Kim 2011 Journal of Operations Management

8/12/2019 Kim 2011 Journal of Operations Management

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Journal of Operations Management 29 (2011) 194211

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Journal of Operations Management

j o u rn a l h o m ep a g e : www.e l sev i e r. co m/ l o ca t e / j o m

Structural investigation of supply networks: A social network analysis approach

Yusoon Kim a, , Thomas Y. Choi b , Tingting Yan b , Kevin Dooley ba Department of Management, Marketing, and Logistics, College of Business Administration, Georgia Southern University, Statesboro, GA 30460, United Statesb Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, Tempe, AZ 85287, United States

a r t i c l e i n f o

Article history:Received 26 October 2009Received in revised form 5 November 2010Accepted 10 November 2010Available online 18 November 2010

Keywords:Supply networksSupply chain managementSecond-tier suppliersSocial network analysisNetwork structureStructural analysisNetwork indices

a b s t r a c t

A systemof interconnected buyers andsuppliers is better modeled as a network than as a linear chain. Inthispaper wedemonstratehow to usesocialnetworkanalysis to investigatethe structuralcharacteristics

of supply networks. Our theoretical framework relates key social network analysis metrics to supplynetwork constructs. We apply thisframework to the three automotive supply networks reported in ChoiandHong (2002) . Eachof thesupply networks is analyzed in terms of both materialsow andcontractualrelationships. We compare the socialnetwork analysis results with the case-based interpretations in Choiand Hong (2002) and conclude that our framework can both supplement and complement case-basedanalysis of supply networks.

2010 Elsevier B.V. All rights reserved.

1. Introduction

Supply chain management has focused on linear relationships of buyers andsuppliers ( Cox et al., 2006; Zhu and Sarkis, 2004 ). Whilea linear perspective may be useful for planning certain mechanicalaspects of transactions between buyers and suppliers, it fails tocapture the complexity needed to understand a rms strategy orbehavior, as both depend on a larger supply network that the rmis embedded in ( Choi and Kim, 2008 ). A rms supply networkconsists of ties to its immediate suppliers and customers, and tiesbetween them and their immediate suppliers and customers, andso on ( Cooper et al., 1997; Croxton et al., 2001 ). In the past decadethere has been increased discussion of the benets of adopting anetwork perspective in supply chain management research ( Choiet al., 2001; Lazzarini et al., 2001; Lee, 2004; Wilding, 1998 ).

Froma supply network perspective, the relative position of indi-vidual rms with respect to one another inuences both strategyand behavior ( Borgatti and Li, 2009 ). In this context, it becomesimperative to study each rms role and importance as derivedfrom its embedded position in the broader relationship structure(Borgatti and Li, 2009; DiMaggio and Louch, 1998 ). For example,BurkhardtandBrass (1990) and Ibarra (1993) claim that power andinuence derive from a rms structural position in its surround-ing network. Others have linked network position to such issues as

Corresponding author. Tel.: +1 912 478 2465; fax: +1 912 478 2553.E-mail address: [email protected] (Y. Kim).

innovation adoption (e.g., Burt, 1980; Ibarra, 1993 ), brokering (e.g.,

Pollock et al., 2004; Zaheer and Bell, 2005 ), and creating alliances(e.g., Gulati, 1999 ).Todate,therehave beenfew studies of real-lifesupplynetworks,

due to the difculties in obtaining data. The studies of real net-works that have been done have relied on qualitative methods toderive theoretical and practical insights (e.g., Harland et al., 2001; Jarillo and Stevenson,1991 ). While qualitative interpretations havetheir merits, their validity is threatened by a researchers boundedrationality, which includes the difculty to conceptualize complexphenomena such as networks. Thus in this paper we propose toanalyze the structural characteristics of supply networks using aformal, quantitative modeling approachsocial network analysis(Borgatti and Li, 2009; Grover and Malhotra, 2003; Harland et al.,1999 ). We will show howsocial network analysis can both supple-ment and compliment more traditional, qualitative interpretationmethods when analyzing cases involving supply networks.

Social network analysis (SNA) has recently gained acceptanceamong scholars for its potential to integrate the operations andsupply management eld with other branches of managementscience ( Autry and Grifs, 2008; Borgatti and Li, 2009; Carteret al., 2007 ). According to Borgatti and Li (2009) , SNA conceptsare particularly suitable for studying how patterns of inter-rmrelationships in a supply network translate to competitive advan-tages throughmanagementof materialsmovement anddiffusionof information.

To date, SNA has not been applied in an empirical study of realsupply networks; in fact there is a general paucity of SNA appli-

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Y. Kim et al. / Journal of Operations Management 29 (2011) 194211 195

cations in operations and supply management, with only a fewexceptions (e.g., Carter et al., 2007; Choi and Liker, 1995 ). This islargely because there is lack of conceptual clarication as to howthekey SNAmetrics(e.g. centrality)canbe theoreticallyinterpretedin the context of supply networks. Therefore in this study we linkdifferent SNA metrics at the node- or rm-level to specic rolesin a supply network. We consider supply networks based on bothmaterials ow and contractual relationships. The metrics yield sixsupply networkrelated constructs:supply load, demand load, oper-ational criticality, inuential scope, informational independence,and relational mediation. Different network-level SNA metrics arealso linked to their implications for supply network performance.

We apply our framework to real supply network data derivedfrom three published case studies of automotive supply networks(Choiand Hong, 2002 ). In thatstudy theauthorscreatedempiricallythree complete network maps of the center console assembly forHonda Accord, Acura CL/TL, and DaimlerChrysler Grand Cherokee.In the present paper, we convert the network data from Choi andHong(2002) intomatrix formsand analyzethem usingthe softwareUCINET 6 (Borgatti et al., 2002 ). These quantitative results are theninterpreted using our theoretical framework. Finally, we discussour quantitative SNA results comparing to the qualitative ndingsof Choi and Hong (2002) and consider the implications.

2. Literature review

2.1. Supply networks

Supply networks consist of inter-connected rms that engagein procurement, use, and transformation of raw materials to pro-vide goodsand services ( Lamminget al., 2000; Harland et al., 2001 ).The relatively recentincorporation of theterm network into sup-plychainmanagementresearchrepresents a pressing need to viewsupplychains as a network forrmsto gain improved performance,operational efciencies, and ultimately sustainable competitive-ness ( Corbett et al., 1999; Dyer and Nobeoka, 2000; Kotabe et al.,

2002 ). Therefore, it is increasingly important to analyze the net-work structure of supply relationships.

In the operations and supply management eld, a complex sys-tem perspective has been used as a theoretical lens for describingsupply networks. Wilding (1998) studied dynamic events in supplynetworks through what he referred to as supply chain complexitytriangle(p. 599). Choietal.(2001) conceptualizedsupply networksas a complex adaptive system (CAS). Surana et al. (2005) proposedhowvarious complex systems concepts can be harnessed to modelsupply networks. Pathak et al. (2007) discussed the usefulness of CAS principles in identifying complex phenomena in supply net-works. Others have examined supply networks from a strategicmanagement perspective. Greve (2009) , using supply networks inthe maritime shipping industry, studied whether technology adop-

tion is morerapidin centrallylocatednetwork positions. Mills et al.(2004) suggested different strategic approaches to managing sup-ply networks depending on whether a rm is facing upstream ordownstream and whether it is seeking its long-term or short-termposition in the supply network.

Methodologically, simulation models have been used to studyhypothetical supply networks ( Kim, 2009; North and Macal, 2007;Pathak et al., 2007 ). Others have studied real-world supply net-works using the case study approach ( Jarillo and Stevenson, 1991;Nishiguchi, 1994; Choi and Hong, 2002 ). Scholars in the industrialmarketing have developed descriptive models of supply networks(Ford, 1990; Hkansson, 1982, 1987; Hkansson and Snehota,1995 ). Descriptive case studies in this genre illustrate how com-panies such as Benetton, Toyota, or Nissan attained competitive

advantage through their supply networks ( Jarillo and Stevenson,

1991; Nishiguchi, 1994 ). Other studies focused on developingtaxonomies of supply networks ( Harland et al., 2001; Lamminget al., 2000; Samaddar et al., 2006 ).

More recently, Borgatti and Li (2009) have highlighted thesalience of SNA to study supply networks. In fact, there have beena few studies in the operations and supply management eld thatused or promoted the useof SNA. Choi and Liker (1995) used SNA toinvestigate the implementation of continuous improvement activ-ities in automotive supplier rms. Carter et al. (2007) providedan example of the application of SNA in a logistics context. Autryand Grifs (2008) applied the concept of social capital, framed aspart of social network theory, to supply chain context. However,still lacking in such studies is a theoretical framework that relatessocial network theory to supply network dynamics and the com-prehensive application of SNA to studying supply networks. In thefollowing section, we provide a brief overview of SNA, focusing onthe key metrics useful for investigating and explaining phenomenawithin supply networks.

2.2. Social network analysis (SNA)

A network is made upof nodes andtiesthatconnect these nodes.In a social network, the nodes (i.e.,persons or rms)have agency inthat they have an ability to make choices. With its computationalfoundation in graph theory ( Cook et al., 1998; Kircherr, 1992; Liand Vitnyi, 1991 ), SNA analyzes the patterns of ties in a network.Naturally, SNA has been used to study community or friendshipstructure ( Kumar et al., 2006; Wallman, 1984 ) and communicationpatterns( Koehly et al.,2003;Zackand McKenney,1995 ). Ithas beenadopted to explore the spreading of diseases (e.g., Klovdahl, 1985 )anddiffusionof innovation (e.g., AbrahamsonandRosenkopf, 1997;Valente, 1996 ). In organization studies and strategic management,scholars have used it to investigate corporate interlocking direc-torships ( Robins and Alexander, 2004; Scott, 1986 ) and networkeffects on individual rms performance (e.g., Ahuja et al., 2009;Burkhardt and Brass, 1990; Gulati, 1999; Jensen, 2003; Rowleyet al., 2005; Stam and Elfring, 2008; Uzzi, 1997 ).

Operations and supply management scholars have also notedthe methodological potentialof SNA.For instance, Choi et al.(2001)stated that one could approach the study of supply networks fromthe social network perspective. Ellram et al. (2006) acknowledgedsocial network theory as a useful tool to study inuence in sup-ply chains. Carter et al. (2007) identied SNA as a key researchmethod to advance the elds of logistics and supply chain man-agement. More recently, Borgatti and Li (2009) and Ketchen andHult (2007) echoed suchsentiments. Theyhavealso recognized thedifculty of collecting network-level data in supply networks butargued its imperativeness for operations and supply managementto be integrated with other management disciplines.

According to Borgatti and Li (2009) , a more systematic adoptionof SNA will be instrumentalin exploringbehavioral mechanisms of

entire supply networks. A SNA approach allows us to better under-stand theoperations of supply networks,both at the individual rmlevel and network levelhow important the individual rms are,given theirpositions inthe network andhow the network structureaffects theindividual rms andperformance of the wholenetwork.Social networkscholars( Everettand Borgatti,1999;Freeman,1977,1979; Krackhardt, 1990; Marsden, 2002 ) have developeda range of network metrics at the node- or network-level to characterize thedynamics inside a social network.

2.3. Key network metrics

Network metrics canbe calculated at two levelsthe node leveland network level. Node-level metrics measure how an individ-

ual node is embedded in a network from that individual nodes


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196 Y. Kim et al. / Journal of Operations Management 29 (2011) 194211

perspective. In this study, we focus on three types of node-levelmetricsdegree, closeness, and betweenness centrality. Network-level metrics compute how the overall network ties are organizedfrom the perspective of an observer that has the birds eye view of the network. The network-level metrics we consider are networkdensity, centralization, and complexity.

2.3.1. Node-level metricsIdentifying the key actors in a social network is one of the pri-

mary uses of SNA ( Tichy et al., 1979; Wasserman and Faust, 1994 ).The concept of centrality is fundamental to node-level networkmetrics ( Borgatti and Everett, 2006; Borgatti and Li, 2009 ). Cen-trality reects the relative importance of individual nodes in anetwork. A nodes central position in a social network has a signi-cant impact on itsand others behaviors andwell-beings ( Mizruchi,1994 ). Centrality has been associated with social status ( Bonacich,1972; Freeman, 1979 ), power ( Coleman, 1973 ), and prestige ( Burt,1982 ).

There are different types of centrality metrics and they identifynodes that are important, in different aspects. Most prominent aredegree centrality, closeness centrality, and betweenness centrality(Everett and Borgatti, 1999; Krackhardt, 1990; Marsden, 2002 ). Of these, the most straightforward is degree centrality . This conceptbuilds on an observation that the more links a node has the morecentral it iswhen a node is connected to a large number of othernodes, the node has high degree centrality. Due to its greater con-nectedness with other nodes, a node with high degree centralitywould necessarily be more visible in the network ( Freeman, 1979;Marsden, 2002 ).

Another centrality concept is closeness centrality . As the termsuggests, this metric focuses on how close a node is to all the othernodes in the network beyond ones that it is directly connected to.A node is central if it can quickly reach all the others, and that iswhy closeness centrality includes indirect ties. This centrality isusually associatedwith nodes autonomyor independence in socialnetworks( Freeman,1979; Marsden,2002 )a nodewithhighclose-ness centrality has morefreedom fromothers inuenceandhigher

capacity for independent actions. Such nodes become less relianton other nodes.

Betweenness centrality measures how often a node lies on theshortest path between allcombinations of pairs of other nodes. Themore a given node connects nodes that wouldotherwise be discon-nected, the more central that node isother nodes are dependenton this node to reach out to the rest of the network. This metricfocuses onthe role of a node as anintermediary and posits that thisdependence of others makes the node central in the network. Assuch, the betweenness centrality usually denotes a nodes poten-tial control or inuence in the network ( Marsden, 2002 ). A nodewith high betweenness centrality has a great capacity to facilitateor constrain interactions between other nodes ( Freeman, 1979 ).

2.3.2. Network-level metricsSNA also yields metrics concerning the structure of the overall

network, suchas network density,network centralization, and net-work complexity. Network density refers to the number of total tiesin a network relative to the number of potential ties. Itis a measureof the overall connectedness of a network ( Scott, 2000 )a networkin which all nodes are connected with all other nodes would giveus a network density of one.

Network centralization captures the extent to which the overallconnectedness is organized around particular nodes in a network(Provan and Milward, 1995 ). Conceptually, network centraliza-tion can be viewed as an extension of the node-level centrality(Freeman, 1979 )if a network had such a highly centralized struc-ture that all connections go through few central nodes, then that

network would be high on network centralization. The network

with highest possible centralization is one with a star structure,wherein a single node at the center is connected to all other nodesandthese other nodes arenot connected toeachother.Likewise, thelowest centralizationoccurs when allnodeshave the same numberof connections to others.

Network centralization and network density are complemen-tary. Whereas centralization is concerned with the distribution of power or controlacross thenetwork,densityreectsnetwork cohe-siveness. A network that has every node connected with everyoneelse would have a highest possible density (i.e., density of one).This network would be a highly cohesive network but would havea diffuse and distributed control structure.

Network complexity is dened as the number of dependencyrelationswithin a network ( Frenken, 2000 , p. 260) and thus woulddependon both thenumber of nodes in the network and the degreeto which they are interlinked ( Frenken, 2000; Kauffman, 1993 ). Inthe context of a supply network, complexity relates to the collec-tive operational burdenborn by the members in the network ( Choiand Krause, 2006 ). For instance, a large number of units in a systemis likely to entail high coordination cost ( Kim et al., 2006; Provan,1983 ). Further, if these units are highly interdependent, then thecollective operational burden would be high and thus more com-plex at the system level.

Network complexity is related to network density and networkcentralization. First, more complex networks require higher opera-tional burden( Lokam, 2003; Pudlk andRdl, 1992, 1994 ). Second,network density is conceptually linked with network complexitybecause a denser network requires more effort to build and main-tain ( Marczyk, 2006 ). Finally, network centralization is associatedwith network complexity because the highest coordination costsrequire when every node is connected to all other nodes (i.e., anetwork with the least centralization) ( Pudlk et al., 1988 ).

3. Conceptual framework for analyzing supply networks

3.1. Two types of supply network

There are a number of different ways in which ties can be estab-lished between rms in the supply network. For example, a tiemight be established between two rms if they were collaboratingon a new product development or if they had overlapping boardmembership or belonged to the same trade organization. In thispaper we focus on two types of ties that Choi and Hong (2002)collected data for in their study.

Firms can be linked because of the delivery andreceipt of mate-rials, or they canbe linked through a contractual relationship( Choiand Hong, 2002 ). In a tree-like structure of materials ow (Berryet al., 1994; Chopra and Sodhi, 2004; Hwarng et al., 2005 ), thenetwork describes which supplier delivers to which customer. Theother type of network is based on contractual relationships . Often,when a buying company wants to control the bill of materials, itengages in directed sourcing,wherein it establishes a contract witha second- or third-tier supplier and directs the top-tier supplierto receive materials from them ( Choi and Krause, 2006; Chopraand Sodhi, 2004; Park and Hartley, 2002 ). In this context, materi-als ow occurs between two rms who do not have a contract andvice versa. These two types of supply networks, although based onthe same set of nodes, can have different network structures and,therefore, different logics and implications ( Borgatti and Li, 2009 ).

3.2. Supply network constructs

3.2.1. Firm-level constructsWe now consider the key node-level SNA metrics and discuss

how they can be used to interpret different roles in supply net-


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Table 1Node-level centrality metrics and their implications for supply networks.

Network type Centralitymetrics

Supply networkconstructs

Conceptual denitions Implication for central nodes

Role a Description Key capabilities

Materials ow Indegreecentrality

Supply load The degree of difculty faced by arm in managing incomingmaterial ows from the upstreamrms

Integrator To put together or transformdifferent parts into a value-addedproduct and ensure it functionswell

System integrationDesign/developmentArchitecturalinnovation

Outdegreecentrality

Demand load The degree of difculty faced by arm in dealing with demands fromthe downstream rms

Allocator To distribute limited resourcesacross multiple customers,focusing on scale economies

Process/manufacturingQuality managementComponent innovationOut-bound logistics

Betweennesscentrality

Operationalcriticality

The extent to which a rm impactsthe nal assemblers operationalperformance in terms of productquality, coordination cost andoverall lead-time.

Pivot To facilitate or control the ows of supply across the whole network

Risk managementIn- and out-boundlogisticsCross-functional integ.

Contractualrelationship

Degreecentrality

Inuential scope The extent to which a rm has animpact on operational decisions orstrategic behavior of other rms inthe supply network

Coordinator To reconcile differences of networkmembers and align their opinionswith the greater supply networkgoals

Contract managementSRM/CRM

Closenesscentrality

Informationalindependence

The extent to which a rm hasfreedom from the controllingactions of others in terms of accessing information in thesupply network

Navigator To explore, access, and collectvarious information with greaterautonomy in the supply network

Information acquisitionStrategic alignmentwith OEM

Betweennesscentrality

Relationalmediation

The extent to which a rm canintervene or has control overinteractions among other rms inthe supply network

Broker To mediate dealings betweennetwork members and turn theminto its own advantage

Information processingStrategic alignmentwith OEM

a Network role given high centrality.

works. Table 1 offers an overview of key centrality metrics, thecorresponding supply network constructs, and their implicationsfor network roles in the context of modeling supply networks. Wepropose this newframework for the interpretationof the SNAmet-rics in the supply network context.

To illustrate these constructs, we rst discuss the calculationof key SNA metrics and the essential properties of each. Then, weintegrate each key SNA metric separately with the two types of supply networks (i.e., materials ow and contractual relation). Weshould note that the supply network based on materials ow isdirectional, whereas the supply network basedon contractual rela-tionship is non-directional as legal obligations are mutually agreedand enacted.

3.2.1.1. Degree centrality in supply network. Degree centrality ismeasured by the number of direct ties to a node. Degree centralityC D(n i) for node i(n i) in a non-directional network is dened as:

C D(n i) = j

xij = j

x ji

where xij is the binary variable equal to 1 if there is a link between

n i and n j but equal to 0 otherwise ( Freeman, 1979; Glanzer andGlaser, 1959; Nieminen, 1973; Proctor and Loomis, 1951; Shaw,1954 ). To accountfor theimpactof network size g , degree centralityis normalized as the proportion of nodes directly adjacent to n i:

C D(n i) =C D(ni) g 1

.

For comparison purposes, in this study, we convert normalizeddegree centrality to a 0100 scale by multiplying by 100.

A high degree centrality points to where the action is in anetwork ( Wasserman and Faust, 1994 , p. 179). Freeman (1979)describes it as reecting the amount of relational activities, andsuch activities make the nodes with high degree more visible. Forinstance,in a non-directional contractual relationship network, the

degree centrality refers to the extent to which the rm inuences

other rms on their operations or decisions as the rm has moredirect contacts with others ( Cachon, 2003; Cachon and Lariviere,2005; Ferguson et al., 2005 ). In contrast, nodes with low degreecentrality are considered peripheral in the same network. If a nodeis completely isolated (i.e., zero degree), then removing this nodefrom the network has virtually no effect on the network. There-fore, a rm who has more contractual ties in the network garners abroad range of inuence on others, andat the same time such a rmwould often be required to reconcile conicting schedules or inter-ests between others. For the nal assembler, for instance, it wouldmake sense to align with suppliers with high degree centrality.

In a directional network of materials ow, the focus is eitheron the ow initiated (out-degree) or ow received (in-degree). Forinstance, out-degree centrality of a node is dened as:

C D(ni) = xi+

g 1.

In-degree centrality and out-degree centrality indicate the sizeof the adjacent upstream tier and downstream tier, respectively.A high in-degree or out-degree can capture transactional intensityor related risks for a rm ( Powell et al., 1996 ). In a materials ow

network, in-degree centrality for a rm can reect the degree of difculty faced by the rm when managing the incoming materialows. In other words, this metric measures the rms operationalload coming from the upstream suppliers. A rm with high in-degree centrality may serve the role of an integrator, as they aretaskedwithorganizing andincorporating a range of parts from var-ious suppliers to maintain the overall integrity of the product orservice ( Parker and Anderson, 2002; Violino and Caldwell, 1998 ).Such members in a supply network are instrumental and vital incarrying out the architectural or technical changes in the currentproduct ( Henderson and Clark, 1990; Iansiti, 2000 ).

Out-degree centrality relates to the rms level of difcultyin managing the needs of customers. The more direct customersthere are in downstream, the more challenging it is for the rm

to ensure on-time delivery, cost-effective inventory, and order


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Table 2Network-level metrics and their implications for supply networks.

Network type Network-levelmetrics

Conceptual denition in supplynetworks

Implication of overall network structure a

Characteristics Performance implications

Materials ow Centralization The extent to which particularfocal rms control and managethe movement of materials in asupply network

Operational authority (e.g., power to makedecisions on materials ow) concentratedin few central rmsCentralized decision implementationprocess

High level of controllability in productionplanningLow level of operational effectiveness atthe network-level (i.e., more time taken toreach a decision and take actions on issuesat a local level)

Complexity The amount of collectiveoperational burden born by themember rms in a supplynetwork

More rms engaged in the delivering andreceiving of materialsMore steps required to move the materialsalong

Low level of operational efciency at thenetwork level (i.e., longer lead time fromthe most upstream to the nal assembleror more parts for the same productfunction)

Contractualrelationship

Centralization The extent to which particularfocal rms exercise bargainingpower or relationshipmanagement control overother rms in a supplynetwork

Lack of interactions between central andperipheral rms in a supply networkDecoupled relationships between rms atdifferent tiers

High level of controllability in productdesign, product quality, and/or costmanagementLow level of responsiveness to or moretime for resolution on issues occurring at alocal level

Complexity The amount of load on thesupply network as a whole thatrequires relationship

coordination

More rms involved in transferringinformation

Low level of robustness or high degree of vulnerability to supply disruptions (i.e.,more time to channel information and a

higher likelihood of information distortionacross a supply network)

Active interactions at a local level

Slow relaying communications fromdownstream to the nal assembler

a Implications given high metric score.

indices are also usedones based on closeness and betweennesscentrality.

Further, there are other proxy measures of centralization usedin this study. They are multiple indices of density that involve thecore and periphery sub-groups in a network (see Table 6 ). Whena network is partitioned into two clusters, a core cluster appearsamong nodes that are densely connected together and a peripheryis formed among nodes that are more connected to core mem-bers thanto eachother ( Borgatti andEverett,2000;Luce andPerry,1949 ). For instance, in Fig. 1, there are 19 rms in the core group(see Table 6 ) around Honda and CVT who appear at the center. Therest appears in the periphery.

3.2.2.2. Supply network complexity. Supply network complexityrefersto theload on the network as a wholethat requires coordina-tion( Choi andHong, 2002 ). While thegeneral state of the literatureregarding the property of complexity at the network level is stillemerging ( Butts, 2001a,b; Everett, 1985; Freeman, 1983 ), we adoptthe idea put forth by Kauffman (1993) and Frenken (2000) . Theypropose that network complexity can be indicated by the numberof nodes and degree of interdependency among nodes in a givennetwork. Therefore, we use two types of SNA output metricssize-type anddensity-typeto represent the number of supply network

members andthe levelof connectedness among them, respectively.The size-type outputs are shown in network size and coresize, and the density-types include network density, core density,periphery density, core-to-periphery (CTP)density, and periphery-to-core (PTC) density. Network size relates to the average pathlength among nodes in the network ( Ebel et al., 2002 ). More rmsin a network translate into more steps and more time needed tocomplete the same task, whereby creating a higher likelihood of the supply being interrupted en route andhigher collective burdenborn at the system level ( Frenken, 2000 ). Likewise, between thetwo networks of identical size, more links imply a higher proba-bility that the functioning of the individual nodes in the networkis likely to be impeded by others, leading to a greater coordinationload on the whole network ( Choi and Krause, 2006 ). For instance,

if an OEM has two top-tier suppliers, the rm would necessarily

incur a greater amount of coordination load, compared to a situ-ation where there is only one top-tier rm. Therefore, a complexsupply networkswould be associatedwith large networksize,largecore size, high network density, high core density, high peripherydensity, high CTP density, and high PTC density. Note that in thecontractual relation supply networks, the PTC and CTP densitiesare identical, since every link in the network is non-directional andthe adjacency matrix representing this network is symmetric.

4. Research methodology

4.1. Data source

Choi and Hong (2002) (hereafter, denoted as C&H) reportedthree supply networks from raw materials suppliers to a nalassembler involved in the production of an automobile center con-sole assembly. The three product lines represented were HondaAccord, Acura CL/TL, and DaimlerChrysler (DCX) Grand Cherokee.Using an inductive casestudyapproach, the authors derived propo-sitions regarding the behavioral characteristics of supply networks.Table 3 provides a review of this particular work.

In our analysis, we include all the rms in the supply networkas identied in C&Hthey are direct suppliers and parts brokers,stretching from raw materials suppliers to the nal assembler.

As indicated before, each supply network contains two differenttypes of network informationone pertaining to materials owandanotherbased oncontractual relationships.These twodifferenttypes of network data yield a total of six supply networksthreebased on directional materials ow and three based on non-directional contractual relationships.

4.2. Data analysis

The network information from C&H is converted into a binaryadjacency matrix ( Wasserman and Faust, 1994 ) that has rms rep-resenting both the rows and columns of the matrix. For instance,cell ( i, j) would equal 1 if the rms i and j were linked either bymaterials ow or contractual relationship, and would be 0 oth-

erwise. Supply networks may yield adjacency matrices that are


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Fig. 1. Materials ow network for Accord.

symmetric (i.e., non-directional) or asymmetric (i.e., directional),depending on the nature of the linkages. As noted earlier, a mate-rials ow network is directional and thus asymmetric, while a

contractual relationship network is non-directional and thus sym-metric. Once generated, the adjacency matrices are imported intoUCINET 6 and areusedas inputsfor network analysis( Borgattiet al.,2002 ).

UCINETis a comprehensive software package for the analysis of social network data. It has been one of the most widely acceptedSNA tools for conducting the structural analysis of interorgani-zational networks (e.g., Gulati, 1995, 1999; Human and Provan,1997; Rowleyet al., 2005;Ahujaet al., 2009 ). The program containsdozens of network analytic methods such as centrality measures,subgroup identication, role analysis, elementary graph analysis,and permutation-based statistical analysis. While performing SNA,UCINET can create network visualizations. A visualization of eachof the six supply networks, also known as a sociogram, is shown inFigs. 16 .

5. Results

5.1. Node-level results

Tables 4 and 5 list key rms in the two types of supply net-works. We identify key rms based on their centrality values.Tables 4 and 5 build on Table 1 . The supply network constructsshown on the top row come from Table 1 , and centrality computa-tions are conducted on the correspondingcentrality metrics shownin Table 1 .

As indicated below each table, there is a cut-off point for eachsupply network construct (e.g., 10 for in-degree, 6 for out-degree).

The cut-off point is determined based on one rule: when there is

a noticeable drop-off in the score, the previous score constitutesthe threshold. In all cases except one, there are multiple key rms.The exception is out-degree centrality for the materials ow type

of DCXs supply network. Every node in the network, except forthe OEM, has only one customer, showing the same value on out-degree centrality; consequently, there was no threshold value. InTables 4 and 5 , the number shown in parenthesis next to a rmname represents the centrality score.

5.2. Network-level results

Tables 68 show SNAresults at thenetworklevel. Tables 6 and 7focus on centralization metrics, respectively, for directional mate-rials ow and non-directional contractual relationships. Table 8summarizes all complexity metrics for both types of networks.

In Table 6 , various network-level indicators are shown acrossthree different supply networks. Beginning with network size

and density, individual node-level centrality scores are averagedfor each supply network. Then, the three network centralizationscores are listed. Up to this point, all values reect network-levelattributes. Below the network-level values, Table 6 lists values atthe group level. It rst shows the size of core group and its density(see Section 3.2.2.1 on supply network centralization for a discus-sion on core and peripheral groups). It then moves on to listingother group-level measures.

Addressing contractual relationship supply networks, Table 7 isconstructed much the same way. Since this supply network typeis non-directional, network measures shown on the left-side col-umnare slightlydifferent from those of Table 6 , as discussed underTable 1 . Also note that most of thenetwork-levelmetricsare in nor-malized form, which allows us to compare them across the three

different supply networks.


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Table 3Summary of case data from Choi and Hong (2002) .

Networkmeasures

Product type

Honda Accord Acura CL/TL DaimlerChrysler (DCX) Grand Cherokee

Centralization Two rms, CVT and JFC, are top-tier suppliersto Honda

One top-tier supplier, Intek, a completeintegrator of this supply network

Textron as the sole top-tier supplier thatintegrates parts and subassemblies

Several second- or third-tier suppliers (e.g.,Emhart, Garden State, and Miliken) directlyselected by HondaSome third-tier suppliers directly selected byCVT, based on Hondas core supplier listHondas penchant for centralized control whenit comes to the product design and supplierselection

Honda engaging in directed sourcing at thesecond, third, and even fourth tiersIntek likewise engages in directed sourcing byselecting its own suppliers and even theirsuppliers suppliers, based on Hondas coresupplier listDirected sourcing generally for high-priced orstrategic itemsHondas centralized control of the productdesign activities

Textron-Farmington and Leon Plastics appearas two key second-tier suppliersTextron assumes the leading role in designingconsoleDirected sourcing occurs only on a limitedbasis

Complexity All together, 50 network entities: 2 rst-tier,21 second-tier, 18 third-tier, 7 fourth-tier, and2 fth-tier suppliersMajority of the suppliers at the second-tierlevelFour different nature of businesses in thenetworkmanufacturing companies, rawmaterials suppliers (e.g., GE Plastics),

distribution centers (e.g., Iwata Bolt), andtrading houses (e.g., Honda Trading)Reciprocal relationship between CVT and JFC,two top-tier suppliers, contributing to eitherreduction or increase of network complexitydepending on the relational nature

76 entities in the network: 1 rst-tier, 20second-tier, 28 third-tier, 17 fourth-tier, 9fth-tier, and 1 sixth-tier suppliersThe coupling between Honda and Intek basedon their shared history may reduce the level of complexityThe decoupling between Intek and JFC, asecond-tier supplier of the critical

subassembly, may further the complexityHondas effort to centralize second-tiersuppliers may increase complexity of thenetwork as a whole

41 entities: 2 rst-tier, 10 second-tier, 22third-tier, and 7 fourth-tier suppliersAt the top-tier level, Textron is engaged inassembly work and also acts as a conduit for apart from Leon as it ships the front console matdirectly to the DCX plant with Textrons labelNo reciprocal relations among suppliersAs per DCXs recommendation, Textron has

consolidated the second-tier suppliers, leadingto reduced number of second-tier suppliersand subsequently reduced complexity

Fig. 2. Materials ow network for Acura.


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Fig. 3. Materials ow network for Grand Cherokee.

Fig. 4. Contractual relationship network for Accord.


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Fig. 5. Contractual relationship network for Acura.

Fig. 6. Contractual relationship network for Grand Cherokee.


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Table 4List of key rms based on materials ow network.

Supply load a Demand load b Operational criticality c

Accord CVT (59 d ), JFC (15), HFI (11) CVT (15), C&C (7.4), JFC (7.4), GE (7.4), Yamamoru (7.4),Industry Products (7.4)

CVT (13), Emhart (2), Yamamoru (1.7), Fitzerald (1.7), JFC (1.3)

Acura Intek (58), Arkay (21), Select Ind. (12) Iwata Bolt (9.1), Tobutsu (6.1), Arkay (6.1), Twist (6.1),Milliken (6.1), Garden State (6.1), Select Ind. (6.1)

Intek (3), Arkay (1.7)

DCX Textron (65), Leon Plastics (31) None Textron (3.8), Leon Plastics (2.5)

a Firms with in-degree> 10.b Firms with out-degree > 6.c Firms with betweenness> 1.0.d Centrality score.

Table 5List of key rms based on contractual relationship network.

Inuential scope a Informational independence b Relational mediation c

Accord CVT (52), Honda (30),Yamamoru (15)

CVT (57), Honda (53), Yamamoru (40) CVT (79), Honda (64), Emhart (21) Yamamoru(15), Fitzerald (14)

Acura Intek (45) , Honda (36), Arkay(18), Select Ind. (15)

Intek (62), Honda (56), Arkay (44), Select Ind.(43), Tobutsu (41), HFI (40)

Intek (77), Honda (63), Select Ind. (14), IwataBolt (12), Arkay (10)

DCX Textron (62), Leon Plastics (35) Textron (72), Leon Plastics (58) Daimler (46) Textron (88), Leon Plastics (53) Daimler (15)

a Firms with degree> 15.b Firms with closeness> 40.c Firms with betweenness> 10.

Table 6Network-level results for materials ow a supply networks.

Network measures Product type

Accord Acura DCX

Network size (rms) 28 34 27Network density 0.046 0.037 0.037Average in-degree 4.630 3.654 3.704Average out-degree 4.630 3.654 3.704Average betweenness 0.809 0.231 0.234Centralization (in-degree) 0.567 0.556 0.641Centralization (out-degree) 0.106 0.056 0.001Centralization (betweenness) 0.128 0.029 0.038

Core group size (rms) 19 23 4Core density 0.067 0.059 0.250

Core to periphery (CTP) density 0.006 0.000 0.000Periphery to core (PTC) density 0.064 0.043 0.250Periphery density 0.000 0.000 0.000

a Represented by asymmetric matrix.

Table 8 re-organizes some information from Tables 6 and 7 . Itlists values for the select indicators of network complexitytheyrepresent the degree of interdependency among rms. Networksize is listed as the rst indicator. Network density is then listed inboth materials ow and contractual relationships networks. Then,group-level indicators are listed in both types of supply networks.

Table 7Network-level results for contractual relationship a supply networks.

Network measures Product type

Accord Acura DCX

Network size (rms) 28 34 27Network density 0.074 0.066 0.074Average degree 7.407 6.595 7.407Average closeness 35.716 37.747 41.959Average betweenness 7.407 5.375 5.778Centralization (degree) 0.479 0.413 0.585Centralization (closeness) 0.459 0.513 0.641Centralization (betweenness) 0.748 0.738 0.854

Core group size (rms) 17 6 3Core density 0.125 0.467 0.667CTP or PTC density 0.048 0.179 0.333Periphery density 0.036 0.000 0.000

a Represented by symmetric matrix.

6. Interpretation of results

In this section, we recapitulate the SNA results shown inTables 48 with reference to the supply network constructs devel-oped in this study (see Tables 1 and 2 ). We provide networkdynamics implicationsof thenode-level results rst and thenthoseof the network-level results. A summary of the SNA results at thenode- and network-level is shown, respectively, in Tables 9 and 10 .

6.1. Node-level implications

6.1.1. Key rms in the materials ow supply networksTable 4 compares groups of rms across supply load, demand

load, and operational criticality (see Table 1 f or denitions). CVT, arst-tier supplier in Accord supply network, appears highly cen-tral, showing the highest scores on all three columns. In otherwords, CVT assumes the most operational burden on both thesupply side and demand side. This rm is tasked with integrat-ing multiple parts into a product, which also means the rm canmake the most of its resources by pooling customer demands andthe related risks. CVT is also the pivotal player in the movementof materials. Without this rm, the entire supply chain would bedisrupted. In contrast, we observe that another top-tier supplierof Accord, JFC, is not as central. Its centrality scores are markedlylower than those of CVT, and there are other second- (i.e., C&C,Emhart, and Yamamoru) and third-tier suppliers (i.e., Fitzerald)who appear more central than JFC. This is because most suppli-

ers supplying to JFC also serve CVT but not the other way around(see Fig. 1).Intek, thetop-tiersupplierfor Acura, appears mostcentral under

both supply load and operational criticality. The bulk of networkresources ow into and through this rm. However, unlike CVT inAccord network, Intek does notappear central under demand load.This is because the rm primarily receives materials (see Fig. 2). Infact, Iwata Bolt, a second-tier supplier for Acura, comes rst underdemand load. This simply means that this rm delivers to a rela-tively large number of buyingrms, which implies thatthis supplierhas leverage in allocating its internal resourcesacross multiplecus-tomers. Another noteworthy nding is that Arkay, a second-tiersupplier, is the only rm that ranks high on all the three centralitymetrics. Without conducting SNA, Arkays central role in the Acura

supply network may very well be overlooked.


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Table 8Key indicators for network complexity.

Network size(rms)

Materials ow network Contractual relationship network

Networkdensity

Core size Coredensity

CTP density PTC density Networkdensity

Core size Coredensity

Peripherydensity

PTC density

Accord 28 0.046 19 0.067 0.006 0.064 0.074 17 0.125 0.036 0.048Acura 34 0.037 23 0.059 0.000 0.043 0.066 6 0.467 0.000 0.179DCX 27 0.037 4 0.250 0.000 0.250 0.074 3 0.667 0.000 0.333

There are a comparatively less number of central rms in DCXssupply network. The implication is that the structure of the DCXnetwork is simpler (see Fig. 3) than those of Honda and Acura. Forone, there are no rms listed under demand load. This is becauseevery supplier in this network has only one customer, includingTextron and Leon, a top-tier and a key second-tier supplier, respec-tively. Thesetwo suppliersappearunder bothsupplyload andoper-ational criticality. Both rms engage in value-adding activities byintegrating parts and facilitating their ows. The supply streams inthis supply network take place primarily through Textron or Leon.

6.1.2. Key rms in contractual relationship supply networksIn Table 5 , CVT is again prominent on all centrality metrics in

Accord supply network. This rm appears as most inuential onthe operation of the contractual relationship supply network, justas it does in the materials ow network. Nonetheless, there area few notable differences. First, Honda does not appear at all inTable 4 , but in this network based on contractual relationships,Honda emerges quite visibly (second to CVT) on all three columns.This is because Honda maintains a contractual relationship withmany of its second- and third-tier suppliers (see Fig. 4). Second, JFC, a top-tier supplier who appears in all three centrality metricsin Table 4 , is gone in Table 5 . In other words, when it comes tomanaging contracts, Honda emerges as central and JFC disappears.Clearly, JFCis moreisolatedin thecontractualrelationshipnetwork.

For Acura supply network, Intek appears yet again as most cen-

tral, while Honda emerges as central also. Thus, Intek looks likemost inuential in thecontractual relation network and none couldbypass Intek to connect with Honda. The network position allowsIntek to take control of information and communication ows. Onesupplier for Acura that appears in Table 5 but did not in Table 4 isHFI. HFI is a lone third-tier supplier that SNA picked up as being akey rm under Informational Independence. This is largely because

HFI does business with other central rms such as Intek and Arkay,and this is how it stays in the loop (see Fig. 5).

Unlike Accord and Acura, the list of rms that appear in Table 5for DCX shows little change. There were two rms (Textron andLeon) in Table 4 and the same rms appear again in Table 5 . Theonlyexception is Daimler.Comparedto thematerialsow network,theOEMis moreprominent in thecontractual relationshipnetwork(Table 5 ), and this is due to its direct links with two third-tier sup-pliers, Irwin and E.R. Wagner(see Fig.6 ). Daimler thus has leverageover the relationships between these two suppliers and Textron,the top-tier supplier.

6.2. Network-level implications

We now turn to discussing the dynamics at the network level.Characterization we make below pertains to the whole supply net-works based on Tables 6 and 7 .

6.2.1. Characteristics of the materials ow supply networksIn Table 6 , Accords supply network shows a comparatively

high density compared to the other two networks of Acura andDCX. Accords supply network also features relatively high aver-age scores on the key centrality metrics. Particularly, on averagebetweenness, Accords lead is substantial. It implies that rmsin this supply network are more engaged in both delivering andreceiving materials than rms in other supply networks. It alsomeans that there are more steps required to move the materials

along. From an operational standpoint, it might indicate that thisnetwork provides less efciency (e.g., longer lead time, more partsused for the same function) as it imposes more managerial atten-tion on the rms in a central position. Looking at centralizationscores for Accord, indegree score stands out, suggesting the inowof materials is concentrated in a small group of rms in the sup-ply network. We also note a rather large discrepancy in the scores

Table 9Node-level overview.


Accord CVT, a 1st-tier suppliers, is most central, and assumes the most operationalburden on both supply and demand sides

CVT is most central under all three measuresoperational exibility,managerial independence, and relational control

JFC, another 1st-tier supplier, is not as much central as CVT Honda emerges as the close second to CVT on all centrality metrics

Honda appears not central in this network JFC is extinct and becomes isolated in this networkHFI and C&C, two 2nd-tier suppliers, need to handle high degrees of supplyload and demand load, respectively

Yamamoru, a 2nd-tier suppliers, emerges as central under managerialindependence

Two 2nd-tier suppliers (Emhart and Yamamoru), and one 3rd-tier (Fitzerald)are also central as a go-between along the materials ow

Emhart, a 2nd-tier supplier, is central under relational control

Acura Intek, the sole 1st-tier supplier, is most central under both supply load andoperational criticality

Intek is again most central on all threecentrality

Arkay, a 2nd-tier supplier, is central on every centrality metric Honda is the close second to Intek on every centrality metricIwata Bolt, a 2nd-tier supplier, is most central underdemand load

HFI, a 3rd-tier supplier, emerges as key under managerial independence due toits ties with other key suppliers

Honda is virtually out of sight in this network Two 2nd-tier suppliers, Arkay and Select Industries, rank consistently high onall three centrality metrics

DCX Textron, the sole 1st-tier supplier, and Leon, a 2nd-tier supplier, are mostcentral under both supply load and operational criticality

Little change from materials ownetwork

No central rm under demand load Textron and Leon are two most central on every centrality metricsDaimler is rather central only under supply load Daimler comes next but by a large margin on all three metrics

No other rms, than the three rms, appear as central in this network


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Table 10Network-level overview.


Accord Comparatively high overall density Relatively high overall densityHighest average score on all three centrality metrics Highest average betweenness but lowest average on closenessRelatively high centralization across all three types, and substantial lead onaverage betweenness score

Largest core group with lowdensity

Much higher indegree centralization than the other two types Relatively high periphery densityNo connectivity among peripheral rms Comparatively low PTC density

Little reciprocity between the core and peripheral rms (much higher PTCdensity than CTP density) There are more interactions overall among moremembersPeripheral rms engage solely in supplying to core rms Rather complex at the network level

Relatively less centralized

Acura Comparatively large overall membership but with low density Largest overall membership but with lowest overall densityRelatively low average scores on all the three centrality metrics Lowest average betweenness scoreComparatively low centralization indices More tightly coupled core groupVery large core group with very low densi ty No interac tions among peripheral rmsVirtually no materials ows among peripheral rms Network activities mostly concentrated around the core groupNo reciprocity between the core and the periphery rms Relatively high PTC densityNetwork activities concentrated around the core group Comparatively more centralized around the smaller core groupComparatively less complex at the network level Comparatively less complex at the network level

DCX Smallest membership with relatively low density Highest average scores on closeness centrali tyComparatively high indegree centralization, but quite low outdegreecentralization

Highest centralizationindices

Smallest and tightly knit core group Smallest core group with very high densityNo materials ows among peripheral rms No interac tions among peripheral rmsLargest discrepancy between PTC and CPT density among three SNs By far higher PTC densityPeripheral rms engage exclusively in supplying to the core rms Majority of network activities centers around the core groupRelatively more centralized and least complex at the network level Peripheral rms engage only in supplying to the core rms

Most centralized and least complex at the network level

of CTP density and PTC density, which signies little reciprocitybetween the core andperipheral rms. Further, the far-off rms donot interact at all, as demonstrated in the periphery density of 0,and this is true for all product types. In other words, the peripheralrms engage solely in supplying to the core rms.

Acuras supply network has comparatively large membershipbut low overall density. The three average centrality scores are rel-atively low. Acuras supplynetwork, comparedto Accords,has lessnumber of links and the overall steps required to get things done

are not as many, which may indicate higher operational efciency.Further, based on centralization scores, Acuras supply networkappears as less centralized than Accords. At the local level,the coregroup has very large membership but with relatively low density.Since there are more rms in the core group, it suggests that thepower in the network is more spreadout; themore atstructure of operational authority again may be an indication that this networkworks more efciently (e.g., less time expended to make a decisionon issues at a local level).

DCXs supply network has the smallest membership, and theoverall density is also relativelylow. The centralization indexbasedon in-degree is comparatively high, as is the case with Accord andAcura. However,notabledifference occurs without-degree central-ization. It is quite low, indicating that most of the materials owout to few common dominant rms, and this observation is alsosupported by the small size of the core group. There is also a hugediscrepancy between CTP density and PTC density, which simplymeans that the majority of materials ow links in the network isconcentratedonasmallnumberofrms.Asexpected,thesermsinthe core group are tightly knit, as evidenced by a high core density.Suchsimple structurecanprovidehighoperationalefciency at thenetwork-level (e.g., shorter lead time from upstream suppliers tothe nal assembler); however, if multiple issues were to happensimultaneously they could overwhelm the few central players andcould require much more time for resolution.

6.2.2. Characteristics of contractual relationship supply networksThe density for Accord is much higher in Table 7 than it was in

Table 6 . This is because contracts can jump across several tiers. As

expected, the same thing happens for Acura and DCX as well. Interms of centrality metrics, Accords supply network shows rela-tively low average closeness but high average betweenness scores.Such a structure may be less responsive or more susceptible tosupply disruptions. It would possibly take more time channelinginformation andthereis a higherchance that information becomesdistorted on its way along the chains as more rms get involved intransferring it. Therefore, such structure is likely to be less robustor less effective when it comes to coping with supply disruptions.

By the same token,the structure would provide greater complexityat the network level for Accord, as also evidenced by Accords rel-atively large core group size (see Table 7 ). Further, it has relativelyhigh periphery density, whichfurther indicates that the network iscomplex because there are more interactions going on even amongperipheral members. Still, more contacts among members at thelocal level might facilitate identifying, if any, supply issues occur-ring locally.

Acuras supply network shows relativelylow overall density butwith large membership, which correspond with less number of contractual linksoverall.Regarding average betweenness, this sup-ply network shows the lowest score, indicating that this networkneeds a smaller number of channels to get things done. Compara-tively, therefore, this supply network appears as more efcient, forinstance, in managing such issues as supply disruptions becausecommunications at the network level can be comparatively fasterandmore organizedthanthoseof Accords,which is also supportedby Acuras comparatively more tightly knit core group and zeroperiphery density.

Interestingly, DCXs supply network shows the highest averagecloseness score. In other words, the rms in the DCXs networkare more readily reachable from each other, indicating that infor-mation can travel faster across the network. To put it differently,the network structure is more conducive to the centralized con-trol by dominant actors. As might be expected, this supplynetworkfeatures the highest centralization indices among all three supplynetworks.There is additional evidencefor DCXs highcentralizationat the network levelthe majority of the activities in the supplynetwork seem to center around a very small group of rms (i.e.,


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three corerms) thatare highly interwoven together (i.e., the high-est core density of 0.667). Further, the rms in the periphery, withno interactions among them, focus on catering to the core rmsneeds, evidenced by high PTC density. Because network informa-tion tends to spread relatively fast and converge at a small groupof dominant actors, the network as a whole would be compara-tively more effective and robust when it comes to dealing withsupply disruptions. Particularly, active interactions between coreand periphery rms would further enhance such capability of thesupply network.

7. Discussion

7.1. Comparisons between SNA results and C&H study

7.1.1. Overlapping and divergent resultsOne of the main ndings of C&H was the three OEMs varying

degrees of centralized control over their supply networks. The SNAresults conrm this. In particular, the nal assemblers practice of directed sourcing is captured in the contractual relationship net-work structure. For instance, the high values in Hondas variouscentralities and overall density in the contractual relationship net-work, compared to those in the materials ow network, is clearlyattributable to the added links that represent Hondas directedsourcing practice involving its second- and third-tier suppliers.Another nding shared by both studies is the relational salienceof those tertiary-level suppliers in the network that are sourceddirectly by OEMs. All of such suppliers (e.g., Emhart for Accord andIwata Bolt for Acura) emerge as visible in the contractual relation-ship network, through their exhibiting high scores on the variouscentrality metrics or becoming a member of the core group in theirrespective supply networks.

Divergent results between the two studies relate largely tonetwork-level properties such as network centralization and com-plexity. First, C&H describe Hondas two supply networks as morecentralized than DCXs. However, SNA suggests the opposite (see

Tables 6 and 7 ). In evaluating network centralization, C&H actu-ally take the perspective of the nal assemblers (i.e., Honda andDCX). They present the argument that Honda is more centralizedcompared to DCX because it has more direct ties with its suppliers(i.e., top-tier as well as second- and third-tier suppliers)Hondahas more centralized control of its supply networks. However,SNA, in contrast, looks at how central all rms are in the supplynetwork, not just the nal assembler. SNA evaluates the relativenode-level centrality scores of all the network members to arriveat the indicators of network centralization. The two studies alsodiverge when considering which supply network is most complex.C&H suggest that Acuras network is most complex. This judgmentis based on the network-level physical attributes (e.g., total num-ber of entities, average geographical distance between companies)

and qualitative evidence regarding the lack of shared history andthe perceived levelof decoupling among members. Contrarily, SNApoints to Accords network as being most complex. This is becauseSNA focuses on how individual rms and their relationships areconnected to one another at the network level. For instance, SNAconsiders various aspects of interdependence among members inthenetwork, such as network density, core density, periphery den-sity, and PTC density.

The two studies, as such, draw different conclusions on someaspects of supply network properties. Nonetheless, we want tocaution that this does not mean one is more accurate; rather, wewant to say that they just focus on different aspects of the samephenomenonthe case approach focuses on contextual informa-tion, whereas SNA operates on numerical breakdown of data onrelative positions of members.

7.1.2. What C&H offer but SNA does not C&Hs qualitative approach offers a contextually rich picture of

network dynamics. For instance, they make statements about thenetwork structure by drawing on such observations as Hondasstrong penchant toward centralized policy with respect to sup-plier selection and product designand DCXs practice of delegatingauthority to the rst-tier supplier as to who will be second-tiersuppliers and how to design the console. Further, the case methodcan provide more detailed accounts of how the supply networksoperate and behave. For instance, in the Hondas supply networks,the second-tier suppliers selected directly by Honda tend to beless cooperative with the top-tier supplier, which contributes tofurthering complexity at the network level; in the DCXs net-work, Daimler commissions the top-tier supplier to consolidatethe second-tier suppliers to reduce operational complexity. Suchndings are context-specic and would be very difcult for SNA tocapture.

Also, C&H offer some propositions representing the overarch-ing principles of the supply networks, derived from the qualitativedata. For instance, the study observes, Formalized rules, norms,and policies lead to the varying degrees of centralization in thesupply network . . . (p. 488); The cost consideration representsthe most salient force that shapes the emergence of the supply-network structure (p.488); and A centralized approach to supplynetwork involves a common list of core suppliers and the designactivities are tightly controlled by the nal assembler (p. 489).Only from case-based qualitative studies could such propositionsbe compiled. SNA would be unable to capture such contextuallyrich information.

7.1.3. What SNA offers but C&H do not Simply, SNA offers many quantitative metrics that qualitative

approaches cannot. By analyzing the structural characteristics of supply networks, SNA brings us new intriguing results that wouldlikely be overlooked by qualitative methods. First, by producingvarious network metrics, from node- to group- and to network-level, SNA facilitated a comprehensive analysis of supply networks.

For instance, SNA evaluated differing roles of the individual nodesand their relative importance with respect to others in the samenetwork (see Tables 4 and 5 ).

Second, SNAallowed for a comparative analysis of two differentnetwork structuresmaterials ow and contractual relationship.Between the two different network structures, we have observedsome divergent results even on the same network metrics (e.g.,density, betweenness centrality). Those discrepancies, as notedearlier, come from the fact that the two structures are constructedbased on different types of relational connection. Thus, it is notproper to say that one type of link is a more accurate depiction of agiven network than the other; but rather the two different types of network information should be considered jointly to fully under-stand a supply network.Further,SNA enableda group-levelanalysis

by partitioning each supply network into two structurally distinctclusterscore and periphery sub-groups. The core-periphery anal-ysis in fact facilitated assessing network-level properties acrossdifferentsupplynetworks(i.e., network centralizationand networkcomplexity).

7.2. Academic contributions

Our goal in this paper has been to introduce SNA as a meansto analyze the structure of supply networks and draw theoreticalconclusions from such analysis. Our framework translates key SNAmetrics into the context of supply networks, and discusses howroles of individual supply network members vary depending ontheir relative structural position in the network. Subsequently, we

suggested a guideline as to how to identify central nodes and eval-


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uate them differently. Central rms require possessing a particularset of capabilities corresponding to the roles they assume in thenetwork (see Table 1 ). For instance, rms with high in-degree cen-trality should focuson developing a capability in system integrationor product architectural innovation ( Parker and Anderson, 2002;Violino and Caldwell, 1998 ); rms with high betweenness central-itymaybe ina betterposition toengagein supplyriskmanagement.Thus, it would be prudent for a buyingcompany(e.g., OEMs), whenselecting or developing a supplier, to consider these issues. Wehope that the theoretical framework of this study would be instru-mental in facilitating future supply network researchadoptingSNAapproach.

The papers methodological contribution is two-fold. First, thisstudy demonstrates the value of SNA in studying supply networks.SNA considers all member rms in a given supply network todetermine which rms are most important, in what aspect, tothe operation of the whole network. Capitalizing on computat-ing power, SNA can generate various analytic outputs reectingeither individual- or group-level behavioral dynamics, which infactfacilitate gaininga morecomprehensive andsystematic viewof network dynamics.Second,applyingthe widely acceptednetwork-level analytical concepts (i.e., network density, centralization, andcore-periphery), SNA can complement qualitative methods in cap-turing the structural intricacy of the whole network in a moreobjective way. As has been demonstrated, SNA has considerablepotential for enhancing our studies of supply networks ( Borgattiand Li, 2009; Carter et al., 2007 ) and can effectively complementqualitative methods.

7.3. Managerial contributions

Based on C&Hs data, our study brings to the fore the salience of two types of supply networksmaterialsow andcontractual rela-tionship. We propose that managers consider these two types forany given supply network, as we have demonstrated how the twonetworks organize and behave differently. For instance, in Acurassupply networks, the size of the core group becomes much larger

whenbasedon materialsow thanthe contractualrelationship(seeTables 6 and 7 for comparison). Also, managers should note thatthere can be different sets of key rms between the two types of supply network (see Tables 4 and 5 ). One rm that does not appearas central in one type (e.g., HFI in the Acura network) may be akey player in another. Depending on which type of link to focus on,individual suppliers position of importance and the strategic roleswill vary. For instance, the key rms in a materials ow networkcan have a considerable effect on the operational quality of overallsupply network, affecting lead time, product quality, OEMs inven-tory level, or stockout costs ( Bourland et al., 1996 ). Key suppliersin a contractual relation network could facilitate the timely inden-tication or resolution of those system-level operational problemsand other supply disruption risks ( Lee, 2002 ).

Further, it may be prudent for a manufacturing rm to identifycentral second- or third-tier suppliers using SNA. Some of thesesuppliersbecome a keyplayer by being linkedto more visible otherkey rms in the supply network. In other words, some tertiary-level suppliers emerge as important because they are vital to othermore prominent suppliers in supply networks.We anticipate thesesecond- and third-tier suppliers that previously went unnoticedwill play a more signicant role in future. As the issue of supplychain scalability takes the center stage for safety and sustainability,large nalassemblers aremoving toward identifying andmanagingkey tertiary-level suppliers. Collecting complete supply networkdata and applying SNA, as we have done in this paper, may serveas a useful approach.

In general, having a pictorial rendition of a supply network will

be useful to managers. SNA can help generate network sociograms

(see Figs. 16 ). As a visual embodiment of relationship patterns insupply networks,these sociograms canbe instrumental in attaininga realistic picture of networking patterns and the dynamics. Just asall graphs, network drawings can help save searchefforts,facilitaterecognition, and provide interesting new perspectives and insightsinto supply networks. Also, SNA provides a methodological framefor collecting and organizing data, which will be useful for plan-ning and monitoring changes in the operation of supply networks.The position of a node in the network affects the opportunities andconstraints of that node and of others ( Gulati et al., 2000; Rowley,1997 ).

7.4. Limitations and future directions

Our study represents a very rst step in theorizing and empir-ically investigating supply networks using SNA concepts. Weacknowledge that our study is limited in ways that suggest oppor-tunities for future research. First, our analysis is conned to aspecic automobile module (i.e., center console assembly). Anyonesupplier in the supply network might be involved in several over-lapping supply networks across differentproduct lines. A suppliersrole based onone supplynetworkwilllookquitedifferent from thatderived by considering the multiple supply networks together it isa member of. Therefore, the central roles a supplier plays in ouranalysis should be qualied to the single product line. It would notbe reasonable to consider the results of our analysis as a generalstatement regarding that supplier.

In a similar vein, supply networks are considered basicallyegocentriccentered around a focal actor ( Hkansson and Ford,2002; Mizruchi and Marquis, 2006 ). The three supply networksstudied here were also mapped based on information obtainedfrom the nal assemblers. Therefore, any possible effect eachsuppliers extended network can have on the rms strategicimportance to the OEM could not be captured in our analysis. Forinstance, one second tier supplier to Honda may have a tie to otherOEMs. If such extended ties were also counted, certain centralitymetrics (e.g., betweenness) for the supplier might have shown dif-

ferent scores fromthose basedon the egocentric network,wherebyplacing the supplier in a different strategicposition with respect toHonda. Suchegocentric network approach, albeit considered a reli-able substitute for complete (sociocentric) network data ( Marsden,2002 ), may notbe enoughto provide a full understandingor poten-tial of a given supplier, embedded within the larger social network(Mizruchi and Marquis, 2006 ).

Third, in quantifying the inter-rm ties, we did not consider thevariances in strength. All the links considered in our analysis weretreated as having the same weight, while the link an OEM has withthe rst-tier suppliers should involve more intensive informationexchanges (i.e., kanban system) or a greater amount of materials(i.e., larger contract size) than those with the second-tier rms, forinstance. Also, we viewed supply networks based on the materi-

als ow and contract connections. However, certainly there aremany other relational connection types that can be consideredin supply networks, such as ownership, technology dependence,intellectual property, and risk sharing. Network ties could be rep-resented bythe numberof jointprograms orof sharedpatents, levelof trust, or perceived transactional risks. Future studies thereforecanincorporate the relative strength of supplyties usingSNA as themethod can effectively illustrate networks with weighted links(Borgatti and Li, 2009; Battini et al., 2007 ). Exchange ties involv-ing a multi-level interface will have differential impact comparedto other comparable supply ties based only on a single type of transaction.

We note that most supply networks are considered a scale-free network, whose degree distribution closely follows a power

law ( Albert and Barabsi, 2002; Pathak et al., 2007 ). That is, most


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nodes have very few links and only a small number of nodes(e.g., core rms) have many connections. Future studies may applythe scale-free network metrics to studying supply networks, suchas clustering coefcient and characteristic path length. Clusteringcoefcient measures the degree to which nodes in a network tendto cluster together around a given node ( Barabsi et al., 2002 ), andit can inform us of how suppliers behave with respect to the nalassembler at both the local and the global level. For instance, itcan tell us how suppliers would come together for better coordi-nation, based on some governance mechanism involving an OEM.Indicating the system-level closeness, characteristic path lengthcan assist in evaluating whether a given supply network is opti-mallydesigned ( Brahaand Bar-Yam,2004; LovejoyandLoch,2003 ).Given a supply network, it can be of considerable interest to knowhow the path length compares to the best or worst possibleconguration for networks with the same number of nodes andlines. This can provide implications for how effectively the net-work is designed and how robust it can be to possible supplydisruptions.

Finally, SNA could be applied to advancing existing theoriesregarding the structure or topology of supply networks. A rangeof SNA metrics can serve as a useful means in this effort. Suchnetworkvariables as densityandvariouscentralitiescouldbe appli-cable to characterizing typological archetypes of supply networkstructures, eventually leading to the development of a portfolioof contingent approaches to supply management. In conclusion,we hope that this paper can serve as a call to other operationsand supply management researchers regarding the importanceof framing supply chains as networks and continuing to developuseful supply network indices. We hope to see more researcherstaking advantage of the usefulness of SNA for untangling andunderstanding the complex phenomena embedded in supplynetworks.

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