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Int. J. Production Economics

Int. J. Production Economics 127 (2010) 372–383

0925-52

doi:10.1

� Cor

E-m

polimi.i

sianesi@

journal homepage: www.elsevier.com/locate/ijpe

Do virtuality and complexity affect supply chain visibility?

Maria Caridi, Luca Crippa, Alessandro Perego, Andrea Sianesi, Angela Tumino �

Politecnico di Milano, Department of Management, Economics and Industrial Engineering, Piazza Leonardo da Vinci, 32, 20133 Milano, Italy

a r t i c l e i n f o

Article history:

Received 30 January 2009

Accepted 14 August 2009Available online 21 August 2009

Keywords:

Supply chain

Virtuality

Visibility

Complexity

Contingency theory

73/$ - see front matter & 2009 Elsevier B.V. A

016/j.ijpe.2009.08.016

responding author. Tel.: +39 02 23994065; fax

ail addresses: maria.caridi@polimi.it (M. Ca

t (L. Crippa), alessandro.perego@polimi.it (A

polimi.it (A. Sianesi), angela.tumino@polimi.

a b s t r a c t

In recent years, scientific literature has devoted much attention to the topic of supply

chain visibility. Nevertheless, further research is needed regarding the variables

affecting the level of visibility that a company has within its supply chain. In accordance

with Contingency Theory, this paper aims to study whether and to what extent the

supply chain configuration, in terms of virtuality and complexity, affects supply chain

visibility. It proposes a structured approach to quantitatively measure supply chain

virtuality, complexity and visibility. This approach has been applied to six case studies

to verify its usability and to gather preliminary empirical evidences.

& 2009 Elsevier B.V. All rights reserved.

1. Introduction

In recent years, scientific contributions on supply chainmanagement have stressed the key role of supply chainvisibility in the competitive arena (e.g. Al-Mudimighaet al., 2004). Most authors agree that visibility providesbenefits, not only in terms of operations efficiency (e.g.Smaros et al., 2003), i.e. increased resource productivity,but also in terms of planning effectiveness (e.g. Petersenet al., 2005).

Several authors attempted to measure visibility and itsimpacts on supply chain performance (see Section 2.1).However, the variables that affect the level of supply chainvisibility and its value have been little explored. Inparticular, little attention has been devoted to the analysisof the context variables that describe the recent supplychain evolution. As a matter of fact, almost all industrieshave experienced increased competitive pressure, de-creased product life cycle, marketplace unsteadiness andglobalisation. These factors prompt industrial companiesto focus on their core competencies, and externalise an

ll rights reserved.

: +39 02 23994067.

ridi), luca.crippa@

. Perego), andrea.

it (A. Tumino).

increasing amount of their activities (Prahalad and Hamel,1990; Prahalad and Krishnan, 2008). Complex virtualisedsupply chains result, whereby the profitability of the focalcompany, i.e. the supply chain leader, relies more andmore on its ability to manage complex relationships withits business partners. In fact, competition has shifted from‘‘company vs. company’’ to ‘‘supply chain vs. supply chain’’(Bowersox, 1997; Christopher, 1998; Bradley et al., 1999;Cox, 1999; Christopher and Lynette, 1999; Lambert andCooper, 2000). In this scenario, visibility and fine-tuningwith supply chain partners become more important, butalso more difficult to obtain.

Consistent with this premise, the ‘‘need’’ for visibility isassumed to depend on the supply chain configuration,which has been analysed in terms of two main dimen-sions. The first is supply chain virtuality, i.e. the extent towhich a company relies on its supply chain for manu-facturing products. In this sense, we expect that the morevirtual a supply chain, the higher the need for visibility,because the focal company (i.e. the supply chain leader)does not directly control ‘‘its’’ supply chain. The seconddimension is supply chain complexity, which is related tothe structure of the supply chain, since more informationis needed to manage a supply chain with many suppliersand tiers. Obviously other variables—not directly relatedto supply configuration—affect the ‘‘need’’ for visibility.One, for instance, is demand and supply variability,

Fig. 1. Research framework.

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383 373

because the higher the variability, the higher the need fortimeliness information to manage the supply chain.Another one is supply chain velocity (how often thecontext changes and how much time is needed to respondto these changes), since the more information available,the faster the supply chain.

The research project presented in this paper has beendeveloped in accordance with the Contingency Theoryapproach (Fiedler, 1964). In particular, this paper presentsthe results of the first step of a broader research project,and it considers the relationship between context andresponse variables (Drazin and van de Ven, 1985), whilethe impact on performance will be included in the nextsteps of the research (Fig. 1). As previously mentioned,supply chain visibility is assumed to be the main responsevariable, and its relations with the configuration contextvariables (i.e. virtuality and complexity) have beenanalysed. More specifically, the paper presents a modelto quantitatively evaluate supply chain virtuality,complexity, and visibility. Six case studies have beencarried out in order to validate the model and to gainevidence about the relation between these variables.

The paper is structured as follows. The literaturereview section provides a classification of the mainscientific contributions on the measure of supply chainvirtuality, complexity and visibility. Section 3 presents theresearch objectives, whereas Section 4 explains themethodology. The proposed approach for measuringvirtuality, complexity, and visibility is described in Section5. Section 6 presents some preliminary results obtained byapplying this approach in several case studies. Finally,some closing remarks and future research paths concludethe paper.

2. Literature review

An in-depth literature review has been carried out inorder to analyse the definitions and measures providedfor the relevant variables of this study (i.e. visibility,

virtuality, and complexity). In the following sections abrief overview of the most interesting literature contribu-tions is reported.

2.1. Supply chain visibility

Visibility is a well-known concept, which has been studiedby many authors. In any case, there is no single definition.Some authors (Lamming et al., 2001; Swaminathan andTayur, 2003) focus their attention on the informationexchange, defining visibility as the ‘‘ability to access/shareinformation across the supply chain’’. A second set ofcontributions looks at the properties of the exchangedinformation, assuming that the level of supply chain visibilityis determined by the extent to which the shared informationis accurate, trusted, timely, useful, and in a readily usableformat (Bailey and Pearson, 1983; Gustin et al., 1995; Mohrand Sohi, 1995; Closs et al., 1997). Finally, other authors stressthe importance of exchanging useful information betweenpartners (Kaipia and Hartiala, 2006; Barratt and Oke, 2007).

In keeping with these definitions, the authors attemptto provide quantitative measures of visibility. Consistentwith the first approach (i.e. visibility as the ability toaccess or share information across the supply chain),Simatupang and Sridharan (2005) consider informationsharing as the basic dimension of coordination, andprovide a way to quantify it in their ‘‘collaboration index’’.However, as the authors highlight, this index is specific toretail and refers to a supply chain consisting of only twomembers, i.e. supplier and retailer, and thus not beingsuitable to assess the visibility level in more complexsupply chains. Other authors, in support of the secondapproach, focus on both the amount and the quality of theexchanged information (Gustin et al., 1995; Mohr andSohi, 1995; Kaipia and Hartiala, 2006; Barratt and Oke,2007; Zhou and Benton, 2007). Finally, consistent with thethird approach (i.e. visibility in terms of exchanging usefulinformation), Petersen (1999) measures the informationquality in terms of how the information exchangedbetween companies meets the needs of the organisations.

Moreover, a very rich research stream investigates howa better visibility can lead to improved business perfor-mance by using different methodologies. However, mostauthors either focus on simplified supply chains (i.e. dyad,two-level supply chain, linear supply chain), which are farfrom the complexity of real environments, or provide only‘‘partial’’ measures, which do not consider the differentdimensions of visibility. As a matter of fact, real supplychains are becoming more and more similar to supplynetworks, and a comprehensive measure of visibilitysuitable to complex supply chains (networks) is lacking.Most of the available literature is based on simulation andmodelling (e.g. Bourland and Powell, 1996; Lee et al., 1997;Chen et al., 2000; Cachon and Fisher, 2000; Lee et al.,2000; Li et al., 2001; Yu et al., 2001; Gavirneni, 2002;Croson and Donohue, 2003; Disney and Towill, 2003a, b;Ryu et al., 2009; Sahin and Robinson, 2005). While theseauthors provide useful contributions to quantify theimpact of visibility on supply chain performance, mostof them focus mainly on two-tier supply chains, without

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383374

providing a quantification of the benefits of increasedvisibility in complex networks. Some authors attempt toconsider more than two-tier supply chains (e.g. Chen,1998; Li et al., 2001; Croson and Donohue, 2003), butfocus only on ‘‘linear’’ supply chains, whereas existingsupply chains are increasingly organised as networks.Other authors rely on empirical studies, where mostattempts are based on surveys (Gustin et al., 1995; Clarkand Hammond, 1997; Closs et al., 1997; Frohlich andWestbrook, 2002; Zhao et al., 2002; Kim et al., 2006; Kim,2009; Zhou and Benton, 2007), although examples ofqualitative research methodologies, i.e. case study, doexist (e.g. Wong and Boon-itt, 2008; Kaipia and Hartiala,2006; Barratt and Oke, 2007; Bartlett et al., 2007; Baileyand Francis, 2008).

However, these contributions mainly explore therelation between the many dimensions of visibility (e.g.information accuracy, timeliness) and supply chain per-formance, without providing a comprehensive measure ofvisibility itself. In most cases a general measure of supplychain visibility is provided and the visibility level of eachcustomer–supplier relation is not investigated (Van derVaart and Van Donk, 2008). Moreover, these studies donot consider the context variables that affect the extent towhich visibility is achieved. In our opinion, a quantitativemeasure of the initial visibility level is fundamental fordiagnostic purposes, in order to support the identificationof the weak points and the value of increased visibility interms of supply chain performance.

2.2. Supply chain virtuality

Three main, distinct meanings of virtuality are referredto in supply chain management scientific literature. Mostauthors consider virtuality as an organisational solution tohelp face increasing environmental complexity and un-certainty (e.g. Byrne et al., 1993; Johansson andGranstrand, 1993; Hedberg et al., 1994; Bultje and Wijkt,1998; Browne and Zhang, 1999; Chandrashekar andSchary, 1999; Bremer et al., 2001; Webster and Sugden,2004; Tarok and Tayebi, 2005; Shekhar, 2006). In theiropinion, virtuality is a managerial practice, and one notstrictly concerning technology. Essentially, in today’sglobal market, virtuality is seen as the ability of anenterprise to collaborate with a large number of suppliersand different customers worldwide to offer a large varietyof products and services, with the enterprise itself havingonly a few proprietary competencies. Therefore, the focusis on the collaboration between the independent compa-nies that form the network, and who collaborate to satisfycustomer needs by combining their distinct core compe-tencies. Consistent with this approach, Webster andSugden (2004) also attempt to provide a quantitativemeasure of virtuality. More specifically, they distinguishbetween ‘‘conventional’’ and ‘‘virtual’’ companies on thebasis of their financial dimensions (e.g. sales per employ-ee, profit per employee), non-financial dimensions(e.g. highly structured vs. flexible operations, extent ofoutsourcing), and the form of supply chain relationships

(e.g. price-focused vs. cooperative). In any case, acomprehensive virtuality index, which can be useful tocompare different enterprises, is not provided.

Other authors refer to ‘‘virtual supply chains’’ asopposed to ‘‘physical supply chains’’, with the formerbeing information-based, and the latter, inventory-based(e.g. Rayport and Sviokla, 1995; Davenport and Pearlson,1998; Christopher, 2005). Therefore, the essence of avirtual organisation is based, not on the sharing ofworkspace, but rather on the sharing of information.

Finally, some authors consider virtuality as strictlyrelated to the use of information and communicationtechnology (e.g. Wiesenfeld et al., 1998; Ho et al., 2003). Inparticular, information technology is portrayed as a keyfacilitator of the ‘‘alliances, simulation, meta-manage-ment, reflection, adaptive structures, and informationsharing characterizing virtual organizing’’.

In this paper, the word ‘‘virtuality’’ is used inaccordance with the first meaning, i.e. virtuality as anorganisational solution, which is the most widely ac-cepted definition in scientific literature.

2.3. Supply chain complexity

Complexity is a key managerial issue that supply chainmanagers should address (e.g. Gottinger, 1983; Waldrop,1992; Kauffman, 1993; Choi et al., 2001; Vachon andKlassen, 2002). Although its meaning has been discussedby a large number of authors, a broad range of definitionsstill exists. As highlighted by Bozarth et al. (2009), muchof this definitional work has been incorporated into theorganisational theory literature (e.g. Stacey, 1996; Staceyet al., 2000), with a focus on studying, predicting andcontrolling chaotic systems. Within operations manage-ment, the concept of complexity has been linked tooperational processes (e.g. Cooper et al., 1992; Khuranaand Talbot, 1999) and manufacturing strategy (Khota andOrne, 1989).

These research streams have been extended to thesupply chain management theory (e.g. Choi et al., 2001;Surana et al., 2005; Pathak et al., 2007). According toliterature, supply chain complexity depends on severaldrivers:

�

the number of supplier relationships that must bemanaged (Choi et al., 2001; Wu and Choi, 2005; Choiand Krause, 2006; Goffin et al., 2006). In fact, addingsuppliers necessarily increases the complexity, due tothe greater number of information flows, physicalflows and relationships that must be managed (Bo-zarth et al., 2009). � The degree of differentiation among these suppliers

(Choi and Krause, 2006), in terms of size, technology,etc.

� The delivery lead time and reliability of suppliers

(Chen et al., 2000; Vollmann et al., 2005).

� The extent of global sourcing (Cho and Kang, 2001;

Nellore et al., 2001), since global linkages potentiallyexpose companies to a wide range of complicatingfactors (e.g. import/export laws, cultural differences).

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383 375

�

The level of inter-relationship among the suppliers(Choi and Krause, 2006), since the greater their level ofinteraction, the greater the operational ‘‘load’’ borne bythe focal company in managing its supply base. Forinstance, a supply base with two independent suppli-ers is less complex than a supply base with two inter-related or linked suppliers.

3. Objectives

The main objective of this paper is to providequantitative measures of:

�

virtuality and complexity of supply networks and � visibility in supply networks.

The measures should be developed in accordance withthe following principles:

�

Usability: The measures should be easy to be computedin real companies, i.e. they will be based on data thatare usually available or easy to obtain. � Diagnosticsupport: The measures should be useful to

find out whether and how visibility is worth improv-ing.

The analysis is carried out from the focal companyperspective, i.e. the supply chain leader that coordinatesthe material and information flows across the supplychain. We considered the focal company as the focal pointthat links two separate networks (i.e. inbound andoutbound) (Fig. 2).

The analysis focuses on the external inbound supply chain,which includes all the suppliers of goods and services, fromthe raw materials producers to the first-tier suppliers. Theinternal supply chain (i.e. the facilities owned by the focalcompany) and the outbound supply chain (from the finishedgoods to the final customers) are not considered, and will beexplored in the next research steps.

The model finds its natural application within anindividual company that is interested in analysing thealignment between its current level of visibility and thescenario in which it operates. Moreover, it can also beused as a benchmarking tool, but in this case, a carefulidentification of the sample is needed.

Fo

Fig. 2. The supply chain o

4. Methodology

In order to achieve the objectives, the researchprogramme was divided into three phases. The first phasewas devoted to developing the model to measure thesupply chain virtuality, visibility and complexity. Thesecond phase consisted in the validation of the model byinvolving a panel of experts. Finally, in the third phase, themodel was applied to real supply chains in order to checkits usability and to get some preliminary results. The threephases resorted to ad hoc research methodologies,according to their specific objectives.

In the first phase, a literature review was carried out inorder to analyse the existing contributions dealing withthe definition and the measures of visibility, virtuality andcomplexity of supply chains (Section 2). Even though themodels found out in the literature either do not suit theprinciples of usability and diagnostic support (Section 3),or cannot be easily applied to supply networks, theliterature review suggested some characteristics ofthe supply chain or of its members which are relevant tothe measure of visibility, virtuality and complexity. Thesecharacteristics were taken into consideration when devel-oping the model. In the validation phase, several meetingswere organised, which involved a panel of nine supplychain experts belonging to leading companies of variousindustries. During the meetings the experts had thechance to analyse the model in depth, and to suggestpossible improvements in order to make it more robust.The third phase resorted to the well-established casestudy methodology (Yin, 2003). Six case studies werechosen in order to test the model in various industries anddifferent scopes of analysis (e.g. family, product orcomponent supply chains) and thus improve the gen-eralisability of the model. Through the case studies, wecould combine both quantitative and qualitative data bymeans of several collection methods (e.g. interviews,questionnaires, archives) (Eisenhardt, 1989). In particular,the more qualitative and softer evidence was necessary todrive the collection of quantitative data (e.g. choosing themore appropriate aggregation level) and to interpret theresults for diagnostic purposes. In short, the case studymethodology allowed us to test both the usability of themodel and its diagnostic value.

The three phases described above (i.e. model construc-tion, validation, case studies) were not strictly sequential,

cal Company

f the focal company.

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383376

but loops were frequent. The possibility to discuss themodel with a panel of expert practitioners, both in theoffice and in the field, added value to the construction ofthe model itself.

5. Measuring virtuality, complexity and visibility

This section is devoted to the presentation of themodel to evaluate the virtuality, complexity and visibilityof supply networks. As will become clearer in thefollowing sections, these measures are calculated byconsidering the contributions of the suppliers belongingto the network. In accordance with the basic principle ofusability (see Section 3), similar suppliers have beengrouped into clusters (nodes). Suppliers belonging to onecluster provide similar goods and have similar size,localisation (i.e. position and role in the supply chain)and level of captivity (i.e. how much the focal companycontrols them) if compared to the other suppliers. There-fore, in order to simplify the analysis, one cluster ofsimilar suppliers is modelled as one aggregated supplychain node.

5.1. Measuring supply chain virtuality

As stated in the literature review, we refer to virtualityas the ability of an enterprise to offer a complete productor service to its customers, with the enterprise itselfhaving only a few proprietary competencies (Bremer et al.,2001). Therefore, the virtuality index attempts to measurethe amount of supply chain activities that are external tothe focal company. In order to evaluate the amount ofexternal activities, it is important to distinguish betweentwo different kinds of suppliers: the so-called ‘‘captive’’suppliers and the external suppliers. A captive supplier iscontrolled by the focal company ‘‘to some extent’’, sincethe products or services it sells to the supply networkrepresent the majority of its turnover. The basic idea isthat the activities carried out by captive suppliers are notcompletely external to the focal company.

The procedure to measure virtuality is structured intothree steps:

(a)

the characterisation of the supply chain nodes (i.e.cluster of suppliers);

(b)

the evaluation of the smoothing captivity coefficient a,which measures the extent to which a node is captive;and

(c)

the assessment of the virtuality index VIRT.

5.1.1. The characterisation of the supply chain nodes

Every node must be characterised as ‘‘captive’’ or‘‘external’’ in relation to its captivity level. In order todistinguish between these two categories, we analyse thepercentage of the node turnover that is generated withinthe considered supply chain. The higher this value, thegreater the node captivity. We also define a specificthreshold value T, depending on the industry, thatdiscriminates between captive and external nodes.

The procedure to classify the nodes of a supplynetwork is structured in four steps:

Step 1: Define T according to the industry.Step 2: Consider all the nodes belonging to tier N=1 (1sttier suppliers). If more than T % of a node’s sales isgenerated by the focal company, then the node iscaptive. Otherwise add the node to the set First-

ExternalNode.Step 3: For each captive node belonging to tier N,consider all its supplying nodes belonging to tier (N+1).

If more than T % of the sales of an (N+1)-tier node isgenerated by the captive node belonging to tier N,then the (N+1)-tier node is captive. Otherwise addthe node to the set FirstExternalNode.

Step 4: If no captive nodes have been identified at tier(N+1), then the procedure ends. Otherwise, N=N+1andgo back to Step 3.

For illustrative purposes, Fig. 3 shows the classificationof the nodes of a supply network consisting of eightsuppliers. The nodes S1 and S3 are captive, while theothers are classified as external. S2, S4 and S6 belong tothe set FirstExternalNode.

5.1.2. The evaluation of the smoothing captivity coefficient aWhen evaluating the virtuality of a supply network, it

should be noted that the contribution of captive suppliersto virtuality is lower than that of external suppliers, giventhat the focal company in reality controls them to someextent. Then a coefficient ak (0rako1) is used to smooththe contribution of a captive supplier k. This coefficientconsiders two kinds of control: ownership, i.e. the extentto which the focal company possesses (e.g. in terms ofshares) its supplier, and ‘‘de facto’’ control, i.e. the extentto which the supplier’s sales rely on the considered supplynetwork. In fact, both these conditions reduce the supplier‘‘independence’’. The formula to compute the captivitycoefficient is as follows:

k ¼ ð1� OkÞ � ð1� SkÞ � ½1=ð1� TÞ� ð1Þ

where Ok is the ownership index, i.e. the percentageamount of shares of the node k owned by the focalcompany; Sk is the percentage of sales to the supplynetwork out of the total sales of the node k; T is thecaptivity threshold value; [1/(1�T)] is a normalisationfactor, so that ak ranges between zero and one.

The higher the ownership ratio and the more relatedthe supplier turnover to the focal company, the lower thecaptivity coefficient a.

5.1.3. The assessment of the virtuality index VIRT

As stated before, the virtuality index VIRT is intendedto measure the amount of supply chain activities which is‘‘really’’ external to the focal company. Basically, the morea company manages a significant business (measured interms of value of production) by controlling only a smallpart of the supply chain (measured in terms of purchas-ing: the higher the purchases, the lower the control level),the more a company is virtual. Therefore, the formula toassess the virtuality index VIRT in a two-tier supply chain

Fig. 3. The classification of the supply chain nodes—example.

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383 377

is the following:

VIRT ¼

Pk2EPk þ

Pk2Cðak � PkÞ

VoPFCð2Þ

where E is the set of external suppliers; C is the set ofcaptive suppliers; ak is the captivity smoothing coefficientassessed for supplier k; Pk is the amount of purchases (e.g.raw materials, components, services) made by the focalcompany from supplier k; VoPFC is the value of productionof the focal company (FC).

When a supply network is considered, the added valueshould be introduced, rather than the total amount ofpurchases, in order to avoid double counting. For longerthan two-tier supply chains, formula (2) should bemodified as follows:

VIRT ¼

Pk2EAVk þ

Pi2Cðak � AVkÞ

VoPFC

¼

Pk2E0Pk þ

Pk2Cðak � AVkÞ

VoPFCð3Þ

where E0 is the set FirstExternalNode; AVk is the addedvalue related to products and services sold to the supplynetwork by the supplier k.

As shown in (3), the evaluation of the virtuality indexVIRT is quite simple for supply networks as well, becauseit is enough to consider all the captive nodes and theexternal nodes which directly supply the focal company ora captive node. This is consistent with the usabilityprinciple described in Section 3. In fact, the sum of allthe added values of the external suppliers can beapproximated as the purchases from the ‘‘nearest’’external supplier.

5.2. Measuring supply chain complexity

As detailed in the literature review, supply chaincomplexity depends on several aspects: the number ofnodes, the number of tiers, the number of nodes per tier,the number of connections, etc. Among the different waysthat can be found to measure supply chain complexity, wedecided to consider the number of connections among thenodes, since it allows to take into consideration both thenumber of nodes and their relations (Choi et al., 2001; Wuand Choi, 2005; Choi and Krause, 2006; Goffin et al., 2006;Bozarth et al., 2009). However, not all the connectionscontribute in the same way to the supply networkcomplexity (Choi and Krause, 2006). As a matter of fact,

the direct connections with the focal company shouldcontribute more. Thus, the contribution of each connec-tion has been weighted according to its position in thenetwork.

Therefore, the formula to evaluate the complexity of asupply network is the following:

COMP ¼Xa2A

Wa ð4Þ

where a is a connection exiting from a generic node k; A isthe overall set of connections exiting from the nodes ofthe supply network; Wa is the weight of the connection a.

The weight Wa depends on the ‘‘distance’’ between theconnected nodes and on the number of suppliers which havebeen included in each node. More specifically, the distancefrom the focal company is measured by considering thevertical integration of the nodes which are on the pathbetween the node itself and the focal company. In fact,although the tier to which the node belongs is significant as ameasure of neighbourhood, a metric which is based just onthe tier would not distinguish between varying degrees ofvertical integration—i.e. the amount of activities directlymanaged and controlled by a firm-which do affect the ‘‘real’’distance between the companies.

Wa ¼Wd;k � nk

¼

nk for first-tier suppliers

for suppliers belonging

1�

Pl2Ik

AVl

Sm;FC

� �� nk to tier N with NZ2

8>>><>>>:

ð5Þ

where a is a connection exiting from node k; nk is the numberof suppliers included in the node k; Wd,k is the weight of nodek, assessed on the basis of its ‘‘distance’’ from the focalcompany (if k belongs to the first tier, then Wd,k=1, else itdepends on the vertical integration of the nodes which are onthe path between the node itself and the focal company); Ik isthe set of nodes belonging to the path from k to the focalcompany; l is a node belonging to Ik; AVl is the added value ofthe node l; m is the first-tier node belonging to Ik; Sm,FC is thevolume of sales from the node m to the focal company FC.

5.3. Measuring supply chain visibility

In a supply network, global visibility is the weightedsum of the visibility that the focal company has on thedifferent nodes of its inbound supply chain. Moreover, as

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383378

stated in the literature review, visibility is defined interms of access to useful information. Therefore, eachnode of the supply chain is characterised by a set ofinformation which may be shared with the focal company.Visibility is measured on the basis of the amount andquality of the information which the focal companyknows, compared to the total information that could beexchanged. For diagnostic purposes, four different types ofinformation flows are considered (Bracchi et al., 2001):transactions (e.g. order confirmation, ASN—Advance Ship-ping Notice), status information (e.g. stock level, residualshelf-life), master data (e.g. bill of materials, commercialinformation), and operational plans (e.g. distribution plan,production plan).

The judgement about the exchanged informationis based on three qualitative scales: one for measuringthe quantity of the exchanged information, two formeasuring its quality, in terms of both freshness andaccuracy (Gustin et al., 1995; Mohr and Sohi, 1995; Kaipiaand Hartiala, 2006; Barratt and Oke, 2007; Zhou andBenton, 2007). The scales have four ordered responselevels, from 1 (low rate) to 4 (best rate). Quantity andquality judgements are collected for each informationflow (i.e. transactions/events, status information, masterdata, operational plans) and for each supply chain node.The procedure to evaluate the visibility index VIS isstructured in three steps. First, the twelve judgments foreach node k (Jk,x,y) should be collected by means of thethree qualitative scales, where:

�

x 2 fq; a; f g, q=quantity, a=accuracy, f=freshness; � y 2 ft; s;m; og, t=transactions, s=status, m=master data,

o=operational plans.

The collected judgements are then combined—usingthe geometric mean—to have a synthetic evaluation of thevisibility that the focal company has on each node. Thegeometric mean, which is obtained multiplying a set of n

numbers and then nth rooting the result—was chosensince it proved to better represent the phenomenon. Thequantity and quality of the shared information areassessed separately, and then combined to evaluate thevisibility index VIS on the node k.

VISQuantityk ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffijk;q;t � jk;q;s � jk;q;m � jk;q;o

4

qð6Þ

VISQualityk ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffijk;a;t � jk;a;s � jk;a;m � jk;a;o

4

q�

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffijk;f ;t � jk;f ;s � jk;f ;m � jk;f ;o

4

qr

ð7Þ

VISk ¼

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiVISQuantityk � VISQualityk

qð8Þ

Finally, the overall visibility that the focal company has onits inbound supply chain is assessed. The contribution ofeach node is weighted on the basis of the node distancefrom the focal company (see Section 5.2), its significancein terms of the value of the supplied goods, and itscriticality, which is measured through a qualitative scalebased on Kraljic (1983) matrix. Therefore, the visibility

index VIS is defined as follows:

VIS ¼XMk¼1

ðVISk �WkÞ ð9Þ

where VISk is the visibility that the focal company has onthe node k; M is the number of nodes; Wk is the weightassigned to the node k in relation to its distance from thefocal company (Wd,k), criticality (Wc,k) and significance(Ws,k). In Section 5.2 we have already discussed how Wd,k

can be assessed.The criticality weight Wc,k is evaluated starting from

the well-known Kraljic (1983) matrix. More specifically, afour response scale is used to measure the criticality of asupply chain node, combining the impact on profits andthe supply risk. Finally, the significance weight Ws,k iscalculated on the basis of the supplier sales within theconsidered supply network, as

Ws;k ¼

Sk;FCPn2N1

Sn;FCfor first-tier suppliers

Sk;ePi2Nz

Si;e�Ws;n for suppliers belonging

to tier N; with NZ2

8>>>>>><>>>>>>:

ð10Þ

where Sb,c (e.g. Sk,FC, Sn,FC, Sk,e, Si,e) are the sales of supplierb to company c; n belongs to the set of first-tier nodes, N1;i belongs to the set of z-tier nodes Nz; e is the (z�1)-tiernode belonging to Ik.

For diagnostic purposes, the overall visibility index VIS

can be split by information quantity and quality, and typeof information.

6. Empirical evidence

This section presents the results obtained by applyingthe proposed model in six real supply chains. Theusefulness of these case studies was twofold. First, theyallowed us to test the proposed model in order to checkboth its accuracy (mainly in terms of methodologicalrigour and diagnostic capability) and its applicability inreal contexts (i.e. usability). Second, they provided somepreliminary empirical evidence useful for addressingfuture research steps. Table 1 summarises the mainfeatures of the case studies.

As can be observed, the case studies are very differentin terms of unit and scope of analysis: two of them arerelated to the supply chain of a family of components (A2and B), two of them to a family of products (A1 and D) andtwo of them to a specific product (C and E). As aconsequence, the results are not directly comparable.Nevertheless, by applying the proposed model to a non-homogeneous sample, we succeeded in testing its usabil-ity in very different situations.

The effort needed to get a result was quite low. Inparticular, the information needed to evaluate virtuality,complexity and visibility indexes were gathered by meansof one or two direct interviews, lasting about three hourseach. In addition to the in-depth interviews, other sourcesof information were analysed (e.g. internal documents,balance sheets). In contrast to that of front-officeactivities, the time required for the back-office activities

Table 1The sample.

Company Annual sales

[2007]

(hbillion)

Finished product Unit of analysis Scope of the analysis No. of

suppliers

A SC1 (A1) 3.4 Cook tops Product family All the inbound supply chain (three tiers) 210

A SC2 (A2) 3.4 Electronic cards Component family All the inbound supply chain (two tiers) 59

B 19.4 Plastic components Component family First two tiers 10

C 3 Helicopter Product First tier 380

D 5 Copper cables Product family First two tiers 61

E 3.7 Video entry phones Product All the inbound supply chain 38

Table 2Virtuality and complexity measures.

A1 A2 B C D E

Virtuality (%) 56 94 96 47 55 45

Complexity 209 50 10 390 60 36

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383 379

(to get the data and internal documents, to analyse theresults of the interviews, to apply the model to evaluatethe index) was more variable, depending both on thescope and unit of analysis (e.g. product vs. product familyvs. component family), as well as on the data availability.

The managers involved in the study greatly appreciatedthe results coming from the application of the model.More specifically, they valued the sound measure ofvariables that are usually not measured at all or analysedonly by means of qualitative aggregated indexes.

The analysed supply chains look very different in termsof virtuality and complexity, as presented in Table 2: infour case studies, average values of virtuality andcomplexity result; in the other two case studies (dealingwith component families), high virtuality and averagecomplexity are observed.

Every supply chain has also been analysed in terms ofvisibility. The results are reported in Table 3.

6.1. Within-case analysis

The application of this model allows to carry out, foreach case study, an internal analysis which aims tounderstand the actual level of visibility in relation to theconfiguration of the supply network.

Since the measures of visibility, virtuality and com-plexity are calculated on the basis of a bottom-upapproach (i.e. combination of elementary contributions),the drilldown of visibility allows to identify criticalities,that should be faced in terms of visibility, and opportu-nities that can be taken. For example, the differentdimensions of visibility (quantity, accuracy and freshness)can be analysed in order to understand the strong and theweak points of the information flows; visibility on eachnode of the supply network can be measured to under-stand which are the most critical relations; the overallmeasure of visibility on the supply network allows to

compare the value of visibility in different supply net-works of the company.

Let us take company E as an example. In this case, weconsider the supply network of a specific product: a videoentry phone.

This specific supply network is characterised by somesuppliers of non-critical items (packaging, manual, fineparts, cables) and by a ‘‘critical branch’’ that is managedby a single supplier (main component supplier) whoprovides the most complex and relevant parts to companyE. The latter also controls the assembly of a WIPcomponent (electronic card) that is then assembled intothe product by the main supplier. As shown in Tables 2and 3, company E controls more than half of theproduction process and the supply chain is not highlycomplex.

The process of computing the measures of virtualityand complexity has shown that a very significant part ofthe virtuality and complexity of the supply network is dueto the branch of the main component supplier. Indeed, ifthe critical branch were directly controlled by company E,the virtuality of the external supply chain would be just33% of the current value and its complexity would be 50%of the current complexity. The other branches of thenetwork are less relevant both in terms of volumes (whichaffect virtuality) and of number and criticality of relationsamong different nodes (which affect complexity). For thisreason, in Fig. 4 this branch is highlighted and betterdetailed than the other ones.

The actual visibility level is very low even on thiscritical branch (see exact values in Fig. 4). Indeed, themeasure is very low where the main component supplieris concerned and even lower on the second-tier suppliers.In next table, the detailed values of the visibility level onthe main component supplier are presented (Table 4).

Visibility on master data has not been considered sincethe components are designed in house by Company E.From the table, it is possible to note how the accuracy ofthe transaction information is quite good, but the amountof the exchanged information is very low. Moreover, as thevisibility level on the other kinds of information is almostzero, it is not possible to qualify the information exchange.This table suggests two main intervention priorities: thefirst one is that it is necessary to speed up the informationexchanges to manage the operational process in anefficient way, and the second, that more medium-term

Ta

ble

3T

he

ass

ess

me

nt

of

the

vis

ibil

ity

lev

el.

Tra

nsa

ctio

ns

Sta

tus

info

rma

tio

nM

ast

er

da

taO

pe

rati

on

al

pla

ns

All

info

rma

tio

n

Qu

an

tity

Acc

ura

cyFr

esh

ne

ssT

ota

lQ

ua

nti

tyA

ccu

racy

Fre

shn

ess

To

tal

Qu

an

tity

Acc

ura

cyFr

esh

ne

ssT

ota

lQ

ua

nti

tyA

ccu

racy

Fre

shn

ess

To

tal

Qu

an

tity

Acc

ura

cyFr

esh

ne

ssT

ota

l

(VIS

)

A1

3.8

3.9

3.8

3.8

3.8

3.9

3.8

3.8

3.8

3.9

2.9

3.5

3.8

3.7

3.7

3.8

3.8

3.9

3.5

3.7

A2

2.1

2.0

1.0

1.5

1.4

2.0

1.0

1.2

2.8

4.0

2.9

1.9

1.0

2.0

1.5

1.4

1.5

2.3

1.3

1.6

B4

.02

.02

.02

.82

.02

.01

.01

.73

.02

.01

.02

.14

.01

.02

.02

.43

.11

.71

.42

.2

C2

.52

.72

.62

.62

.62

.82

.42

.62

.62

.92

.62

.72

.72

.92

.42

.72

.52

.72

.42

.6

D1

.82

.82

.82

.11

.93

.13

.02

.32

.53

.93

.92

.92

.23

.73

.42

.62

.43

.33

.22

.7

E1

.03

.01

.01

.31

.0–

–1

.02

.93

.01

.01

.71

.0–

–1

.01

.23

.21

.41

.5

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383380

information is needed to get some benefits in productionplanning as well.

Company A provides another interesting example, astwo different supply networks have been considered.Table 3 presents that the visibility level is different in A1and A2 supply networks. This is due to the adoption of asupplier portal in A1, which is not used by suppliers in A2.The analysis shows how the portal supports bettervisibility, in terms of both quantity and quality of theexchanged information. Moreover, the results show thatthe first supply network is more complex and less virtualthan the second one. Indeed, greater efforts have beenmade to strictly interconnect the supply network A1 withthe processes of the focal company, since the productionprocess of this family of products is not completelyexternalised and it requires fine tuning between theprocesses of company A and partners in order to work.

6.2. Cross-case analysis

Even if generalisability is still weak due to the limitation ofthe number of case studies, some first interesting cross-caseevidences result. Virtuality and complexity seem to be twosignificant variables in explaining the current level ofvisibility that the focal company has on its inbound supplychain. These preliminary evidences allow to formulate someresearch hypotheses, which should be better investigated inthe next research steps.

More precisely, from the considered case studiesemerged that the higher the quantity of collectedinformation, the higher its quality (Table 3). In fact a‘‘critical mass’’ effect and a ‘‘learning phenomenon’’ havebeen observed in the case studies: information requiresbetter systems that lead to better quality.

Moreover, the relation between the two dimensions ofvisibility (quality and quantity of shared information)proved to be affected by supply chain virtuality in terms ofsuppliers’ captivity. Fig. 5 shows that visibility on captivenodes is, on average, higher in terms of quantity ofexchanged information than visibility on external nodes.This is not necessarily true when considering the qualityof shared information. Moreover, both quantity andquality of information shared with captive suppliers hasa lower variance than that shared with external ones.

An interesting relation results from the analysis of thecomplexity of the external supply chain and the visibilityof the focal company on its first-tier suppliers. As shownin Fig. 6, when the overall complexity of the inboundsupply chain is higher, the focal company achieves ahigher visibility on the first-tier suppliers. We could arguethat firms that have to manage a more complex supplychain are more likely to invest to enhance visibility.

However, the results of our study show that, in almost allthe case studies, the focal company has a very low visibilityon the suppliers’ suppliers (e.g. 2nd-tier suppliers, 3rd-tiersuppliers) regardless of the supply chain complexity. Thismeans that the effectiveness of the efforts of the focalcompany is limited to the first tier suppliers, and it representsa serious obstacle in the path towards supply chainintegration.

Fig. 4. Supply network of company E.

Table 4Visibility on the major supplier of Company E.

Transactions Status Operational

plans

Quantity 1 No

information

exchange

No

information

exchange

Accuracy 3 – –

Freshness 1 – –

Fig. 5. Empirical evidences—the impact of captivity on visibility.

Fig. 6. Empirical evidences—the relation between complexity and

visibility.

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383 381

7. Concluding remarks

This paper is part of a broader research that aims toinvestigate the relation between supply chain visibility,context variables (i.e. visibility and complexity) and theimpact on supply chain performance, in accordance withthe Contingency Theory. It is focused on the developmentof a quantitative model to measure, on the one hand, thevisibility that a focal company has of its supply network,and on the other hand, two main features of supplynetwork configuration, i.e. virtuality and complexity.Seven case studies were carried out in order to verifythe measures usability and to gather some preliminaryempirical evidence about the relation between the contextvariables (virtuality and complexity) and visibility.

M. Caridi et al. / Int. J. Production Economics 127 (2010) 372–383382

By studying the visibility on complex supply chainnetworks, this work intends to bridge the gap existingbetween literature contributions about supply chain visibility(mainly focused on simplified supply chains) and practi-tioners’ need for managing complex supply networks.

The results presented in this paper are relevant forboth researchers and practitioners. Indeed, as the formerare concerned, this paper provides a model to measurevisibility, virtuality and complexity of supply networks,supporting the analysis of the relation between thesevariables, in accordance with the Contingency Theory.Moreover, the paper provides some interesting prelimin-ary evidence about the relation between visibility andcontext variables which could be further investigated. Asthe relevance for practitioners is concerned, the proposedmodel provides a methodology that can be used bymanagers to analyse their supply networks in order todiscover the opportunities provided by information shar-ing. By measuring the three indexes proposed in thispaper, a company can compare its visibility to that of thebest-in-the-class, thereby gathering useful hints for im-proving the visibility inside its supply networks. Forinstance, taken two companies having similar visibilityon their supply networks, it may happen that one of themshares a lot of low-quality information, whereas the otherone has access to less, but higher-quality information.Therefore, the first company should invest in informationaccuracy and freshness, whereas the second one shouldincrease the amount of shared information.

Finally, four main paths for future research can beoutlined. First, the model to assess the virtuality, complex-ity and visibility indexes should be extended to considerthe internal and the outbound supply chains as well. Onone side, the analysis of the internal supply chain could bevery important when the focal company manages anumber of facilities in several countries; on the otherside, the analysis of the outbound supply chain is crucialwhen distributors and retailers have a key role in thesupply network between the focal company and themarket. Second, a larger number of case studies wouldallow to perform benchmarks, and to better explore theimpact of virtuality and complexity on supply chainvisibility. Different samples should be analysed fordifferent industries to take into consideration thosespecific features (e.g. product complexity) that may havea significant impact on the visibility level, thus renderingless meaningful any cross-industry comparison. Third,other context variables that can significantly impactvisibility should be analysed (e.g. velocity, demandvariability). Fourth, the model should also include perfor-mance indicators (see Fig. 1) that would allow to analysehow the match between context variables and visibilityaffects supply chain performance.

Acknowledgement

We wish to thank IBM Italia for funding this researchproject.

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