second step of the chain. The third branch is the component suppliers: manufacturers of plastic and rubber components, steel and metal components, and electronics. These companies supply smaller parts, like bearings or gaskets, to the system suppliers. The system suppliers are the fourth branch in the value chain. These companies make complete systems and parts to deliver to the car manufacturers, which then take care of the assembly of the final
Benchmarking working capital management 127
product, the car. The finished cars reach the end customers via car dealers. The sample is based on the Top 100 Automotive Suppliers Global Ranking (Automobilproduktion, 2010) and on discussions with professionals working in the branch of system suppliers. The automotive industry, including the names of companies is presented in Figure 4.
4 Results and analysis
4.1 The role of working capital in assets management
To gain understanding of the importance of working capital management for the ICT, pulp and paper and automotive industry, the structure of assets of the industries is studied. Figure 5 presents these structures. The values are five-year averages (2006−2010) of each industry.
Figure 5 The structure of the assets of the ICT, pulp and paper and automotive industries
0102030405060708090
100
ICT P&P Automotive
% of assets
Long term assets
Other current assets
Inventories andaccounts receivable
Figure 5 reveals the differences in tied-up working capital. In the ICT and pulp and paper industries the tied-up working capital (inventories and accounts receivable) is slightly over 20% and in the automotive industry 32% of the total assets. For the automotive industry, the management of working capital is therefore even a more important part of assets management than in the ICT and pulp and paper industries. The automotive industry is well known for adapting the Lean philosophy. Supposing that the possibilities of Lean are exploited, a new method to improve working capital management will be welcome.
The small size of the tied-up working capital in the ICT industry can be explained by the nature of the business. Services are a significant part of the business, and they do not tie up capital to inventories, as physical products do. More cash is needed to ensure liquidity. The pulp and paper industry can be described as capital intensive, and therefore the role of long term assets is emphasised.
Working capital components are a part of current assets whit the length of rotation less than a year. Quite often this type of assets is financed by short-term debt. During the financial crisis, the refinancing of working capital was crucial for companies regardless the industry.
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4.2 Benchmarking of CCC, DIO, DSO and DPO
To study how benchmarked industries have performed regarding working capital management, the average cycle time of working capital and its components measured by the CCC [defined in equation (1)] for the ICT, pulp and paper and automotive industries have been calculated. Figure 6 shows the results of these calculations.
Figure 6 CCC and its components in the ICT, pulp and paper and automotive industries
Information and
communication
technology
Pulp and paper
Automotive
Benchmarking working capital management 129
When the CCCs of the ICT, pulp and paper and automotive industries are compared, it can be seen that the length of the CCC increases respectively. The CCC of ICT is 40 days, that of pulp and paper 60 days, and the CCC of the automotive industry 70 days, approximately. The results do not show remarkable changes in the CCC during the observation period, and therefore the relationship between sales and working capital can be considered constant. Figure 6 shows that the length of the cycle time of inventories causes the main differences in the CCCs of the industries. Our results support the findings of Farris and Hutchison (2003) and Hill et al. (2010). The cycle time of working capital varies between the industries, and the main reason for this is the cycle time of inventories. The DIO of the ICT industry is around 20 days, while in the pulp and paper industry and automotive industry the cycle time of inventories is around 40 days and 50 days, respectively. The variation of the DIO has been modest between the years 2006 and 2010 (max five days). Findings of Losbichler et al. (2008) are similar. They found that the cycle time of inventories of European companies improves only slightly between the years 1995 and 2004. Losbichler et al. conclude that the supply chain management projects companies may have carried out have not affected to the cycle time of inventories in broader than a single company context. The average payment terms of purchases and sales are more similar among the ICT, pulp and paper and automotive industries, and when they are netted, the difference between the industries is even smaller.
4.3 The effect of the 2008 financial downturn on working capital management
To study the effects of the 2008 financial downturn, it has been assumed to appear in the 2009 figures. The year 2009 can be considered as a recession year because of the change of sales (ICT: −1.8%, P&P: −6.0%, Automotive: −21.2%). Therefore the change between the years 2008 and 2009 has been observed to examine the impact of the financial downturn on working capital management. The change of the CCC had been modest in each industry before the year 2009, from one to three days. Between the years 2008 and 2009 the sales of each industry decreased and in light of the results of the study, the ICT and pulp and paper industries managed to adapt their tied-up working capital to maintain or they even improved the CCC. Meanwhile the automotive industry could not maintain its efficiency in working capital management. The cycle time of accounts receivable increased in each industry between the years 2008 and 2009. Simultaneously, the cycle time of accounts payable increased, but the increase of it was not big enough to offset an increase of the DSO in the automotive industry. The lengthening of the cycle times of accounts receivable and payable from the year 2008 to 2009 suggest that the companies in the industries used more trade financing than before the crisis. The companies may have needed to borrow more from value chain partners to balance the shortage of bank loans. The companies may have changed their payment policies to prefer longer payment terms than the cash discount term to achieve more financing from the value chain where they operated. Regardless of the reason, the prolonged cycle time of accounts receivable and payable had an effect throughout the value chain where the companies operated. Our results differ from those of Kestens et al. (2012), which suggest a decrease of accounts receivable and payable. Their measurement is based on assets; accounts receivable and payable are divided by the total assets. It measures the structural change of the assets. In 2010 the automotive industry managed to restore the CCC to its established level. The
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pulp and paper industry improved the efficiency of working capital management in 2010 compared to 2009 by shortening the cycle time of accounts receivable and lengthening the cycle time of accounts payable simultaneously.
4.4 Benchmarking the variance of working capital management of the industries
To study the characteristics of working capital management of each industry, the more specific level of analysis is the branch level (Figure 7). The branches of each industry were presented in Section 3 where we introduced the data used in this study.
Figure 7 The CCCs of the branches of the ICT, pulp and paper and automotive industries in a descending order
Network hardware
Network operators
0
20
40
60
80
100
120
140
days
ICT
Car manufacturers
Raw material suppliers
Automotive
Machinery
Publishers
P&P
Figure 7 demonstrates the five-year averages of the CCC of the branches of each industry. They have been calculated to visualise how the length of the CCC varies inside each industry. The difference of the CCC between the branches that have the shortest and the longest CCC is six-fold in the ICT industry, two-fold in the pulp and paper industry, and three-fold in the automotive industry. In days, the biggest difference is in the automotive industry (80 days). In the ICT industry the difference is 50 days, and in the pulp and paper industry 41 days. Figure 7 shows that the shapes of the curves differ. The middle of the curve of the pulp and paper industry is flat, which indicates that the CCC is around 60 days for various branches. The variance of the cycle time of working capital may be accepted inside the value chains because of different cycle times of working capital components. An interesting point is why the CCCs of the value chains differ from each other. Should the pulp and paper and automotive industry aim to operate at same working capital management level as the ICT industry?
When the components of the CCC are studied, it can be noted that five branch names of the six presented in Figure 7 appear when the longest and shortest cycle times of the CCC components are viewed (see Appendix). The branches car manufacturers and network hardware producers have the longest cycle times of accounts receivable in their industries. The machinery producers have the longest cycle time of inventories and the publishers the shortest one in the pulp and paper industry. The raw material suppliers of
Benchmarking working capital management 131
the automotive industry have the shortest cycle time of inventories. Only the branch network operators of the ICT industry have not achieved the longest or shortest cycle time in any component of the CCC.
In the ICT industry, the difference between the branches that have the shortest and longest CCC is mainly explained by the cycle time of inventories. The branches internet software and software carry in practice no inventories at all, while the cycle time of inventories of the component manufacturers, contract manufacturers and network hardware producers is around 40 days. The CCC of the network hardware manufacturers is the longest in the ICT industry. It also allows more favourable payment terms to its customers than it gets from its suppliers. The difference of the trade credit terms is 19 days. The branch network operators, which has the shortest CCC, has a very short cycle time of inventories (five days). The network operators do not favour their customers, as the difference of the DSO and DPO is only four days. The branches computers and computer peripherals producers and contract manufactures have been able to collect payments from their customers faster than they have paid to their suppliers. This means that the payment terms of purchases are much longer than the payment terms for customers, as the value of the product increases during the manufacturing process.
The CCC and cycle time of inventories place the branches of pulp and paper industry almost in the same order. Only the printers change place with the brand owners when the DIO is the ordering criterion. The publishers, with the shortest CCC, have a more than twofold advantage in the DIO compared to the machinery producers who have the longest DIO. The publishers lose the advantage by offering favourable payment terms to their customers. The market pulp producers, who have the second longest CCC, have the shortest cycle times of accounts receivable and payable. They favour their customers by 17 days, however.
In the automotive industry, the cycle time of working capital of the car manufactures differs from the other branches of the industry significantly. The major reason for the long CCC is the long cycle time of accounts receivable. This is because the financing business of car manufacturers requires long credit periods. If the DSO of the car manufacturers did not include the receivables of their financing business, their CCC would be significantly lower (approximately 50−75 days). The raw material suppliers have the lowest CCC, on average 33 days, which results from the short cycle time of their inventories, and they collect the payments from their customers almost at the same terms as they pay to their suppliers. The car dealers have managed to match the cycle time of accounts receivable and payable, as the difference is only one day.
5 Conclusions
This study has analysed the working capital management of the information and communication technology (ICT), pulp and paper, and automotive industries. The contribution of the study is deepening the understanding of working capital management in the inter-organisational context by benchmarking the working capital management of these three industries. The analysis has been carried out with the financial value chain analysis method that observes an industry that is in the form of a value chain, instead of a single company. The period of analysis has been the years from 2006−2010.
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The analysis of asset structure disclosed the role of working capital in assets management. Working capital management is important for all the studied industries. For the automotive industry, managing the working capital is the most significant, as the tied-up working capital is about a third of industry’s assets, while in the ICT and pulp and paper industries the proportion is about a fifth of the assets.
The analysis of the CCC of the industries showed that the CCC of the ICT industry is the shortest. Services are a distinctive characteristic of the ICT industry compared to the pulp and paper and automotive industries. The pulp and paper industry represents a process industry and the automotive industry can be considered to be batch and series production. The analysis showed that the length of the cycle time of inventories causes the main differences in the CCCs of the industries. The terms of payment are more homogenous between the industries.
The 2008 financial crisis affected the working capital management of the industries similarly. When the financial crisis occurred, the refinancing of working capital was crucial for companies. Companies borrowed more from their value chain partners than earlier. In the analysis, this was shown by the increase of cycle times of accounts receivable and accounts payable in each industry between the years 2008 and 2009.
The cycle times of working capital of branches inside the industries differ from each other. The difference of the CCC between the branches that have the shortest and longest CCC is six-fold in the ICT, two-fold in the pulp and paper industry and three-fold in the automotive industry. More understanding of the effect of the business concept on working capital management is needed before the ultimate reason for the differences can be revealed. It would be important to study the process-related working capital with the case of an actual value chain of companies, i.e., to follow the flow a product moves towards the end customer.
In the light of this study, a company should benchmark its working capital management to companies that operate in similar business, not necessarily in the same industry, but in manufacturing business if the company is a manufacturer, and so on. Actions that increase the efficiency of working capital management should be implemented together with the value chain partners of the company, as in light of this study the CCC of industries seems to be constant. Single-company actions just change the tied-up working capital forward or backward in the chain but do not decrease the total need for it. The 2008 financial crises demonstrated that viable working capital financing solutions for the value chain partners are as vital for a company as its own liquidity.
The public accessibility of financial statements and unspecified information were a major barrier during the data collection of this study. It would be worthwhile to study the process-related working capital with the case of an actual value chain of companies. The average cycle time of working capital may be an inadequate approach for the decision makers of a firm. This study has only described the working capital in the value chain context and firm-specific issues have not been considered. Normative research would instruct the managers of companies on working capital management in a more concrete manner than descriptive research. This study is among the first to consider what kind of working capital management models are implemented in companies.
Benchmarking working capital management 133
Acknowledgements
The authors would like to thank the reviewers and the editor for their constructive comments on the earlier version of this manuscript.
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Appendix
Cycle time of the components of the cash conversion cycle
Figure A1 Cycle time of inventories
Component manufacturers
Internet software
0
10
20
30
40
50
60
70
80
days
ICT
Machinery
Publishers
P&P
Refined raw material suppliers
Raw material suppliers
Automotive
Figure A2 Cycle time of accounts receivable
Network hardware
Internet software
0
20
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60
80
100
120
days
ICT
Car manufacturers
Car dealers
Automotive
Printers
Market pulp
P&P
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Figure A3 Cycle time of accounts payable Network hardware
Software
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Article 5
Pirttilä, M., Kivinen, K., Monto, S. and Kärri T. (2013) Working capital management in a Finnish pharmaceutical supply chain 22nd Annual IPSERA conference, March 24-27, 2013, Nantes, France.
Working capital management in a Finnish pharmaceutical supply chain
Miia Pirttilä, Karri Kivinen, Sari Monto and Timo Kärri Lappeenranta University of Technology
P.O.Box 20, 53851 Lappeenranta, Finland e-mail: [email protected], phone: +358 294463198
e-mail: [email protected], phone: + 358 408229252 e-mail: [email protected], phone:+358 294463211 e-mail: [email protected], phone:+358 294463176
Abstract The aim of this study is to analyse the working capital management of a Finnish pharmaceutical supply chain. The study applies the mixed methods approach, as it utilises a quantitative financial statement analysis and a qualitative interview study. The numerical analysis shows that the cycle time of working capital varies substantially in the stages of the supply chain. However, the analysis of interviews suggests that the situation is satisfying for each actor of the supply chain.
Keywords: working capital management, cash conversion cycle, pharmaceutical supply chain
Introduction
Working capital management is still in the focus of many companies after the financial crisis of 2008. The aim of this study is to analyse the working capital management of a Finnish pharmaceutical supply chain, which revived the financial crisis with small consequence. The challenges that the pharmaceutical supply chain has faced in recent years have been set by the Finnish government. Due to a need of preventing an increase in public health care costs new laws and regulations have been introduced and implemented at a fast pace. These actions have threatened the good profitability of the supply chain. More efficient working capital management as a part of the asset management would balance the loss of profits that is a probable consequence of the acts of the government. The research questions of the study are:
How is working capital managed in the Finnish pharmaceutical supply chain and its stages? What affects working capital management in the stages of the Finnish pharmaceutical supply chain?
Figure 1 shows the structure of the Finnish pharmaceutical supply chain. The stages are: pharmaceutical companies (located in Finland); importers from foreign pharmaceutical companies (Finnish subsidiaries of groups that have plants in some other countries than in Finland and independent Finnish companies); pharmaceutical wholesalers that deliver the products to the pharmacies; and pharmacies that serve the end customers.
Figure 1. The structure of the Finnish pharmaceutical supply chain
Literature review
This section concerns the importance of working capital management, presents how the working capital and profitability of a company are tied to each other, explains why the cycle time -based measure is the best one, and emphasizes the importance of managing working capital on the supply chain level in addition to the company level.
The importance of managing working capital effectively increased dramatically when the financial crisis started to depress the economy in 2008. In dynamic business environments, companies should be able to manage their assets in order to maintain their relative profitability (Navarro, 2009; Gibson, 2000). The familiar Du Point chart helps in understanding this phenomenon. Figure 2 shows how the components of working capital affect the profitability of a company, measured by the return on investment (ROI).
Figure 2. Components and calculation method of the return on investment
Pharmaceutical companies
Importers from foreign pharmaceutical companies
Pharmaceutical wholesalers Pharmacies
End customers
Income statement SALES
OPERATING PROFIT
COSTS
Balance sheet FIXED ASSETSIntangible assets ROITangible assetsInvestments
SHAREHOLDERS' CURRENT ASSETSEQUITY Inventories (*
Accounts receivable (*LONG-TERM Other receivablesLIABILITIES Current financial assets
Cash and cash equivalents
CURRENT LIABILITIESAccounts payable (*Other liabilities
(* Working capital components
CAPITAL INVESTED
The upper part of the figure describes the income statement and the lower part the balance sheet, assets and liabilities. During a downturn, the sales of a company usually decrease more rapidly than the costs can be cut, and therefore the operating profit (EBIT) of the company weakens. A company that wants to maintain its ROI should be able to manage its assetsquickly. In theory, a company has several options to decrease the amount of capital invested, but the haste to do this limits the possibilities. Sharing dividends or repaying long-term loans would decrease the amount of capital invested, but a company has seldom enough cash for these kinds of actions. Dividends could be paid by raising a short-term loan, but during a downturn the willingness of banks to lend decrease and the costs of loans increase. The structure of fixed assets follows the strategic decisions of a company. A company that has no pressure to update its strategy does not have an opportunity to sell its property. Fixed assets can be decreased also by some other actions, but all these will have a negative effect on the operating profit. Therefore a company can affect its ROI more rapidly with the balance sheet items current assets and liabilities. Working capital forms a remarkable proportion of the net working capital (current assets – current liabilities). Therefore a company should pay attention to its working capital management.
The managers of manufacturing companies worry about the physical flow of products, including inventories (Pirttilä et al., in press), and financial managers simply manage working capital by monitoring the daily cash balance of their business. The popular method used is “collecting the accounts receivable fast enough and delaying the accounts payable as long as possible”. This kind of management of working capital may lead to sub-optimization even at the company level. Gupta and Dutta (2011, p. 47) argue that ‘for an effective supply chain system, the management of upstream flow of money is as important as the management of downstream flow of goods’. Working capital management is first of all management of operational time, and less management of the monetary value of working capital, because the monetary value depends on the amount of sales. The flow of goods or money does not last longer even if the sales increase, and therefore the cash conversion cycle (CCC) is an appropriate measure. It calculates the length (days) of the time a company has funds tied up in working capital, starting from the payment of purchases to the supplier and ending when remittance of sales is received from the customer. The CCC, also known as C2C, has been used by several scholars to measure working capital management (e.g. Hofmann and Kotzab, 2010; Lind et al., 2012a). The importance of the CCC from the perspective of supply chain management is that it bridges the purchasing activities with suppliers, internal supply chain activities, and sales activities with the customer (Farris and Hutchison, 2002). Lind et al. (2012b) suggest that a firm can follow a substantially different working capital management model than its competitors and still achieve similar results.
Companies should show interest in the tied-up working capital of their supply chain partners in addition to improving their own working capital management. Companies are often so tightly coupled that the domino-effect of suboptimal working capital management can lead to financial glitches at a single actor of the supply chain, and even bankruptcy (Grosse-Ruyken et al., 2011). Kesten et al. (2012) have studied whether the financial crisis of 2008 had an impact on Belgian companies’ accounts receivable and payable, i.e. the financial flow. They found that the financial crisis had a negative impact on the overall availability of accounts payable. The companies’ suppliers were not able to offer accounts receivable to the same extent as before the financial crisis. This, together with the degreased willingness of banks to lend, was a challenge for liquidity.
Research design
The research process
This section introduces how the present study has been conducted. The methods, data and the equation of the cash conversion cycle (CCC) are introduced.
This study applies the mixed methods approach. The data collection started by fulfilling the requirements of financial statement analysis and was continued with interviews.
In the first part of the study, working capital management is analysed by the cash conversion cycle (CCC) developed by Richards and Laughlin (1980). The CCC consists of three components: cycle times of inventories (DIO), accounts receivable (DSO) and accounts payable (DPO). The equation of the CCC is:
= +
=Inventory
Sales× 365 +
Accounts receivableSales
× 365Accounts payable
Sales× 365
(1)
There are several definitions for the CCC in the literature (Farris and Hutchison, 2002), but probably the most widely accepted one is that of Schilling’s (1996, p.4–5) “The cash conversion cycle, which mirrors the operating cycle, measures the interval between the time cash expenditures are made to purchase inventory for use in the production process and the time funds are received from the sales of the finished products. This time internal is measured in days and is equal to the net of the average age of the inventory plus the average collection period minus the average of accounts payable.” The present study concentrates on the financial year-end figures. The pharmaceutical industry can be described as defensive, and the consumption of medicines is non-cyclical. Therefore the financial year-end figures describe the working capital management of the supply chain well enough.
The data for the financial statement analysis has been gathered from the Voitto+ database that Suomen Asiakastieto Oy publishes twice a year. Suomen Asiakastieto Oy is a company that offers credit and risk management services for companies, and Voitto+ is offered for scholars as well. The database includes the financial statements of Finnish companies extensively. The comprehension of the financial statements of companies operating in the pharmaceutical supply chain varied a lot by the stages of the supply chain, however. Firstly, the number of Finnish pharmaceutical companies is small. Secondly, importers from foreign pharmaceutical companies are numerous but the financial statements of only the biggest ones are available in the database. Thirdly, the method of wholesaling has resulted in the situation that in practice two pharmaceutical wholesalers operate in the field, and the third one is just a minor actor (not included to the sample). Lastly, the database contains mainly limited companies, which only a few pharmacies are. The sample of the study includes 19 companies divided to stages as follows: 3 pharmaceutical companies, 5 importers from foreign pharmaceutical companies, 2 pharmaceutical wholesalers, and 9 pharmacies. The names of the companies included in the sample are listed in the Appendix. The data has been collected in the period 2006 to 2010.
After the results of financial statement analysis were ready, the interviews were carried out. The results of the first part of the study were introduced to the interviewees. In total three
persons from the supply chain were interviewed, one from each stage, except for the importers from foreign pharmaceutical companies.
The Finnish pharmaceutical industry
This section introduces the characteristics of the Finnish pharmaceutical industry that have an impact on the results of this study. The business is regulated, as the medicine policy covers the entire supply chain of the Finnish pharmaceutical industry (depicted in Figure 1), and therefore radical changes of the structure of the supply chain have not been witnessed in Finland.
The pharmaceutical companies and the importers from foreign pharmaceutical companies are obligated to stock specific medicines or their ingredients to ensure the availability of medicines in case of emergency. The government pays compensation to the companies for the tied-up capital of inventories held because of preparing for an emergency. Therefore the cycle time of inventories of the pharmaceutical companies and the importers from foreign pharmaceutical companies are longer than they would be solely on the operational basis. The pharmaceutical companies and the importers from foreign pharmaceutical companies are not satisfied with the situation, and therefore Pharma Industry Finland presented a report of the defects of obligatory storage to the Finnish Medicines Agency on 1st October 2010 (Pharma Industry Finland, 2010).
Pharmaceutical distribution is organised with a single-channel model in which pharmaceutical companies and the importers from foreign pharmaceutical companies focus the distribution of their products on a single pharmaceutical wholesaler. The pharmaceutical wholesaler has a monopoly on a certain medicine. In Finland, the pharmaceutical wholesale business is in practice divided between two companies. Medicine sale is divided to pharmacies, 75% of the whole, and to hospitals 22% (the rest is nicotine replacement treatments and medicinesimported for a single patient, otherwise these medicines are not available in Finland). At wholesale prices the value of market was 2 billion euros in 2011 (Pharma Industry Finland, 2013). From the year 2007 the monetary value of wholesale has not increased, though the amount of medicines sold has increased with each year. This is a consequence of the need to prevent the increase of public health care costs. New regulations, such as medicine substitution and are reference price system have been implemented.
The number of pharmacies in Finland was 816 on 31st December 2011 (The Association of Finnish Pharmacies , 2012). The pharmacies are owned by a pharmacist, except in university pharmacies, the legislation forbids merges, and acquisitions are controlled as well. The sales of pharmaceuticals consist of prescription-only medicines and self-care medicines. The largest part of the sales is prescription-only medicines, and reimbursements are paid on 90% of these medicines. The retail value of medicine sales was 2.7 billion euro in 2011. The prices of medicines for the end customers are the same in all pharmacies (Pharma Industry Finland, 2013).The reliability of the availability of medicines for end customers was 98.5% in 2011 (The Association of Finnish Pharmacies , 2012). When the medicine has the reimbursement status, the end customer pays the retail price of the medicine less the health insurance part of the medicine price to the pharmacy. Besides medicines, pharmacies sell consumer health products such as vitamins, food supplements, cosmetics and skincare products. The prices of these kinds of products are not regulated. 14% of the pharmacies had separated the consumer health care business from licensed pharmacy operations in 2010 (The Association of Finnish Pharmacies , 2012)
Results and discussion
The results of the financial statement analysis of the pharmaceutical supply chain are presented in Figure 3. The cash conversion cycle and the cycle times of its components represent averages cycle times in days. The values of the CCC and its components in the Finnish pharmaceutical supply chain are depicted in the figure.
Figure 3. The Finnish pharmaceutical supply chain (adapted from Rinta (2008)) and the cycle times of working capital and the components of the stages in days.
The CCC of the stages varies from -4 days to 108 days in the Finnish pharmaceutical supply chain. The pharmaceutical wholesalers that achieve a negative cycle time of working capital are able to finance their business because the pharmaceutical industry and the importers from foreign pharmaceutical companies offer them favorable payment terms. The CCC of pharmacies, 39 days, is the second shortest. The logistics of medicine supply is effective, and pharmacies do not need to stock medicines. Also the available space in pharmacies is limited and there is no room for bigger inventories. Both the pharmaceutical companies (CCC 81 days) and the importers from foreign pharmaceutical companies (CCC 108 days) have to stock medicines to ensure their availability in a crisis situation. Beside this, they both favor their customers because the DSO is longer than the DPO. Certainly the good profitability explains this as well, as the value of raw materials increases during the process, and the value of sold goods is much higher than the value of raw materials.
All the interviewees said that working capital management is important for them. This is an interesting result in the light of the CCC that was not a familiar measure for the interviewees. The interviewees explained that the cycle times of inventories, accounts receivable and accounts payable were controlled, but the CCC that bridges the activities together was not in use. Also the amount of working capital in euros was monitored. The findings of the financial statement analyses suggest that the financial crisis had not had a remarkable effect on the pharmaceutical industry, and the interviewees confirmed the finding. New laws and regulations related to medicine substitution and the reference price system had affected the working capital management of the pharmaceutical industry at least as much as the financial crisis.
Sales forecast 6-18 months before delivery
PharmaciesPharmac. wholesalers
Importers fromforeign
pharmaceutical companies
Pharmaceutical companies
Different locations of production
(EU, USA, Asia)
Central warehose in
Europe (optional)
Sales
Purchase
Info
rmat
ion
of
sale
s and
st
orag
es o
n ha
nd
Purc
hase
Delivery
Purchase 1.5-12 months
CCC 108DIO 56DSO 66DPO 14
CCC 81DIO 58DSO 36DPO 13
CCC 39DIO 37DSO 12DPO 10
CCC -4DIO 46DSO 24DPO 74
The results of the financial statement analysis
The DIO was the cycle time that was monitored broadly, as well as the tied-up euros of inventories. The DIO of the stages varied from 37 days to 60 days. The cycle time of the pharmacies’ inventories was the shortest. They form interface with the end customers and can analyze the sales to forecast the demand of medicines. The cycle time of inventories of the pharmaceutical companies and the importers from foreign pharmaceutical companies were 58 days and 56 days, respectively. Firstly, the Finnish law explains these figures. Both are obligated to stock medicines (finished goods) or the ingredients of medicines (raw materials or work in process) to the amount that is equivalent to the demand of 3, 6 or 10 months. Secondly, companies that produce generic medicines have a wide variety that increases the inventories. Thirdly, the pharmaceutical wholesalers are in practice distributors of medicines. The pharmaceutical wholesalers offer a storage space for the local and foreign pharmaceutical companies, and their call-offs are not made long before the medicine is ordered by a pharmacy. The cycle times of the accounts receivable and payable were mentioned as important measures by the interviewees. In the biggest companies of the supply chain, the DSO and DPO were set as personal goals for those who negotiate with customers or suppliers. The substantially longer DPO of the pharmaceutical wholesalers, 74 days, reflects the fact that the pharmaceutical companies and the importers from foreign pharmaceutical companies finance the business of the pharmaceutical wholesalers, as the accounts receivable of the suppliers are the accounts payable of the customers. This was explained by an interviewee as “otherwise the pharmaceutical wholesalers would go bankrupt”. The margins of medicines included in the reimbursement system are regulated, and that is the main part of sales,although the pharmaceutical wholesalers have tried to extend their business. The pharmaceutical companies and the importers from foreign pharmaceutical companies sell their consumer health products also to other customers, in addition to the pharmaceutical wholesalers. The payment terms vary a lot from the terms used in medicine sales, as the DSO of the pharmaceutical industry and the importers from foreign pharmaceutical companies is shorter than the DPO of the pharmaceutical wholesalers.
This study confirms the idea presented by Lind et al. (2012b). Companies of the same branch may follow different kinds of working capital management strategies and achieve assuccessful outcomes. The figures in Table 1 that presents the CCC and its components of the pharmaceutical wholesalers explain this phenomenon. The CCC of both of the companies, Oriola Oy and Tamro Oy, is negative but the management of the DIO, DSO and DPO varies substantially.
Table 1. Average CCC, DIO, DSO and DPO of two pharmaceutical wholesalers, period 2006-2010
Oriola Oy Tamro Oy CCC -5 -3 DIO 64 28 DSO 30 17 DPO 100 49
The supply chain of the medicines in Finland has stayed unchanged for a long time. The actors in the chain seemed to be satisfied with the situation. The interviewees were interested in developing the working capital management of their own company. The improvement of the working capital management of the entire supply chain did not interest the interviewees.
Conclusions
The length of the cash conversion cycle differs in the stages of the Finnish pharmaceutical supply chain substantially. New laws and regulations related to medicine substitution and the reference price system have affected the working capital management of the pharmaceutical industry at least as much as the financial crisis of past years. The pharmaceutical wholesalers have the shortest and negative CCCs, which is considered as good working capital management in general. The CCCs of the pharmacies, pharmaceutical companies and importers from pharmaceutical companies are positive, and lengthen respectively. The situation is satisfying to each actor in the supply chain. The companies are interested to develop their own working capital management, but collaboration with the supply chain partners is not found to be interesting. The pharmaceutical supply chain has been regulated for a long time and continues to be so. The authors consider that the findings of the study reflect the past business environment well. Similar studies in other countries would help to analyze the results of this study.
Acknowledgement The authors would like to thank the reviewers for their constructive comments on the extended abstract of this paper. We wish to thank the interviewed practitioners as well.
References
The Association of Finnish Pharmacies (2012) The Association of Finnish Pharmacies 2011. http://www.apteekkariliitto.fi/media/pdf/annual_review_2012.pdf [accessed December 2012]
Farris, M.T. and Hutchison, P.D. (2002) ‘Cash-to-cash: the new supply chain management metric’, International Journal of Physical Distribution & Logistics Management, Vol. 32, No. 4, pp.288–298.
Gibson, V. (2000) ‘Property portfolio dynamics: the flexible management of inflexible assets’, Facilities, Vol. 18, No. 3, pp.150–154.
Grosse-Ruyken, P.T., Wagner, S.M. and Jönke, R. (2011) ‘What is the right cash conversion cycle for your supply chain?’, International Journal of Services and Operations Management, Vol. 10, No. 1, pp.13–29.
Gupta, S. and Dutta, K. (2011) ‘Modeling of financial supply chain’, European Journal of Operational Research, Vol. 211, No. 1, pp.47–56.
Hofmann, E. and Kotzab, H. (2010) ‘A supply chain-oriented approach of working capital management’, Journal of Business Logistics, Vol. 31, No. 2, pp.305-330.
Kestens, K., Van Cauwenberge, P. and Vander Bauwhede, H. (2012) ‘Trade credit and company performance during the 2008 financial crisis’, Accounting and Finance (article first published online 10 November 2011).
Lind, L., Pirttilä, M., Viskari, S., Schupp, F. and Kärri, T. (2012a) ‘Working capital management in the value chain of automotive industry: financial value chain analysis’, Journal of Purchasing and Supply Management, Vol. 18, No. 2, pp.92–100.
Lind, L., Pirttilä, M., Viskari, S., Schupp, F. and Kärri, T. (2012b) ‘Competing with the negativecycle time of working capital in ICT value network’, 21th Annual IPSERA Conference, 1–4 April 2012, Naples, Italy.
Navarro, P. (2009) ‘Recession-proofing your organization’, MIT Sloan Management Review, pp. 45–51, spring, 2009.
Pharma Industry Finland (2010) Velvoitevarastoinnin epäkohdat ja ehdotuksemme niiden ratkaisemiseksi, http://www.laaketeollisuus.fi/Banners/LTry_lausunto_01102010_velvoitevarastointi_LIITE1%20%28ID%2017850%29.pdf [accessed January 2013] (in Finnish)
Pharma Industry Finland (2013) Area of operations. http://www.pif.fi/ [accessed January 2013]
Pirttilä, M., Viskari, S.,Lind, L. and Kärri, T. (in press) ‘Benchmarking working capital management in the inter-organisational context’, Int. J. Business Innovation and Research
Richards, V.D. and Laughlin, E.J. (1980) ‘A cash conversion cycle approach to liquidity analysis’, Financial Management, Vol. 9, No. 1, pp.32-38.
Rinta, S. 2008. Lääkkeiden jakelu – nykyinen tilanne. In: Saarinen, A. and Tamminen, N., (Eds), Medicines and health 2008, Lääketietokeskus Oy, Helsinki, pp. 68-72 (in Finnish).
Schilling, G. (1996) ‘Working capital’s role in maintaining corporate liquidity’, TMA Journal, Vol. 16, No. 5, pp. 4–7.
Appendix
This appendix lists the companies included in the sample of the study.
The data has been gathered from Finnish company information. The companies of pharmaceutical industry export their medicines and consumer health care products. Those figures cannot be separated from business concerning Finnish markets. For example the foreign operations of Orion Oyj covered more than 70% of its sales. The database Voitto+ lacks annual financial data of pharmacies. The data for most of the pharmacies was available only for one or two years of the observation period 2006-2010.
Pharmaceutical companies Orion Oyj Vitabalans Oy Bayer Oy
Importers from foreign pharmaceutical companies Ratiopharm Oy Astra Zeneca Oy GlaxoSmithKline Oy Pfizer Oy Oy Verman Ab
Pharmaceutical wholesalers Oriola Oy Tamro Oy
Pharmacies Yliopiston apteekki Hyvän mielen apteekit Oy Forssan apteekkarin rohto Oy Kanta-apteekki Kaurialan apteekki Medielo Oy Terveyssampo Oy Ålands Apoteks Produkter Ab Outokummun apteekki
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