jurnal manajemen operasi

Penyelidikan struktur dari jaringan pasokan : Pendekatan analisis jaringan social.Yusoon Kima,∗, Thomas Y. Choib, Tingting Yanb, Kevin Dooleyb

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 2010 Available online 18 November 2010

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

a b s t r a c t

Sebuah sistem pembeli saling berhubungan dan pemasok lebih baik dimodelkan sebagai jaringan selain sebagai rantai linear . Dalam tulisan ini kami menunjukkan bagaimana menggunakan analisis jaringan sosial untuk mengetahui karakteristik struktural jaringan pasokan . Kerangka teoretis berhubungan kunci metrik analisis jaringan sosial untuk menyediakan jaringan konstruksi . Kami menerapkan kerangka kerja ini untuk tiga jaringan pasokan otomotif dilaporkan pada Choi dan Hong (2002 ) . Setiap jaringan pasokan dianalisis dari segi aliran bahan dan hubungan kontrak . Kami membandingkan hasil analisis jaringan sosial dengan interpretasi berbasis kasus di Choi dan Hong (2002 ) dan menyimpulkan bahwa kerangka kita bisa baik suplemen dan melengkapi analisis kasus berbasis jaringan pasokan .

© 2010 Elsevier B.V. All rights reserved.a Department of Management, Marketing, and Logistics, College of Business Administration, Georgia Southern University, Statesboro, GA 30460, United States b Department of Supply Chain Management, W. P. Carey School of Business, Arizona State University, Tempe, AZ 85287, United States

1. Pengenalan

Manajemen rantai pasokan telah difokuskan pada hubungan linear dari pembeli dan pemasok

(Cox et al, 2006;. Zhu dan Sarkis, 2004). Sementara perspektif linear mungkin berguna untuk perencanaan aspek mekanik tertentu transaksi antara pembeli dan pemasok, gagal untuk menangkap kompleksitas diperlukan untuk memahami strategi perusahaan atau perilaku, baik sebagai bergantung pada jaringan pasokan yang lebih besar bahwa perusahaan tertanam di (Choi dan Kim, 2008). Sebuah perusahaan "jaringan pasokan" terdiri dari hubungan dengan pemasok langsung dan pelanggan, dan hubungan antara mereka dan pemasok langsung mereka dan pelanggan, dan sebagainya (Cooper et al, 1997;. Croxton et al., 2001). Dalam dekade terakhir telah terjadi peningkatan diskusi tentang manfaat dari mengadopsi perspektif jaringan dalam penelitian manajemen rantai pasokan

(Choi et al, 2001;.. Lazzarini et al, 2001; Lee, 2004; Wilding, 1998).Dari perspektif jaringan pasokan, posisi relatif perusahaan individual terhadap satu sama lain pengaruh baik strategi dan perilaku (Borgatti dan Li, 2009). Dalam konteks ini, menjadi penting untuk mempelajari peran masing-masing perusahaan dan pentingnya sebagai berasal dari posisinya tertanam dalam struktur hubungan yang lebih luas (Borgatti dan Li, 2009; DiMaggio dan Louch, 1998). Misalnya, Burkhardt dan Kuningan (1990) dan Ibarra (1993) menyatakan bahwa kekuasaan dan pengaruh berasal dari jabatan struktural perusahaan di jaringan sekitarnya. Lain telah dikaitkan posisi jaringan untuk masalah-masalah seperti

∗ Corresponding author. Tel.: +1 912 478 2465; fax: +1 912 478 2553. E-mail

address: [email protected] (Y. Kim).

0272-6963/$ – see front matter © 2010 Elsevier B.V. All rights reserved.doi:10.1016/j.jom.2010.11.001

mwww.el sev i er .c om/ loc at e/ j ojourna l hom epage :

Journal of Operations Management

tScienceDirecContents lists available at

Journal of Operations Management 29 (2011) 194–211

195 Y. Kim et al. / Journal of Operations Management 29 (2011) 194–211innovation 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,therehavebeenfewstudiesofreal-lifesupplynetworks, due to the difficulties in obtaining data. The studies of real networks that have been done have relied on qualitative methods to derive theoretical and practical insights (e.g., Harland et al., 2001; Jarillo and Stevenson, 1991). While qualitative interpretations have their merits, their validity is threatened by a researcher’s bounded rationality, which includes the difficulty to conceptualize complex phenomena such as networks. Thus in this paper we propose to analyze the structural characteristics of supply networks using a formal, quantitative modeling approach—social network analysis (Borgatti and Li, 2009; Grover and Malhotra, 2003; Harland et al., 1999). We will show how social network analysis can both supplement and compliment more traditional, qualitative interpretation methods when analyzing cases involving supply networks.

Social network analysis (SNA) has recently gained acceptance among scholars for its potential to integrate the operations and supply management field with other branches of management science (Autry and Griffis, 2008; Borgatti and Li, 2009; Carter et al., 2007). According to Borgatti and Li (2009), SNA concepts are particularly suitable for studying how patterns of inter-firm relationships in a supply network translate to competitive advantagesthroughmanagementofmaterialsmovementanddiffusionof information.

To date, SNA has not been applied in an empirical study of real supply networks; in fact there is a general paucity of SNA applications in operations and supply management, with only a few exceptions (e.g., Carter et al., 2007; Choi and Liker, 1995). This is largely because there is lack of conceptual clarification as to how thekeySNAmetrics(e.g.centrality)canbetheoreticallyinterpreted in the context of supply networks. Therefore in this study we link different SNA metrics at the node- or firm-level to specific roles in a supply network. We consider supply networks based on both materials flow and contractual relationships. The metrics yield six supplynetworkrelatedconstructs:supplyload,demandload,operational criticality, influential scope, informational independence, and relational mediation. Different network-level SNA metrics are also linked to their implications for supply network performance.

We apply our framework to real supply network data derived from three published case studies of automotive supply networks (ChoiandHong,2002).Inthatstudytheauthorscreatedempirically three complete network maps of the center console assembly for Honda Accord, Acura CL/TL, and DaimlerChrysler Grand Cherokee. In the present paper, we convert the network data from Choi and Hong(2002)intomatrixformsandanalyzethemusingthesoftware UCINET 6 (Borgatti et al., 2002). These quantitative results are then interpreted using our theoretical framework. Finally, we discuss our quantitative SNA results comparing to the qualitative findings of Choi and Hong (2002) and consider the implications.

2. Literature review

2.1. Supply networks

Supply networks consist of inter-connected firms that engage in procurement, use, and transformation of raw materials to provide goods and services (Lamming et al., 2000; Harland et al., 2001). The relatively recent incorporation of the term “network” into supply chain management research represents a pressing need to view supply chains as a network for firms to gain improved performance, operational efficiencies, and ultimately sustainable competitiveness (Corbett et al., 1999; Dyer and Nobeoka, 2000; Kotabe et al., 2002). Therefore, it is increasingly important to analyze the network structure of supply relationships.

In the operations and supply management field, a complex system perspective has been used as a theoretical lens for describing supply networks. Wilding (1998) studied dynamic events in supply networks through what he referred to as “supply chain complexity triangle”(p.599).Choietal.(2001)conceptualizedsupplynetworks as a complex adaptive system (CAS). Surana et al. (2005) proposed how various complex systems concepts can be harnessed to model supply networks. Pathak et al. (2007) discussed the usefulness of CAS principles in identifying complex phenomena in supply networks. Others have examined supply networks from a strategic management perspective. Greve (2009), using supply networks in the maritime shipping industry, studied whether technology adoption is more rapid in centrally located network positions. Mills et al. (2004) suggested different strategic approaches to managing supply networks depending on whether a firm is facing upstream or downstream and whether it is seeking its long-term or short-term position in the supply network.

Methodologically, simulation models have been used to study hypothetical supply networks (Kim, 2009; North and Macal, 2007; Pathak et al., 2007). Others have studied real-world supply networks using the case study approach (Jarillo and Stevenson, 1991; Nishiguchi, 1994; Choi and Hong, 2002). Scholars in the industrial marketing have developed descriptive models of supply networks (Ford, 1990; Håkansson, 1982, 1987; Håkansson and Snehota, 1995). Descriptive case studies in this genre illustrate how companies such as Benetton, Toyota, or Nissan attained competitive advantage through their supply networks (Jarillo and Stevenson, 1991; Nishiguchi, 1994). Other studies focused on developing taxonomies of supply networks (Harland et al., 2001; Lamming et al., 2000; Samaddar et al., 2006).More recently, Borgatti and Li (2009) have highlighted the salience of

SNA to study supply networks. In fact, there have been a few studies in the operations and supply management field that used or promoted the use of SNA. Choi and Liker (1995) used SNA to investigate the implementation of continuous improvement activities in automotive supplier firms. Carter et al. (2007) provided an example of the application of SNA in a logistics context. Autry and Griffis (2008) applied the concept of social capital, framed as part of social network theory, to supply chain context. However, still lacking in such studies is a theoretical framework that relates social network theory to supply network dynamics and the comprehensive application of SNA to studying supply networks. In the following section, we provide a brief overview of SNA, focusing on the key metrics useful for investigating and explaining phenomena within supply networks.

2.2. Social network analysis (SNA)

Anetworkismadeupofnodesandtiesthatconnectthesenodes. In a social network, the nodes (i.e., persons or firms) have agency in that they have an ability to make choices. With its computational foundation in graph theory (Cook et al., 1998; Kircherr, 1992; Li and Vitányi, 1991), SNA analyzes the patterns of ties in a network. Naturally, SNA has been used to study community or friendship structure (Kumar et al., 2006; Wallman, 1984) and communication patterns(Koehlyetal.,2003;ZackandMcKenney,1995).Ithasbeen adopted to explore the spreading of diseases (e.g., Klovdahl, 1985) anddiffusionofinnovation(e.g.,AbrahamsonandRosenkopf,1997; Valente, 1996). In organization studies and strategic management, scholars have used it to investigate corporate interlocking directorships (Robins and Alexander, 2004; Scott, 1986) and network effects on individual firms’ performance (e.g., Ahuja et al., 2009; Burkhardt and Brass, 1990; Gulati, 1999; Jensen, 2003; Rowley et al., 2005; Stam and Elfring, 2008; Uzzi, 1997).

Operations and supply management scholars have also noted the methodological potential of SNA. For instance, Choi et al. (2001) stated that one could approach the study of supply networks from the social network perspective. Ellram et al. (2006) acknowledged social network theory as a useful tool to study influence in supply chains. Carter et al. (2007) identified SNA as a key research method to advance the fields of logistics and supply chain management. More recently, Borgatti and Li (2009) and Ketchen and Hult (2007) echoed such sentiments. They have also recognized the

Y. Kim et al. / Journal of Operations Management 29 (2011) 194–211 196difficulty of collecting network-level data in supply networks but argued its imperativeness for operations and supply management to be integrated with other management disciplines.

According to Borgatti and Li (2009), a more systematic adoption of SNA will be instrumental in exploring behavioral mechanisms of entire supply networks. A SNA approach allows us to better understand the operations of supply networks, both at the individual firm level and network level—how important the individual firms are, given their positions in the network and how the network structure affects the individual firms and performance of the whole network. Socialnetworkscholars(EverettandBorgatti,1999;Freeman,1977, 1979; Krackhardt, 1990; Marsden, 2002) have developed a range of network metrics at the node- or network-level to characterize the dynamics inside a social network.

2.3. Key network metrics

Network metrics can be calculated at two levels—the node level and network level. Node-level metrics measure how an individual node is embedded in a network from that individual node’s perspective. In this study, we focus on three types of node-level metrics—degree, closeness, and betweenness centrality. Networklevel metrics compute how the overall network ties are organized from the perspective of an observer that has the bird’s eye view of the network. The network-level metrics we consider are network density, centralization, and complexity.

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

of SNA (Tichy et al., 1979; Wasserman and Faust, 1994). The concept of centrality is fundamental to node-level network metrics (Borgatti and Everett, 2006; Borgatti and Li, 2009). Centrality reflects the relative importance of individual nodes in a network. A node’s central position in a social network has a significant impact on its and others’ behaviors and well-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 identify nodes that are important, in different aspects. Most prominent are degree centrality, closeness centrality, and betweenness centrality (Everett and Borgatti, 1999; Krackhardt, 1990; Marsden, 2002). Of these, the most straightforward is degree centrality. This concept builds on an observation that the more links a node has the more central it is—when a node is connected to a large number of other nodes, the node has high degree centrality. Due to its greater connectedness with other nodes, a node with high degree centrality would necessarily be more visible in the network (Freeman, 1979; Marsden, 2002).

Another centrality concept is closeness centrality. As the term suggests, this metric focuses on how close a node is to all the other nodes 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 is why closeness centrality includes indirect ties. This centrality is usually associated with node’s autonomy or independence in social networks(Freeman,1979;Marsden,2002)—anodewithhighcloseness centrality has more freedom from others’ influence and higher capacity for independent actions. Such nodes become less reliant on other nodes.

Betweenness centrality measures how often a node lies on the shortest path between all combinations of pairs of other nodes. The more a given node connects nodes that would otherwise be disconnected, the more central that node is—other nodes are dependent on this node to reach out to the rest of the network. This metric focuses on the role of a node as an intermediary and posits that this dependence of others makes the node central in the network. As such, the betweenness centrality usually denotes a node’s potential control or influence in the network (Marsden, 2002). A node with high betweenness centrality has a great capacity to facilitate or constrain interactions between other nodes (Freeman, 1979).

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

such as network density, network centralization, and network complexity. Network density refers to the number of total ties in a network relative to the number of potential ties. It is a measure of the overall connectedness of a network (Scott, 2000)—a network in which all nodes are connected with all other nodes would give us a network density of one.

Network centralization captures the extent to which the overall connectedness is organized around particular nodes in a network (Provan and Milward, 1995). Conceptually, network centralization can be viewed as an extension of the node-level centrality (Freeman, 1979)—if a network had such a highly centralized structure 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 nodes andtheseothernodesarenotconnectedtoeachother.Likewise,the lowest centralization occurs when all nodes have the same number of connections to others.

Network centralization and network density are complementary. Whereas centralization is concerned with the distribution of powerorcontrolacrossthenetwork,densityreflectsnetworkcohesiveness. A network that has every node connected with everyone else would have a highest possible density (i.e., density of one). This network would be a highly cohesive network but would have a diffuse and distributed control structure.

Network complexity is defined as “the number of dependency relations within a network” (Frenken, 2000, p. 260) and thus would depend on both the number of nodes in the network and the degree to which they are interlinked (Frenken, 2000; Kauffman, 1993). In the context of a supply network, complexity relates to the collective operational burden born by the members in the network (Choi and Krause, 2006). For instance, a large number of units in a system is likely to entail high coordination cost (Kim et al., 2006; Provan, 1983). Further, if these units are highly interdependent, then the collective operational burden would be high and thus more complex at the system level.

Network complexity is related to network density and network centralization. First, more complex networks require higher operational burden (Lokam, 2003; Pudlák and Rödl, 1992, 1994). Second, network density is conceptually linked with network complexity because a denser network requires more effort to build and maintain (Marczyk, 2006). Finally, network centralization is associated with network complexity because the highest coordination costs require when every node is connected to all other nodes (i.e., a network with the least centralization) (Pudlák 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 established between firms in the supply network. For example, a tie might be established between two firms if they were collaborating on a new product development or if they had overlapping board membership or belonged to the same trade organization. In this paper 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 and receipt of materials, or they can be linked through a contractual relationship (Choi and Hong, 2002). In a tree-like structure of materials flow (Berry et al., 1994; Chopra and Sodhi, 2004; Hwarng et al., 2005), the network describes which supplier delivers to which customer. The other type of network is based on contractual relationships. Often, when a buying company wants to control the bill of materials, it engages in directed sourcing, wherein it establishes a contract with a second- or third-tier supplier and directs the top-tier supplier to receive materials from them (Choi and Krause, 2006; Chopra and Sodhi, 2004; Park and Hartley, 2002). In this context, materials flow occurs between two firms who do not have a contract and vice versa. These two types of supply networks, although based on the

197 Y. Kim et al. / Journal of Operations Management 29 (2011) 194–211same 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-

Y. Kim et al. / Journal of Operations Management 29 (2011) 194–211 198

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

Materials flow Indegree centrality Supply load The degree of difficulty faced by a firm in managing incoming material flows from the upstream firms

Integrator To put together or transform System integration different parts into a value-added Design/development product and ensure it functions Architecturalwell innovation

Outdegree centrality

Demand load The degree of difficulty faced by a firm in dealing with demands from the downstream firms

Allocator To distribute limited resources Process/manufacturing across multiple customers, Quality management focusing on scale economies Component innovation

Out-bound logisticsBetweenness centrality

Operational criticality The extent to which a firm impacts the final assembler’s operational performance in terms of product quality, coordination cost and overall lead-time.

Pivot To facilitate or control the flows of Risk managementsupply across the whole network In- and out-bound

logisticsCross-functional integ.

Contractual relationship

Degreecentrality

Influential scope The extent to which a firm has an impact on operational decisions or strategic behavior of other firms in the supply network

Coordinator To reconcile differences of network Contract management members and align their opinions SRM/CRM with the greater supply network goals

Closeness centrality Informational independence

The extent to which a firm has freedom from the controlling actions of others in terms of accessing information in thesupply network

Navigator To explore, access, and collect Information acquisition various information with greater Strategic alignment autonomy in the supply network with OEM

Betweenness centrality

Relational mediation The extent to which a firm can intervene or has control over interactions among other firms in the supply network

Broker To mediate dealings between Information processing network members and turn them Strategic alignment

into its own advantage with OEM

Network type Centrality Supply network Conceptual definitions Implication for central nodes metricsconstructs

a Network role given high centrality.works. Table 1 offers an overview of key centrality metrics, the corresponding supply network constructs, and their implications for network roles in the context of modeling supply networks. We propose this new framework for the interpretation of the SNA metrics in the supply network context.

To illustrate these constructs, we first discuss the calculation of key SNA metrics and the essential properties of each. Then, we integrate each key SNA metric separately with the two types of supply networks (i.e., materials flow and contractual relation). We should note that the supply network based on materials flow is directional, whereas the supply network based on contractual relationship is non-directional as legal obligations are mutually agreed and enacted.

3.2.1.1. Degree centrality in supply network. Degree centrality is measured by the number of direct ties to a node. Degree centrality CD(ni) for node i(ni) in a

non-directional network is defined as:

CD

xji j

where xij is the binary variable equal to 1 if there is a link between ni and nj but equal to 0 otherwise (Freeman, 1979; Glanzer and Glaser, 1959; Nieminen, 1973; Proctor and Loomis, 1951; Shaw, 1954). To account for the impact of network size g, degree centrality is normalized as the proportion of nodes directly adjacent to ni:

CD(ni)

CD g − 1 .

For comparison purposes, in this study, we convert normalized degree centrality to a 0–100 scale by multiplying by 100.

A high degree centrality points to “where the action is” in a network (Wasserman and Faust, 1994, p. 179). Freeman (1979) describes it as reflecting the amount of relational activities, and such activities make the nodes with high degree more visible. For instance, in a non-directional contractual relationship network, the degree centrality refers to the extent to which the firm influences other firms on their operations or decisions as the firm has more direct contacts with others (Cachon, 2003; Cachon and Lariviere, 2005; Ferguson et al., 2005). In contrast, nodes with low degree centrality are considered peripheral in the same network. If a node is completely isolated (i.e., zero degree), then removing this node from the network has virtually no effect on the network. Therefore, a firm who has more contractual ties in

the network garners a broad range of influence on others, and at the same time such a firm would often be required to reconcile conflicting schedules or interests between others. For the final assembler, for instance, it would make sense to align with suppliers with high degree centrality.In a directional network of materials flow, the focus

is either on the flow initiated (out-degree) or flow received (in-degree). For instance, out-degree centrality of a node is defined as:

xi+

Key capabilitiesDescriptionaRole

199 Y. Kim et al. / Journal of Operations Management 29 (2011) 194–211

.

g − 1

In-degree centrality and out-degree centrality indicate the size of the adjacent upstream tier and downstream tier, respectively. A high in-degree or out-degree can capture transactional intensity or related risks for a firm (Powell et al., 1996). In a materials flow network, in-degree centrality for a firm can reflect the degree of difficulty faced by the firm when managing the incoming material flows. In other words, this metric measures the firm’s operational load coming from the upstream suppliers. A firm with high indegree centrality may serve the role of an integrator, as they are tasked with organizing and incorporating a range of parts from various suppliers to maintain the overall integrity of the product or service (Parker and Anderson, 2002; Violino and Caldwell, 1998). Such members in a supply network are instrumental and vital in carrying out the architectural or technical changes in the current product (Henderson and Clark, 1990; Iansiti, 2000).

Out-degree centrality relates to the firm’s level of difficulty in managing the needs of customers. The more direct customers there are in downstream, the more challenging it is for the firm to ensure on-time delivery, cost-effective inventory, and order management for their customers. The number of direct customers is thus positively associated with the operational load relatedtodemandintegrationandresourceallocation(Frohlichand Westbrook, 2002). In a materials flow supply network, a firm with high out-degree centrality tends to be a common supplier to multiple downstream firms. Such supplier can economize and capitalize on its own internal resources as it aggregates demands from a range of customers (Nobeoka, 1996). Further, this firm is more likely than others to gain access to proprietary assets or information of its customer firms. This firm is in the best position to allocate or channel production or technical information to others in the network (Cassiman and Veugelers, 2002).

3.2.1.2. Closeness centrality in supply network. The calculation of closeness centrality is based on geodesic distance d(ni, nj) —the minimal length of a path between two nodes ni and nj (Hakimi, 1965; Sabidussi, 1966). In this study, closeness centrality is considered only in contractual relationship networks, as shown in Table 1. In a directed network (e.g., materials flow), the geodesic(s) from ni to nj may not be the same as the one(s) from nj to ni, or there can be two geodesics between two non-adjacent nodes. In the case of supply

networks, this does not make physical sense. Therefore, typical node closeness is defined as: ⎤−1

where (ni,nj) is the total distance between ni and all other nodes. At a maximum, the index equals (g −1)−1, which happens when the node is adjacent to all other nodes. When all the other nodes are not reachable from the node in question, the index reaches its minimum value of zero. The index can be normalized by multiplying CC(ni) by g −1. The value then ranges between 0 and 1 regardless of network size (Beauchamp, 1965). In this study, the normalized index is converted to a 0–100 scale.

Nodes with high closeness need not much rely on others for relaying information or initiating communications (Bavelas, 1950; Beauchamp, 1965; Leavitt, 1951). This metric, in a supply network context, thus can represent the extent to which a firm can act autonomously and navigate freely across the network to access resourcesinatimelymanner.Suchafirmhascomparativelyshorter supply chains, both upstream and downstream. Shorter chains translate into less distortion of information and better ability to access reliable information (e.g., demand forecasts, supply disruption) in a timelier manner (Lee et al., 1997; Chen et al., 2000). Such accessibility to high-quality information increases the firm’s capability to match supply and demand (Cachon and Fisher, 2000), resulting in less inventory and lower operational costs (Lee et al., 2000).

3.2.1.3. Betweenness centrality in supply network. Betweenness centrality appears under both types of networks. A firm can lie between a pair of non-adjacent firms either along their materials flow or contractual relationship. The intermediary will have different effects on the firms it links, whether directionally or non-directionally. Measuring betweenness centrality begins with an assumption that a connection between two nodes, nj and nk, follows their geodesics. Therefore, betweenness centrality can be expressed as (Freeman, 1977):

gjk

(ni

)CB(ni) =

gjk

j<k

where gjk is the total number of geodesics linking the two nodes, and gjk(ni) is the number of those geodesics that contain ni. The ni’s betweenness is then simply the sum of the probabilities that the node lies between other nodes. The betweenness reaches the maximum when n i

falls on all geodesics and has a minimum of zero when ni falls on no geodesics. We normalize it to a value between 0 and 100:

CB (ni) = [(g − 1)(CB(gn−i) 2)/2] × 100.


The betweenness can be viewed as indicating

how much “gatekeeping” ni does for the other nodes (Borgatti and Everett, 2006; Freeman, 1980; Spencer, 2003). Gatekeeping occurs because a node on geodesic can control the flows of materials or communication (Marsden, 2002). When applying to materials flow networks, firms with high

betweenness act as a hub or pivot that transmits materials along the supply chains, and betweenness centrality relates to theextenttowhichafirmpotentiallyaffectsthedownstreamfirms’ daily operations (e.g., lead time) and eventually the performance (e.g., final product quality) of the whole network. For instance, if a firm with high betweenness transmits materials to a wrong place or does not respond to changes in demand in a timely manner, it can easily lead to supply disruptions (Chopra and Sodhi, 2004). Similarly, the effects of poor-quality outputs from these firms can easily infect the broader supply network, interfering with normal product flows (Kleindorfer and Saad, 2005). Therefore, operational hiccups caused by such firms can surely hamper the functioning of the entire supply network (Hendricks and Singhal, 2005). Considering the significance of negative impacts such members can have, it would be prudent of the final assembler to ensure high or, at least, consistent operational performance of these firms (Hendricks and Singhal, 2003).

In a contractual relationship network, the metric can denote the extent to which a firm can affect the interactions among others in the same supply network. A firm with high betweenness centrality mediates many pathways and thus can either facilitate or interfere with the network communications. The social network literature suggests that a node linking dense regions of relationships enjoys the benefits of non-redundant information to increase its control over others (Burt, 1992, 1998). Supply network research also

postulates that a buyer can enjoy the increased

sourcing leverage when it lies between two disconnected, competiting suppliers (Choi and Wu, 2009; Wu and Choi, 2005). For instance, the buyer can play two rival suppliers off each other to drive down the purchasing price.

3.2.2. Network-level constructsWe now discuss the key network-level metrics.

Table 2 summarizes the theoretical interpretation of the metrics and their implications for network performance in the context of supply networks.

3.2.2.1. Supply network centralization. Recall that CD(ni) is nodelevel degree centrality, and CD(n∗

i ) is its maximum value in the network. Then, a general definition for network centralization is (Freeman, 1979):

CD .Given g nodes in the network, the denominator

reduces to (g −1)(g −2).ThevalueofCD

reachesthemaximumvalueof1when one node is connected with all other g −1 nodes, and the others interact only with this node. Its minimum value of 0 occurs when all degree centrality values are equal. In supply networks, centralization can refer to how much power or control the core firms exercise over other network members (Choi and Hong, 2002). In this study, besides centralization based on degree, two other centralizationa Implications given high metric score.

indices are also used—ones based on closeness and betweenness centrality.

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

Network type Network-level Conceptual definition in supply Implication of overall network structurea

metrics networks

Materials flow Centralization The extent to which particular focal firms control and manage the movement of materials in a supply network

Operational authority (e.g., power to make decisions on materials flow) concentrated in few central firmsCentralized decision implementation process

High level of controllability in production planningLow level of operational effectiveness at the network-level (i.e., more time taken to reach a decision and take actions on issues at a local level)

Complexity The amount of collective operational burden born by the member firms in a supply network

More firms engaged in the delivering and receiving of materialsMore steps required to move the materials along

Low level of operational efficiency at the network level (i.e., longer lead time from the most upstream to the final assembler or more parts for the same product function)

Contractual relationship

Centralization The extent to which particular focal firms exercise bargaining power or relationship management control over other firms in a supply network

Lack of interactions between central and peripheral firms in a supply network Decoupled relationships between firms at different tiers

High level of controllability in product design, product quality, and/or cost managementLow level of responsiveness to or more time for resolution on issues occurring at a local level

Complexity The amount of load on the supply network as a whole that requires relationship coordination

More firms involved in transferring informationActive interactions at a local level Slow relaying communications from downstream to the final assembler

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 distortion across a supply network)

Characteristics Performance implications


Further, there are other proxy measures of centralization used in this study. They are multiple indices of density that involve the core and periphery sub-groups in a network (see Table 6). When a network is partitioned into two clusters, a core cluster appears among nodes that are densely connected together and a periphery is formed among nodes that are more connected to core members than to each other (Borgatti and Everett, 2000; Luce and Perry, 1949). For instance, in Fig. 1, there are 19 firms in the core group (see Table 6) around Honda and CVT who appear at the center. The rest appears in the periphery.

3.2.2.2. Supply network complexity. Supply network complexity refers to the load on the network as a whole that requires coordination (Choi and Hong, 2002). While the general state of the literature regarding the property of complexity at the network level is still emerging (Butts, 2001a,b; Everett, 1985; Freeman, 1983), we adopt the idea put forth by Kauffman (1993) and Frenken (2000). They propose that network complexity can be indicated by the number of nodes and degree of interdependency among nodes in a given network. Therefore, we use two types of SNA output metrics—sizetype and density-type—to represent the number of supply network membersandthelevelofconnectednessamongthem,respectively.

The size-type outputs are shown in network size and core size, and the density-types include network density, core density, periphery density, core-to-periphery (CTP) density, and peripheryto-core (PTC) density. Network size relates to the average path length among nodes in the network (Ebel et al., 2002). More firms in a network translate into more steps and more time needed to complete the same task, whereby creating a higher likelihood of the supply being interrupted en route and higher collective burden born at the system level (Frenken, 2000). Likewise, between the two networks of identical size, more links imply a higher probability that the functioning of the individual nodes in the network is likely to be impeded by others, leading to a greater coordination load on the whole network (Choi and Krause, 2006). For instance, if an OEM has two top-tier suppliers, the firm would necessarily incur a greater amount of coordination load, compared to a situation where there is only one top-tier firm. Therefore, a complex supply networks would be associated with large network size, large core size, high network density, high core density, high periphery density, high CTP density, and high PTC density. Note that in the contractual relation supply networks, the PTC and CTP densities are identical, since every link in the network is non-directional and the adjacency matrix representing this network is symmetric.

4. Research methodology

4.1. Data source

Choi and Hong (2002) (hereafter, denoted as C&H) reported three supply networks from raw materials

suppliers to a final assembler involved in the production of an automobile center console assembly. The three product lines represented were Honda Accord, Acura CL/TL, and DaimlerChrysler (DCX) Grand Cherokee. Using an inductive case study approach, the authors derived propositions regarding the behavioral characteristics of supply networks. Table 3 provides a review of this particular work.

In our analysis, we include all the firms in the supply network as identified in C&H—they are direct suppliers and parts brokers, stretching from raw materials suppliers to the final assembler. As indicated before, each supply network contains two different types of network information—one pertaining to materials flow andanotherbasedoncontractualrelationships.Thesetwodifferent types of network data yield a total of six supply networks—three based on directional materials flow and three based on nondirectional contractual relationships.


4.2. Data analysis

The network information from C&H is converted into a binary adjacency matrix (Wasserman and Faust, 1994) that has firms representing both the rows and columns of the matrix. For instance, cell (i,j) would equal “1” if the firms i and j were linked either by

materials flow or contractual relationship, and would be “0” otherwise. Supply networks may yield adjacency matrices that are symmetric (i.e., non-directional) or asymmetric (i.e., directional), depending on the nature of the linkages. As noted earlier, a materials flow network is directional and thus asymmetric, while a contractual relationship network is non-directional and thus symmetric. Once generated, the adjacency matrices are imported into UCINET6andareusedasinputsfornetworkanalysis(Borgattietal., 2002).

UCINET is a comprehensive software package for the analysis of social network data. It has been one of the most widely accepted SNA tools for conducting the structural analysis of interorganizational networks (e.g., Gulati, 1995, 1999; Human and Provan, 1997; Rowley et al., 2005; Ahuja et al., 2009). The program contains dozens of network analytic methods such as centrality measures, subgroup identification, role analysis, elementary graph analysis, and permutation-based statistical analysis. While performing SNA, UCINET can create network visualizations. A visualization of each of the six supply networks, also known as a sociogram, is shown in Figs. 1–6.

5. Results

5.1. Node-level results

Tables 4 and 5 list key firms in the two types of supply networks. We identify key firms based on their centrality values. Tables 4 and 5 build on Table 1. The supply network constructs shown on the top row come from Table 1, and centrality computations are

conducted on the corresponding centrality metrics shown in Table 1.

As indicated below each table, there is a cut-off point for each supply 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 constitutes the threshold. In all cases except one, there are multiple key firms. The exception is out-degree centrality for the materials flow type of DCX’s supply network. Every node in the network, except for the OEM, has only one customer, showing the same value on outdegree centrality; consequently, there was no threshold value. In Tables 4 and 5, the number shown in parenthesis next to a firm name represents the centrality score.

5.2. Network-level results

Tables 6–8 show SNA results at the network level. Tables 6 and 7 focus on centralization metrics, respectively, for directional materials flow and non-directional contractual relationships. Table 8 summarizes all complexity metrics for both types of networks.

Fig. 1. Materials flow network for Accord.


In Table 6, various network-level indicators are shown across three different supply networks. Beginning with network size and density, individual node-level centrality scores are averaged for each supply network. Then, the three network centralization scores are listed. Up to this point, all values reflect network-level attributes. Below the network-level values, Table 6 lists values at the group level. It first shows the size of core group and its density (see Section 3.2.2.1 on supply network centralization for a discussion on core and peripheral

groups). It then moves on to listing other group-level measures.

Addressing contractual relationship supply networks, Table 7 is constructed much the same way. Since this supply network type is non-directional, network measures shown on the left-side column are slightly different from those of Table 6, as discussed under Table 1. Also note that most of the network-level metrics are in normalized form, which allows us to compare them across the three different supply networks.

Table 3Network measures Product type

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

Centralization Two firms, CVT and JFC, are top-tier suppliers to Honda

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

Textron as the sole top-tier supplier that integrates parts and subassemblies

Several second- or third-tier suppliers (e.g., Emhart, Garden State, and Miliken) directly selected by HondaSome third-tier suppliers directly selected byCVT, based on Honda’s core supplier list Honda’s penchant for centralized control when it comes to the product design and supplier selection

Honda engaging in directed sourcing at the second, third, and even fourth tiersIntek likewise engages in directed sourcing by selecting its own suppliers and even their supplier’s suppliers, based on Honda’s core supplier listDirected sourcing generally for high-priced or strategic itemsHonda’s centralized control of the product design activities

Textron-Farmington and Leon Plastics appear as two key second-tier suppliersTextron assumes the leading role in designing consoleDirected sourcing occurs only on a limited basis

Complexity All together, 50 network entities: 2 first-tier,21 second-tier, 18 third-tier, 7 fourth-tier, and2 fifth-tier suppliersMajority of the suppliers at the second-tier levelFour different nature of businesses in the network—manufacturing companies, raw materials suppliers (e.g., GE Plastics), distribution centers (e.g., Iwata Bolt), and trading houses (e.g., Honda Trading)Reciprocal relationship between CVT and JFC, two top-tier suppliers, contributing to either reduction or increase of network complexity depending on the relational nature

76 entities in the network: 1 first-tier, 20 second-tier, 28 third-tier, 17 fourth-tier, 9 fifth-tier, and 1 sixth-tier suppliersThe coupling between Honda and Intek based on their shared history may reduce the level of complexityThe decoupling between Intek and JFC, a second-tier supplier of the critical subassembly, may further the complexity Honda’s effort to centralize second-tier suppliers may increase complexity of the network as a whole

41 entities: 2 first-tier, 10 second-tier, 22 third-tier, and 7 fourth-tier suppliers At the top-tier level, Textron is engaged in assembly work and also acts as a conduit for a part from Leon as it ships the front console mat directly to the DCX plant with Textron’s label No reciprocal relations among suppliersAs per DCX’s recommendation, Textron has consolidated the second-tier suppliers, leading to reduced number of second-tier suppliers and subsequently reduced complexity

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


Fig. 2. Materials flow network for Acura.


Fig. 3. Materials flow network for Grand Cherokee.

Fig. 4. Contractual relationship network for Accord.


Fig. 5. Contractual relationship network for Acura.

Fig. 6. Contractual relationship network for Grand Cherokee.


Table 4List of key firms based on materials flow network.

Supply load1 Demand loadb Operational criticalityc

Accord CVT (59d), 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 firms based on contractual relationship network.

Influential scopea Informational independenceb Relational mediationc

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 flowa supply networks.

Network size (firms) 28 34Network density 0.046Average in-degree 4.630Average out-degree 4.630Average betweenness 0.809Centralization (in-degree) 0.567Centralization (out-degree) 0.106Centralization (betweenness) 0.128

Core group size (firms) 19 23

Core density 0.067Core to periphery (CTP) density 0.006Periphery to core (PTC) density 0.064

Periphery density 0.000 0.000 0.000

a Represented by asymmetric matrix.

Table 8 re-organizes some information from Tables 6 and 7. It lists values for the select indicators of network complexity—they represent the degree of interdependency among firms. Network size is listed as the first indicator. Network density is then listed in both materials flow and contractual relationships networks. Then, group-level indicators are listed in both types of supply networks.

Table 7Network-level results for contractual relationshipa supply networks.

Network size (firms) 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 (firms) 17 6 3

Core density 0.125 0.467 0.667CTP or PTC density 0.048 0.179 0.333Periphery density 0.036 0.000 0.000

6. Interpretation of results

In this section, we recapitulate the SNA results shown in Tables 4–8 with reference to the supply network constructs developed in this study (see Tables 1 and 2). We provide network dynamics implications of the node-level results first and then those of the network-level results. A summary of the SNA results at the node- and network-level is shown, respectively, in Tables 9 and 10.

1 Represented by symmetric matrix.

DCXAcuraAccord

Product typeNetwork measures

DCXAcuraAccord

Product typeNetwork measures


6.1. Node-level implications

6.1.1. Key firms in the materials flow supply networks

Table 4 compares groups of firms across supply load, demand load, and operational criticality (see Table 1 for definitions). CVT, a first-tier supplier in Accord supply network, appears highly central, showing the highest scores on all three columns. In other words, CVT assumes the most operational burden on both the supply side and demand side. This firm is tasked with integrating multiple parts into a product, which also means the firm can make the most of its resources by pooling customer demands and the related risks. CVT is also the pivotal player in the movement of materials. Without this firm, the entire supply chain would be disrupted. In contrast, we observe that another top-tier supplier of Accord, JFC, is not as central. Its centrality scores are markedly lower 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 suppliers supplying to JFC also serve CVT but not the other way around(see Fig. 1).

Intek,thetop-tiersupplierforAcura,appearsmostcentralunder both supply load and operational criticality. The bulk of network resources flow into and through this firm. However, unlike CVT in Accord network, Intek does not appear central under demand load. This is because the firm primarily receives materials (see Fig. 2). In fact, Iwata Bolt, a second-tier supplier for Acura, comes first under demand load. This simply means that this firm delivers to a relativelylargenumberofbuyingfirms,whichimpliesthatthissupplier has leverage in allocating its internal resources across multiple customers. Another noteworthy finding is that Arkay, a second-tier supplier, is the only firm that ranks high on all the three centrality metrics. Without conducting SNA, Arkay’s central role in the Acura supply network may very well be overlooked.

There are a comparatively less number of central firms in DCX’s supply network. The implication is that the structure of the DCX network is simpler (see Fig. 3) than those of Honda and Acura. For one, there are no firms listed under demand load. This is because every supplier in this network has only one customer, including Textron and Leon, a top-tier and a key second-tier supplier, respectively.Thesetwosuppliersappearunderbothsupplyloadandoperational criticality. Both firms engage in

value-adding activities by integrating parts and facilitating their flows. The supply streams in this supply network take place primarily through Textron or Leon.

6.1.2. Key firms in contractual relationship supply networks

In Table 5, CVT is again prominent on all centrality metrics in Accord supply network. This firm appears as most influential on the operation of the contractual relationship supply network, just as it does in the materials flow network. Nonetheless, there are a few notable differences. First, Honda does not appear at all in Table 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 with many of its second- and third-tier suppliers (see Fig. 4). Second, JFC, a top-tier supplier who appears in all three centrality metrics in Table 4, is gone in Table 5. In other words, when it comes to managing contracts, Honda emerges as central and JFC disappears. Clearly,JFCismoreisolatedinthecontractualrelationshipnetwork.

For Acura supply network, Intek appears yet again as most central, while Honda emerges as central also. Thus, Intek looks like most influential in the contractual relation network and none could bypass Intek to connect with Honda. The network position allows Intek to take control of information and communication flows. One supplier for Acura that appears in Table 5 but did not in Table 4 is HFI. HFI is a lone third-tier supplier that SNA picked up as being a key firm under Informational Independence. This is largely because

Accord 28 0.046 19 0.067 0.006 0.064 0.074 17 0.125 0.036 0.048 Acura 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

Table 8Key indicators for network complexity.

Network size(firms )

Materials flow network Contractual relationship network

Networkdensity

Core size Coredensity

CTP density PTC density Networkdensity

Core size Coredensity

Peripherydensity

PTC density


Table 9 Node-level overview.

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

Unlike Accord and Acura, the list of firms that appear in Table 5 for DCX shows little change. There were two firms (Textron and Leon) in Table 4 and the same firms appear again in Table 5. The onlyexceptionisDaimler.Comparedtothematerialsflownetwork, theOEMismoreprominentinthecontractualrelationshipnetwork (Table 5), and this is due to its direct links with two third-tier suppliers, Irwin and E.R. Wagner (see Fig. 6). Daimler thus has leverage over 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 networks based on Tables 6 and 7.

6.2.1. Characteristics of the materials flow supply networks

In Table 6, Accord’s supply network shows a comparatively high density compared to the other two networks of Acura and DCX. Accord’s supply network also features relatively high average scores on the key centrality metrics. Particularly, on average betweenness, Accord’s lead is substantial. It implies that firms in this supply network are more engaged in both delivering and receiving materials than firms in other supply networks. It also means that there are more steps required to move the materials along. From an operational standpoint, it might indicate that this network provides less efficiency (e.g., longer lead time, more parts used for the same function) as it imposes more managerial attention on the firms in a central position. Looking at centralization scores for

Accord, indegree score stands out, suggesting the

inflow of materials is concentrated in a small group of firms in the supply network. We also note a rather large discrepancy in the scores


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

CVT is most central under all three measures—operational flexibility, 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 network

HFI and C&C, two 2nd-tier suppliers, need to handle high degrees of supply load and demand load, respectively

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

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

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

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

Intek is again most central on all three centrality

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

Iwata Bolt, a 2nd-tier supplier, is most central under demand load HFI, a 3rd-tier supplier, emerges as key under managerial independence due to its 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 on all three centrality metrics

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

Little change from materials flow network

No central firm under demand load Textron and Leon are two most central on every centrality metrics

Daimler is rather central only under supply load Daimler comes next but by a large margin on all three metricsNo other firms, than the three firms, appear as central in this network


of CTP density and PTC density, which signifies little reciprocity between the core and peripheral firms. Further, the far-off firms do not interact at all, as demonstrated in the periphery density of 0, and this is true for all product types. In other words, the peripheral firms engage solely in supplying to the core firms.

Acura’s supply network has comparatively large membership but low overall density. The three average centrality scores are relatively low. Acura’s supply network, compared to Accord’s, has less number of links and the overall steps required to get things done are not as many, which may indicate higher operational efficiency. Further, based on centralization scores, Acura’s supply network appears as less centralized than Accord’s. At the local level, the core group has very large membership but with relatively low density. Since there are more firms in the core group, it suggests that the power in the network is more spread out; the more flat structure of operational authority again may be an indication that this network works more efficiently (e.g., less time expended to make a decision on issues at a local level).

DCX’s supply network has the smallest membership, and the overall density is also relatively low. The centralization index based on in-degree is comparatively high, as is the case with Accord and Acura.However,notabledifferenceoccurswithout-degreecentralization. It is quite low, indicating that most of the materials flow out to few common dominant firms, and this observation is also supported by the small size of the core group. There is also a huge discrepancy between CTP density and PTC density, which simply means that the majority of materials flow links in the network is concentratedonasmallnumberoffirms.Asexpected,thesefirmsin the core group are tightly knit, as evidenced by a high core density. Such simple structure can provide high operational efficiency at the network-level (e.g., shorter lead time from upstream suppliers to the final assembler); however, if multiple issues were to happen simultaneously they could overwhelm the few central players and could require much more time for resolution.

6.2.2. Characteristics of contractual relationship supply networks

Table 10Network-level overview.


Accord Comparatively high overall density Relatively high overall density

Highest average score on all three centrality metrics Highest average betweenness but lowest average on closeness

Relatively high centralization across all three types, and substantial lead on average betweenness score

Largest core group with low density

Much higher indegree centralization than the other two types Relatively high periphery density

No connectivity among peripheral firms Comparatively low PTC density

Little reciprocity between the core and peripheral firms (much higher PTC density than CTP density)

There are more interactions overall among more members

Peripheral firms engage solely in supplying to core firms Rather complex at the network levelRelatively less centralized

Acura Comparatively large overall membership but with low density Largest overall membership but with lowest overall density

Relatively low average scores on all the three centrality metrics Lowest average betweenness score

Comparatively low centralization indices More tightly coupled core group

Very large core group with very low density No interactions among peripheral firms

Virtually no materials flows among peripheral firms Network activities mostly concentrated around the core group

No reciprocity between the core and the periphery firms Relatively high PTC density

Network activities concentrated around the core group Comparatively more centralized around the smaller core group

Comparatively 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 centrality

Comparatively high indegree centralization, but quite low outdegree centralization Highest centralization indices

Smallest and tightly knit core group Smallest core group with very high density

No materials flows among peripheral firms No interactions among peripheral firms

Largest discrepancy between PTC and CPT density among three SNs By far higher PTC density

Peripheral firms engage exclusively in supplying to the core firms Majority of network activities centers around the core group

Relatively more centralized and least complex at the network level Peripheral firms engage only in supplying to the core firmsMost centralized and least complex at the network level


The 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. In terms of centrality metrics, Accord’s supply network shows relatively low average closeness but high average betweenness scores. Such a structure may be less responsive or more susceptible to supply disruptions. It would possibly take more time channeling information and there is a higher chance that information becomes distorted on its way along the chains as more firms get involved in transferring it. Therefore, such structure is likely to be less robust or less effective when it comes to coping with supply disruptions. By the same token, the structure would provide greater complexity at the network level for Accord, as also evidenced by Accord’s relatively large core group size (see Table 7). Further, it has relatively high periphery density, which further indicates that the network is complex because there are more interactions going on even among peripheral members. Still, more contacts among members at the local level might facilitate identifying, if any, supply issues occurring locally.

Acura’s supply network shows relatively low overall density but with large membership, which correspond with less number of contractual links overall. Regarding average betweenness, this supply network shows the lowest score, indicating that this network needs a smaller number of channels to get things done. Comparatively, therefore, this supply network appears as more efficient, for instance, in managing such issues as supply disruptions because communications at the network level can be comparatively faster and more organized than those of Accord’s, which is also supported by Acura’s comparatively more tightly knit core group and zero periphery density.

Interestingly, DCX’s supply network shows the highest average closeness score. In other words, the firms in the DCX’s network are more readily reachable from each other, indicating that information can travel faster across the network. To put it differently, the network structure is more conducive to the centralized control by dominant actors. As might be expected, this supply network features the highest centralization indices among all three supply networks.ThereisadditionalevidenceforDCX’shighcentralization at the network level—the majority of the activities in the supply network seem to center around a very small group of firms (i.e., three core firms) that are highly interwoven together (i.e., the highest core density of 0.667). Further, the firms in the periphery, with no interactions among them, focus on catering to the core firms’ needs, evidenced by high PTC density. Because network information tends to spread relatively fast and converge at a small group of dominant actors, the network as a whole would be comparatively more effective and robust when it comes to dealing with supply disruptions. Particularly, active interactions between core and periphery firms would further enhance such capability of the supply network.

7. Discussion

7.1. Comparisons between SNA results and C&H study

7.1.1. Overlapping and divergent resultsOne of the main findings of C&H was the

three OEMs’ varying degrees of centralized control over their supply networks. The SNA results confirm this. In particular, the final assemblers’ practice of directed sourcing is captured in the contractual relationship network structure. For instance, the high values in Honda’s various centralities and overall density in the contractual relationship network, compared to those in the materials flow network, is clearly attributable to the added links that represent Honda’s directed sourcing practice involving its second- and third-tier suppliers. Another finding shared by both studies is the relational salience of those tertiary-level suppliers in the network that are sourced directly by OEMs. All of such suppliers (e.g., Emhart for Accord and Iwata Bolt for Acura) emerge as visible in the contractual relationship network, through their exhibiting high scores on the various centrality metrics or becoming a member of the core group in their respective supply networks.

Divergent results between the two studies relate largely to network-level properties such as network centralization and complexity. First, C&H describe Honda’s two supply networks as more centralized than DCX’s. However, SNA suggests the opposite (see Tables 6 and 7). In evaluating network centralization, C&H actually take the perspective of the final assemblers (i.e., Honda and DCX). They present the argument that Honda is more centralized compared to DCX because it has more direct ties with its suppliers (i.e., top-tier as well as second- and third-tier suppliers)—Honda has more centralized control of its supply networks. However, SNA, in contrast, looks at how central all firms are in the supply network, not just the final assembler. SNA evaluates the relative node-level centrality scores of all the network members to arrive at the indicators of network centralization. The two studies also diverge when considering which supply network is most complex. C&H suggest that Acura’s network is most complex. This judgment is based on the network-level physical attributes (e.g., total number of entities, average geographical distance between companies) and qualitative evidence regarding the lack of shared history and the perceived level of decoupling among members. Contrarily, SNA points to Accord’s network as being most complex. This is because SNA focuses on how individual firms and their relationships are connected to one another at the network level. For instance, SNA considers various aspects of interdependence among members in the network, such as network density, core density, periphery density, and PTC density.

The two studies, as such, draw different conclusions on some aspects of supply network properties. Nonetheless, we want to caution that


this does not mean one is more accurate; rather, we want to say that they just focus on different aspects of the same phenomenon—the case approach focuses on contextual information, whereas SNA operates on numerical breakdown of data on relative positions of members.

7.1.2. What C&H offer but SNA does notC&H’s qualitative approach offers a contextually

rich picture of network dynamics. For instance, they make statements about the network structure by drawing on such observations as Honda’s strong penchant toward centralized policy with respect to supplier selection and product design and DCX’s practice of delegating authority to the first-tier supplier as to who will be second-tier suppliers and how to design the console. Further, the case method can provide more detailed accounts of how the supply networks operate and behave. For instance, in the Honda’s supply networks, the second-tier suppliers selected directly by Honda tend to be less cooperative with the top-tier supplier, which contributes to furthering complexity at the network level; in the DCX’s network, Daimler commissions the top-tier supplier to consolidate the second-tier suppliers to reduce operational complexity. Such findings are context-specific and would be very difficult for SNA to capture.

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

7.1.3. What SNA offers but C&H do notSimply, SNA offers many quantitative metrics

that qualitative approaches cannot. By analyzing the structural characteristics of supply networks, SNA brings us new intriguing results that would likely be overlooked by qualitative methods. First, by producing various network metrics, from node- to group- and to networklevel, SNA facilitated a comprehensive analysis of supply networks. For instance, SNA evaluated differing roles of the individual nodes and their relative importance with respect to others in the same network (see Tables 4 and 5).

Second, SNA allowed for a comparative analysis of two different network structures—materials flow and contractual relationship. Between the two different network structures, we have observed some divergent results even on the same network metrics (e.g., density, betweenness centrality). Those discrepancies, as noted earlier, come from the fact that the two structures are constructed based on different types of relational connection. Thus, it is not proper to say that one type of link is a more accurate

depiction of a given network than the other; but rather the two different types of network information should be considered jointly to fully understandasupplynetwork.Further,SNAenabledagroup-levelanalysis by partitioning each supply network into two structurally distinct clusters—core and periphery sub-groups. The core-periphery analysis in fact facilitated assessing network-level properties across differentsupplynetworks(i.e.,networkcentralizationandnetwork complexity).

7.2. Academic contributions

Our goal in this paper has been to introduce SNA as a means to analyze the structure of supply networks and draw theoretical conclusions from such analysis. Our framework translates key SNA metrics into the context of supply networks, and discusses how roles of individual supply network members vary depending on their relative structural position in the network. Subsequently, we suggested a guideline as to how to identify central nodes and evaluate them differently. Central firms require possessing a particular set of capabilities corresponding to the roles they assume in the network (see Table 1). For instance, firms with high in-degree centralityshouldfocusondevelopingacapabilityinsystemintegration or product architectural innovation (Parker and Anderson, 2002; Violino and Caldwell, 1998); firms with high betweenness centralitymaybeinabetterpositiontoengageinsupplyriskmanagement. Thus, it would be prudent for a buying company (e.g., OEMs), when selecting or developing a supplier, to consider these issues. We hope that the theoretical framework of this study would be instrumental in facilitating future supply network research adopting SNA approach.

The paper’s methodological contribution is two-fold. First, this study demonstrates the value of SNA in studying supply networks. SNA considers all member firms in a given supply network to determine which firms are most important, in what aspect, to the operation of the whole network. Capitalizing on computating power, SNA can generate various analytic outputs reflecting either individual- or group-level behavioral dynamics, which in factfacilitategainingamorecomprehensiveandsystematicviewof network dynamics. Second, applying the widely accepted networklevel analytical concepts (i.e., network density, centralization, and core-periphery), SNA can complement qualitative methods in capturing the structural intricacy of the whole network in a more objective way. As has been demonstrated, SNA has considerable potential for enhancing our studies of supply networks (Borgatti and Li, 2009; Carter et al., 2007) and can effectively complement qualitative methods.

7.3. Managerial contributions

Based on C&H’s data, our study brings to the fore the salience of two types of supply networks—materials flow and contractual relationship. We propose that managers consider these two types for any given supply network, as we have demonstrated


how the two networks organize and behave differently. For instance, in Acura’s supply networks, the size of the core group becomes much larger whenbasedonmaterialsflowthanthecontractualrelationship(see Tables 6 and 7 for comparison). Also, managers should note that there can be different sets of key firms between the two types of supply network (see Tables 4 and 5). One firm that does not appear as central in one type (e.g., HFI in the Acura network) may be a key player in another. Depending on which type of link to focus on, individual suppliers’ position of importance and the strategic roles will vary. For instance, the key firms in a materials flow network can have a considerable effect on the operational quality of overall supply network, affecting lead time, product quality, OEM’s inventory level, or stockout costs (Bourland et al., 1996). Key suppliers in a contractual relation network could facilitate the timely indentification or resolution of those system-level operational problems and other supply disruption risks (Lee, 2002).

Further, it may be prudent for a manufacturing firm to identify central second- or third-tier suppliers using SNA. Some of these suppliers become a key player by being linked to more visible other key firms in the supply network. In other words, some tertiarylevel suppliers emerge as important because they are vital to other more prominent suppliers in supply networks. We anticipate these second- and third-tier suppliers that previously went unnoticed will play a more significant role in future. As the issue of supply chain scalability takes the center stage for safety and sustainability, large final assemblers are moving toward identifying and managing key tertiary-level suppliers. Collecting complete supply network data and applying SNA, as we have done in this paper, may serve as 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. 1–6). As a visual embodiment of relationship patterns in supplynetworks,thesesociogramscanbeinstrumentalinattaining a realistic picture of networking patterns and the dynamics. Just as all graphs, network drawings can help save search efforts, facilitate recognition, and provide interesting new perspectives and insights into supply networks. Also, SNA provides a methodological frame for collecting and organizing data, which will be useful for planning and monitoring changes in the operation of supply networks. The position of a node in the network affects the opportunities and constraints of that node and of others (Gulati et al., 2000; Rowley, 1997).

7.4. Limitations and future directions

Our study represents a very first step in theorizing and empirically investigating supply networks using SNA concepts. We acknowledge that our study is limited in ways that suggest opportunities for future research. First, our analysis is confined to a specific automobile module (i.e., center console assembly). Any one supplier in the supply network might be involved in several overlapping supply networks across different product lines. A supplier’s

rolebasedononesupplynetworkwilllookquitedifferentfromthat derived by considering the multiple supply networks together it is a member of. Therefore, the central roles a supplier plays in our analysis should be qualified to the single product line. It would not be reasonable to consider the results of our analysis as a general statement regarding that supplier.

In a similar vein, supply networks are considered basically “egocentric”—centered around a focal actor (Håkansson and Ford, 2002; Mizruchi and Marquis, 2006). The three supply networks studied here were also mapped based on information obtained from the final assemblers. Therefore, any possible effect each supplier’s extended network can have on the firm’s strategic importance to the OEM could not be captured in our analysis. For instance, one second tier supplier to Honda may have a tie to other OEMs. If such extended ties were also counted, certain centrality metrics (e.g., betweenness) for the supplier might have shown different scores from those based on the egocentric network, whereby placing the supplier in a different strategic position with respect to Honda. Such egocentric network approach, albeit considered a reliable substitute for complete (sociocentric) network data (Marsden, 2002), may not be enough to provide a full understanding or potential of a given supplier, embedded within the larger social network (Mizruchi and Marquis, 2006).

Third, in quantifying the inter-firm ties, we did not consider the variances in strength. All the links considered in our analysis were treated as having the same weight, while the link an OEM has with the first-tier suppliers should involve more intensive information exchanges (i.e., kanban system) or a greater amount of materials (i.e., larger contract size) than those with the second-tier firms, for instance. Also, we viewed supply networks based on the materials flow and contract connections. However, certainly there are many other relational connection types that can be considered in supply networks, such as ownership, technology dependence, intellectual property, and risk sharing. Network ties could be representedbythenumberofjointprogramsorofsharedpatents,level of trust, or perceived transactional risks. Future studies therefore can incorporate the relative strength of supply ties using SNA as the method can effectively illustrate networks with “weighted” links (Borgatti and Li, 2009; Battini et al., 2007). Exchange ties involving a multi-level interface will have differential impact compared to other comparable supply ties based only on a single type of transaction.

We note that most supply networks are considered a scalefree network, whose degree distribution closely follows a power law (Albert and Barabási, 2002; Pathak et al., 2007). That is, most nodes have very few links and only a small number of nodes (e.g., core firms) have many connections. Future studies may apply the scale-free network metrics to studying supply networks, such as clustering coefficient and characteristic path length. Clustering coefficient measures the


degree to which nodes in a network tend to cluster together around a given node (Barabási et al., 2002), and it can inform us of how suppliers behave with respect to the final assembler at both the local and the global level. For instance, it can tell us how suppliers would come together for better coordination, based on some governance mechanism involving an OEM. Indicating the system-level “closeness,” characteristic path length can assist in evaluating whether a given supply network is optimallydesigned(BrahaandBar-Yam,2004;LovejoyandLoch,2003). Given a supply network, it can be of considerable interest to know how the path length compares to the “best” or “worst” possible configuration for networks with the same number of nodes and lines. This can provide implications for how effectively the network is designed and how robust it can be to possible supply disruptions.

Finally, SNA could be applied to advancing existing theories regarding the structure or topology of supply networks. A range of SNA metrics can serve as a useful means in this effort. Such networkvariablesasdensityandvariouscentralitiescouldbeapplicable to characterizing typological archetypes of supply network structures, eventually leading to the development of a portfolio of contingent approaches to supply management. In conclusion, we hope that this paper can serve as a call to other operations and supply management researchers regarding the importance of framing supply chains as networks and continuing to develop useful supply network indices. We hope to see more researchers taking advantage of the usefulness of SNA for untangling and understanding the complex phenomena embedded in supply networks.

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