evaluation factors of supplier selection for direct
TRANSCRIPT
EVALUATION FACTORS OF SUPPLIER
SELECTION FOR DIRECT-PROCUREMENT
TOWARDS PURCHASING OPERATION
(Case Study in L’Oreal Manufacturing Indonesia)
By
Ardisa Pramudita
014201100185
A Skripsi Presented to the
Faculty of Business President University
In partial fulfillment of the requirements for
Bachelor Degree In Economics Major In Management
January 2015
2
SKRIPSI ADVISOR
RECOMMENDATION LETTER
This Skripsi entitled “EVALUATION FACTORS OF
SUPPLIER SELECTION FOR DIRECT-PROCUREMENT
TOWARDS PURCHASING OPERATION (Case Study In
L’Oréal Manufacturing Indonesia)” prepared and submitted by
Ardisa Pramudita in partial fulfillment of the requirements for
the degree of Bachelor in the Faculty of Business has been
reviewed and found to have satisfied the requirements for a Skripsi
fit to be examined. I therefore recommend this Skripsi for Oral
Defense.
Cikarang, Indonesia, January 5th 2015
Acknowledged by, Recommended by,
Vinsensius Jajat K. SE, MM, M. B. A. Filda Rahmiati, M. B. A.
Head of Management Study Program Skripsi Advisor
3
DECLARATION OF ORIGINALITY
I declare that this Skripsi, entitled “EVALUATION FACTORS
OF SUPPLIER SELECTION FOR DIRECT-
PROCUREMENT TOWARDS PURCHASING
OPERATION (Case Study In L’Oréal Manufacturing
Indonesia)” is, to the best of my knowledge and belief, an
original piece of work that has not been submitted, either in
whole or in part, to another university to obtain a degree.
Cikarang, Indonesia, January 5th, 2015
ARDISA PRAMUDITA
4
PANEL OF EXAMINERS
APPROVAL SHEET
The Panel of Examiners declares that the Skripsi entitled
“EVALUATION FACTORS OF SUPPLIER SELECTION
FOR DIRECT-PROCUREMENT TOWARDS
PURCHASING OPERATION (Case Study In L’Oréal
Manufacturing Indonesia)” that was submitted by Ardisa
Pramudita majoring in Management from the Faculty of Business
was assessed and approved to have passed the Oral Examinations
on January 22nd, 2015.
Dr. ERWIN RAMEDHAN, MA
Chair - Panel of Examiners
Ir. ERNY ESTIURLINA HUTABARAT, M.B.A.
Examiner I
FILDA RAHMIATI, M. B. A.
Examiner II
5
ABSTRACT
The focus of this research is to analyze the factor evaluation of Supplier Selection towards Purchasing Operation in L‟Oreal Manufacturing
Indonesia. The analytical method used is quantitative analysis. Non
probability sampling is used as sampling technique which is judgemental
sampling. The result of this research shows that the most dominant variable
is Quality of Materials. In this research, the data collected are primary data,
by spreading questionnaires to the sample size of 50 respondents. Inside the
questionnaire, tool for measure the degree of agreement from respondents is
Likert Scale. Tests that include in quantitative analysis are reliability and
validity test, classical assumptions test, and linear multiple regression to
conduct the hypothesis testing through F-test, t-test, and coefficent of determination (R
2). Results found in the analysis that three of the evaluation
factors variable measured in this research have significant influence towards
Purchasing Operation.
Keywords: Lead Time, Cost Criteria, Quality of Materials, Purchasing
Operation.
6
ACKNOWLEDGEMENT
First of all, I would like to express my gratitude to Allah SWT, and all
my praise to Allah SWT, for the guidance, motivation, encourage, and
strength in writing and finishing this research. Despite there were many
obstacles had been encountered by the researcher, this research would
would never be completed without any support from my surroundings
which in either way have contributed significantly to my research.
Through this opportunity, I would like to express my gratitude to
those who have been supporting me and kind enough to help me graduate
from President University both direct and indirectly:
1. My beloved father, Denny L. Kondoy, thank you for the endless
support and motivation for all these years, my gratitude cannot
be describe in words, I am proud to have you as my father.
2. My beloved mother, Nurul Syamsiah, for all pray, sacrifices,
and care for over the years. Thank you for always being my
friend, someone that I always count on with. Thank you.
3. My little brother, Ardan Saputra, thank you for always
supporting me with all your cheerful jokes until we‟re fall
asleep.
4. Mr. Orlando R. Santos, MBA, thank you so much for your
guidance, attention, patience, and kindness during I‟m writing
this research. Thank you for being the best lecturer and advisor
I‟ve ever had in my entire life.
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5. Mrs. Filda, thank you so much for the guidance, support, and all
advises given during the process of making this research.
6. L‟Oreal Manufacturing Indonesia, for the greatest opportunity to
enhance my skills and knowledges. Thank you for the huge
support during my hardest time in doing my research while I
still doing my duties as an intern. Thank you for all the
opportunities and experiences, such a best experience I‟ve ever
had. Special credits addressed to Mr. Tedy Purwoko for being a
very gentle superior I‟ve ever met in my entire life, Ms. Endah
Lestari, Mr. Erwin Fanani, Mr. Elan Suherlan, Mr. Mustakim,
Cecillia Lukardi, Indra Pramuji, and Kurnia Putra Anes, for all
the willingness to help me to finish this research, and all of my
other colleagues in Manufacturing Supply Chain Department,
thank you.
7. Factory Magang Community, Yusrina Husna, Muhammad
Faizin Rissa, Endin Zainuddin, Tia Darlina, Luqman Nur
Hakim, Angga Mahatman, Kristantho Sulistiohadi, and Icha
Maratun Sholihah, the interns in L‟Oreal Manufacturing
Indonesia who have been in the same situation, finishing each
researches while doing duties as an intern.
8. Dearest Soul Sisters, Kartika Pradypta Sari, Rinda Putri Sari,
Annysa Yuliaty, Pinia Agista Helmi, Rizky Wulandari, Laras
Hening Basuki, and Ayu Amelia, thanks for always giving your
shoulders and support each other.
9. Dearest Viva La Vida, Andri Prayoga Putra, Darren Kristofer
Kosasih, Edwin Tanbowi, Gerry Jonathan Prasetya, Jonathan
Canny, Giovanni Septio, Odelia Wilanda, Halim Nathanael
Sutjipto, Nashrullah, Petrus Suryaputra.
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10. Dearest mates, Tiara Hutami, Savitri, Kenny Prasetya, Dita
Nurul Akbarani, Melinda Situmorang, Mangesti Nugraheni.
Cikarang, Indonesia, January 5th,
2015
ARDISA PRAMUDITA
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TABLE OF CONTENTS
RECOMMENDATION LETTER...................................................................II
DECLARATION OF ORIGINALITY.......................................................... III
APPROVAL SHEET ..................................................................................... IV
ABSTRACT..................................................................................................... V
ACKNOWLEDGEMENT ............................................................................. VI
TABLE OF CONTENTS ........................................................................... VIII
LIST OF TABLES ........................................................................................ XI
LIST OF FIGURES...................................................................................... XII
CHAPTER I .....................................................................................................1
INTRODUCTION ............................................................................................1
1.1 Background of The Study ..........................................................................1
1.2 Problem Identification ...............................................................................6
1.3 Statement of The Problem .........................................................................7
1.4 Research Objectives ..................................................................................8
1.5 Definition of Terms ...................................................................................8
1.6 Scope and Limitations ...............................................................................9
1.7 Research Benefits .................................................................................... 10
CHAPTER II .................................................................................................. 11
REVIEW OF LITERATURE ........................................................................ 11
2.1 Theoretical Review ................................................................................. 11
2.1.1 Supply Chain Management ............................................................... 11
2.1.2 Purchasing ........................................................................................ 15
2.1.3 Purchasing Operation ........................................................................ 17
2.1.4 Direct Procurement ........................................................................... 18
2.1.5 Lead Time ........................................................................................ 19
2.1.6 Lead Time Relationship Towards Purchasing Operation .................... 20
2.1.7 Cost Criteria ..................................................................................... 21
2.1.8 Cost Criteria Relationship Towards Purchasing Operation.................. 22
2.1.9 Quality of Materials .......................................................................... 23
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2.1.10 Quality of Materials Relationship Towards Purchasing Operation..... 24
2.2 Previous Research ................................................................................... 25
2.3 Theoretical Framework ............................................................................ 26
2.4 Operational Definition ............................................................................. 27
2.5 Hypothesis .............................................................................................. 28
CHAPTER III ................................................................................................ 29
METHODOLOGY ......................................................................................... 29
3.1 Research Design ...................................................................................... 29
3.2 Research Framework ............................................................................... 31
3.3 Sampling Design ..................................................................................... 32
3.3.1 Population ........................................................................................ 32
3.3.2 Sample ............................................................................................. 33
3.4 Research Instrument ................................................................................ 35
3.4.1 Primary Data .................................................................................... 35
3.4.2 Scaling ............................................................................................. 36
3.5 Statistical Treatment ................................................................................ 38
3.5.1 Descriptive Analysis ......................................................................... 38
3.6 Reliability and Validity............................................................................ 38
3.6.1 Reliability ......................................................................................... 38
3.6.2 Validity ............................................................................................ 40
3.7 Data Collection Procedure ....................................................................... 43
3.8 Hypothesis Testing .................................................................................. 44
3.8.1 Classical Assumption Test ................................................................ 44
3.8.2 Linear Multiple Regression ............................................................... 45
3.8.3 t-Test ................................................................................................ 47
3.8.4 F-Test ............................................................................................... 48
3.8.5 R2 Test ............................................................................................. 49
CHAPTER IV ................................................................................................ 50
ANALYSIS AND INTERPRETATION ......................................................... 50
4.1 Company Profile ..................................................................................... 50
4.1.1 L‟Oreal Worldwide ........................................................................... 50
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4.1.2 L‟Oreal in Indonesia ......................................................................... 50
4.1.3 PT Yasulor Indonesia (L‟Oreal Manufacturing Indonesia).................. 51
4.1.4 Vision, Mission, Values, and Ethical Principles ................................. 52
4.1.5 Organizational Structure ................................................................... 55
4.2 Data Analysis .......................................................................................... 55
4.2.1 Respondents Profile .......................................................................... 55
4.2.2 Reliability Test ................................................................................. 58
4.2.3 Validity Test ..................................................................................... 59
4.2.4 Descriptive Analysis ......................................................................... 61
4.2.5 Classical Assumptions Test ............................................................... 62
4.2.6 Multiple Regression Equation ........................................................... 67
4.2.7 Hypothesis Testing ........................................................................... 71
4.3 Interpretation of Results .......................................................................... 73
CHAPTER V .................................................................................................. 76
CONCLUSION AND RECOMMENDATION .............................................. 76
5.1 Conclusion .............................................................................................. 76
5.2 Recommendation..................................................................................... 78
REFERENCES ............................................................................................... 82
APPENDICES ................................................................................................ 92
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LIST OF TABLES
Table 2.4 Operational Definition…………….......……….…..…....... 27
Table 3.3.1 Research Population………………..………………….…... 33
Table 3.6.1 Cronbach Alpha……………………………..…………...... 39
Table 3.6.2 Pearson-Moment Correlation……………………………… 41
Table 4.1.4.3 Six Values of L‟Oreal…………………….………….....…. 53
Table 4.1 Gender Distribution………………….....…………………. 56
Table 4.2 Age Distribution….…………………………….…………. 57
Table 4.2.2 Reliability Test: Cronbach Alpha‟s………………....……. 59
Table 4.2.3 Validity Test: Pearson Correlation Coefficient..…………. 60
Table 4.2.4 Descriptive Statistical Analysis……………………….…... 61
Table 4.2.5.2 Multicollinearity Test: Tolerance and VIF Value...……… 64
Table 4.2.6 Linear Multiple Regression………………….....…………. 68
Table 4.2.6.2 Model Summary…………………….………………….…. 70
Table 4.2.7.1 F-test…………………………….…………………..….…. 71
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LIST OF FIGURES
Figure 1.1 Growth in Cosmetics Industries....................................... 2
Figure 1.1.1 Top 10 Players‟ Value Shares.......................................... 3
Figure 2.1.1 Integration in Supply Chain Management ............……... 13
Figure 2.3 Theoretical Framework…..……………………………..... 26
Figure 3.2 Research Framework………..………..………….……….. 31
Figure 3.4.2 Likert Scale…………………………………....………….. 37
Figure 3.4.2.1 Likert Scale Questionnaire……………………...………… 37
Figure 3.7 Data Collection Procedure………………………......……. 43
Figure 4.1.2 Historical Timeline of L‟Oreal…………......……...……... 50
Figure 4.1.4.4 Yasulor 7 Values………………………………………..… 54
Figure 4.1.5 Organizational Structure…………………………….……. 55
Figure 4.1 Pie Chart of Gender Distribution……………………....… 56
Figure 4.2 Pie Chart of Age Distribution………….………….....…… 57
Figure 4.2.5 Normality Test: Histogram………………………….……. 62
Figure 4.2.5.1 Normality Test: Normal P-Plot......................................... 63
Figure 4.2.5.3 Scatterplot of Heteroscedasticity Test…………………… 66
Figure 4.2.6 Multiple Regression Equation…………………………… 68
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CHAPTER I
INTRODUCTION
1.1 Background of The Study
Supply chain management is the strategic management of activities
involved in the acquisition and conversion of materials to finished products
delivered to the customer. It is also the system by which companies source,
make, and deliver their products or services according to market demand.
According to Keith Oliver (2003 cited in Burgess, 2010), supply chain
management is a highly complex undertaking that involves multiple
functional areas of an organization, including procurement (purchasing) of
raw materials, transportation (logistics) throughout the manufacturing
process, inventory (warehousing), and distribution. It also includes the
process of forecasting demand, and ideally will tie in with sales and
marketing programs as well. With responsibility for moving products all the
way from mine to driveway or farm to refrigerator, SCM can deliver
powerful results yet reducing costs, boosting revenues, and increasing
customer satisfaction and heads of manufacturing, purchasing, or logistics
(38%) or members of general management (18%). Supply chain as defined
by experienced practitioners extent from suppliers‟ suppliers to customers‟
customers. The operations and decisions of supply chain management are
ultimately triggered by demand signals at the ultimate consumer level. In
today‟s global marketplace, supply chain management practice is seen as
competitive advantage for companies that conducts supply chain planning
activities (aberdeen.com, 2014). The application of supply chain
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management is applicable among industries, for one example, cosmetic
industries.
Cosmetic industries are having a rapid growth nowadays due to
positive response from consumers all over the world. According to Brandon
Gaille (2013), total global beauty sales have increased 14% in 2012 with
revenue from makeup sales for $932million, skincare sales for $844 million,
and fragrance sales for $501 million. The market of those cosmetic brands
are widen in any area of the world and has created a lot of revenue to the
owners and shareholders. One of the successful cosmetic brand that has
established for more than 20 years is L‟Oreal. L‟Oreal has known for its
many kinds of cosmetic fields, concentrating in skin care, hair care, make-
up, perfumes, and etc. It is the world‟s largest cosmetics industry and has its
headquarters in Paris.
Figure 1.1 Growth in Cosmetic Industries
Source: www.ey.com
L‟Oreal products are found in a wide variety of distribution channel,
either supermarkets, pharmacies, salons, and any other outlets. In the role of
distribution channel, supply chain plays an important key as a part of the
group‟s development. Supply chain management practice is being applied to
many of L‟Oreal manufacturers, one of the largest L‟Oreal manufacturer in
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Indonesia that focuses on skin care and hair care is L‟Oreal Manufacturing
Indonesia, or as known in Indonesia as PT Yasulor Indonesia.
Figure 1.1.1 Top 10 Players’ Value Shares
Source: http://euromonitor.typepad.com
L‟Oreal Manufacturing Indonesia which located in Jababeka
Industrial Park is one of the largest manufacturing business in Indonesia that
produce consumer goods for cosmetics products, concentrating in hair care
and skin care, based on its capacity of production. L‟Oreal Manufacturing
Indonesia has been well-known for its efficiency on maintaining supply
chain management procedures. As it is established in 2012 (previously the
factory was located in Ciracas since the year of 1986), L‟Oreal
Manufacturing Indonesia applies supply chain management practice in its
operation and 70% of the goods will be exported throughout Indonesia
(Loreal.com, 2014).
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Since the focus of this Skripsi will rely on supply chain management
practice as a wide range of logistics concept, there are many important
aspects that deal with supply chain management, which are the purchase of
raw materials, the process of product manufacturing, quality assurance of
the goods, the storage of the goods, the inventory management, and the
distribution of the goods. Logistics is a narrow range in supply chain
management which more about activities such as physical distribution or as
known as export, warehouse of the goods, inventory management, shipping
activities, and etc. In other words, supply chain management is a one whole
integration of the operations within the manufacturer, whereas buyers,
suppliers, external and internal customers, and sub-contractors are gathered.
To ensure the production activities run properly, it needs raw
materials and packaging materials to complete the process of making the
finished goods. Those raw materials and packaging materials are acquired
from suppliers. L‟Oreal Manufacturing Indonesia utilize suppliers to fulfill
procurement activities. Procurement is a key for manufacturing business
process which started as a way to integrate purchasing into supply chain
management. Suppliers required towards a manufacturer is in terms of
direct-procurement. Direct-procurement is the main key to ensure the
production of the goods. Direct-procurement is the act for acquiring raw
materials and packaging materials for production, and purchased by large
quantity.
There is a need to understanding of the supplier selection criteria. The
role of purchasing in supply chain management has received and continues
to receive increasing attention as the years goes by. According to Cox (1999
cited in Mikwali and Kavale, 2012), some of the factors firms consider
include trust and commitment, adequate finance, quality, reliable delivery
times, adequate logistics, and technological capabilities. While Harps (2000
cited in Mikwali and Kavale, 2012) mentioned that other criteria such as
ISO certification, reliability, credibility, good references, and product
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development were also necessary. The purchasers play an important role in
L‟Oreal‟s economic performance and their mission is inseparable from the
main challenges faced by the group. They also the one who is in charge to
make transaction of procure all necessary materials needed for production
and daily operation of the manufacturer. The purchasing teams contribute to
the company‟s growth, particularly by presenting the other entities in the
group with innovative solutions developed by the network of suppliers. In
several ways they also play an active part in risk management. Purchasing
teams are also responsible for operational risk control for example quality of
procurements, compliance with timelines, and etc. Lastly, the purchasers
play a major part in cost reduction and cost control. The purchasing role is
currently dynamic in line with the group‟s strategy in order to meet the
requirements.
From above mentioned factors which involved in the supplier
selection criteria in this research, there are three major factors which will
affect the supplier selection criterias which are lead time, cost criteria, and
quality of materials.
The first is the lead time, according to Beamon (1999 cited in
Shepherd and Gunter, 2011), long lead time has the impression that the
specific supplier is less efficient or he just has more customers than he can
serve thus delaying deliveries, every purchasing firm will be comfortable
when the lead time is shortest possible. The shorter the lead time, the better
the supplier, and the maximum efficiency for procurement.
The second factor is cost criteria. The aim of this criterion is to
identify vital element of cost associated with purchase. According to Stanley
and Gregory (2001 cited in Mikwali and Kavale, 2012), the most common
cost related with a product is purchase price, transportation cost and taxes.
Operational costs are also being considered during the supplier selection.
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The operational cost includes transaction processing for one example cost of
rejects.
Quality of materials also affect the third factor analysis of supplier
selection. Quality of materials is a key factor of suppliers by which they can
improve and maintain quality and delivery performance. It is very important
for the manufacturer and suppliers.
The supplier selection towards L‟Oreal Manufacturing Indonesia
happen because the manufacturer need to adjust with a condition that the
manufacturer need suitable suppliers in order to ensure the procurement
activities in terms of supply chain management practice which involve one
whole integration from supplies, production, until distribution. Therefore, at
some point, the manufacturer will adjust their need of particular suppliers
for raw materials and packaging materials of which one can provide more
suitable performance before the bidding process.
1.2 Problem Identification
In the procurement activities, L‟Oreal Manufacturing Indonesia
utilizes import suppliers and local suppliers to supply raw materials and
packaging materials. But in terms of purchasing operation, a manufacturer
needs a certain improvement and it involves going beyond suppliers that
interface with the manufacturer to the suppliers. These considerations of
improvement may include the reduction of cost, increasing quality of the
product, and minimizing the lead time from suppliers to buyer. The main
problem from suppliers are lead time, followed by cost criteria and quality
of materials. Therefore, it can be concluded if the main problem identified
that the lead time should be reduced, using local suppliers or localisation,
can be an option at one point for L‟Oreal Manufacturing Indonesia to
maximize the procurement activities efficiency. With this, the researcher
20
would like to do research about Evaluation Factors of Supplier Selection for
Direct-Procurement towards Purchasing Operation (Case Study in L‟Oreal
Manufacturing Indonesia).
1.3 Statement of The Problem
As the problem identified stated that L‟Oreal Manufacturing Indonesia
currently needs to select local suppliers in order to minimize the lead time to
maximize the efficiency, therefore it can be formulated into 4 (four)
questions related to the research topic:
a. Is there any partial significant influence of lead time for direct-
procurement towards purchasing operation in L‟Oreal Manufacturing
Indonesia?
b. Is there any partial significant influence of cost criteria for direct-
procurement towards purchasing operation in L‟Oreal Manufacturing
Indonesia?
c. Is there any partial significant influence of quality of materials for
direct-procurement towards purchasing operation in L‟Oreal
Manufacturing Indonesia?
d. Is there any significant simultaneous influence of lead time, cost
criteria, and quality of materials for direct-procurement towards
purchasing operation in L‟Oreal Manufacturing Indonesia?
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1.4 Research Objectives
Based on the research questions that has been mentioned above, the
objectives of this research can be formulated into 4 (four) statements as
follows:
a. To analyze partial influence of lead time for direct-procurement
towards purchasing operation in L‟Oreal Manufacturing Indonesia.
b. To analyze partial influence of cost criteria for direct-procurement
towards purchasing operation in L‟Oreal Manufacturing Indonesia.
c. To analyze partial influence of quality of materials for direct-
procurement towards purchasing operation in L‟Oreal Manufacturing
Indonesia.
d. To analyze simultaneous influence of lead time, cost criteria, and
quality of materials for direct-procurement towards purchasing
operation in L‟Oreal Manufacturing Indonesia.
1.5 Defitinition of Terms
a. Direct Procurement: encompasses all items that are part of finished
products, such as raw material, components and parts. Direct
procurement, which is the focus in supply chain management, directly
affects the production process of manufacturing firms (Lewis, M.A.
and Roehrich, J.K., 2009).
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b. Manufacturing: the production of merchandise for use or sale using
labor and machines, tools, chemical and biological processing, or
formulation (Friedman, David, 2006).
c. Procurement: the acquisition of goods, services or works from an
outside external source. It is favorable that the goods, services or
works are appropriate and that they are procured at the best
possible cost to meet the needs of the purchaser in terms of quality
and quantity, time, and location (Weele, Arjan J. van, 2010).
d. Purchasing: To buy materials of the right quality, in the right
quantity from the right source delivered to the right place at the right
time at the right price (Lysons, K., & Farrington, B., 2006).
e. Supplier: a party that supplies goods or services. A supplier may be
distinguished from a contractor or subcontractor, who commonly adds
specialized input to deliverables.
f. Supply Chain Management: the management of the flow of goods.
It includes the movement and storage of raw materials, work-in-
process inventory, and finished goods from point of origin to point of
consumption (Cooper et al, 1997, cited in Shahabuddin, 2011).
1.6 Scope and Limitations
In this research, the researcher meant to get a deep analysis of supplier
selection for direct-procurement which involve raw materials and packaging
materials towards purchasing operation in L‟Oreal Manufacturing
Indonesia, which the factory is located in Jababeka Industrial Park,
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Jababeka, Cikarang, whom the respondents are in Manufacturing Supply
Chain Department, in analyzing the selection method for selecting the local
suppliers in order that the researchers‟ colleagues will get a clear picture
when it comes to a bidding process.
1.7 Research Benefits
Based on the objective of the research, this research is meant to be
able to give benefits and contributions for both academic and professional
aspects.
a. For Company
This research can be used for their tools to select suppliers in order to
obtain as much information of evaluations before the bidding process,
and to assist their decision maker based on the method use in this
research.
b. For Researcher
This research represents a big opportunity for the researcher to do the
research about supplier selection, which apply supply chain
management practices and theories that the researcher‟s acquired
during the study in the university, and broaden the researcher‟s
knowledge.
24
c. For University
This research may be used as an additional material about the supply
chain management practice, and is also very useful for lecturers as an
additional.
d. For Future Researcher
This research is expected to be used as a reference and reference
material for other researchers who want to examine the analysis
factors for supplier selection towards purchasing operation.
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CHAPTER II
REVIEW OF LITERATURE
2.1. Theoretical Review
2.1.1. Supply Chain Management
Martin Christopher (2000 cited in Ellram and Cooper, 2014)
conceptualized the thoughts of supply chain management is a reciprocal
relationship between suppliers and customers to deliver highly optimized
values to customers with low cost yet to provide the advantage of supply
chain as a whole. The focus of supply chain management is basically the
relationship management in order to create optimal results and benefits for
all parties who are members of the chain. According to Heizer and Render
(2004), supply chain management is the activity of management activities in
order to obtain raw materials to be processed into manufactured products
and finished goods, then the goods are sent to consumers through
distribution channels. The activities include the traditional purchasing
function plus other important activities related to the suppliers to
distributors. The supply chain conceptually covers the entire physical
process from obtaining the raw materials through all process steps until the
finished goods reaches the end consumers. Aitken (2011) claimed that
supply chain management is the network of organizations that are
interconnected and need each other and they work together to regulate,
supervise, and improve the flow of commodities and information from
supplier point to the end user. Supply chain management is directly related
to the cycle of raw materials from suppliers to production, warehouse, and
distribution then to the consumers. Handfield and Nichols (1998 cited in
Chen and Paulraj, 2004) stated supply chain management as an integrated
function and managerial towards the parts related to supply chain through
26
collaborative relationships, the effectiveness of business processes, and the
information that can be achieved in a certain managerial level values to
create a high performance thus providing a competitive advantage.
Companies that can run the supply chain activities will get benefit not only
the short-term but it is for long-term. According to Laudon and Laudon
(2006), supply chain management described as a philosophy and business
planning that can make a business entity to coordinate their activities with
suppliers, distributors, to consumers and retailers. A supply chain provides a
company and the activities involved in the business what is needed in
designing, making, using, and delivering of products and services. Any
business depends on the supply chain in providing it with necessary survival
and thriving methods. The concept of supply chain management which is
the most advanced is embraced by the company in their business process
integration with related parties. Coyle, Bardi, and Novack (2000 cited in
Saifudin, 2012) stated that supply chain integrates product, information and
money flow among company which start from the point of origin to the
point of consumption with the aim of minimize the cost and maximum
customer satisfaction. As stated by Himolla (2007), advantages in terms of
costs, flexibility, customer satisfaction, accuracy and time that can be
produced by supply chain management is the reason why supply chain
management can be developed rapidly.
27
Figure 2.1.1 Integration in Supply Chain Management
Source: Coyle, Bardi and Novack (2000 : 9 cited in Saifudin, 2012)
According to Miranda and Tunggal (2005), supply chain management
can be seen from five different point of view:
a) A process by which companies move material, parts, and products to
customers. Various forms of industry in the world has put supply chain
management as the main agenda that must be observed seriously. High
pressure to compete in a variety of ways, has made companies in each
industry are trying to send their commodities in the right amount, the right
location and the right time.
Demand Forecasting
Purchasing
Requirements Planning
Production Planning
Manufacturing
Inventory
Warehousing
Materials Handling
Industrial Packaging
Finished Goods
Inventory
Distribution Planning
Order Processing
Transportation
Customer Service
Strategic Planning
Information Technology
Marketing/Sales
Finance
Physical Distribution
Materials Management
SCM Logist
28
b) Supply chain management is a management philosophy that is constantly
looking for sources of business functions that are competent for combined
internal and external company as business partners who are in the supply
chain for competitive advantage to enter the supply system for customer
needs, and focused on developing innovative solutions and synchronizing
the flow of products, services and information sources to create customized
customer value.
c) Supply chain management is a network of organizations which involved
upstream and downstream relationships and the activities of the different
processes and provide value in the form of products and services to
customers.
d) Supply chain management is closely related to the flow of materials
management, and financial information in one network consisting of
suppliers, corporate, distributors, and customers.
e) Supply chain management is a set approach applied to integrate suppliers,
entrepreneurs, warehouses, and other storages efficiently in order the
product is produced and distributed in the right quantities, exact location,
and the right time to minimize costs and satisfy customer needs
Supply chain management essentially is a further development of
logistics management. Supply chain management has "operational
sequence" which is longer than the management of logistics. Supply chain
management involves the entire enterprise networking organizations ranging
from upstream to downstream. Supply chain management concept is a new
concept of looking at logistics problem. As stated by David Simchi Levi et
al (1999 cited in Verma and Seth, 2011), supply chain management is a set
of approaches utilized to efficiently integrate suppliers, manufacturers,
warehouses, and stores, so that merchandise is produced and distributed at
the right quantities, to the right locations, at the right time, in order to
29
minimize systemwide costs while satisfying service level requirement. By
looking from previous definition, it can be said that supply chain is the
logistics network. To extent with this, David Simchi Levi et al (1999 cited
in Verma and Seth, 2011) also conceptualized five main factors where
companies have the same interest, they are:
1. Suppliers: the first chain of supply chain management originated from a
source that provides the first ingredient named supplier.
2. Manufacturer: then the first chain associated with the second chain, it is
called the manufacturer or plants or assembler or fabricator or other form
that does the job of making or completing the process of manufacture of
goods.
3. Distribution: finished goods from the factory transferred to warehouse and
distributed through the warehouse‟s distributor or wholesaler or mass trader.
4. Retail Outlets: at this stage, the goods are in temporary storage before it
gets to the consumer. This stage is usually a location that is geographically
or commercially easily achieved by consumers.
5. Customers: the supply chain stages has completely done when the goods
arrived at the end user.
2.1.2. Purchasing
According to Van Weele (2010), purchasing is the management of the
company‟s external resources in such a way that the supply of all goods,
services capabilities and knowledge which are necessary for running,
maintaining and managing the company‟s primary and support activities is
secured under the most favourable conditions. As Brandes (1994 cited in
Lintukangas, 2013) has argued, there are two contradictory forces that
influence the supply strategies of companies which are the standardization
and efficiency pressures pushing purchasing towards worldwide
centralization, and the customization and responsiveness pressures that push
30
purchasing and supply management to a more decentralization. Jain and
Laric (1979 cited in Jaaskelainen, 2013) presented a conceptual framework
for selecting an industrial purchasing startegy, additionally they urge more
purchasing involvement in startegic decisions regarding on price
determination. According to both of them, they believe that the purchasing
function‟s importance increased when marketing practitioners were
concerned about external forces such as the cost of inputs. Meanwhile,
according to Kannan and Tan (2002 cited in Shah et al, 2013), the firm‟s
purchasing management objective is to consistently obtain the good
qualities and delivery performance from its suppliers. However, the analysis
of the objectives of purchasing by other researchers is much more developed
based on the following perspectives. Smeltzer et al (2003 cited in Eltantawy
et al, 2014) claimed that purchasing functions are not just the matter of
price and delivery time, but they are aligned with the organization‟s long-
term goals. He also argue that in order to complete firm‟s strategic goals,
selecting the right suppliers to ensure their dependable and flexible supply is
one of purchasing management objectives.
According to Carr and Smeltzer (1999 cited in Castaldi et al, 2011),
the ability of strategic purchasing and supply management to influence the
suppliers in the supply chain with respect to meeting the requirements of the
firm is defined to be supplier responsiveness. Moreover, Stanley and Wisner
(2001 cited in Nassiry et al, 2012) pointed out that purchasing function has
changed significantly in the last 15 years from pure transactions-oriented
order processors to supply managers with an emphasis on supply chain
management practice for adding value for customers and meet the
company‟s long-term goals. Though it can be concluded that many
evidences have identified that the objectives of purchasing has been
upgraded from the traditionally functional levels which only focuses on
purchasing at lower price or within shorter delivery time, up to strategic
decision levels which is aligned with company‟s long term goals.
31
On the contrary, the very opposite of performing a traditional
relationship with suppliers, within a supply chain system, firms are
generally interested in partnerships rather than adversarial relationships
because the believe that the former can provide a number of advantages as
mentioned by Olorunniwo and Hartfield (2001 cited in Hayat et al, 2012).
While similar opinion hold by Tan et al (1999 cited in Prajogo and Olhager,
2012), that in the face of a competitive global market, organizations have
downsized, focused on core competencies, and attempted to achieve
competitive advantage by more effectively managing purchasing activities
and partnership relationships with suppliers. Nonetheless, Carr and Pearson
(2002 cited in Schneider and Wallenburg, 2012) stated since demand from
customers will probably change at will, one of the important firm‟s
objectives of purchasing management is to make sure that it can obtain the
dependable and flexible supplies from it suppliers.
2.1.3. Purchasing Operation
According to Lysons (2006), purchasing can be depicted as a
sequential chain of events leading to the acquisition of supplies. The events
according to Lysons, are defined as recognition of needs, specification,
make or buy decisions, source identification, source selection, contracting,
contract management, receipt, possible inspection, payment and fulfillment
of needs. On the other hand, Carter (1993 cited in Ates, 2014) explained that
purchasing is the department within materials management which is
concerned with the process of ascertaining the company‟s material and
service need, selecting suppliers, agreeing terms, placing orders and
receiving goods and services. Dobler et al (1996) conceptualized the cost of
supplies, that is raw materials and purchase parts, partially completed goods,
work-in-process, finished goods, inventories (manufacturing firms),
constitute the highest single expenditure of firms engaged in basic
32
manufacturing or production. Therefore, Jones et al (2004) stated that
extreme care is required to ensure that the materials and parts purchased
meet quality specification at the lowest possible total cost of procurement.
2.1.4. Direct Procurement
The key activities in supply chain are procurement, purchasing,
producing, distributing, storing, and selling. For successful flow in supply
chain it will need coordination role like sales forecasting, production
planning, supplier management, and logistics management. Detail of
production planning is scheduled with procurement as the benchmark.
Therefore, procurement have a role in supplier management, in which the
activity that make sure supplier will fulfill the request properly. Kraljic
(1983 cited in Weele, 2010), and Ellram and Cooper (2014) indicated
procurement activity is focused on the acquisition of commodities or
standard materials readily available in the market. However, differentiated
products are distinguished by specialized components and materials.
Direct procurement purchase raw materials and packaging materials
which are related directly to production of finished goods. As stated by
Rajagopal and Bernard (1993 cited in Brewer et al, 2013), and Kotabe and
Mol (2009), because the procurement function has played a vital role in
supplier selection, contract management, and evaluation, firms are reluctant
to outsource it. Nonetheless, according to Edwards et al (2004),
procurement service providers have improved their ability to achieve
economies of scale by pooling the purchases of different firms and obtaining
low product and transaction costs. Moreover, Edwards also stated that for
some firms, procurement is not considered core and outsourcing presents an
opportunity to obtain efficient purchasing services with less management
responsibility.
Although purchasing personnel are reduced and supply management is
able to focus more on strategic purchasing, competitive advantage may not
33
materialize. Koskie (2002 cited in Brewer et al, 2013) claimed purchasing
savings are shared in comparison to a direct contribution to firm profits
achieved by internal procurement savings. Even more important is the potential
to lose direct relationships with suppliers. According to O‟Brien et al (2014),
loss of contact can negatively impact competitive advantage in industries
where suppliers control the technology or where the competitive environment
drives a high level of coordination or cooperative relationships. Firms are
reluctant to release strategic or direct materials procurement responsibility to
another firm. Ellram and Maltz (1997 cited in Brewer et al, 2013) indicated
that firms view internal procurement functions superior to third party service
providers in their ability to manage strategic purchases. Their study indicated
low levels of selection for strategic purchases to include direct materials.
However, Ulku, Toktay, and Yucesan (2007) conceptualized the contract
manufacturing context firms do outsource direct materials procurement
responsibility. Over time, procurement functions have built trust, goodwill, and
strong cooperative relationships to create and maintain an adequate supply
base, as stated by Monczka et al (1998 cited in Yang et al, 2014). Meanwhile,
Cox (2001 cited in Ahmed and Hendry, 2012) mentioned in the case where
critical suppliers of other products also produce materials or components for a
product under consideration for procurement selection activities, certainly
needs to maintain close relationships and higher purchase volumes with critical
suppliers to ensure they retain a level of importance with suppliers, resulting in
beneficial prices, service, or quality.
2.1.5. Lead Time
Lead time can be an extremely important competitive advantage when
stock is not held in advance. According to Woeppel, M.J (2000 cited in
Honiball, 2013), lead time is a very important component in a customers‟
perception of business performance. In a make-to-order business the lead
34
time has a direct impact on the business and on the customer. Woeppel also
stated that total lead time is the result of total work in process
(manufacturing lead time), which primarily driven by:
a) Excessive queue time or work-in-process.
b) Batching of product.
c) Batching in time.
According to Fisher (1997 cited in Prajogo and Olhager, 2012), supply
chain management theory clearly addresses the limitations to improving
demand chain performance through the transfer of demand information
when lead times are long. In practice, however, supply chain improvement
efforts frequently are observed to implement information transfer
improvements before addressing long lead times. According to Hariga
(2000 cited in Glock, 2012), lead-time is made up of several components
besides the fabricating itself, which are moving time, waiting time, setup
time, lot size, and rework time. Furthermore, Tersine (1994 cited in Glock,
2012) states that lead-time normally also includes the following elements
which are order transit, supplier lead-time, order preparation, delivery time
and set-up time.
Hopp et al (1990 cited in Glock, 2012) mentioned supplier lead time
cannot be seen apart from its variance which is basically from the reliability
of the supplier. The concept of supplier lead time is defined as the time that
lapses between the time an order is received by a supplier and his shipment
of the items, as conceptualized by Liao and Shyu (1991 cited in Glock,
2012). Latter has the same definition, but includes transport time (order
transit) from supplier to the customer organization.
35
2.1.6. Lead Time Relationship Towards Purchasing Operation
According to Manoocheri (1984), Hahn et al (1986 cited in
Punniyamoorthy et al, 2011), Spekman (1988 cited in Li et al, 2012), Pilling
and Zhang (1992 cited in Kleemann and Essig, 2013), and Kekre et al (1995
cited in Chen and Paulraj, 2004), many firms or manufacturers are reducing
the number of primary suppliers and allocating a majority of the purchased
material requirements to a single source. They also stated that this action
provides multiple benefits including reduced lead times due to dedicated
capacity and work-in-process inventory from the suppliers. According to
Forrester (1961 cited in Treville, Shapiro, and Hameri, 2004), lead time
reduction has long been considered a fundamental objective for overall
business improvement and a cornerstone for lean thinking of many
purchasers. Iansiti (1995 cited in Tuulenmaki and Valikangas, 2011) noted
that overlapping product development tasks (concept development and
implementation) in lead time reduction design also reduced uncertainty and
improved the flexibility to react to market and technology changes. Clark
and Fujimoto (1991 cited in Marquez, 2010) found the overlapping of
activities in purchasing operation as an effective time compression strategy
for new product development, which is in form of make-to-order process.
But still, the primary concern is to determine the optimal planned lead
times for the component to minimize the expected cost for procurement in
purchasing operations. Further from research, according to Yano (1987),
stochastic procurement and assembly times are two main components, Hopp
and Spearman (1993 cited in Glock, 2012), considered n components with
random procurement lead times and instantneous assembly, Chu et al (1993
cited in Dolgui et al, 2006), considered n components with random
procurement times and a deterministic assembly time, while Gurnani et al
(1996 cited in Motlagh et al, 2013) considered a finished product with two
components and a single random demand. These are to impose a constraint
on the probability of achieving on-time delivery. There is an independent
36
supplier for each component and a joint supplier that can supply
components in pairs. And all of them came up to a key decisions in which
how much to purchase from each supplier.
2.1.7. Cost Criteria
According to Williamson and Tadelis (2012), doing a transaction has
a cost; conceptually, the transaction cost phenomenon becomes easily
accepted. The major difficulty is in the operationalization of these costs.
Consequently, it can be said that transaction costs represent more a way of
giving arguments than an efficiency indicator of empiric nature. Meanwhile,
North et al (2012) conceptualized the costliness of information is the key to
the costs of transacting, which consist of the costs of measuring the valuable
attributes of what is being exchanged and the costs of protecting rights and
policing and enforcing agreements.
The formal theory of human action is based on the recognition of the
fact that the cost phenomenon, which is impossible to be separated from the
choice process, has a subjective nature regarding value and utility, as stated
by Menger (1871/1994 cited in Marinescu, 2012). This perspective reveals
inseparable difficulties as to when to make transaction costs operational or
when to appreciate their influences. Rothbard (1997 cited in Marinescu,
2012) mentioned if costs, like utilities, are subjective, nonadditive, and
noncomparable, then of course any concept of social costs, including
transaction costs, becomes meaningless. And third, even within each
individual, costs are not objective or observable by any external observer.
For an individual‟s cost is subjective and ephemeral; it appears only ex-
ante1, at the moment before the individual makes a decision. The cost of any
individual‟s choice is his subjective estimate of the value ranking of the
highest value foregone from making his choice.
1 The term ex-ante is a phrase meaning "before the event".
37
2.1.8. Cost Criteria Relationship Towards Purchasing Operation
According to Newman (1988 cited in Norbis and Meixell, 2011) and
Helper (1991 cited in Sarkar and Mohapatra, 2006), reduction of the
supplier base, however, is a unique characteristic of contemporary buyer-
supplier relationships, and the statement of reason also stated by Dyer (2000
cited in Ploetner & Ehret, 2006), that the administrative or transaction costs
associated with managing a large number of vendors often outweigh the
benefits. Dyer (2000 cited in Ploetner & Ehret, 2006) also mentioned the
transaction costs and inventory holding costs associated with arm‟s-length
bidding practices, characterized by short-term relationships with a large
number of short-term suppliers, can actually outweigh the costs of the parts
themselves.
In the narrowest definition, the cost includes only the purchase price
of the product. Arrow (1959 cited in Mirowski, 2013) mentioned that the
transaction consists of the cost of specifying details of procurement contract,
the cost of discovering what prices should be, the cost of negotiating the
procurement contract, and the cost of monitoring the fulfillment contract.
Nishiguchi (1994 cited in Choi and Krause, 2006) stated the transaction
costs tend to be significant for manufacturers particularly if the product is
acquired through competitive bidding. Wortmann et al (1997 cited in Li et
al, 2011) argued that as competition stiffens, the cost reduction and quality
improvement of products require minimization of transaction cost towards
the purchase price.
2.1.9. Quality of Materials
According to Harvey and Green (1993 cited in McKenna and Quinn,
2012), quality attributed into five following meanings:
a) quality as exceptional, i.e., exceptionally high standards of academic
achievement;
38
b) quality as perfection (or consistency), which focuses on processes and their
specifications and is related to zero defects and quality culture;
c) quality as fitness for purpose, which judges the quality of a product or
service in terms of the extent to which its stated purpose, defined either as
meeting customer specifications or conformity with the institutional mission
is met;
d) quality as value for money, which assesses quality in terms of return on
investment or expenditure and is related to accountability;
e) quality as transformation, which defines quality as a process of qualitative
change with emphasis on adding value towards the product.
Harvey and Green also stated the concept quality lends itself to varied
and ambiguous interpretations. While Vidovich (2009) stated most sources
in literature avoid defining quality per se2. According to Pirsig (1974 cited
in Kenyon and Sen, 2012), “Quality” is a popular term and people tend to
rely on intuitive connotations of today everyday word, for example quality
of life or quality products, presents a lenghty metaphysical argument that
although quality exists, it cannot be defined, on which only one intuitive
knows what quality is. Gabor (1990 cited in Said, 2013) claimed that quality
as an innovation, in which customers must be loyal and return again and
again for leading-edge products and services. Ultimately management
should embrace holistic initiatives to anticipate the customers‟ needs and
wants in so doing, “make the leap from continual improvement to continual
innovation.”
2.1.10. Quality of Materials Relationship Towards Purchasing Operation
According to Dickson (1966 cited in Zubar and Parthiban, 2014),
aside from deliver products on time and performance history, the abilities to
meet quality standards is the most critical determinant in selecting suppliers.
2 Latin etymology, per se (“by itself”), per (“by, through”), and se (“itself, himself, herself,
themselves”).
39
He also stated that quality has always been one of the most important
performance criteria even with the conventional purchasing strategy.
Manoochehri (1984 cited in Punniyamoorthy et al, 2011) claimed that
many conceptual studies also emphasize that supply management must have
quality focus. Meanwhile, Helper (1991) stated that the importance of
quality has increased the most during the period. Furthermore, Handfield et
al (2011) elaborated the effective integration of suppliers into new product
development can yield such benefit as improved quality of purchased
materials. In addition, Lyons et al (1990 cited in Al-Abdallah et al, 2014)
stated that incentives such as long range relationship and contracts as well
as commitment are expected to encourage suppliers to improve the quality
of their products as suppliers account for almost 30% of quality related
problems.
According to Juran (1974 cited in Kenneth, 2012), the quality
movement developed a significant body of work around the value concept
of a product and emphasized that the most important objective in any
manufacture to satisfy customer needs, whereas value from purchasing can
be understood to also include the sacrifice to meet the needs.
2.2. Previous Research
1. Indra Cahyadi, 2004, in his research entitled “The Analysis of Multi Criteria
Decision In the Process of Supplier Selection”, explained that in the supplier
selection process, the decision often concerned with more than one criterion,
and the selection process refers to decisions faced with variety of objectives
and intended to help decision makers to obtain the best solution.
2. Elly Wintania, 2012, in her research entitled “Supplier Selection Strategy
Indirect Material – Procurement Department (Case Study: PT Merck Tbk)”,
explained that uncertainty supply chain management system shall be
40
developed responsively by improving the ability to response, adapt, and
transform the changes in the market in limited time. Therefore, develop
source data between suppliers and customers also play an important role in
providing fast and accurate source data. Also she added that supplier
selection is one of the key activities in procurement which supports success
in supply chain, and served as a tool to get trust and reliable suppliers to
fulfilling request as a part of business process.
3. Yusuf Andriana, 2012, in his research entitled “Evaluation and Selection of
Suppliers In Supply Chain Management of Industry Guava Juice (Juice
Industry Case Study XYZ Guava, Sabang, West Java)”, explained that
suppliers are important element to reduce cost of raw materials for supply
chain management towards an industry. Moreover he explained, at the level
of the upper echelons of a supply chain, the evaluation and selection of
suppliers is a key element in the ordering process (purchasing process) and
became the main activity of the professional firms.
4. David Gunawan, 2009, in his research entitled “Analysis and Design of
Informations Systems E-Procurement for Supplier Selection at PT. Baria
Tradinco”, explained that purchasing role today is not just the purchase
process, it can be seen from the ability to create value added to a product by
doing supplier selection, evaluate supplier performance, and build a good
partnership with suppliers. Though, evaluation and selection of suppliers
became one of the fundamental role of purchasing.
41
2.3. Theoretical Framework
Figure 2.3 Theoretical Framework
Source: Developed by Researcher
The Figure 2.3 above illustrated four variables which consist of
independent variables and dependent variable. Lead Time, Cost Criteria, and
Quality of Materials are the independent variable. While the Purchasing
Operation is dependent variable.
Lead Time (X1)
Cost Criteria (X2)
Quality of
Materials (X3)
Purchasing Operation
(Y)
42
2.4. Operational Definition
Table 2.4 Operational Definition
Terms Meaning Benefit
Lead Time latency (delay) between the
initiation and execution of a
process
Gaining the
competitive
advantage of turning
inventory towards the
manufacturing
business process
Cost Criteria A metric that is totaling up
as a result of a process or as
a differential for the result
of a decision
The company could
receive the estimated
value of projected
costs for worth
undertaking
Quality of
Materials
A value measured for
verifying and maintaining a
desired level of quality in an
existing product or service
Setting a standardized
value for the product
for continuous
improvement, and,
people and machines
efficiency
Purchasing
Operation
The activity of making
transaction towards supply
of commodities, equipment,
and services as requested by
various units, at the lowest
price consistent with
required quality from the
one who supply and deliver
the items
Accuracy of business
forecasts for reducing
inventory levels,
faster time to market,
significant cost
savings, and reduce
development costs
43
2.5. Hypothesis
2.5.1. Partial Significant Influence of Lead Time (X1) Towards Purchasing
Operation (Y).
H01: There is no partial significant influence of lead time towards
purchasing operation.
Ha1: There is a partial significant influence of lead time towards purchasing
operation.
2.5.2. Partial Significant Influence of Cost Criteria (X2) Towards Purchasing
Operation (Y).
H02: There is no partial significant influence of cost criteria towards
purchasing operation.
Ha2: There is a partial significant influence of cost criteria towards
purchasing operation.
2.5.3. Partial Significant Influence of Quality of Materials (X3) Towards
Purchasing Operation (Y).
H03: There is no partial significant influence of quality of materials towards
purchasing operation.
Ha3: There is a partial significant influence of quality of materials towards
purchasing operation.
2.5.4. Significant Simultaneous Influence Towards Lead Time (X1), Cost
Criteria (X2), and Quality of Materials (X3) Towards Purchasing
Operation (Y).
H04: There is no significant simultaneous influence of lead time, cost
criteria, and quality of materials towards purchasing operation.
Ha4: There is a significant simultaneous influence of lead time, cost criteria,
and quality of materials towards purchasing operation.
44
CHAPTER III
METHODOLOGY
3.1. Research Design
There are two methods in doing scientific research those are
qualitative and quantitative research. The differences between qualitative
and quantitative research are the type of data, research process, instrument
in collecting data and the purpose of research.
• Qualitative method usually gathered by observations, interviews or focus
groups and the data also is gathered from written documents and through
case studies, it less emphasis on counting numbers of people who think or
behave in certain ways and more emphasis on explaining why people think
and behave in certain ways.
• Quantitative method emphasis on objective measurements and numerical
analysis of data collected through polls, questionnaires, or surveys. In,
quantitative method pieces of information that can be counted
mathematically, it is usually gathered by surveys from large numbers of
respondents selected randomly and it is analyzed using statistical method,
best used to answer what, when and who questions (Civicpartnership.org,
2013). Quantitative research focuses on gathering numerical data and
generalizing it across groups of people (Babbie, Earl R, 2010).
Based on the explanation above, this research will be categorized as
quantitative research, the aim of this research is to measure the criterias of
supplier selection as the independent variable, towards purchasing operation
as the dependent variable.
45
There are four variables which are measured in this research. Three
variables are independent variable as the indicators are Lead Time (X1),
Cost Criteria (X2), and Quality of Materials (X3). Another variable which is
dependent variable as the indicator is Purchasing Operation (Y), in which
the variable influenced by the independent variables. It will be reflected on
the questionnaires which will be given to the colleagues of whom the
researcher can obtain accurate informations, and questions provided will be
regarding on evaluation factors of supplier selection for direct-procurement
towards purchasing operation in L‟Oreal Manufacturing Indonesia.
46
3.2. Research Framework
The researcher framework is the structure of the research that show
the process of the analysis in order to achieve the best results. The flow
chart is shown as below:
NO
NO YES
Figure 3.2 Research Framework
Source: Developed by Researcher
Problem Statement
Literature Review
Pre Questionnaire
Real Questionnaire
Data Collection
Data Analysis and Interpretation
Conclusion and Recommendation
Validity
Reliability
47
3.3. Sampling Design
Sampling Design is part of statistical methodology that related in
taking a portion of the population. If a sampling is done correctly, statistical
analysis can be used to generalize a whole population. There are two major
types of sampling design: probability and non-probability sampling. In
probability sampling, the elements in the population have some known non-
zero chance or probability of being selected as sample subjects. In non-
probability sampling, the elements do not have a known or predetermined
chance of being selected as subjects (Sekaran, Bougie, 2010).
3.3.1. Population
Polit and Hungler (1999 cited in Bell, 2010) refer to the population as
an aggregate or totality of all the objects, subjects or members that conform
to a set of specifications. Population is the set of elements that the research
focuses upon and to which the results obtained by testing the sample and
should be generalized. It is absolutely essential to describe accurately the
target population (Bless, 2006). The Population refers to the entire group of
people, events, or things of interest that the researcher wishes to investigate
(Sekaran, Bougie, 2010). This research is aimed to evaluate the factors of
supplier selection for direct procurement towards purchasing operation,
therefore, the population is the purchasing department, especially the one
who are responsible in procurement function.
In this research, the population are the people in Manufacturing
Supply Chain Department, concentrating in purchasing and procurement
function. The total population is 85 people in which as shown on below
table:
48
Table 3.3.1 Research Population
Source: Developed by Researcher
3.3.2. Sample
Sample is a subset of population (Sekaran, Bougie, 2010). Sample on
this research will be used to investigate the research problems. According to
Ferdinand (2006), if the sample is subset of the population that consists of
some member population, these subset should be taken because in many
case it is impossible to conduct the research with all members of population,
therefore, we formed a representative population that is called sample.
In this research, sample will be chosen by using non-probability
sampling. Non-probability is a technique which the probability of any
particular member of the population being chosen is unknown (William G.
Zikmund, 2007). In non-probability sampling, every element in the
population doesn‟t have the opportunity or the same opportunities to be
selected as the sample (Santoso, 2009). Sample will be taken by using
judgemental sampling technique. Judgemental sampling is when the
Position Number
Procurement Leader 1
Procurement Logistics 12
Logistic Manager 1
Supply Staff of Raw Materials 9
Supply Staff of Packaging Materials 12
Asia Pacific Sourcing Center 50
49
researcher specifies the characteristics of a population of interest and then
tries to locate individuals who have those characteristics (Burke Johnson,
2010). Once the group is located, the researcher will ask those who meet the
criteria to participate in the research study. From the mentioned population
of 85 people, the sample size will be taken according to the following
formula:
n = N
1 + Ne2
n = 85
1 + (85)(0.05)2
n = 85 ; n = 70
1 + 0.2125
Where:
n = sample size
N = population
e2 = level of confidence 95%
Therefore, the final respondents of the questionnaires will be 70
people, for as much as 20 people will be pre-test, and the other people 50
will be real test. The researcher will spread the questionnaires based on
researcher‟s personal judgement towards the respondent who meet the
criteria and have the certain knowledge or background towards the study.
50
3.4. Research Instrument
Research Instrument is the tool that used to answer the research
questions that stated in the previous chapter, which also used to gather,
examine, investigate an issue or collecting, process, analyze and present the
data in a systematic and objective in order to solve the problem or to test a
hypothesis. The researcher intention is to gather the information from as
much various sources.
3.4.1. Primary Data
Primary data is the specific information collected by the person who is
doing the research. It can be obtained through clinical trials, case studies,
observation, discussion, interview, true experiments and randomized
controlled studies. This information can be analyzed by other experts who
may decide to test the validity of the data by repeating the same experiments
(Ehow.com, 2013). Data that collected on primary sources come from actual
hands-on situation when an event was happen (Silalahi, 2006).
There are other several methods of collecting primary data which are:
1. Direct personal investigation;
2. Indirect oral investigation;
3. Through local correspondents;
4. Through questionnaire mailed to correspondents;
5. Through schedules filled in by enumerators (Gupta, 2005).
Direct personal investigation is when the researcher directly comes in
contact to the correspondents to collect data. The researcher herself visits
the different correspondents (Gupta, 2005).
51
Through questionnaire mailed to correspondents is the method which
the correspondents are not directly contacted by the researcher, but instead
the researcher sends the questionnaires by post to the correspondents with
the request of sending them back after fill the questionnaire (Gupta, 2005).
Primary data in this research of “Evaluation Factors of Supplier
Selection For Direct Procurement Towards Purchasing Operation (Case
Study In L‟Oreal Manufacturing Indonesia)” is obtained directly from the
questionnaires that used for survey and the selection method is by using
direct personal investigation. Questionnaires are a technique of data
collection done by giving series of written statements that are consists of
research variables. These questionnaires will be spread to the numbers of
samples.
3.4.2. Scaling
This research use Likert Scale as a tool to measure the degree of
agreement from the respondents. In the Likert Scale, the distance between
different categories cannot be quantified. The only possible operation is to
determine whether a certain state is greater or smaller than another. In this
sense, the measured properties are considered to be continuous, while its
states are reviewed as discrete (Davino, 2012). The Likert Scale is designed
to examine how strongly subjects agree or disagree with statements on a
five-point scale with the following anchors (Sekaran, Bougie, 2010):
52
Figure 3.4.2 Likert Scale
Source: Sekaran, Bougie, 2010
The Questionnaire uses Likert Scale and all statements that express
either a favorable and unfavorable attitude will be scaled through Strongly
Disagree, Disagree, Neither Agree or Disagree, Agree, and Strongly Agree.
Figure 3.4.2.1 Likert Scale Questionnaire
Source: Developed by Researcher
Note:
1. For Strongly Disagree
2. For Disagree
3. For Neutral
4. For Agree
5. For Strongly Agree
No. Statements 1 2 3 4 5
1
2
3
4
5
53
Each of the five responses would have a numerical value which would
be used to measure the attitude under investigation.
Likert Scale have the advantage that they do not expect a simple yes /
no answer from the respondent, but rather allow for degrees of opinion, and
even no opinion at all. Therefore quantitative data is obtained, which means
that the data can be analyzed with relative ease.
3.5. Statistical Treatment
3.5.1. Descriptive Analysis
Descriptive analysis provide a general view of the data such as mean,
standard deviation, variance, maximum value, minimum value, sum, range,
and skewness (Ghozali, 2005). In this research, descriptive statistical
analysis that is being used are mean and standard deviation of respondents‟
responds to the question given in the questionnaire. Descriptive statistical
analysis is aim to show the dispersion of the respond to a questionnaire.
3.6. Reliability and Validity
3.6.1. Reliability
Reliability refers to the consistency or stability of a measuring
instrument. It is to determine a measure to measure exactly the same way
each time it is used (Jackson, 2011). According to Imam Ghozali (2005),
reliability is actually a tool to measure a questionnaire which is an indicator
of the variables or constructs. As with Validity, Reliability testing in this
research will be conducted by using software SPSS 16.0. Accurate
questionnaire may deflect the right question which is means when the
54
question is asked for several times, the interpretation would be the same
from one respondent to another.
Measurement of Reliability (Internal-Consistency) in this research
will use the Cronbach‟s Alpha Coefficient in which the equation is:
Where:
k = number of items
r = average correlation between any two items
α = reliability of the average or sum
Table 3.6.1 Cronbach Alpha
Source: Imam Ghozali (2005)
Cronbach's alpha Internal consistency
α ≥ 0.9 Excellent
0.8 ≤ α < 0.9 Good
0.7 ≤ α < 0.8 Acceptable
0.6 ≤ α < 0.7 Questionable
0.5 ≤ α < 0.6 Poor
α < 0.5 Unacceptable
55
3.6.2. Validity
The purpose of validity testing is to eliminate the proper question that
will answer the research objectives. Validity test is used to determine
whether the questionnaire is valid or not. The Pearson product-moment
correlation coefficient (or Pearson correlation coefficient for short) is a
measure of the strength of a linear association between two variables and is
denoted by r. Basically, a Pearson product-moment correlation attempts to
draw a line of best fit through the data of two variables, and the Pearson
correlation coefficient, r, indicates how far away all these data points are to
this line of best fit (how well the data points fit this new model/line of best
fit) (Statistic.laerd.com, 2013). The valid data is a representative statement
of variables that are ready to spread to the respondents.
In Pearson Correlations, results are between -1 and 1. A result of -1
means that there is a perfect negative correlation between the two values at
all, while a result of 1 means that there is a perfect positive correlation
between the two variables. A result of 0, on the other hand, means that there
is no linear relationship between the two variables. Most research will very
rarely get a correlation of 0, -1 or 1. Result would be somewhere in
between. The closer the value of r gets to zero, the greater the variation the
data points are around the line of best fit.
The Quantitative interpretation of the degree of linear relationship
existing is shown in the table below:
56
Table 3.6.2 Pearson-Moment Correlation
Guidelines for Interpreting Pearson Product-Moment Correlation
(Applicable For Both Postive and Negative Correlation)
.8 – 1 Is considered a very strong
relationship
.6 - .79 Is considered a strong
relationship
.4 - .59 Is considered a moderate
relationship
.2 - .39 Is considered a weak
relationship
Less than .2 Is considered a very weak
relationship
Correlation r formula:
For any two variables, X and Y, the correlation coefficient between them is
given by the formula:
Where:
n = number pair of scores
∑ = sum of the products of pair scores
∑ = sum of x scores
∑ = sum of y scores
57
∑ = sum of squared x scores
∑ = sum of squared y scores
The first requirement of a good instrument was validity. Thus, the
researcher chooses Pearson Product Moment Correlation by using the
software SPSS 16.0 to fulfill the requirement of the instrument‟s validity.
58
3.7. Data Collection Procedure
In this research, the researcher use primary data as a tool for data
collection by spreading questionnaires for the survey as a purpose that the
data is obtained from first-hand of respondents. Surveys only consist of two
components which are questions and responses. In fact, surveys are
typically selected when information is to be collected from a large number
of people or when answers are needed to a clearly defined set of questions.
Surveys are good tools for obtaining information on a wide range of topics
when indepth probing of responses is not necessary, and they are useful for
both formative and summative purposes.
Figure 3.7 Data Collection Procedure
Source: Developed by Researcher
RESEARCH
DATA COLLECTION
PRIMARY DATA
SURVEY
DATA SELECTION
59
3.8. Hypothesis Testing
3.8.1. Classical Assumption Test
Classical assumption is the statistical requirements that must be met in
multiple linear regression analysis. In order to use multiple regression
models, classic assumption test need to implement such as normality testing,
multicollinearity, and heteroscedasticity testing.
3.8.1.1. Normality Test
Normality tests are used to determine if a data set is well-modeled by
a normal distribution and to compute how likely it is for a random variable
underlying the data set to be normally distributed. The basic indicator that
stated if the data is normally distributed is when the histogram chart shows
the bell-shaped curve, and if the P Plot of regression standardized residual
shows the residual distributed in the pattern of diagonal line. Normality can
be detected by analyzing the distribution of residuals in diagonally shaped
on the Normal P-Plot of Regression Standardized Residual graph (Santoso,
2009).
3.8.1.2. Multicollinearity Test
Multicollinearity is a term used when to x variables are highly
correlated. Means that one can be linearly predicted from the others with a
non-trivial degree of accuracy. In this situation, the coefficient estimates of
the multiple regressions may change erratically in response to small changes
in the model or the data. In SPSS 16.0 software for Windows, to compute
tolerance for each independent variable, SPSS runs a separate regression
analysis as stated by Tabachnick and Fidell (2001b cited in Asteriou and
Hall, 2011).
60
3.8.1.3. Heteroscedasticity Test
Collection of random variables is heteroscedastic if there are sub-
populations that have different variability from others. Variability could be
quantified by the variance or any other measure of statistical dispersion.
Thus, heteroscedasticity is the absence of homoscedasticity.
Heteroscedasticity typically occurs when a variable is not distributed
in a normal manner or when a data transformation procedure has produced
an anticipated distribution of a variable as stated by Tabachnick and Fidell
(2001b cited in Asteriou and Hall, 2011). Heteroscedasticity reflects
inconstant error variance, which in turn may compromise the validity of
significance tests and goddess-of-fit indicators. Specifically, the variance of
residuals may vary with expected values of the dependent variable and with
individual explanatory variables (Farag, 2009).
3.8.2. Linear Multiple Regression
The researcher will be using linear multiple regression in order to
answer the research questions in this research. Regression analysis is a
statistical process for estimating the relationships among variables. The
regression analysis is basically done to determine the dependency of the
dependent variable towards one or more independent variable (Ghozali,
2005). Regression model in which the independet variables are more than
one is called multiple regression or multiple linear regression, therefore
since the researcher have three independent variables, the research will
conducted with multiple linear regressions.
The multiple regression model contain dependent variable (Y), more
than one independent variables (X1, X2, X3,.....,Xn), the β‟s are the
regression coefficients and the random error term (ε), where Y depend on
Xs, and, Y and the Xs are continuous variables. The regression model in this
61
research is applied to determine the impact of the independent variables
which are Lead Time (X1), Cost Criteria (X2), and Quality of Materials (X3)
towards Purchasing Operation (Y).
The mathematical model for above regression model is as follows:
Y = a + β1X1 + β2X2 + β3X3 + e
Where:
Y = Purchasing Operation
a = Constanta
β1 = Coefficient between Lead Time towards Purchasing Operation
β2 = Coefficient between Cost Criteria towards Purchasing Operation
β3 = Coefficient between Quality of Materials towards Purchasing Operation
X1 = Lead Time
X2 = Cost Criteria
X3 = Quality of Materials
e = Error disturbance
The result from this regression analysis will be used to accept or reject
the hypothesis as to observe whether there is any dependency between
dependent variable and independent variables.
62
3.8.3. t-Test (Partial Test)
The t-test is a test to determine the effect of the independent variables
could individually affect the dependent variable (Ghozali, 2005). The t-test
can be used to determine if two sets of data are significantly different from
one another, and mostly applied when the statistics test follow the normal
distribution.
The formula of t-test for manual calculation is stated as follows:
t = bj – βj
Sbj
Where:
t = statistic test for t-distribution
bj = sample slope
βj = slope of the population
Sbj = standard error of the slope
To interpret the result of the t-test, the following criteria needs to be as
follows:
a. Null Hypothesis (Ho) and Alternative Hypothesis (Ha) Formulation:
Ho : β1 = 0
Then;
The independent variables have neither positive nor negative impact
towards the dependent variable.
63
Ha : β1 ≠ 0
Then;
The independent variables have either positive or negative impact towards
the dependent variable.
b. The significant factor being used for the t-test significant;
Significant Factor = 0.05 (5%)
3.8.4. F-Test (Simultaneous Test)
The F-test is a test to determine whether the independent variable
could simultaneously or collectively affect to dependent variable. For the F-
testing that will be conducted in this research, the researcher uses
confidence interval at 95% , and the 5% significance (α = 0.05) outside the
confidence level will leads to the rejection of null hypothesis. The
significance level at 5% is applied since the research is categorized as social
science, in which the 5% significance is customary.
The formula of F-test for manual calculation is stated as follows:
F = [ R2 / k ]
[ ( 1 – R2 ) / ( n – k – 1 ) ]
Where:
F = statistics test for F distribution
R2
= coefficient of determination
k = number of independent variables in the regression model
n = number of samples
64
To interpret the result of the F-test, the following criteria needs to be
as follows:
a. Ho : β1 = β2 = β3 = 0
Then;
The independent variables are not simultaneously affecting the dependent
variable.
b. Ha : β1 = β2 = β3 ≠ 0
Then;
The independent variables are simultaneously affecting the dependent
variable.
3.8.5. R2 Test (Coefficient of Determination)
The R2
test is a test to determine how far the independent variables
could describe the dependent variable. The determination coefficient value
goes around zero to one. Low R2
value means that the ability of the
independent variables to describe the dependent variable are limited. If the
value of R2
goes near one, it means that the independent variables give
almost all information that needed to predict the dependent variable
(Ghozali, 2005).
65
CHAPTER IV
ANALYSIS AND INTERPRETATION
4.1. Company Profile
4.1.1. L’Oreal Worldwide
L‟Oreal firstly comes from Eugene Schueller, a chemist who invents
the first non-offensive hair color for human hair in 1907 and he patented it
on 1908. Starting 1909, he started to distribute the hair color to salons in
Paris.
Currently, L‟Oreal World Wide has 27 global brands and presents in
130 countries. In financial aspect, L‟Oreal generate € 23 billion consolidated
sales in 2003, € 665 million in R&I investments. L‟Oreal has 580 patents
filed in 2011, 77,450 employees over the world. In 2013 L‟Oreal produce
6.7 billion units and until now, L‟Oreal becomes the number 1 cosmetics
company worldwide.
4.1.2. L’Oreal in Indonesia
Figure 4.1.2 Historical Timeline of L’Oreal
66
Firstly, L‟Oreal starts its journey in Indonesia by opening a
LANCOME Paris store in Indonesia on 1979 and L‟Oreal Paris store in
1985. Due to the positive response of consumers in Asia-Pacific, L‟Oreal
worldwide started to establish a factory in Indonesia to fulfill product
demand comes from Asia-Pacific region. At that time, regarding with
Indonesian regulation about foreign company, it stated that a foreign
company couldn‟t have 100%, so L‟Oreal did a joint-venture with a local
cosmetic company in Indonesia.
In line after the opening, L‟Oreal grew step by step by keep
introducing their product. In 1987 they sell L‟Oreal Professional for salon
consumption, Kerastase Paris in 1994, and Maybelline New York in 1996.
L‟Oreal saw a lot of business opportunities in Indonesia which underlying
the establishment of PT. L‟Oreal Indonesia in 2000 as the basis of L‟Oreal
business headquarters for Indonesian Area.
Currently, L‟Oreal Indonesia has 15 brands in the market, 7% market
share. L‟Oreal Indonesia also recorded as No. 3 player in cosmetic and No.
3 in mass market – fastest growing for the last two years after Unilever and
Procter & Gamble. Number 1 in professional market and number 2 in luxury
market. L‟Oreal Indonesia also grew for 25% larger for the last 5 years.
4.1.3. PT Yasulor Indonesia (L’Oreal Manufacturing Indonesia)
On 1985, L‟Oreal worldwide established PT. Yasulor as a joint
venture with a local cosmetic Company and starting its first production on
1986. L‟Oreal becomes the 100% owner of PT. Yasulor at 1993. Due to
1998‟s economic crisis, production was stuck at 14 million units and at that
time was the hardest time that L‟Oreal needs to survive for the continuity of
the factory.
67
On 2008, L‟Oreal business becomes better which requires to transfer
all finish good to warehouse in Cibinong, followed by redeployment if the
plant on 2009. On 2010, PT. Yasulor had budget for its development by the
production of 100 million units and preparation its factory transfer to
Jababeka area. Finally L‟Oreal did inauguration on Jababeka factory at
2012, followed by the realization of 210 million units on 2013 and 220
million units finish good production in 2014.
4.1.4. Vision, Mission, Values, and Ethical Principles
1. The Vision
To offer everyone, all over the world, the best cosmetics in terms of
quality, efficacy, and safety; to give everyone access to beauty by offering
products in harmony with their needs, culture and expectations.
2. The Mission
Beauty is a Language
For more than a century, L‟Oreal has devoted itself solely to one
business; beauty. It is a business rich in meaning, as it enables all
individuals to express their personalities, gain self-confidence and open up
to others.
Beauty is Universal
L‟Oreal has set itself the mission of offering all women and men
worldwide the best of cosmetics innovation in terms of quality, efficacy, and
safety. It pursues this goal by meeting the infinite diversity of beauty needs
and desires all over the world.
68
Beauty is a Science
Since its creation by a researcher, the group has been pushing back the
frontiers of knowledge. Its unique Research arm enables it to continually
explore new territories and invent the products of the future, while drawing
inspiration from beauty rituals the world over.
Beauty is a Commitment
Provides access to products that enhance well-being, mobilizes its
innovative strength to preserve the beauty of the planet and supporting local
communities. These are exacting challenges, which are a source of
inspiration and creativity of L‟Oreal.
L’Oreal Offering Beauty For All
By drawing on the diversity of its teams, the richness and the
complementary of its brand portfolio, L‟Oreal has made the universalization
if beauty its project for the years to come.
3. Values
L‟Oreal values are embedded in L‟Oreal genetic code. They continue
to this day to express themselves in the daily actions of our teams across the
globe. Here is the list on the Group‟s six founding values:
Table 4.1.4.3 Six Values of L’Oreal
Passion Innovation Entrepreneurial
Spirit
Open-mindedness Quest for Excellence Responsibility
69
4. PT Yasulor Indonesia (L’Oreal Manufacturing Indonesia) 7 Values
Figure 4.1.4.4 Yasulor 7 Values
5. Ethical Principles
L‟Oreal principles are Integrity, Respect, Courage, and Transparency.
L‟Oreal Ethical Principles shape its culture, underpin their reputation, and
must be known and recognized by all L‟Oreal employees.
Integrity: because acting with integrity is vital to build and maintain trust
and good relationship
Respect: because what we do has an impact on many people‟s lives.
Courage: because ethical question are rarely easy but must be addressed.
Transparency: because we must always be truthful, sincere and be able to
justify our actions and decisions.
Yasulor 7
Values
Human Sensitivity
Spirit of Initiative
Active Engagement
Respect
Commitment to Excelence
Team Spirit
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4.1.5. Organizational Structure
Figure 4.1.5 Organizational Structure
`
4.2. Data Analysis
4.2.1. Respondents Profile
The respondent profiles data gathered has purpose to gain insight the
characteristics of respondents in this study through the questionnaire. The
populations in this study were those who are the expertise of the field in this
research. Data obtained that shows the characteristics of this research were
recorded as below table:
71
1. Gender
Figure 4.1 Pie Chart of Gender Distribution
Source: Developed by Researcher
Table 4.1 Gender Distribution
Source: Developed by Researcher
Male 33
Female 37
Gender
Gender Frequency Percentage (%)
Male 33 47%
Female 37 53%
Total 70 100%
72
From table 4.1 as shown on above, we can see that most respondents
of this research are female, as many as 27 people with percentage of 54%.
The second largest followed with male, as many as 23 people with
percentage of 46%. Moreover, based on the table, we can conclude that
most gender of the respondents in this research is dominantly female.
2. Age
Figure 4.2. Pie Chart of Age Distribution
Source: Developed by Researcher
Table 4.2 Age Distribution
Source: Developed by Researcher
20-35 years old, 18
36-40 years old, 36
Above 40 years old,
16
Age Frequency Percentage
(%)
20-35 years old 18 26%
36-40 years old 36 51%
73
From table 4.2 as shown on above, we can see that most respondents
of this research are in the category age of 36-40 years old, as many as 26
people that represents 52% of the total respondents. The second largest is in
the category age of 20-35 years old, as many as 13 people with percentage
of 26% of the total respondents. Then followed by the lowest number of
respondents which in category age above 40 years old as many as 11 people
with the percentage of 22% of the total respondents.
4.2.2. Reliability Test
Before the instrument of the research is properly spread to the
respondents, the researcher needs to test the reliability of every question in
the questionnaire. Reliability test was conducted by computing statistics
data in SPSS and the data was arranged from Microsoft Excel to tabulate
Cronbach’s Alpha of the research instrument. The Alpha value of each
variable if it is greater than 0.700 is acceptable; if it is greater than 0.800 is
good (Sekaran, Bougie, 2010). The results of reliability testing for each
variable are shown in Table 4.2.2. Based on the result, all variable has alpha
value are greater than 0.700. Therefore, we can conclude that each of
variables is reliable to be used for further research activity.
Above 40 years old 16 23%
Total 70 100%
74
Table 4.2.2 Reliability Test: Cronbach Alpha’s
Source: self-construct, processed through SPSS 16.0 Software for Windows
Variable Number of
Questions
Cronbach’s
Alpha Result
Lead Time (X1) 4 0.731 Acceptable (0.7 ≤ a ≤ 0.9)
Cost Criteria (X2) 4 0.800 Good (0.8 ≤ a ≤ 0.9)
Quality of Materials (X3) 4 0.724 Acceptable (0.7 ≤ a ≤ 0.9)
Purchasing Operation (Y) 4 0.770 Acceptable (0.7 ≤ a ≤ 0.9)
Cronbach’s Alpha should be more than 0.6 to be considered as
reliable, therefore it can be concluded from table 4.2.2 of the researcher‟s
data reliability has exceeded the require number of Cronbach’s Alpha.
Hence, the researcher‟s data is reliable as the source of questionnaire and
the variables used in this research are also reliable and can be applicable to
be used for further research activities as the result of the Cronbach’s Alpha
is 0.731, 0.800, 0.724, and 0.770.
4.2.3. Validity Test
Validity testing is used to determine whether the questionnaire is valid
or not. In validity testing of the data, the researcher used Peason Product-
Momment Coefficient of Correlation, conducted in SPSS 16.0 Software for
Windows. The measure that is valid measure is what it claims to measure.
Validity is measured by the use of correlation coefficient. For validity
coefficients, the important thing is that they are statistically significant at
the level greater than 0.05 levels ( Jackson, 2011).
75
Table 4.2.3 Validity Test: Pearson Correlation Coefficient
Source: self-construct, processed through SPSS 16.0 Software for Windows
Variable R Table (a=5%) Pearson
Correlation Result
Lead Time (X1-1) .468 .533 Valid
Lead Time (X1-2) .468 .562 Valid
Lead Time (X1-3) .468 .626 Valid
Lead Time (X1-4) .468 .722 Valid
Cost Criteria (X2-1) .468 .771 Valid
Cost Criteria (X2-2) .468 .886 Valid
Cost Criteria (X2-3) .468 .587 Valid
Cost Criteria (X2-4) .468 .822 Valid
Quality of Materials (X3-1) .468 .632 Valid
Quality of Materials (X3-2) .468 .571 Valid
Quality of Materials (X3-3) .468 .719 Valid
Quality of Materials (X3-4) .468 .754 Valid
Purchasing Operation (Y-1) .468 .926 Valid
Purchasing Operation (Y-2) .468 .561 Valid
Purchasing Operation (Y-3) .468 .879 Valid
Purchasing Operation (Y-4) .468 .959 Valid
Based on the table shown above, we can conclude that the
significances of all questions are greater than 0.05. Therefore, all of the
questions in the questionnaire are valid, and the variables used in this
research are also valid and can be applicable to be used for further research
activities.
76
4.2.4. Descriptive Analysis
In the descriptive statistic show the mean and standard deviation on
indicators of Lead Time, Cost Criteria, and Quality of Materials according
to respondent responses. Mean is the most widespread way to find out
which variable is the most dominant from all variables. Standard deviation
is a measure of how spreads out numbers are. The result is show in below
table:
Table 4.2.4 Descriptive Statistical Analysis
Source: self-construct, processed through SPSS 16.0 Software for Windows
Descriptive Statistics
Variables Mean Std. Deviation
Lead Time 2.14 0.84008
Cost Criteria 2.56 1.64865
Quality of Materials 2.10 0.90699
Purchasing Operation 2.72 0.86610
From table 4.2.4 it can be concluded that the most dominant factor of
Purchasing Operation in this study is Quality of Materials with the mean
value of 2.10 means that the respondent response have tendency to choose
suppliers for direct procurement by looking and judging the Quality of
Materials of product offered by suppliers. Then followed by Lead Time with
the mean value of 2.14 means that the respondent have tendency to consider
the Lead Time given from suppliers from the moment of product being
purchased until arrival at the manufacture. And the least dominant factor is
the Cost Criteria with the mean value of 2.56. By looking from the variable,
it shows that the respondent response has less considerations about Cost
Criteria compared to another two dominant factors mentioned.
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4.2.5. Classical Assumptions Test
This research use classical assumption to analyze the data which
firstly has to be processed through assumptions testing, they are normality,
multicollinearity, heteroscedasticity.
4.2.5.1. Normality Test
The first test is normality test. Normality test is used to determine if a
data set is well-modeled by a normal distribution and to compute how likely
it is for a random variable underlying the data set to be normally distributed.
The basic indicator that stated if the data is normally distributed is when the
histogram chart shows the bell-shaped curve, and if the P Plot of regression
standardized residual shows the residual distributed in the pattern of
diagonal line. Normality can be detected by analyzing the distribution of
residuals in diagonally shaped on the Normal P-Plot of Regression
Standardized Residual graph (Santoso, 2009).
Figure 4.2.5 Normality Test: Histogram
78
Based on figure 4.2.5, Histogram of Normal Distribution, it shows the
histograms are bell-shaped. It can be concluded that the data in this research
is normally distributed.
Figure 4.2.5.1 Normality Test: Normal P-Plot of Regression
Standardized Residual
Based on figure 4.2.5.1, graph of Normal P-Plot of Regression
Standardized Residual on above suggest that data is spread around the
diagonal line and follow the direction of the diagonal line or histogram
graph. Then again, it can be concluded that the data in this research is
normally distributed.
79
4.2.5.2. Multicollinearity Test
Multicollinearity test has a purpose to test whether the regression
model found a correlation between the independent variables. A good
regression models should have no correlation between independent
variables. If the independent variables are correlated, then this variable is
not orthogonal. Orthogonal variable is the independent variable in which the
correlation value between the members of independent variables is equal to
zero (0). Multicollinearity is indicated for a particular variable if the
tolerance value is 0.1 or less and if the VIF greater than 10 as indicative of
multicollinearity (Meyers et al, 2006). To detect the variables has
multicollinearity or not in the regression model are as follows:
Table 4.2.5.2 Multicollinearity Test: Tolerance and VIF Value
Source: self-construct, processed through SPSS 16.0 Software for Windows
Model
Collinearity Statistics
Tolerance VIF
1 (Constant)
LeadTimeTotal .294 3.435
CostCriteriaTotal .258 3.879
QualityQfMaterialsTotal .413 2.423
a. Dependent Variable: PurchasingOperationTotal
Based on the table 4.2.5.2 as shown on above, all variables show the
tolerance which all of them are greater than 0.1 and Variance Inflation
Factor (VIF) score of lower than 10. The indication shows that there is no
multicollinearity that is used by any variables of this research.
80
4.2.5.3. Heteroscedasticity Test
Heteroscedasticity is a linear correlation between independent
variables in multiple regressions. The purpose of heteroscedasticity test in
the regression model is to test whether the regression model has the variance
inequality from one residual observation to the other. If the variance of
residual is fixed, then it is called as homoscedasticity, otherwise if the
residual is different, it is called as heteroscedasticity. Heteroscedasticity
reflects inconstant error variance, which in turn may compromise the
validity of significance tests and goddess-of-fit indicators. Specifically, the
variance of residuals may vary with expected values of the dependent
variable and with individual explanatory variables (Farag, 2009).
The heteroscedasticity test will be conducted through scatter plot
generated by SPSS 16.0 software for Windows. X Axis is the predicted
value of ZPRED = Regression Standardized Predicted Value while Y Axis
is the predicted value of ZRESID = Regression Standardized Predicted
Value. If the graphic shows any certain kind of pattern, it means the
heteroscedasticity is occurs. If the graphic shows of spread plots and did not
indicates any form of pattern, it means there is no occurence of
heteroscedasticity (Purwoto, 2007).
81
Figure 4.2.5.3 Scatterplot of Heteroscedasticity Test
Based on figure 4.2.5.3 Scatterplot of Heteroscedasticity Test as
shown on above, the pattern of residuals did not indicate any kind of certain
clear pattern. It also indicates that the residuals are spread above and below
the number 0 on the Y Axis. Hence, it can be concluded that the data in this
research is normal since there is no occurence of heteroscedasticity and the
data is cleared to be used for further research process.
82
4.2.6. Multiple Regression Equation
Multiple regression analysis is the analysis to assess the strength of a
relationship between one dependent and two or more independent variables
(Mark Saunders, Philip Lewis, et al, 2011). Multiple regression analysis is
used to examine the influence of several independent variables (variables X)
on the dependent variable (variable Y). Formally we can say that if the
significance value is greater than 0.05, it means that the independent
variable being measured does not have significant influence towards the
dependent variable (Santoso, 2009). When variables are standardized,
regression weights are called beta weights. Beta weight for an independent
variable indicates the expected increase or decrease in the dependent
variable, in standard deviation units, given a one standard deviation increase
in independent variable with all other independent variables held constant
(Hoyt et al, 2006).
The interpretation of regression analysis in this research will be using
unstandardized regression coefficient. Because the independent variables are
rely on one measurement scale only, which is Likert Scale (Newton, 2012).
When variables are not standardized, regression weights are called B
weights. B weight also indicates how much a one unit increase in the
independent variable results in an increase in the dependent variable with all
other variables held constant. However, this increase is scaled in term of the
variable‟s original scaling metric, rather than in a standardized metric (Hoyt
et al, 2006).
From the theory above the regression written in unstandardized
coefficient, because all variable in this study have a same scale and the
entire variables are significance so it shouldn‟t to standardize.
83
Y = a + β1X1 + β2X2 + β3X3 + e
Figure 4.2.6 Multiple Regression Equation
Table 4.2.6 Linear Multiple Regression
Source: self-construct, processed through SPSS 16.0 Software for Windows
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity
Statistics
B
Std.
Error Beta Tolerance VIF
1 (Constant) -1.374 .977 -1.406 .166
LeadTimeTotal .331 .135 .289 2.727 .009 .294 3.435
CostCriteriaTotal .246 .119 .227 2.064 .045 .258 3.879
QualityOfMaterials
Total .605 .080 .595 7.559 .000 .413 2.423
a. Dependent Variable: PurchasingOperationTotal
Based on the result in table 4.2.6 as shown on above, if written in the
unstandardized form of the equation, the regression is as follows:
Y = a + β1X1 + β2X2 + β3X3 + e
Y = -1.374 + .331 X1 + .246 X2 + .605 X3 + e
84
Where:
Y = Purchasing Operation
a = Constanta
X1 = Lead Time
X2 = Cost Criteria
X3 = Quality of Materials
e = Error Disturbance
Based on the result of linear multiple regressions in table 4.2.6, we
can conclude that the results of this research are as follows:
4.2.6.1. Coefficient Regression (b)
1. Coefficient Regression of Lead Time (X1) is 0.331; means that if the values
of Lead Time increase in one of unit while the other variables is constant,
then Purchasing Operation decision (variable Y) will increase as much
0.331 of unit.
2. Coefficient Regression of Cost Criteria (X2) is 0.246; means that if the
values of Cost Criteria increase in one of unit while the other variables is
constant, then Purchasing Operation decision (variable Y) will increase as
much 0.246 of unit.
3. Coefficient Regression of Quality of Materials (X3) is 0.605; means that if
the values of Quality of Materials increase in one of unit while the other
variables is constant, then Purchasing Operation decision (variable Y) will
increase as much 0.605 of unit.
85
Based on the explanation above, it can be concluded that from all of
independent variables (Lead Time, Cost Criteria, and Quality of Materials),
the most dominant variable that has the most significant influence for
Purchasing Operation is Quality of Materials with the value of 60.5%.
4.2.6.2. Coefficient of Correlation and Determination (R2)
Table 4.2.6.2 Model Summaryb
Source: self-construct, processed through SPSS 16.0 Software for Windows
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1 .933a .871 .862 1.64858 2.630
a. Predictors: (Constant), QualityOfMaterialsTotal, CostCriteriaTotal, LeadTimeTotal
b. Dependent Variable: PurchasingOperationTotal
1. Coefficient Correlation (R) is 0.933.
2. Coefficient of Determination (R2) is 0.862.
Coefficient Correlation (R) shows the value of 0.933, it means that the
correlation between dependent and independent variable is 93.3%. It can be
concluded that Purchasing Operation has strong correlation with Lead Time,
Cost Criteria, and Quality of Materials.
Meanwhile, for the Coefficient of Determination (R2), it can be
concluded that 86.2% changes in the dependent variable (Purchasing
Operation) is influenced by independent variables (Lead Time, Cost
86
Criteria, and Quality of Materials). The other 13.8% is explained by another
variable outside the four variables used within this research.
4.2.7. Hypothesis Testing
4.2.7.1. F-Test
F-test is basically used to seek the influence of independent variables
(Lead Time, Cost Criteria, and Quality of Materials) towards the dependent
variable (Purchasing Operation) simultaneously.
Table 4.2.7.1 F-Test
Source: self-construct, processed through SPSS 16.0 Software for Windows
ANOVAb
Model Sum of Squares Df Mean Square F Sig.
1 Regression 840.980 3 280.327 103.144 .000a
Residual 125.020 46 2.718
Total 966.000 49
a. Predictors: (Constant), QualityofMaterialsTotal,
LeadTimeTotal, CostCriteriaTotal
b. Dependent Variable: PurchasingOperationTotal
Formally we can say that if the significance value is lower than 0.05
(α) we need to reject the null hypothesis. The general ways to evaluate
influence of independent variables towards dependent variable
simultaneously is by analyzing the F column in ANOVA table (Johar,
2008).
87
Related hypothesis to be used in this research is:
Ha4: There is a significant simultaneous influence of lead time, cost criteria,
and quality of materials towards purchasing operation.
Based on F-table and table 4.2.7.1 ANOVA, F-table shows the
number of 2.81, and F column in table 4.2.7.1 ANOVA shows the number
of 103.144 with significance level of 0.000. It means that there is significant
simultaneous influence of lead time, cost criteria, and quality of materials
towards purchasing operation, because the number in F-column in table
4.2.8 ANOVA is greater than the number in F-table (103.144 > 2.81) and
has significance level of 0.000 (< 0.05).
Therefore from table 4.2.7.1 ANOVA, it can be concluded that Ha4 is
accepted; because it is proven that there is a significant simultaneous
influence of lead time, cost criteria, and quality of materials towards
purchasing operation.
4.2.7.2. t-Test
t-Test is the test performed to determine the effect of partially
independent variable (X) on the dependent variable (Y), then the test is used
to test how far the influence of Lead Time, Cost Criteria, and Quality of
Materials on Purchasing Operation.
The requirements of this test is Ha1, Ha2, and Ha3, is accepted if
significance value is lower than 0.05 on α = 5%, and if the number in t-
column is greater than the value in t-table (Johar, 2008).
The result of the t-Test is as referred from table 4.2.6 Linear Multiple
Regression, which shown that none of the variables are rejected. Moreover
it can be concluded that all of the independent variables have significant
88
impact towards the dependent variable. Also from the result in the t-column
of Table 4.2.6 Linear Multiple Regression, we can conclude that Quality of
Materials (7.559) give the most significant impact towards Purchasing
Operation, though it is also supported by the significant value which is
0.000. The second one is Lead Time (2.727) with significant value of 0.009
and the last is Cost Criteria (2.064) with significant value of 0.045. Hence,
hypothesizes that accepted because has proven to have influence towards
Purchasing Operation in this research are Ha1, Ha2, and Ha3.
4.3. Interpretation of Results
Based on gender, most respondents of this research are female as
many as 37 people with percentage of 54%. The second largest is male, as
many as 33 people with percentage of 46%. Based on age, the most
respondents in this research are in the age of 36-40 years old, as many as 36
people with the percentage of 52% of the total respondents. The second
largest respondents are in the age of 20-35 years old as many as 18 people,
with the percentage of 26% of the total respondents. And the lowest number
of respondents are in age above 40 years old as many as 16 people with the
percentage of 22% of the total respondents. The research instrument is
reliable and valid to be used for the test. Because according to reliability and
validity test, all variables has Cronbach Alpha value which are greater than
0.700, and significances of all questions are greater than 0.05.
The data in this research is normally distributed, because according to
Histogram of Normal Distribution, it shows the histograms are bell-shaped
and P Plot of Regression Standardized Residuals is in the pattern of
diagonal line. The data in this research is also non-multicollinearity, causes
by „Coefficient Table‟ of all variables shows the tolerance which greater
than 0.100 and Variance Inflation Factor (VIF) score of lower than 10.
Besides non-multicollinearity, the data in this research is free from
89
heteroscedasticity, because the pattern of residuals in the scatterplot of
heteroscedasticity test did not indicate any certain kind of clear pattern.
Though the residuals are also spread below and above of number 0 on Y
Axis.
Independent variables (Lead Time, Cost Criteria, and Quality of
Materials) has significant influence towards dependent variable (Purchasing
Operation), with the most dominant variable which is Quality of Materials
with the value of 60.5%, as results obtained from multiple regression
analysis are as follows:
Y = -1.374 + .331 X1 + .246 X2 + .605 X3 + e
1. Coefficient Regression of Lead Time (X1) is 0.331; means that if the values
of Lead Time increase in one of unit while the other variables is constant,
then Purchasing Operation decision (variable Y) will increase as much
0.331 of unit.
2. Coefficient Regression of Cost Criteria (X2) is 0.246; means that if the
values of Cost Criteria increase in one of unit while the other variables is
constant, then Purchasing Operation decision (variable Y) will increase as
much 0.246 of unit.
3. Coefficient Regression of Quality of Materials (X3) is 0.605; means that if
the values of Quality of Materials increase in one of unit while the other
variables is constant, then Purchasing Operation decision (variable Y) will
increase as much 0.605 of unit.
In fact, there are 86.2% changes in dependent variable (Purchasing
Operation) which influenced by independent variables (Lead Time, Cost
Criteria, and Quality of Materials). While the other 13.8% are influenced by
90
other factors used exclude variables of this research, causes by Coefficient
of Determination (R2) that shows the number of 0.862.
Ha4, “There is a simultaneous influence of lead time, cost criteria, and
quality of materials towards purchasing operation” is accepted, because
based on F-Test, the significance of „ANOVA table‟ shows the number of
0.000 which is certainly not greater than 0.005. Besides, the F column in
„ANOVA table‟ shows the number of 103.144 which is greater than the F-
table value of 2.81. And it is proven that there is a significant simultaneous
influence of lead time, cost criteria, and quality of materials towards
purchasing operation.
Ha1, “There is a partial significant influence of lead time towards
purchasing operation”, Ha2, “There is a partial significant influence of cost
criteria towards purchasing operation”, and Ha3, “There is a partial
significant influence of quality of materials towards purchasing operation”,
are also accepted, because the three independent variables have been tested
individually through the t-Test. Three variables in this study have significant
influence on Purchasing Operation. The most dominant variable that
influence the Purchasing Operation is Quality of Materials with t-value of
7.559 (> 1.676; value of t-table) and significance value of 0.000 (< 0.05).
The next variable that has a significant influence in Purchasing Operation is
Lead Time with t-value of 2.727 (> 1.676) and significance value of 0.009
(< 0.05). And the last variable that has the least significant influence on
Purchasing Operation is Cost Criteria with t-value of 2.064 (> 1.676) and
significance value of 0.045 (< 0.05).
91
CHAPTER V
CONCLUSION AND RECOMMENDATION
In the last chapter of this research, the researcher draws the conclusion
and the recommendation which developed from all integration of
quantitative analysis, to be specific in the multiple regression analysis about
the evaluation factors of supplier selection for direct-procurement towards
purchasing operation, a case study in L‟Oreal Manufacturing Indonesia.
5.1. Conclusion
The result of this research based on the evaluation factors of supplier
selection towards purchasing operation in L‟Oreal Manufacturing Indonesia
come to a conclusion as follows:
1. Independent variable, Lead Time, does have significant influence to
dependent variable, Purchasing Operation. Therefore Ha1 “There is a
significant influence of lead time towards purchasing operation” is proven
to be accepted. Another proof which state the first hypothesis is accepted is
from the regression coefficient of Lead Time (X1) is showing the number of
0.331 which means that if the values of Lead Time increase in one of unit
while the other variables is constant, then Purchasing Operation decision
variable (Y) will increase as much as 0.331 units. Thus, it also supported by
the survey that the buyer (or in this research as the manufacturer), would
prefer shorter lead time as the main consideration for supplier evaluation,
because shorter lead time could increase advantages from both sides of
supplier and buyer such as greater flexibility and responsiveness, maintain
safety stock from the reduction of firm horizon, and shorter time to market.
Therefore according to Elly Winata (2012), suppliers shall adapt and
92
transform with the market changes and develop competitve lead time as a
tool to get trust and reliable suppliers.
2. Independent variable, Cost Criteria, does have significant influence to
dependent variable, Purchasing Operation. Therefore Ha2 “There is a
significant influence of cost criteria towards purchasing operation” is proven
to be accepted. Another proof which state the second hypothesis is accepted
is from the regression coefficient of Cost Criteria (X2) is showing the
number of 0.246 which means that if the values of Cost Criteria increase in
one of unit while the other variables is constant, then Purchasing Operation
decision variable (Y) will increase as much as 0.246 units. Thus, it also
supported by the survey that the buyer (or in this research as the
manufacturer), would prefer cost criteria as the second main consideration
for supplier evaluation, because when selecting the best suppliers to bid, it
always lead to a negotiation for costs, either it is contract cost,
administrative cost, purchasing cost, and other costs which will be include
in the agreement, in which the purchasers needs to be thoroughly towards it.
Therefore according to Yusuf Andriana (2012), cost is an important element
in purchasing process as one of the purpose is to reduce cost of raw
materials for supply chain management towards an industry.
3. Independent variable, Quality of Materials, does have significant influence
to dependent variable, Purchasing Operation. Therefore Ha3 “There is a
significant influence of quality of materials towards purchasing operation”
is proven to be accepted. Another proof which state the third hypothesis is
accepted is from the regression coefficient of Quality of Materials (X3) is
showing the number of 0.605 which means tha t if the values of Quality of
Materials increase in one of unit while the other variables is constant, then
Purchasing Operation decision variable (Y) will increase as much as 0.605
units. Thus, it also supported by the survey that the buyer (or in this research
as the manufacturer), would prefer high conformity of quality of materials
as the third main consideration for supplier evaluation, because buyer is
93
really attracted with supplier who could offer products with good physical
attributes that meet buyer specifications, has International Organization for
Standardization, continuous improvement and zero defect guarantee, and
more importantly if material used and chemical composition are
environmental friendly. Therefore according to David Gunawan (2009),
purchase products is not just a purchase process, it can be seen from the
supplier‟s ability to create value added to a product.
4. Adjusted R Square indicates that independent variables (Lead Time, Cost
Criteria, and Quality of Materials) do have significant simultaneous
influence towards Purchasing Operation. There are 86.2% changes in
dependent variable (Purchasing Operation) is influenced by the independent
variables (Lead Time, Cost Criteria, and Quality of Materials). The other
13.8% are influenced by other factors exclude variables of this research.
Another proof which state the independent variables have significant
simultaneous influence towards dependent variable is shown by the result of
variable test simultaneously conducted with F-Test that showed the F
number of 103.144 with significance level of 0.000. Therefore, Ha4 “There
is a significant simultaneous influence of lead time, cost criteria, and quality
of materials towards purchasing operation.” is proven to be accepted.
5.2. Recommendation
Based on the conclusion drew in this study, the recommendation
proposed as a complement to the results of the study as follows:
5.2.1. For L’Oreal Manufacturing Indonesia
The lack of influence of Cost Criteria (X2) towards purchasing
operation in supplier selection in this research could be happen because
when it comes to the supplier qualification screening process, most of the
time-consumed is in the process of considering other two primary factors
94
listed which are delivery or lead time and quality. Also, the challenge of
supplier selection lies in constructing the tradeoff between two factors in a
way that could reflects the manufacturer‟s preferences. For example, if the
manufacturer wishes to evaluate suppliers for bidding process between lead
time and cost criteria, a bid with short lead time and high cost criteria to a
bid with long lead time and low cost criteria, are two different preferences
for manufacturer who‟s in charge as the buyer, based on the survey, what
makes cost criteria has lack of influence because the respondents prefered a
bid with short lead time and high cost criteria, so that is why variable Lead
Time (X1) have more influence. Same thing as in bid with low cost criteria
and minimum conformity quality to a bid with high cost criteria and
maximum conformity quality, based on the survey the respondents prefered
a bid with high cost criteria and maximum conformity quality, which means
low cost criteria doesn‟t have much influence compared to Quality of
Materials (X3) that can have highly conformity.
My recommendation is that L‟Oreal Manufacturing Indonesia needs to
enhance the influence of cost criteria during suppliers evaluation. Those cost
criteria are include costs such as, qualification screening cost, cost of
purchasing, cost of testing the products, structural costs (e.g: labor cost),
cost of travel to supplier facilities abroad (transportation cost), product life
cycle cost analysis, contract cost, adminstrative costs, and sum of all total-
cost.
It is proven that Purchasing Operation is the dependent variable that
influences Quality of Materials the most. Therefore, L‟Oreal Manufacturing
Indonesia should guarantee suppliers to maintain and improve the quality
and ensure the quality has maximum conformity towards the products.
95
5.2.2. For Future Researcher
The researcher of this study has built up the effort to conduct this
research until the researcher has managed to find out the significant
influence by 86.2% through some limitations while conducting this research
whereas more could be improved by increasing the number of variables. It is
advisable to enhance potential expansion of independent variables outside
the three variables used in this research. Either way, the future researcher
could take the rest of 13.8% potential variables for future research. In
addition, to meet the expectation of variables expansion that would give
contribution to supplier selection analysis factors on purchasing operation, it
is advisable for future researcher to widen the population scope. Hence, the
sample used will be much more than previous one, and future researcher
will be able to provide more specific analysis factors of supplier selection
on purchasing operation.
96
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106
APPENDICES
107
APPENDIX A
QUESTIONNAIRE
108
Evaluation Factors of Supplier Selection For Direct-Procurement
Towards Purchasing Operation (Case Study In L’Oréal
Manufacturing Indonesia)
Dear Sir/Madam,
First of all I would like to introduce myself, my name is Ardisa Pramudita, I am a
student majoring in Management Faculty of Business, concentrating in
International Business. Currently I am working on my Skripsi project which
requires me to do research by using questionnaire as a tool for survey. My
research entitled “Evaluation Factors of Supplier Selection For Direct-
Procurement Towards Purchasing Operation” is intended to provide an analysis of
supplier selection for direct procurement towards purchasing operation. Therefore,
it will be very helpful if you assist to fill this questionnaire to expedite my
research process. Please fill the questionnaire with the most honest answer, all
answers are correct and there would be no wrong answers. Please give a check list
() inside the box provided in the column to answer which is the most correct one
or highly agreed based on your opinions and experiences.
Thanks for your kind favor!
Regards,
Ardisa
109
PART I
Respondent Profile
Research questionnaire is intended for respondents who has the knowledge
and experience towards the purchasing operation function, and areas that
interconnected with purchasing operation in terms of supply chain
management.
1 Name
2 Gender a. Male b. Female
3 Age a. 20-35 yo
b. 36-40 yo
c. Above 40 yo
110
PART II
INSTRUCTION QUESTIONNAIRE FILLING
In this session, you will be asked to give your personal opinion towards
Purchasing Operation. Please choose one answer that is the most
appropriate, by making check list (). The following description of
alternative response option available, namely:
a) 1= Strongly Disagree
b) 2= Disagree
c) 3= Neutral
d) 4= Agree
e) 5= Strongly Agree
111
PART III
Lead Time
Lead Time is refers to the length of time between when a manufacturer
place a new frame order and when it is ready to ship.
No Lead Time 1 2 3 4 5
1 Weather or climate affects the
reliability of the supplier lead time □ □ □ □ □
2
Delivery lead time products have a
significant effect on the number of
manufacturer‟s demand
□ □ □ □ □
3
Lead time is a very important
component in a customers‟
perception of business performance
□ □ □ □ □
4
Shorter production lead time from
supplier to minimize time spend for
activity that give less additional
value
□ □ □ □ □
112
Cost Criteria
Cost Criteria is refers to the assessment of value of money before the
individual makes a decision to require a specified payment.
No Cost criteria 1 2 3 4 5
1
Transaction costs represent more a
way of giving arguments than an
efficiency indicator than it should be
□ □ □ □ □
2
Cost is a subjective nature regarding
value and utility which is impossible
to be separated from the choice
process
□ □ □ □ □
3 Costs are not objective or observable
by any external observer □ □ □ □ □
4
Costliness of information between
buyer and supplier is the key to the
costs of transactions
□ □ □ □ □
113
Quality of Materials
Quality of Materials is refers to measurement procedure for verifying and
maintaining a desired level of quality in an existing product or service
No Quality of Materials 1 2 3 4 5
1
Quality as transformation with
emphasis on adding value towards
the product
□ □ □ □ □
2
Quality as value for money in terms
of return on investment or
expenditure
□ □ □ □ □
3
Quality as fitness for purpose, which
judges the quality of a product in
extent to which its stated purpose to
meet buyer specifications
□ □ □ □ □
4 Quality as consistency that is related
to zero defects □ □ □ □ □
114
Purchasing Operation
Purchasing Operation is refers to the activity of making transaction towards
supply of commodities at the lowest price consistent with required quality
No Purchasing Operation 1 2 3 4 5
1
Purchasing operation‟s importance
increased when marketing
practitioners concerned about
external forces (cost of inputs)
□ □ □ □ □
2
Purchasing operation focuses on
lower price within shorter delivery
lead time
□ □ □ □ □
3 Purchasing operation aligned with
company‟s long term goals □ □ □ □ □
4
Purchasing operation always involve
in strategic decisions regarding on
price determination
□ □ □ □ □
115
APPENDIX B
RAW DATA MATERIAL
116
Independent Variables
No. L.T.1 L.T.2 L.T.3 L.T.4 L.T.Total C.C.1 C.C.2 C.C.3 C.C.4 C.C.Total Q.M.1 Q.M.2 Q.M.3 Q.M.4 Q.M.Total
1 5 1 2 5 13 4 2 5 5 16 5 4 5 5 19
2 4 3 5 5 17 4 5 5 5 19 5 4 5 4 18
3 3 3 4 4 14 5 4 5 4 18 5 4 4 5 18
4 5 4 5 4 18 5 5 4 5 19 5 4 4 5 18
5 5 3 2 4 14 4 2 5 3 14 5 4 4 5 18
6 5 5 4 1 15 5 4 4 5 18 5 4 4 5 18
7 3 5 1 5 14 4 1 4 5 14 5 3 5 4 17
8 5 1 5 1 12 5 5 5 4 19 5 5 4 5 19
9 3 4 4 4 15 4 4 5 4 17 5 5 4 5 19
10 3 4 1 1 9 4 1 4 4 13 4 4 4 5 17
11 1 2 4 1 8 4 4 5 4 17 4 4 4 4 16
12 5 5 5 3 18 5 5 4 4 18 4 4 4 4 16
13 5 4 5 5 19 4 5 4 5 18 5 4 4 5 18
14 5 2 2 5 14 4 2 4 5 15 5 5 5 5 20
15 5 5 2 5 17 4 2 4 4 14 4 4 5 5 18
16 4 1 5 5 15 5 5 4 5 19 4 4 4 5 17
17 4 1 5 1 11 5 5 4 5 19 4 4 4 4 16
18 4 5 2 5 16 4 2 5 5 16 5 4 4 5 18
19 2 4 5 5 16 5 5 5 4 19 5 4 5 4 18
20 5 5 1 1 12 4 1 4 5 14 5 5 4 5 19
21 4 5 5 5 19 5 4 4 4 17 5 4 4 4 17
22 5 5 5 4 19 4 4 5 5 18 5 4 4 5 18
23 5 5 5 5 20 5 4 5 5 19 4 4 5 5 18
24 5 5 5 4 19 4 4 5 4 17 4 4 5 5 18
25 5 5 5 5 20 4 4 5 4 17 5 4 5 4 18
26 5 5 5 5 20 4 4 4 4 16 4 4 4 4 16
27 5 4 5 5 19 4 5 5 4 18 5 4 5 4 18
28 5 5 5 5 20 5 4 4 5 18 4 5 4 4 17
29 5 4 5 5 19 4 5 5 4 18 5 5 5 4 19
30 5 5 5 5 20 4 4 4 4 16 5 4 4 4 17
31 5 5 5 5 20 5 5 5 4 19 4 4 4 5 17
32 5 4 5 5 19 4 5 4 4 17 4 4 5 5 18
33 4 5 5 5 19 5 4 5 5 19 5 4 5 4 18
34 4 5 4 5 18 4 5 4 5 18 5 5 4 4 18
35 5 5 5 5 20 4 4 4 5 17 5 5 4 5 19
36 5 5 5 5 20 4 5 5 5 19 4 4 4 4 16
37 4 5 5 5 19 4 4 4 5 17 4 5 5 4 18
38 5 4 5 5 19 4 5 4 5 18 4 4 4 4 16
117
39 5 5 5 5 20 4 4 5 4 17 4 5 4 4 17
40 5 5 5 5 20 4 4 5 5 18 4 4 4 4 16
41 5 5 4 5 19 4 4 4 5 17 5 5 5 4 19
42 5 5 5 4 19 4 4 5 5 18 5 5 5 5 20
43 5 5 5 4 19 4 4 5 4 17 4 4 4 5 17
44 5 5 4 5 19 4 4 4 4 16 4 4 4 5 17
45 5 5 5 5 20 5 4 5 4 18 4 4 5 4 17
46 5 4 5 5 19 4 4 5 4 17 4 4 5 4 17
47 5 4 5 5 19 4 5 5 5 19 4 4 4 4 16
48 5 5 5 5 20 5 4 5 4 18 4 5 5 5 19
49 5 5 5 5 20 4 5 4 5 18 4 5 5 4 18
50 5 5 5 5 20 5 5 4 4 18 4 5 5 4 18
Dependent Variable
P.O.1 P.O.2 P.O.3 P.O.4 P.O.Total
4 5 5 4 18
4 5 5 5 19
4 5 4 5 18
4 4 4 4 16
4 4 5 5 18
5 5 5 4 19
4 4 5 5 18
5 5 5 5 20
5 4 4 5 18
4 5 5 5 19
4 4 5 5 18
4 4 5 5 18
4 4 4 5 17
4 5 5 4 18
4 5 4 5 18
4 5 5 5 19
4 5 4 4 17
4 5 4 5 18
5 5 4 4 18
5 4 4 5 18
5 4 4 5 18
5 4 5 4 18
118
5 4 5 5 19
5 4 4 4 17
4 5 5 4 18
5 5 5 5 20
4 5 4 5 18
4 4 5 5 18
5 4 5 4 18
4 4 4 5 17
4 4 5 5 18
4 4 4 4 16
5 4 5 4 18
5 4 4 5 18
5 4 5 4 18
4 4 5 5 18
4 5 5 4 18
5 5 5 4 19
5 5 5 4 19
4 5 4 4 17
4 5 4 4 17
5 4 4 5 18
5 4 5 5 19
4 4 5 5 18
4 4 4 5 17
4 4 5 4 17
4 4 5 5 18
4 5 5 4 18
4 4 4 5 17
4 5 4 5 18
119
APPENDIX C
CRONBACH’S ALPHA
120
1. Lead Time 2. Cost Criteria
Reliability Statistics
Cronbach's
Alpha N of Items
.731 5
3. Quality of Materials 4. Purchasing Operation
Reliability Statistics
Cronbach's
Alpha N of Items
.724 5
Reliability Statistics
Cronbach's
Alpha N of Items
.800 5
Reliability Statistics
Cronbach's
Alpha N of Items
.770 5
121
APPENDIX D
PEARSON CORRELATION COEFFICIENT
122
1. Lead Time
Correlations
LT1 LTT
LT1 Pearson Correlation 1 .533*
Sig. (2-tailed)
.016
N 20 20
LTT Pearson Correlation .533* 1
Sig. (2-tailed) .016
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
LT2 LTT
LT2 Pearson Correlation 1 .562**
Sig. (2-tailed)
.010
N 20 20
LTT Pearson Correlation .562**
1
Sig. (2-tailed) .010
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
123
Correlations
LT3 LTT
LT3 Pearson Correlation 1 .626**
Sig. (2-tailed)
.003
N 20 20
LTT Pearson Correlation .626**
1
Sig. (2-tailed) .003
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
LT4 LTT
LT4 Pearson Correlation 1 .722**
Sig. (2-tailed)
.000
N 20 20
LTT Pearson Correlation .722**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
124
2. Cost Criteria
Correlations
CC1 CCT
CC1 Pearson Correlation 1 .771**
Sig. (2-tailed) .000
N 20 20
CCT Pearson Correlation .771**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
CC2 CCT
CC2 Pearson Correlation 1 .886**
Sig. (2-tailed) .000
N 20 20
CCT Pearson Correlation .886**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
125
CC3 CCT
CC3 Pearson Correlation 1 .587**
Sig. (2-tailed) .006
N 20 20
CCT Pearson Correlation .587**
1
Sig. (2-tailed) .006
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
CC4 CCT
CC4 Pearson Correlation 1 .822**
Sig. (2-tailed) .000
N 20 20
CCT Pearson Correlation .822**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
126
3. Quality of Materials
Correlations
QM1 QMT
QM1 Pearson Correlation 1 .632**
Sig. (2-tailed) .003
N 20 20
QMT Pearson Correlation .632**
1
Sig. (2-tailed) .003
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
QM2 QMT
QM2 Pearson Correlation 1 .571**
Sig. (2-tailed) .009
N 20 20
QMT Pearson Correlation .571**
1
Sig. (2-tailed) .009
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
127
Correlations
QM3 QMT
QM3 Pearson Correlation 1 .719**
Sig. (2-tailed) .000
N 20 20
QMT Pearson Correlation .719**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
QM4 QMT
QM4 Pearson Correlation 1 .754**
Sig. (2-tailed) .000
N 20 20
QMT Pearson Correlation .754**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
128
4. Purchasing Operation
Correlations
PO1 POT
PO1 Pearson Correlation 1 .926**
Sig. (2-tailed) .000
N 20 20
POT Pearson Correlation .926**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
PO2 POT
PO2 Pearson Correlation 1 .561*
Sig. (2-tailed) .010
N 20 20
POT Pearson Correlation .561* 1
Sig. (2-tailed) .010
N 20 20
*. Correlation is significant at the 0.05 level (2-tailed).
129
Correlations
PO3 POT
PO3 Pearson Correlation 1 .879**
Sig. (2-tailed) .000
N 20 20
POT Pearson Correlation .879**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
Correlations
PO4 POT
PO4 Pearson Correlation 1 .959**
Sig. (2-tailed) .000
N 20 20
POT Pearson Correlation .959**
1
Sig. (2-tailed) .000
N 20 20
**. Correlation is significant at the 0.01 level (2-tailed).
130
APPENDIX E
CLASSICAL ASSUMPTIONS
131
1. Normality Test
132
2. Multicollinearity Test
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity
Statistics
B
Std.
Error Beta Tolerance VIF
1 (Constant) -1.374 .977 -1.406 .166
LeadTimeTotal .331 .135 .289 2.727 .009 .294 3.435
CostCriteriaTotal .246 .119 .227 2.064 .045 .258 3.879
QualityOfMaterials
Total .605 .080 .595 7.559 .000 .413 2.423
3. Heteroscedasticity Test
133
APPENDIX F
MULTIPLE REGRESSION EQUATION
134
Model Summaryb
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1 .933a .871 .862 1.64858 2.630
a. Predictors: (Constant), QualityOfMaterialsTotal, CostCriteriaTotal, LeadTimeTotal
b. Dependent Variable: PurchasingOperationTotal
ANOVAb
Model Sum of Squares Df Mean Square F Sig.
1 Regression 840.980 3 280.327 103.144 .000a
Residual 125.020 46 2.718
Total 966.000 49
a. Predictors: (Constant), QualityofMaterialsTotal,
LeadTimeTotal, CostCriteriaTotal
b. Dependent Variable: PurchasingOperationTotal
135
Coefficientsa
Model
Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
Collinearity
Statistics
B
Std.
Error Beta Tolerance VIF
1 (Constant) -1.374 .977 -1.406 .166
LeadTimeTotal .331 .135 .289 2.727 .009 .294 3.435
CostCriteriaTotal .246 .119 .227 2.064 .045 .258 3.879
QualityOfMaterials
Total .605 .080 .595 7.559 .000 .413 2.423
a. Dependent Variable: PurchasingOperationTotal
136
R-TABLE
137
F-TABLE
138
139
T-TABLE