The business value of digital supply networks: A program of research on the impacts of globalization

Journal of International Management 11 (2005) 201 – 227

The business value of digital supply networks: A program of research on the impacts of globalization Jonathan Warehama,b,*, Lars Mathiassena, Arun Raia, Detmar Strauba, Richard Kleinc a

Department of Computer Information Systems, Center for Process Innovation, Robinson College of Business, Georgia State University, Atlanta, GA, United States b Deparment of Information Systems, ESADE, Barcelona, Spain c Department of Management, Clemson University, Clemson, SC, United States Accepted 20 September 2004 Available online 13 June 2005

Abstract The bNetworked EconomyQ describes alliances of firms that manage globally distributed supply networks. In the best of all worlds, this interactive flow of information among member firms will result in efficient and effective balance of supply and demand. Unfortunately, supply networks suffer from poor and inexact information, and, in the worst case, information is unavailable where and when it is needed. Such entropy creates errors and limits responsiveness of processes leading to situations where there is too much or too little inventory at a given stage in a supply network. These complications are exacerbated across transnational supply chain networks. This paper offers theoretical perspectives, a case study, and outline of a research proposal to help address these challenges and develop insights into the best practices of transnational digital supply networks. High level questions include: What are the defining characteristics of high performing digital supply networks? How does information sharing impact the error and responsiveness of supply network processes and, consequently, supply network performance? How do international outsourcing practices affect network outcomes? These questions are theoretically examined and used to formulate specific hypotheses. An initial investigation of this theoretical formulation is conducted using a case study approach of a global plastics supply network.

* Corresponding author. Department of Computer Information Systems, Center for Process Innovation, Robinson College of Business, Georgia State University, Atlanta, GA, United States. E-mail address: [email protected] (J. Wareham). 1075-4253/$ - see front matter D 2005 Elsevier Inc. All rights reserved. doi:10.1016/j.intman.2005.03.007

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We propose a program of follow-up empirical work based on a broad field study of high performing supply networks. After a rigorous process for developing the instrumentation through semi-structured interviews, we expect to gather information about over 300 network configurations. As initial empirical evidence for going beyond dyadic exploration of supply networks, we present a case analysis of a plastic industry supply network, the Omnexus electronic trading network. In this analysis, we show how network externalities lead to more efficient flows of information and to more dynamic responses. The overall performance of all firms participating in this network should be enhanced over time. The major contribution of these ongoing studies will be new theoretical and empirical insights into the pathologies and metabolism of global digital supply networks. Specifically, we intend to delineate externalities of supply networks that are not embraced by exclusively dyadic perspectives, as well as the salient factors that complicate the interaction of supply network behavior across national boundaries. We will develop, test and measure novel constructs such as networked organizational performance and error amplification, unveiling systematic knowledge about the relationships between these factors across socio-geographic regions. D 2005 Elsevier Inc. All rights reserved. Keywords: Digital supply networks; Dyadic versus k-configuration networks; Information sharing; Error amplification; Responsiveness; Networked organizational performance

1. Introduction Every new day sees the economies of the world more intricately networked. The lifeblood of organizations – information – is exchanged via digital networks and strategic partnerships. How well do these digital supply networks function? What are their underlying moti vivendi? Are their behaviors explicable by scientific theory? Can they be improved? Based on prior work with organizational partnerships, several things become clear in the larger picture of designing globally-distributed, inter-firm networks. Increasing levels of market uncertainty challenge the traditional segmented and hierarchical assumptions that firms employ to design their inter-firm networks and interactions with partners. Traditional information-lean designs are ineffective in balancing customer demand with supplies of raw materials, components, and finished goods (Choi et al., 2001). Fig. 1 illustrates this supply– demand imbalance as the so-called bullwhip effect that resulted from automakers not sharing information about demand with machine tool makers (Fine, 1998). This led to cycles of gross over-supply and gross under-supply (cf. the most volatile line in Fig. 1). There are other key factors that explain why bullwhip-like error amplifications take place in supply networks. Focusing on structural elements, market mediation costs can be reduced by controlling order variance amplification across the network. Sterman (2001) investigated the impact of lead times and information sharing on the market mediation costs and concluded that lead time compression and centralization of demand information reduce amplification of order variation across a multi-tiered supply chain and, consequently, reduce market mediation costs (Simchi-Levi et al., 2000). In addition, past research has investigated structural impediments, such as pricing and incentive schemes that not only increase demand variability, but also constrain collaboration and information sharing between partners (Lee et al., 1997a).

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Data from United States, 1961-1991 (GDP, vehicle production, and machine tool orders

% change GDP % change vehicle production index % change net new orders machine tool industry Fig. 1. Bullwhip effect of information invisibility in the supply chain (Fine, 1998).

Radical innovations in information technology can be deployed to replace sequential and time-delayed partner interaction with reciprocal and near real-time partner interaction. Implicit in assertions about the potential of IT innovations is the need to recalibrate patterns of interactions between partners so as to enhance the capability of core processes, which, in turn, yield increased business value. We focus the proposed research on the sales planning process in global, multi-tiered, digital networks and the effect of information sharing on error amplification and responsiveness. In doing so, we draw on relevant theories and view the firm as a member of a network of firms who collaborate in order to gain network-wide business value. The overall network configuration for the research is illustrated in Fig. 3. The specific questions that will be examined are: ! How can information sharing improve the operational efficiency of global, digital supply networks? ! How can information sharing improve the market effectiveness of global, digital supply networks? ! Do cultural and international characteristics affect the nature and form of information sharing in these digital networks? ! What positive externalities are manifested in supply networks that are not captured in an exclusively dyadic perspective?

2. Literature review of information sharing in digital supply networks Previous research has found that successful cooperative arrangements between partners can be achieved through direct (including web) communications (Ellram and Carr, 1994; Hunter et al., 1996; Kearney, 1994; Korczynski, 1996; Lamming, 1993; Macbeth and Ferguson, 1994; Matthyssens and Bulte, 1994; McMillan, 1990; Mudambi and Schru¨nder, 1996; Sako, 1992; Spekman et al., 1996, 1994; Venkatesan, 1992). Communications

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represent a major form of information sharing, and thus are highly indicative of a link between sharing of valuable information and performance. Information exchange is one of nine evaluative metrics that Lamming (1993) describes as instrumental in achieving blean supply.Q Such cooperation is said to be of bmutual benefitQ to firms engaging in exchange (p. 246), but a firm must guard against deleterious effects such as information leakage to competitors. Consequently, Lamming (1993) argues that, in relationship assessment of a customer and a supplier, one needs to set minimum, maximum, actual, and target levels of information exchange. He also argues that dyadic exchanges can sometimes be more effective than bnetworked relationshipsQ (p. 206) because of the greater control dyads have in holding strategic information closely. Lamming gathered interview data from 181 assemblers and suppliers at various tiers to draw these conclusions. Using transaction cost theory, Kotabe et al. (2003) posit that knowledge transfer between single customer and buyer dyads can be moderated by the duration of the relationship, and that this leads to increases in supplier performance. This study was a field study (mailed questionnaire instruments) of 123 suppliers in the US and Japanese automotive industry. Based on monadic supplier performance measures, results indicate that the duration of a relationship in both countries impacts information sharing that transfers technological knowledge. Kotabe et al.’s suppliers averaged $440 M in sales, indicating that the research drew to some extent from medium to large-sized firms. In Mudambi et al.’s study of SME dyads (2004) enhanced communications was a key factor in distinguishing cooperative versus non-cooperative at the 0.001 significance level. Prior research, thus, has found effects at various scales. 2.1. Limitations of the dyadic view in the literature Our literature review suggests that prior work has stressed dyads of manufacturer and customers, or, more generally stated, vendors and clients. The vast majority of the studies mentioned above fall into this category. If one believes that the essence of the network phenomenon can be described as relationships between two firms in a chain, then this conceptualization may be sufficient to gain understanding of digital supply networks. Several authors, however, criticize this point of view, including Anderson et al. (1994), Iacobucci (1996), Levy and Grewal (2000), Mo¨ller and Wilson (1995), Ha˚kansson and Ivan (1995), and Wilkinson (2001). According to these critics, it is a notably unrealistic assumption that other relationships in a chain, Tier 2 to Tier 3 suppliers, for example, will respond similarly to manufacturer–customer dyads, ceteris paribus. Consequently, there are likely serious limitations to dyadic studies, especially within the intellectual frame of supply networks. One important limitation is that previous research has tended to view supply networks as linear systems composed of multiple dyadic relationships between a focal company (FC), suppliers (S), and customers(C) (Kemppainen and Vepsa¨la¨inen, 2003), as depicted in Fig. 2. Focal companies could be companies like component assemblers who then deal with wholesalers (dark gray Cs), who in turn deal with ultimate customers (light gray Cs). Dark and light gray Ss refer to tiers of suppliers. The immediate suppliers to a focal company are shown as dark gray. Tier 2 and Tier 3 suppliers are depicted as light gray.

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Fig. 2. Traditional bchainQ view of supplier–customer interactions (Kemppainen and Vepsa¨la¨inen, 2003).

Chains such as Fig. 2 imply linearity and a sequential or reciprocal flow of both materials and information between dyads. Suppliers at a given level of the chain, say two tiers removed from the focal company (light gray Ss), are conceptualized graphically as having exactly the same kind of relationship as the focal company with its Tier 1 supplier. This also holds for the customer-side of the chain. From a research standpoint, this is an overly stylized depiction of what happens in exchanges between various tiers of suppliers, customers, and a focal company. Fig. 3 shows that inter-firm exchanges do not have to be linear, lock-step in relation to the focal company. Not only is this a feasible set of nonlinear interactions, but we find that firms are increasingly assuming this organizational design (Malhotra et al., 2000). These considerations lead to a second key assumption underpinning our research. The appropriate unit of analysis should not be individual firms and their dyadic relationships, but the network itself. There are still poignant issues about the minimal size of a btrueQ network, but it likely goes beyond the dyad. In one sense, if firms are linked to just one other firm, and that firm to many others, and onward in a long progression, a firm’s network can be hypothetically extended to include a large number of other firms globally. Obviously, from the standpoint of tractability, the definition of a network must be constrained. The key observation is that the configuration of linkages across a network creates positive externalities in the supply network. These externalities include standardized communication protocols, business interfaces, and operational processes. In short, there are synergies generated by interactions within the entire system that make networks highly effective, especially accompanied by greater information visibility among its nodes. Dyadic views neglect the higher order benefits of these networked exchanges. Therefore, one of the central contributions of the program of research articulated in this paper is that networks involve k-configurations of exchanges. The dyad is the simplest

Fig. 3. Emergent bnetworkQ view of supplier–customer interactions (Kemppainen and Vepsa¨la¨inen, 2003).

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form of a network. However, triads, quadrads, and quintad networks, etc., more closely approximate the activity that falls under the concept of bdigital supply network.Q Indeed, the size of the network configuration being studied can be a control variable that will help to determine how and when network size is important. 2.2. Other literature bases Other literature bases could inform the information exchanges of global, digital supply networks. The strategic alliances and partnerships that firms form are, for example, a form of outsourcing. Firms could, after all, acquire raw materials themselves and manufacture their own components. So, the setting in which these networks operate raises the traditional questions of make-or-buy. When a firm transacts business exchanges with other firms, it has opted for a sourcing solution that is beyond its corporate boundaries. Yet, as in the network partnership literature, the extant outsourcing research does not shed much light on how firms’ network relationships should develop and mature. With respect to work on strategic alliances, Henderson (1990) and Henderson and Venkatraman (1993) argue that information and knowledge sharing is important in successful partnerships, but they do not specify the nature of this sharing and apply their thinking to digital networks. Much of the other outsourcing literature focuses on why firms outsource and the benefits they receive or fail to receive (Lacity and Willcocks, 2001; Lee et al., 2003). In sum, insights into the form and extent of digital interactions are generally not dealt with in this literature (Anonymous, 2003; Heeks and Nicholson, 2001; McKeen et al., 2002). There is no doubt that thinkers like Forrester (1961), Churchman (1968) and Sterman (2001) have promoted concepts of cybernetic flows in supply chains and networks. In fact, one view of this work is that it more fully developed the generic concepts of system theory in the 1930s (Bertalanffy, 1968). Consistent with these thinkers, we embrace systems thinking as a viable framework for interpreting what is happening in supply networks. Our approach is heavily oriented to testing connections between specific constructs in what is admittedly a cybernetic flow with feedback loops. By developing constructs and measures and positing linkages to testing such models, we are hoping to avoid the criticism of systems theory that it is essentially not falsifiable (Popper, 1959), and, therefore, unscientific. Our approach has limitations in that we are not modeling only the first stage in a dynamic process that will, of course, involve feedback. But even this approach has generally not been taken in examining the complexities of supply networks.

3. Theoretical foundations of the digital supply network value model In Fig. 4, we propose a model of Digital Supply Network Value. The constructs suggested in the model – strategic and operational information sharing, responsiveness and error, and market and operational performance – will be linked primarily via cooperative game theory, discussed in greater detail in the next sections. An overview of the model and its importance will orient the reader for the more specific theoretical discussion that follows. What explains higher performance in a network of organizations? While performance is a goal of supply networks as it is of individual firms,

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Responsiveness

Market Performance

Error

Operational Performance

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Strategic Information Sharing Operational Information Sharing

Control Variables Fig. 4. Digital supply network value model.

supply networks differ in the degree of information sharing and the symmetry of that sharing. The performance outcomes manifest themselves on two levels: at the internal operational level and at the external market level. We posit that a high level of symmetrical strategic information sharing between network partners will increase responsiveness and impact market performance. The sharing of operational information, on the other hand, will reduce error and thereafter lead to greater efficiencies in operations. Innovative aspects of the model are: (1) adaptation of well understood constructs to the supply network context, (2) differentiation in testing both degree and symmetry in the key model variables, (3) distinction between strategic and operational information sharing and (4) two distinct levels – operational and strategic – of value creation processes. Fig. 4 presents an overview of the model. As Strategic Information and Operational Information are fundamentally two different derivatives of the same supply chain activities, they are combined in the dashed box. The difference between the two is often a function of aggregation, where operational data can be combined to form strategic data. However, their use in managerial decision making is fundamentally different. As such, two separate causal paths are depicted in the remainder of the model. Specific definitions of the constructs follow. 3.1. Strategic information sharing Information sharing has received particular attention in the economics discipline in an attempt to understand how changes in the ownership of information and decision making roles affect competitive balances between agents and firms. Where the most highly recognized insights of information sharing have come from game theory (Kreps, 1996, 1990; Nash, 1951), other streams of economics also embrace information asymmetries as a

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focal variable, including Agency Theory (Ross, 1973), Transaction Cost Economics (Williamson, 1975, 1985, 1996), and Property Rights Theory (Hart, 1995). Game theory analyzes the interactions of rational, decision making individuals who may not be able to predict fully the outcome of their decisions. One consistent trait of game theory is the use of constrained optimization techniques to model self-interested behavior in strategically interdependent settings. A key insight of game theory was derived by John Nash (1951), who showed that the performance of cooperative behavior between profit maximizing agents can be superior to the performance of purely self-interested behavior, contradicting a basic tenet of the economic theory of capitalism. The value of information sharing, commonly illustrated in the prisoner’s dilemma example, where two captured prisoners can reduce their overall sentences if they collude, has been verified repeatedly, not only through simulations, but also empirically (Friedman, 1967; Lave et al., 1962; Plott, 1982). We believe, as a result, that the level of information sharing in a supply network affects its performance. In addition, we believe that the relative symmetry in the information sharing between firms has equally positive effects on network performance as it will facilitate a convergence towards a Pareto optimal outcome. Using dyadic data for Strategic Information Sharing and Networked Organizational Performance, Straub et al. (in press) present evidence that degree symmetric information sharing leads to heightened levels of degree symmetric networked organizational performance. Degree-symmetric measures are premised on the idea that performance at the network level has two components: the level of the shared performance and its symmetry. Networks that are symmetric in the antecedent information sharing, as well as the benefits endowed to the partners, will exhibit greater longevity. In the present paper, we conceptualize information sharing as either Strategic Information Sharing or Operational Information Sharing. Strategic information is typically characterized by a longer temporal perspective and is not related to specific process operations. The strategy literature (Barney, 1991; Eisenhardt, 1989; Eisenhardt and Martin, 2000; Makadok, 2001) notes that strategic information can span cognition about the environment, scarce and valuable resources, as well as dynamic capabilities focused on acquiring, jettisoning, or reconfiguring resources. We draw on this literature base to frame strategic information as being concerned with cognition about the environment and planned actions, current status and projected availability of scarce and valuable resources, as well as the intermediate trajectories and bounds of dynamic capabilities. The measurement of this construct will be discussed below. What needs to be said before is that the very word bsharingQ implies a mutuality that is generally unrecognized in prior literature. Game theory suggests that this bsharingQ should be as symmetrical as possible for long-term relationships and long-term benefits. But the choice of language in the word bsharingQ is interesting because the connotations of symmetry and degree have been embedded in the construct from the very beginning. One of our contributions, we believe, is to develop this conceptually and examine it empirically in the context of supply networks. 3.2. Operational information sharing Operational Information Sharing includes data that can be related to the planning or execution of a specific process or transaction. Seidmann and Sundararajan (1997) note that

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sharing operational information can be used to leverage economies of scale. Inventory holding information, an important operational information item, when shared, can reduce total inventory in the supply network (Lee et al., 1997a). Similarly, production and delivery schedules can be shared to enhance operational efficiencies through improved coordination of allocated resources, activities, and roles across the supply network (Lee and Whang, 2000). This can include order status data, manufacturing process data, logistics flow data, stock keeping unit movement information, or other sources of information that are directly pertinent to the planning or execution of operations. 3.3. Responsiveness Responsiveness is defined as the reaction of the supply network to unpredictable changes in environmental and regulatory conditions, shifts in competitor strategy, demand, and supply conditions, or internal process disruptions. Lee et al. (2002) note the critical importance of such agile responses by supply networks in uncertain demand and supply conditions to create market value. The innate ability for networks to respond to endogenous and exogenous shocks has often been modeled through an information processing metaphor that replicates a network of firms as a system of decisions subject to a number of design choices, including managerial capability, communication paths, utilization rates, and the overall resources needed to process such decisions. Viewing organizations as information processing entities is well established (Galbraith, 1973; 1977; Malone, 1989; Malone and Rockart, 1991; Simon, 1976). From this point of view, goal attainment of an organizational entity can be enhanced by implementing information focus strategies, where relevant information is acquired, and capacity-enhancing strategies, where bounded rationality constraints can be reduced by processing increased quantities of focused information. If a network of firms is a distributed system, its goal is to achieve global optima from local activities. This defines the problem as one of designing the supply network’s components, processors, and communication links, in a manner that enables sharing of the information required to respond proactively to exogenous as well as endogenous uncertainty. To the extent that strategic information is shared extensively and symmetrically by partners, the responsiveness of a supply network to both exogenous and endogenous uncertainty should be enhanced. Accordingly, we formulate our first hypothesis: H1. High levels of strategic information sharing are associated with high levels of responsiveness. 3.4. Error Error invokes an information processing metaphor of communication links, information flow, as well as the consequent delays, amplifications, and distortions that affect overall entropy and efficiency in the network (Landauer, 1996; Shannon and Weaver, 1959). In the context of our study, supply network error refers to the extent to which variances associated with key operational processes are amplified across a supply network.

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A lack of sharing of operational data can lead to severe aberrations in supply–demand equilibriums among trading partners in a network, resulting in high market mediation costs (Simchi-Levi et al., 2000). Symmetric and high levels of operational information sharing should enable activities and outcomes to be monitored and errors detected across a supply network. The resulting coordination in operations should reduce variance in orders, replenishment schedules, purchasing and production cycles, as well as returns. Hypothesis 2 follows from this logic. H2. High levels of operational information sharing are associated with low levels of error. 3.5. Market performance Market Performance is not performance constrained to a specific manufacturing or logistics process, but rather, conceptualized as measures that capture the overall effectiveness of the supply network from a customer perspective. To the extent that a supply network meets or exceeds changing customer requirements, its service levels, customer satisfaction and loyalty, and revenue growth will be superior to supply networks that are unresponsive to such shifts in customer requirements (Goldhar and Lei, 1991; Simchi-Levi et al., 2000). Highly structured, or rigid, network structures with limited information sharing can excel in the efficiency in which information flows are directed and processed (Malone, 1987), yet may lag in their ability to contend with uncertainty or significant variance in information sources. They may not perform well in highly complex and unstable environments, where more pliable and dynamic links and extensive information sharing are needed to redirect process and control information (Galbraith, 1974), acquire or jettison resources, or reconfigure resources in the pursuit of temporary advantages in the market (Eisenhardt and Martin, 2000). As such, the pursuit of responsiveness does not come without trade offs. Additional latent flexibility can come at the expense of speed, inefficient coordination, and underutilized slack resources, which compromises short-term static efficiency objectives, but is often requisite in instances of high environmental uncertainty (Galbraith, 1973, 1977). Accordingly, we expect that responsiveness will enable improved customer-focused performance in the face of shifts in the environment, regulation, demand and supply conditions, or competition. H3. High levels of responsiveness are associated with high levels of market performance. 3.6. Operational performance Operational Performance refers to the global optimization of operational processes across multiple trading partners in the network. Operational performance reflects the network’s ability to reduce the baseline of buffered resources and to mitigate expensive variance in resource requirements. High operational performance translates to increase in utilization of resources, reduction in inventory levels, and improvement in process yields.

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Operational inefficiencies, such as sub-utilization of key resources and inventory expenses, are prevalent across virtually every vertical industry segment. This inefficiency is evidenced by the recent inventory build-ups that occurred in several hi-tech industries (notably Cisco’s value network) in 2001, due to an abrupt decline in customer demand and the presence of duplicate orders due to inefficient information management among original equipment manufacturers and contract manufacturers. The variability in operational efficiencies across virtually every vertical industry suggests that there are huge efficiency improvement opportunities (Simchi-Levi et al., 2000), and we posit that these improvements can be realized by increasing the symmetry and degree of operational information sharing across a supply network. As such, Hypothesis 4 is formulated. H4. Low levels of error are associated with high levels of operational performance. 3.7. Controls Researchers frequently measure additional variables to control for influences that lie outside of their theoretical model, but could affect these relationships. For the sake of parsimony, these variables are examined in the statistical analysis, but not included in the theoretical foundations. In this work, several constructs fall into this category: (1) partner trust, (2) nodes and links in a network, (3) tier configuration, and (4) international dimensions. The role of these controls in reducing variance in the theoretical links will be discussed in the next section.

4. Research methods and design 4.1. Possible methodological choices There are many viable avenues for exploring networks more complex than dyads. These would include but are not constrained to: (1) simulation, (2) field study, and (3) field experiments. Simulation presents some intriguing possibilities. If the range and strength of the linkages can be specified and the nature of the feedback loops identified, a simulation modeling the supply network impacts over time could be constructed and executed. The assumptions underlying such a simulation would, however, need to be better understood before proceeding in this vein, and, therefore, options (2) or (3) should be carried out first. Option (3), a field experiment, is an interesting approach that would yield valuable insights. The usual problem with conducting field experiments, the unwillingness of firms to sub-optimize one or more of their performing units in order to uncover the underlying relationships, is one of the primary obstacles researchers would face (Bouchard, 1976). 4.2. Plans for a field study Option (2) could be chosen to examine these critical issues in its full-fledged form. Stone (1978) discusses the benefits of the field study method, including excellent range

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and strength in the independent variables and, of course, the naturalness of the setting that leads to higher external validity. Semi-structured interviews, which combine structured-quantitative questions–responses and free format, qualitative questions–responses, will be the research technique or data collection vehicle of choice. Among the advantages of this technique are: (1) its ability to probe respondents and elicit more detailed answers to complex questions (Stone, 1978); (2) increased motivation on the part of interviewees to engage in providing accurate and full responses (Bouchard, 1976; Stone, 1978); and (3) flexibility in how questions and answers are structured to elicit rich data (Bouchard, 1976). Interviews also permit gathering archival data and documents, systematic observation of sites, and field note insights. We have initial high level and encouraging contacts with an original equipment manufacturer to study its digital supply network. This firm has indicated its willingness to fully participate in the study as a focal company. It provides a preeminent example of networked organizational design and, for that reason, it would be a superb site and source from which to begin the study. As depicted in Fig. 5, we will use a snowballing approach with their managers, and trace upstream suppliers, lateral and downstream partners within a limited product set. With this contact information, we would be able to branch out from their home sites, and gather data wherever it takes us. With focal companies’ imprimatur on the project, we anticipate high levels of cooperation among their partners. We are reasonably certain that this approach is tractable. We have one Fortune 500 firm committed at this point, and anticipate others being interested when we fully explain the pragmatic advantages of the knowledge generated. 4.3. Operationalization of constructs 4.3.1. Unit of analysis The unit of analysis for the proposed study is, as we have argued initially, the network (i.e., a network configuration or group of networked organizations). Examining the effects of digital supply networks on individual firms is not as insightful as seeing how entire

(i)

(ii) Fig. 5. Three-step snowballing approach.

(iii)

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systems profit or lose when they are digitally connected, as in game theory. Klein (2002) and others have developed dyadic network perspectives, and these provide valuable suggestions on how to constitute measures at other network levels. The networks we propose to examine include not only dyads, but also triads, quadrads, quintads, etc. Therefore, a novel approach to measurement is needed. The conceptualization of this is reported in Straub et al. (in press) and is extended here to k-configurations. The unit of analysis is the network and the operationalization is known as degree-symmetric measurement. 4.3.2. Degree, symmetric measures Straub et al. (in press) have argued for degree-symmetric measures as de rigeur for research in networked organizations. Performance at the network level has two components: the degree of the shared performance and its symmetry. Consistent with game theory, it is argued that networks that are symmetric in the antecedent information sharing, as well as the benefits endowed to the partners, will exhibit greater longevity. Obviously, higher levels of performance are also more desirable than lower levels. Therefore, it follows that high levels of partner performance that are also symmetrical are Pareto optimal. Straub et al.’s (in press) empirical analysis found evidence that dyadic information sharing between clients and vendors resulted in higher performance. It is critical, however, that researchers go beyond dyads and use new measurement approaches since the effects of information are expected to be system-wide and not confined to individual firms. In our field study, we propose to use degree-symmetric measures for all constructs in the study. Extension of their measurement approach to k-configurations in a network is presented below in Table 1. 4.3.3. Dependent variables All model variables for the proposed study are shown in Table 2. Control variables are shown in Table 3. Previous work has focused on the dependent variable, performance, and so we begin the table with that variable. Market Performance is the outcome construct for the strategic level of the model. The construct focuses on the overall effectiveness of the supply network from a customer’s perspective. Organizations differ in their ability to meet customer expectations in terms of service levels and, consequently, satisfaction and loyalty (Simchi-Levi et al., 2000). In addition, organizations differ in their ability to grow over time (Goldhar and Lei, 1991). Accordingly, our measures will capture customer loyalty, customer satisfaction, and revenue growth. The direct relationship between networked organizational information sharing and performance has been demonstrated in Klein (2002). Our model offers several key variations on the Klein model, including mediating variables and the critical distinction of strategic performance versus operational performance impacts. Operational Performance is the outcome construct for the transactional level of the model. The construct focuses on the overall efficiency of the supply network. As Lee et al. (1997b) and Rajgopalan and Malhotra (2000) note, inventory ratios related to materials and suppliers, work-in-process, and finished goods provide key performance indicators of the efficiency of manufacturing firms that are impacted by supply chain-related capabilities. In

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Table 1 Degree symmetric information sharing algorithm for k-configuration Network structure representation a. Set of nodes b. Link activity

c. Number of feasible dyadic links in k-configuration d. Number of dyadic links in k-configuration

N i i = 1,2,. . .. . .k L ij i = 1,2,. . .. . .k L ij = 0 if there is no link between N i and N j L ij = 1 if there is a link between N i and N j k(k
1)/2 Lk ¼

k X i
1 X

Lij 0bLk Vk ðk
1Þ=2

i¼2 j¼1

Information sharing representation a. Information shared by N i with N j

b. Degree of information shared between dyadic links

c. Symmetry of information shared between dyadic links

ISij For i = 1,2,. . .. . .k and j = 1,. . .i
1, i + 1,. . .k a. If L ij = 0, then ISij = 0 b. If L ij = 1, then [(0 b ISij V 1) or (0 b ISji V 1)] or [(0 b ISij V 1) and (0 b ISji V 1)] DISij For i = 1,2,. . .. . .k and j = 1,. . .i
1, i + 1,. . .k DISij = (ISij + ISji )/2, 0 b DISij V 1 SISij For i = 1,2,. . .. . .k and j = 1,. . .i
1, i + 1,. . .k a. If ISij b ISji , then SISij = ISij /ISji b. If ISij V ISji , then SISij = ISji /ISij

Aggregated measures a. Average degree of information shared across dyadic links in k-configuration

k X i
1 X

ADISk ¼ b. Average symmetry of information shared across dyadic links in k-configuration

c. Degree symmetric measure of information shared across dyadic links in k-configuration

0bADISk V1

L k X i
1 X

ASISk ¼

DISij

i¼2 j¼1

i¼2 j¼1

L

ISij 0bASISk V1

DSISk = (ADIS + ASIS)/2 0 b DSISk V 1

addition, Tyndall et al. (1998) note that operating costs and capital efficiency are important efficiency-related performance indicators that, too, should be impacted by supply chainrelated capabilities. The possible set of measures for the construct includes inventory turnover, days of supply, operating costs, and capital efficiency. 4.3.4. Independent variables Strategic Information Sharing, or the degree of symmetric information sharing, represents the extent or degree to which firms share information and the symmetry of this sharing in a network. Measures will be adapted from Klein (2002) and will capture sharing of information related to cognition about the environment and the market, plans pertaining to scarce and valuable resources, and initiatives related to development of core capabilities.

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Table 2 Degree symmetric constructs and measures for proposed study Construct

Definition

Dependent variables (DV) Market performance Measures that capture the overall effectiveness of the supply network from a customer perspective Operational Efficiency of the supply network performance

Independent variables Strategic information sharing

Operational information sharing

Mediating variables Responsiveness

Error

(IVs) Information sharing that changes the ownership of information and decision making roles and affects the relative competitive balances between agents and firms Data relevant to the planning or execution of specific processes or transactions

Responsiveness of the network to unpredictable changes in demand and supply, environmental and regulatory conditions, shifts in competitive strategy, or internal process disruptions Extent to which variances associated with key operational processes are amplified across a supply network; nonlinear expansion of order variability as different tiered partners make purchase decisions lacking complete information

Possible measures (all measures are network level degree-symmetric) Revenues/sales per employee average deltas over 5 years; customer loyalty measures; customer satisfaction measures Inventory turnover; inventory performance statistics and ratios; working capital employed; capacity utilizations statistics

Measures used by Straub et al. (in press) and Klein (2002) to capture strategic level information sharing

Order status data; manufacturing process data; logistics flow data; stock keeping unit movement information; other information directly pertinent to the planning or execution of operations

Questions about speed of product introduction, adjustment of product and service offerings, and agile alignment of offerings with changes in customer requirements associated with different channels, geography, and segments Measures of information flow as well as the consequent delays, amplifications, and distortions that affect overall entropy and efficiency in the network; ratio of change in order variability at different tiers of the supply network

Table 3 Control variables for proposed study Construct Control variables Network structure International scope Tier configuration Partner Trust

Definition

Possible measures (all measures are network level degree-symmetric)

Number of dominant partners and links between them in a network Number of separate cultural regions that the network traverses Number of tiers in a network

Straight-forward mapping of network nodes and links Straight-forward counting of cultural regions traversed Straight-forward counting of network links by tier Measures based on McKnight et al. (2002)

Mutual perception of ability, intentions, predictability, and disposition of partner

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Operational Information Sharing, or the degree of symmetric information sharing, represents the extent or degree to which firms share information and the symmetry of this sharing in a network. Measures will be adapted from Klein (2002) and will focus on sharing of production and delivery schedules, order status data, manufacturing process data, logistics flow data, stock keeping unit movement information, or other sources of information that are directly pertinent to the planning or execution of operations. 4.3.5. Mediating variables Error, or amplification of order variance, is the nonlinear expansion of order variability as different tiered partners make purchase decisions without complete information. It can be expressed as: AOV ¼ VarðD1 Þ=VarðDk Þ where D 1 is the order variance at stage 1 in the supply chain and D k is the order variance at stage k in the supply chain. When order variability and its amplification are high, there is an even greater need to be able to enact a flexible, adaptable network. Otherwise, a precipitous bullwhip effect will eventuate. When these numbers are low, there is still a need to reduce error, although it may not define the competitiveness of the industry. This form of error can be measured, of course, and the presence of this variable in the model implies that it is useful to seek out firms that are in hypercompetitive markets as well as in tranquil markets. Responsiveness is the speed with which a network introduces new products, adapts its product offerings and service levels, and dynamically aligns its offerings based on changes in customer requirements associated with different channels, geography, and segments. 4.3.6. Control variables As noted above, we will gather data about and consider the effects of a set of control variables on the phenomenon. We are not hypothesizing about these variables, although other models could be construed where they did play a more central role. 4.3.7. Cultural and international context Global supply networks are a major instance of globalization. Firms are increasingly looking abroad for suppliers, partners, and markets. The effects of culture on network interactions are of great interest in the context of the current study. Not all cultures share information as readily as many Western technologically developed countries. In Arab countries, for example, there are cultural barriers to open sharing of information (Patai, 1973). In cultures with such values, overseas partners in the digital network may withhold information or not be willing to participate in the research. These effects need to be accounted for. The research design calls for semi-structured interviews and, even in the presence of some cultural resistance, it should be possible to gain understanding about information sharing behaviors and outcomes across cultural boundaries. Similar obstacles have been dealt with (although not completely overcome) in other settings (Hill et al., 1998), and there is every reason to hope that it will work out well in this case. There are other international factors that need to be accounted for, including differences in regulatory environments among and between partners, local economic

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conditions, availability of computing infrastructure, and so on. The research design calls for gathering information on all of these phenomena to see what international dimensions affect supply networks. The elicitation of these factors will come primarily through the semi-structured interviews. We anticipate that a relatively high percentage of partners will be off American shores. It will also be possible to explore correlations and clusters based upon geo-cultural regions through categorical data coding of the regions represented in the network. 4.3.8. Other measurement and research design issues To hold constant certain conditions of the model, we propose to limit our inquiries to the demand planning, forecasting, and replenishment process. This information task is extremely important in order variability and the ability of firms to control inventories and, for this reason, is well matched to the research model. Other information tasks can be studied for generalizability, but it is important not to have excessive variance across task in this initial test. The demand planning, forecasting, and replenishment process impacts the ability of supply networks to balance demand with supply. Strategic information sharing in the context of this process should enhance sensing of demand shifts for existing and new products and reconfiguring resources and processes to respond to demand shifts. Operational information sharing should promote streamlined execution of production and distribution plans and supply network-wide mitigation of operational errors. It is also critical that there not be excessive variance across product or service line, and for this reason, we will restrict the inquiry to a particular, but not too narrowly defined, product line. Firms that supply the focal company with their components would thus qualify for the interview sampling, as would Original Equipment Manufacturers (OEM) for the corporate market. As Straub et al. (in press) show, there are numerous interactions that can be constituted for study as networks, even from a relatively small group of bcoreQ partners. By examining different configurations within an over network, the number of smaller bnetworksQ can be greatly multiplied. Elaborated by Straub et al. (in press), the feasible set of network interactions with just 6 firms, are 53, for example (2004). Expanding the bcoreQ group to 60 in our situation will yield several hundred network configurations. With this multiplier technique, we will be able to examine network configurations across tiers in the network as well as across layers (Straub et al., in press). Network layers acknowledge directness of the links, whereas both tiers and layers recognize stages in the movement of a product from raw material to customer (Straub et al., in press). The other result of the multiplier effect is the efficiency with which we can gather a sample to simultaneously test the research model linkages. LISREL requires 100–150 cases (Gefen et al., 2000), and we should be able to achieve this sample size, using the approach described above, with relative ease. 4.4. Project phases The six phases of a project that implements the described research program are delineated in Table 4.

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Table 4 The six phases of a field study project Phase

Task

Objectives and procedures

1

Project planning

2

Sample design and instrumentation

3

Training

4

Data gathering and analysis

5

Document results and dissemination

6

Project wrap-up

Project planning will involve detailed specification of activities for the next 2 years. With cooperation of focal company managers, we will identify the appropriate firms and contacts for snowballing. Design of the interview script, based on prior work of Straub et al. (in press), will also be carried out in this phase. Research assistants who will take part in the interviewing and data gathering will be trained for deployment in the field. We anticipate a continual process of data gathering and immediate analysis, so that we can gain maximal benefit from time on site. The budget includes a cadre of doctoral students assisting faculty in the field work. Near the end of the data gathering process, we will be able to formulate models, analyze the data qualitatively, and also test our theoretical framework quantitatively. This phase will include final preparation of white papers and reports and transference of materials to the project web site. This transference to the public forum will go on long beyond the end of the project as papers are published.

4.5. Initial empirical examination of the digital supply network value model As an initial examination of the relationships articulated in the model, we offer a case study of the digital supply network embedded in the Omnexus B2B Web site.

5. Case study of Omnexus 5.1. The plastics industry According to the Society of the Plastics Industry, this is one of the largest manufacturing industries in the world, accounting for approximately $589 billion dollars in annual volume, directly employing more than 1.5 million people. Plastics are used in a large variety of industries and products so the industry is essentially as vertical as it is horizontal. Plastics penetrate market boundaries, ranging from packaging and construction to transportation; consumer and institutional products; furniture and furnishings; electrical and electronic components; adhesives, inks, coatings; and others.1 On April 5, 2000, BASF (Germany), Bayer (Germany), Dow (USA), Dupont (USA), and Ticona/Celanese (Germany) signed a letter of intent committing to the creation of Omnexus, a neutral company to serve as a global eMarketplace for the plastics industry. The founders had identified the thermoplastic processing market, specifically tapping the 1

www.socplas.org/industry/index.htm.

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global $50 billion injection and blow molding industry as its initial target market. On October 2, 2000, Omnexus launched the initial version of the marketplace and allowed buyers and suppliers to apply for membership and browse the product catalog. On November 30, 2000, Omnexus completed its first live transaction when an injection blow molder purchased $40,000 worth of resin product from Dow Chemical. In December 2000, Omnexus completed its first multi-supplier transaction, when a large plastics processor transacted simultaneously with Dow Chemical and Ticona/Celanese in breal timeQ for their respective products, at prices, credit terms, and delivery schedules unique to each relationship. The plastics industry can be segmented into processing methods including: injection molding, blow molding, thermoforming, transfer molding, reaction injection molding, compression molding, and extrusion. Omnexus selected the Injection and Blow Molding segments as its initial target market. This market segment generates $50 billion in sales, $40 billion in polymers, and $10 billion in equipment, material, repair and operations, etc., out of the total $589 billion for the plastics industry. Inputs for the injection and blow molding industry mainly consist of resins, equipment, material, repair and operations, tooling, and other supplies. Analysis of global resin activity indicates that sales are rather evenly distributed among three regions: $16 billion for North America, $10 billion for Europe, and $14 billion for Asia. Omnexus segmented the injection and blow molding industry into four different customer types: molders, OEM’s, compounders, and distributors. According to the Freedonia report, there were approximately 8000 molders in the United States, Canada, and Mexico. Of those 8000, 2.5%, or about 200 molders, accounted for half of the molded product sales, and these injection and blow molding companies were primarily based in the United States with annual revenues of at least $30 million each per year. Another 2700 molders were categorized as medium-sized, with annual revenues of $6 million each. 5.2. Standardizing processes and network externalities The injection and blow molding business was well-suited for a migration to global, digital supply network collaboration because it was characterized by a concentrated group of suppliers and a fragmented group of buyers; an inefficient buying process with high data requirements; and, large transaction volumes. Standardizing these transaction processes across the highly heterogeneous digital supply network was one of the greatest challenges for Omnexus. This presented substantial change management challenges for their sales staff. The online marketplace was a disrupting technology that would impact an entire organization, but specifically the traditional sales channel. While Omnexus recognized the impact their model would bring, it believed the marketplace will have positive, rather than negative, effects on their suppliers’ sales organizations. Mr. Thaler, the company’s strategy manager stated: Omnexus’ biggest challenge is to communicate and demonstrate how our services will benefit sales persons and get their bbuy-in.Q By diverting customers to the emarketplace, Omnexus will take care of all of the paperwork, processing and customer service that many studies conclude take up 50% of a salespersons’ day.

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This will allow them to concentrate on selling, relationship building, and providing high value-added expertise to their customers. In order to attract a wide variety of global suppliers, Omnexus built their model to achieve several supply-side benefits such as: full integration with the Omnexus marketplace; increased transaction speed; key cost reductions in customer acquisition and retention; automation of the demand chain; and, real-time inventory and price updating. Omnexus provided single-point access to multiple suppliers increasing buyer capabilities for multi-sourcing while decreasing related search and administrative costs. Omnexus achieves efficiencies by allowing buyers to: streamline and standardize purchasing process; use a physical properties-based search tool to enable retrieval of chemical compound profiles; submit Request For Quotes (RFQs) to various suppliers; outsource order facilitation and management; and, use electronic bill presentment and payment tools. In addition, the full evolution of the Omnexus platform aimed towards value-added services to increase its viability and add dimension to its revenue model. These services included: ! Full integration services: full buyer-side and supplier-side integration into the Omnexus marketplace. Various levels of integration would be offered to customers, ranging from basic EDI/XML communication to ERP connectivity. ! Supply network services: virtual distribution services ranging to full supply-chain outsourcing for the plastics industry. To fully leverage the efficiency offered by the Omnexus marketplace, management was evaluating a relationship with distributor(s) to provide consolidated shipping to resin buyers. In the long-term, Omnexus would determine whether it will offer supply chain outsourcing to suppliers to achieve breakthrough logistics performance. These stages of buyer and supplier integration into the Omnexus platform promoted the increased symmetry of information sharing, as well as a redefinition of purchasing and fulfillment processes according to Omnexus standards. By promoting an increased symmetry in information sharing across the supply network, significant operational benefits were realized by both buyers and suppliers. In addition, standardization of procurement and logistics procedures across previously fragmented, heterogeneous systems, Omnexus enabled the realization of network externality effects. These effects go beyond the benefits of dyadic information sharing as the increased demand- and supply-side information visibility promotes improved collaboration across the end-to-end supply network processes between participating firms. By standardizing the disparate business interfaces, communication standards, and operational processes, Omnexus was able to realize higher-order externalities that benefited the entire network. Such positive externalities would not be realized in a purely dyadic relationship. It also enabled each of these firms to explore how they individually and collectively learned to improve supply network capability. It is important to recognize that great care was taken by Omnexus to preserve the confidentiality and privacy issues associated with how collected information from any source could be aggregated, shared, or used for purposes of broader supply network improvements.

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5.3. International issues Soon after its inception, Omnexus selected and entered into strategic alliances to further enhance its product and service offerings and place them in a position to expand internationally. On November 17, 2000, Omnexus entered a strategic alliance with ChemCross, the leading Asian B2B e-marketplace for chemicals and plastics. This alliance enabled both companies to extend their reach globally. ChemCross provided expertise, networked relationships, and a strong customer base in the Asian market, while Omnexus provided similar access to the US for ChemCross. By the end of March 2001, Omnexus received funding from nine additional suppliers and distributors, beyond the five who founded Omnexus. These suppliers were: PolyOne (USA), Solvay (Belgium), DSM Engineering Plastics (The Netherlands), Resinex & Ravago (Belgium), Clariant (Switzerland), The Biesterfeld Group (Germany), Ellis and Everard (UK), Atofina (USA & Europe), and Engel (Canada). The increasing diversity of the global players made it critical for Omnexus to efficiently manage global messaging and semantic consistency. It was imperative for them to manage this need for global standards within the framework of differentiated requirements associated with subsets of local supply networks. With Omnexus’ launch in Europe in March, 2001 and Asia in 2002, a global marketing approach allowed for a united front and greater control over the global message. The suppliers Omnexus had contracted with were global corporations that maintained a global strategic focus. However, the global focus was complemented with local sensitivity. For example, sales for resins could be segmented regionally, requiring much more of a local approach to establishing relationships with various molders. The sales division had two dedicated teams, customer-focused and supplier-focused. The customer-focused team segmented potential buyers by organizational size, large, medium, and small. The large and medium sized processors were actively targeted and approached by the regional sales teams. Omnexus did not exclude the small processors and will facilitate their requests for integration if they approached the marketplace. 5.4. Summary Our case analysis illustrates the power and importance of going beyond dyadic conceptualizations in global, digital supply networks. Omnexus was able to create significant network effects though a focus on communication and operational standards and increased symmetry in information sharing. The case analysis also emphasizes the importance of mutual trust and respect in building multilateral symmetric information sharing capabilities and network-wide process standards and the case shows how these critical intangibles can be promoted through commitment of key stakeholders. Finally, the case analysis recognizes the importance of balancing the global need for standards with the local need for differentiated processes. Omnexus’ early initiation of a strategic alliance with ChemCo, a firm with significant expertise in Asia, enabled them to discover differentiated business practices that could be meaningfully standardized and the ones where identified differences needed to be preserved. Their integration strategy also

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supports local sensitivity as it enables firms to preserve operating procedures and achieve process integration through the Omnexus marketplace.

6. Future research The proposed research is expected to have significant impacts on research and practice. Innovative aspects of our model of achieving business value in digital supply networks are: (1) adaptation of well understood constructs to a global, digital supply network context, (2) differentiation in the nature of information sharing, and (3) a differentiated understanding of two levels – operational and strategic – of value creation processes. 6.1. Knowledge generated Through this research, we expect to generate in-depth knowledge and understanding of the business value of global, digital supply networks. We will gain a better understanding of how the application of network technologies impacts professional interaction and collaboration across organizational and international boundaries. We will also increase our knowledge of how network configurations can foster or create barriers towards improved network and business performance. Unless demand information is shared across the extended supply network, minor variations in customer demand become amplified upstream in the network and result in the bullwhip effect. While the bullwhip effect, or order variance amplification, is widely known, it is only in recent years that firms have had access to the kinds of information technology that allow them to effectively share information with their network partners. To date, little is known about the impact of more complex network structures such as triadic and quadratic interactions that can exist among trading partners across multiple tiers of a supply network. This research will, in particular, generate new knowledge of the higher order effects resulting from information sharing. 6.2. International legal and cultural effects It is widely recognized that differences in cultural norms are manifested in operational practices such as manufacturing, logistics, financial accounting and control, personnel management, information revelation, and formal governance. Clearly, supply chains that traverse such boundaries will encounter conflicts in cultural norms that are manifested in these practices. An assessment of the legal and cultural conflicts that are discernible at international interfaces is an additional goal of this research. Understanding how differences in cultural biases will affect transnational supply chain operations will be extremely helpful in designing effective relationships. In addition to obvious factors such as legal and regulatory structures, more subtle cultural characteristics, such as views on information sharing, social hierarchy, centralization, and temporal perspectives, will also influence the nature of international supply chain relationships. It is at the interface of many of these more subtle, informal differences that the greatest conflicts are likely to occur and the standardization of business communication and

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processes can be severely obscured. Empirical research should be employed to illuminate these sensitive situations where companies can learn a great deal about how others have maximized benefits, even in trying circumstances. 6.3. Impact of this knowledge on research and researchers The research will develop and test a theory-based model of network information dynamics. This approach will contribute to knowledge on global, digital supply networks in a number of ways. First, by studying a large number of network interactions, we will be able to test the model statistically and determine how well the model fits the data. By introducing variables such as network responsiveness, and network information sharing, we will be able to understand the relative impact that these variables has on network performance. By moving beyond dyadic relationships and gathering data that allows us to test more complex network interactions, we expect to be able to shed light on how the depth of information sharing across multiple tiers of the network affects network performance. 6.4. Impact of this knowledge on practice Supply network integration has proven difficult to achieve in practice. The fact that a box of cereal typically spends an average of 104 days in the supply network underscores the problems that most firms and industries face today. Many firms still rely on a relatively high degree of vertical integration, preferring to keep knowledge pertaining to products within the firm’s boundaries. Increasingly, firms are sharing limited product and process knowledge with network partners, but this has sometimes resulted in fragmented information sharing between suppliers, manufacturers, distributors, and retail firms. Achieving end-to-end integration requires an integrated network perspective rather than an enterprise-centric mindset. During the past decade, we have begun to see the emergence of disruptive information technologies in terms of e-business models, supply chain management solutions, enterprise resource planning (ERP), and customer relationship management applications that promise to drive down the cost of internal and external coordination, allowing a new level of application integration and process coordination among firms. Despite these advances, there is little knowledge to guide practitioners concerning the relative performance advantages or disadvantages associated with different network configurations. The proposed research promises to generate useful knowledge concerning different types of network structures that can exist among trading partners across multiple tiers of a supply network and their associated strengths and weaknesses. This research will also guide practice by shedding light on how managers can reduce errors in the supply network and optimize network performance.

Acknowledgement We would like to thank our colleagues Ricardo Checchi, Mark Keil, Xinlin Tang, and Richard Welke for inspiring discussions and helpful assistance in preparing this manuscript.

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$1,600 billion 70% of industry $1,110 billion

30% of industry

Chemicals

$480 billion

Commodity $1,010 billion

$320 billion

$112 billion

Petrochem

Inorganic

Intermediate

Chlor-alkali

Polymers2

Acids

Thermosets Thermoplastics

$100 billion

Performance

Fine Chemicals

Plastic Additives

Pharma

Pigments

Flavors & Fragrances

Adhesives & Sealants

Soaps and Detergents

Coatings

Food and Feed

2

Industrial Gases (3%)

Monomers

Omnexus

Specialty $80 billion

Agro Crop Protection/ Fertilizers Pesticides Herbicides Insecticides Biotech

Food products

Titanium Dioxide

Resins Other1

Elastomers 1 Other

includes: Water treatment chemicals, textile chemicals, mining chemicals, metal processing, oil field, paper and pulp, printing inks, lubricants, construction, mineral processing, electronic chemicals, etc.

2 Excludes

composites (BMC, SMC, TMC), fillers, and reinforcements. To evaluate later.

Customer Portal Customer Registration/ Log on Omnexus e-mail accounts Source

(Marketplace)

User Registration/ Log on

Static Content Services

Order

Settle

Catalog

Invoicing

Order Processing

Payment

Order Change

Cash Management

Reporting

Reporting

RFQ Catalog

Manual Message Browser

Integration Hub

Physical connectivity (VPN)

Message Handling Release 1.a

Supplier

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Appendix A. Omnexus position within the chemicals industry

Appendix B. Omnexus’ initial flexibility

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