Successfully reported this slideshow.

An Empirical Examination of the Relation between Performance ...

602 views

Published on

Published in: Business, Technology
  • Be the first to comment

  • Be the first to like this

An Empirical Examination of the Relation between Performance ...

  1. 1. An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership Ranjani Krishnan Michigan State University & Harvard Business School Fabienne Miller Worcester Polytechnic Institute Karen Sedatole Michigan State University May 24, 2010 ____________________________________ We thank Ella Mae Matsumara, Vallabh Sambamurthy, Geoff Sprinkle, participants of the 2008 Management Accounting Research Conference of the American Accounting Association, and workshop participants at Indiana University, and Michigan State University. This research was supported by the Center for the Leadership of the Digital Enterprise (CLODE) of the Broad School of Business, Michigan State University. We also thank our research partners for their assistance.
  2. 2. An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership ABSTRACT: This study uses insights from transaction cost economics and agency theory to posit that uncertainty in inter-firm relations increases the difficulty in measuring contractual performance thereby leading to contractual incompleteness. To protect from the resultant contractual opportunism, firms are more likely to use collaborative contracting. Results using data from 87 contracts with customers of a leading provider of electronics manufacturing services indicate that services such as supply chain management have higher uncertainty than production services. In addition, performance measurement difficulty due to high uncertainty is associated with greater use of collaborative contracting. Empirical results also show that suppliers are more likely to invest in relation-specific assets when there is a greater reliance on collaborative contracting. Our study suggests that uncertainty has implications for inter-firm contracting in the presence of relation-specific investments. Keywords: Collaboration; uncertainty; performance measurement; transaction cost; agency theory; contracting. Data availability: A confidentiality agreement with the firm precludes us from publicly sharing the data.
  3. 3. An Empirical Examination of the Relation between Performance Measurement, Collaborative Contracting, and Asset Ownership I. INTRODUCTION Most inter-firm relationships impose risks to both the supplier and the customer (Das and Teng 1996, 2001). Contractual safeguards can be employed to limit such risks if the contracting parties can achieve ex ante consensus about which mechanisms can be used to measure and monitor performance and determine the extent to which each party is meeting its contractual obligations. For example, in the manufacturing of routine components that have pre-determined standards, supplier performance can be assessed by the extent to which the specifications of supplied components fall within tolerance limits or deliveries are made as scheduled (Anderson and Dekker 2005). Prior literature suggests that contracts are one of the most frequently used mechanisms parties use to protect themselves against potential trade opportunism (Tirole 1988; Joskow 1985). Under certain circumstances, however, contracting parties are unable to find or ex ante agree on which performance measures will be used for assessing contractual performance. One such circumstance is when there is uncertainty about the factors that influence performance. The contracting parties are exposed to the risk of ex-post hazards that may arise subsequent to the initial contractual agreement. While the supplier may be able to negotiate a favorable initial contract, transaction cost economics (hereinafter TCE) suggests that this contract will likely be incomplete, and, therefore, unable to take into account all the contingencies that may arise in the future (Williamson 1985, 1991). Prior TCE research shows that when transactional hazards are high, collaborative relationships are more likely than arm's-length transactions (Artz and Brush 2000; Jap and 1
  4. 4. Anderson 2003; Judge and Dooley 2006). Milgrom and Roberts (1992) define a collaborative contract (which they refer to as a “relational contract”) as one that “does not attempt the impossible task of complete contracting but instead settles for an agreement that frames the relationship” (p. 131, emphasis added) and relies on “unarticulated but (presumably) shared expectations that the parties have concerning the relationship” (p. 132).1 A collaborative relation entails sharing not only information and resources, but also risks and rewards (Kumar 1996). Indeed, confidence and mutual trust exist between the parties because each expects the other to cooperate (Das and Teng 1998; Holmstrom and Roberts 1998). Thus, trust and the repeated exchange associated with collaboration compensate for the lack of adequate performance measures necessary to enforce contractual provisions. Collaboration, which is also associated with alignment of strategic objectives and temporal horizons, can therefore facilitate contracting by increasing trust between the contracting parties. We extend this area of research by first examining the antecedents of collaboration. Specifically, we explore measurability of contractual performance as a factor that drives whether a buyer-seller relation will be collaborative. We next examine the effect of collaborative contracting on relation-specific investments, i.e., investments in assets that have a low value outside the relationship. Ex-post contractual risks are especially pronounced when a supply relationship entails relation-specific investments and uncertainty is high. One example of such a relation-specific investment is an in-process die used in the automobile industry to shape steel sheets into parts for a specific vehicle (Klein, Crawford, and Alchian 1978). These dies, which require significant capital investments by the parts supplier, have little to no value outside the relationship between the automaker and the parts supplier. Moreover, once the supplier invests in the dies, it is exposed to the “hold-up” 1 Anderson and Dekker (2005) define collaborative arrangements as transactions conducted via close partnership relationships rather than through arm’s-length market transactions. Subramani and Venkatraman (2003) define collaboration as the linkage between supplier and customer that are reflected in communication and information exchange, and the extent to which the supplier orients its resources toward serving the customer's distinctive needs. 2
  5. 5. problem, i.e., the customer may later force unfavorable exchange terms on the supplier. To the extent the supplier anticipates potential hold-up, it may choose either to not invest in relation- specific assets at the socially optimal level or to spend resources to protect itself, both of which lead to inefficiencies (Williamson 1995; Holmstrom and Roberts 1998).2 We empirically examine whether the collaborative nature of the relationship affects the likelihood of the supplier making a relation-specific investment. We predict that, because collaboration helps protect firms from contract incompleteness, collaboration reduces the risk of hold-up by the customer and thereby increases the supplier's willingness to invest in relation-specific assets (Parkhe 1993). We use data from 87 customer contracts obtained from a Fortune 500 corporation to empirically test the above predictions. This corporation is a leading provider of electronics manufacturing and integrated supply chain services. We analyze and code the contracts to measure the degree of collaboration, the relation-specific investments made by the supplier and the customer, and other important contract characteristics. Then, based on field interviews with managers from the firm, we examine how difficult it would be for customers to evaluate the supplier's performance based on the types of services the supplier provided, uncertainty, and difficulty in monitoring performance. Our results suggest that collaborative contracting is more likely to be used when the measurement of contractual performance is more ambiguous because of uncertainty. Our results also show that the supplier is more likely to invest in relation-specific assets when there is greater reliance on collaborative contracting. These results indicate that when performance measurement is difficult, firms reduce their risks by making greater use of collaborative 2 Relation-specific investments have the potential to benefit both the supplier and the customer, and thus both parties stand to be worse off if the investment is not made. 3
  6. 6. contracting, presumably because it is not feasible (or too costly) to write contracts that offer protection from all the contingencies that may arise. This study makes an important contribution to the accounting literature. Prior contracting literature in accounting has examined how the properties of performance measures influence their use in contracting within the firm (Banker and Datar 1989; Feltham and Xie 1994; Datar, Kulp, and Lambert 2001). We add to this literature by examining the role of performance measurement in an inter-firm setting. Using a unique data set of actual customer-supplier contracts,3 our results show that firms that make relation-specific investments protect themselves by collaborative contracting to mitigate the risks of opportunism posed by incomplete contracts, as suggested by transaction cost economics. Our results, therefore, imply that the TCE framework can enrich researchers' insights into inter-firm contracting in the presence of relation- specific investments. Our results also have practical implications for firms interested in determining the conditions under which risk management via collaborative contracting is advantageous; namely, conditions wherein performance measures either are not available or unable to effectively evaluate contractual performance such as with high uncertainty. The remainder of this study is organized as follows. Section II discusses the relevant theory and poses research questions. Section III provides details about the data and empirical models. Section IV presents the results of our empirical analysis and Section V offers concluding remarks. II. THEORY AND RESEARCH QUESTIONS Performance Measurement and Contracting 3 Our study is one of the few to use actual contract data. Others include Joskow (1985), Crocker and Reynolds (1993), and Saussier (2000). 4
  7. 7. A significant body of research in economics, strategy, and accounting has explored contracting relationships within and between firms. The two theoretical frameworks most frequently used to examine these contractual relations are contracting theory using an agency lens and TCE. 4 From an agency perspective, a contract can be used to assign responsibilities, determine outcomes and shares of contracting parties, and specify penalties for non-compliance (Poppo and Zenger 2002). Under certain circumstances contract can be “complete”, i.e., fully specify the terms of exchange. A complete contract is defined by Baiman and Rajan (2002) as follows: “there are no restrictions on the feasible set of contracts from which the contracting parties can choose; all information that will be observed by the contracting parties can be specified by them at the time of contracting and will be verifiable by a court; and there are no out of pocket costs associated with writing or enforcing contracts” (p. 214). An essential feature of complete contracting is the ability to measure supplier performance, and use such performance information in monitoring and assignment of rewards. Considerable research in contract design has therefore focused on the choice of appropriate performance measures and the extent of their use in contracting (e.g., Holmstrom 1979; Banker and Datar 1989; Feltham and Xie 1994; Datar, Kulp, and Lambert 2001). The general consensus from these models is that the optimal design of incentives and monitoring mechanisms depends on the availability of precise, sensitive, and congruent performance measures. Some routine production services, for example, can often be monitored using performance measures such as cost or quality, where performance is defined as a cost or quality measure that falls within an acceptable range of a target level (i.e., is within a “tolerance limit”). To the extent that routine 4 While agency theory has predominantly been used to examine contracting within firms (such as between the board and the CEO), the basic tenets of agency theory are applicable to inter-firm contracting. Agency theory primarily deals with contracting to minimize agency losses arising from hidden information and/or hidden action without explicitly considering the boundaries of the firm. Moreover, Baiman and Rajan (2002) state “there is no consensus on what distinguishes inter-firm from intra-firm transactions” (page 214). 5
  8. 8. production services use established technologies, the variability of the parameters associated with those services and their corresponding target levels are known. The degree of acceptable deviation from the target could therefore be agreed upon ex ante by both the supplier and the customer. In some instances however, the degree of uncertainty associated with some parts of the process imposes difficulties in measuring performance. In the case of activities such as innovation and R&D, even simply defining, let alone measuring performance is difficult. Additionally, when tasks are nonseparable, i.e., when each party's contribution to the outcome cannot be easily identified, outcome performance measures cannot be relied upon to assess contractual performance. Measuring a supplier's performance may also be complicated by “systems effects,” in which the performance of one supplier depends to some extent on either the performance of another supplier or of the customer itself. The aforementioned automotive die illustrates the latter case in that a particular die's performance is defined as the fit of the parts it produces with the other parts of the same automotive subassembly (Anderson, Glenn, and Sedatole 2000). Thus, identifying performance standards and using those standards to evaluate performance is more difficult for certain services than for others. In such instances, monitoring the supplier's performance and accordingly, determining whether the supplier has fulfilled the terms of the contract becomes more difficult. Under such circumstances, contracting between the parties may be hampered by the supplier's unwillingness to risk expending effort without the guarantee of some return. At the same time, the customer may be unwilling to guarantee a return in the absence of measurable outcomes. As a result, formal contractual safeguards cannot be employed. 6
  9. 9. Transaction cost economics offers useful insights into circumstances in which firms are unable to find or agree upon performance measures. Originally propounded by Coase (1937) and developed by Williamson (1975, 1985, 1995, 2002) and Klein et al. (1978), transaction cost economics addresses the following question: What factors determine whether a firm is better off vertically integrating with its suppliers and customers or contracting with another firm? As Shelanski and Klein (1995, 336) point out, the basic insight of transaction cost economics is that “transactions must be governed as well as designed and carried out, and certain institutional arrangements effect this governance better than others.” Transaction cost economics proposes that, in a complex world, contracts are inherently incomplete. This incompleteness can arise from many factors such as the bounded rationality of the agents, their inability to anticipate all the changes that may occur after the contract has been signed, and the unobservability or unverifiability of outcomes (Shelanski and Klein 1995). Indeed, because of the incomplete nature of contracts, they are often not enforceable in a court of law. The incomplete nature of contracts gives rise to a number of problems, not the least of which is how partners can protect themselves from each other's opportunistic behavior. Transaction cost economics posits that collaborative contracting offers protection to the contracting parties when the risk of opportunism is high because of incomplete contracting. Collaborative contracting can provide safeguards against opportunism by both parties through mechanisms such as trust and repeated transactions (Gulati 1995; Dyer 1997). Importantly, such collaborative relationships are often associated with increased information sharing and sharing of risks and benefits (Dekker 2003, Tomkins 2001). A variety of collaborative relationships can be used such as alliances, joint ventures, and strategic supplier relationships (Anderson and Sedatole 7
  10. 10. 2003).5 Drawing on agency and TCE theories, we propose that firms will employ collaborative contracting to address the challenges associated with measuring outcome performance. Thus, when an activity has higher uncertainty then performance measures are noisier; they cannot be incorporated in contracts and contract incompleteness increases. We also assume that lower monitoring is a proxy for unavailability or the poor-quality of performance measures and, as a result, similar contracting risks exist when an activity is less amenable to monitoring. That is, we expect that uncertainty and lack of monitoring proxy for performance measurement difficulty and consequently we are more likely to observe collaborative contracting in such instances. Hypothesis 1: Collaborative relationships are more likely when an activity’s performance is more difficult to measure. Investments in Relation-Specific Assets Collaborative Contracting The risk of opportunistic behavior intensifies in the presence of relation-specific investments when uncertainty is high and contractual outcomes cannot be specified ex ante. Williamson (1983) identifies four types of relation-specific investments. These include: (a) site specificity, where the supplier and the customer are located in a “cheek-by-jowl” relation to reduce inventory and transportation expenses, (b) physical asset specificity, where one party or the other must invest in an asset that has no (or significantly less) value outside the relationship for which the investment was made (e.g., the aforementioned dies required to produce an automotive component for a specific vehicle), (c) human-asset specificity which arises when employees acquire knowledge and skills that do not extend beyond the given relation, and (d) 5 Researchers have observed that collaborative customer-supplier relationships fall along a continuum that ranges from arm’s length to complete integration. Indeed, there is a “vast middle ground” of collaborative supplier relationships that includes licensing arrangements, joint ventures, and strategic alliances (Anderson and Sedatole 2003). 8
  11. 11. dedicated assets committed to a particular supply arrangement that, if terminated, would leave the firm with considerable excess capacity. To protect themselves from opportunism when investments in relation-specific assets are made, contracting parties will attempt to employ various safeguards including formal contracts (Dyer 1997). A comprehensive contract that stipulates the obligations and expected actions of each party, as well as the ramifications in the event of unexpected environmental conditions, decreases the risks that a supplier would be exposed to from the relation-specific investments. Indeed, from an agency perspective, when complete contracting is feasible, asset ownership is irrelevant because the contract can assign rights associated with asset ownership (Baiman and Rajan 2002). However, TCE argues that as the complexity of the contract increases, so does the cost of contracting for both parties. In extreme cases, contracting costs can increase to such a degree that they become prohibitive, requiring the contracting parties to explore other options for safeguarding their relation-specific investments (Dyer 1997). Moreover, some contracting contingencies, while foreseeable, are ultimately indescribable (Tirole 1999). As a result, contracting parties need to establish governance procedures whose safeguards against opportunism are sufficient to increase the supplier’s willingness to invest in relation-specific assets. While both the supplier and customer stand to gain from the supplier’s investments in relation-specific assets, the distribution of risk thereafter is uneven (recall that once the supplier invests in the relation-specific asset, it exposes itself to the risk of hold-up by the customer). We explore collaborative contracting as a governance safeguard firms use to facilitate investments in relation-specific assets (Dyer 1997; Bensaou and Anderson 1999; Dekker 2004; Lee and Cavusgil 2006). Collaborative contracting, which is associated with increased trust 9
  12. 12. between contracting parties, establishes a sense of reciprocity that reduces the probability that the parties will behave strategically (Ring and Van De Ven 1992; Baiman and Rajan 2002; Jap and Anderson 2003). We therefore predict: Hypothesis 2: The supplier is more likely to invest in relation-specific assets when the relation between the supplier and the customer is collaborative. In sum, we predict that uncertainty and lack of monitoring proxy for difficulty in measuring performance, and are associated with collaborative contracting (Figure 1). Collaborative contracting in turn is associated with higher probability of relation-specific investments. The next section describes the data and methods. [Insert Figure 1 here] III. DATA AND METHODS Research Setting We analyze data from a firm (hereinafter, "EMS") that is a leading provider of electronics manufacturing services.6 With a global supply base and short product life cycles, the electronics manufacturing services business is very competitive. Firms in this industry have suffered from a downturn during the high-tech industry slump of 2001, driving margins to all-time lows. In response, firms have implemented several strategies to adapt to this new environment. Some firms have increased their emphasis on product design and helping customers to reduce their R&D costs, while others have focused on supply chain management and providing lean configuration. Additionally, while some firms have chosen to emphasize quality, others have aimed to become low-cost providers. Materials account for as much as 80 percent of the costs incurred, leading all firms to manage their inventories very closely. In addition, electronics 6 Owing to a confidentiality agreement with the firm, we refer to the firm as “EMS” (not its real name) and do not provide any financial or operating data. 10
  13. 13. manufacturing services firms across the board have attempted to shorten time-to-market and increase manufacturing efficiency. Exclusivity of customer-supplier relationships is rare in this industry, but most firms have a few large customers that comprise the majority of their revenue. Finally, most customers do not commit to long-term production schedules and margins are very low. In sum, the firm we analyze operates in a very competitive environment characterized by a high degree of uncertainty. Data Like most of its competitors, EMS offers services ranging from production and repairs to supply chain management. Production (i.e., manufacturing of electronics ranging from components of mobile phones to set-top boxes) constitutes the core of EMS business and is characterized by a focus on quality, flexibility, and efficiency. EMS also provides its customers with end-to-end services that encompass production modification (i.e., manufacturing with the goal of reducing cost), repairs (i.e., aftermarket services such as warranty support and reverse logistics), and supply chain management (i.e., supply chain optimization). EMS is the market leader in repairs and aftermarket services. Our research team initially conducted exploratory interviews with senior EMS executives to gain an understanding of the industry and of the relationships EMS encounters with its customers. Subsequently, we obtained all of EMS's contracts with its customers. From these contracts, we selected agreements that were in force and that included the provision of manufacturing services. In other words, all the contracts we selected were production contracts. From this group, we identified those contracts that, in addition to production, provided production modification, repair, or supply chain management services. We excluded all contracts written in a foreign language, with the exception of those written in French because a member of our research team was able to translate these contracts 11
  14. 14. into English. Three raters then independently rated the contracts from the 33 largest customers as well as 54 additional customers. EMS identified its 33 largest customers based on the size of the revenues each customer generated for the firm. The other 54 contracts were selected at random from the sample of all manufacturing contracts. In sum, we analyzed 87 of 179 manufacturing contracts, or 49 percent of the total manufacturing contracts. Importantly, EMS disclosed to our research team that its ten largest customers account for over 60 percent of its revenues. Thus, we estimate that our sample (comprising the 33 largest customers plus 54 additional customers) represents between 80 and 90% of EMS sales revenue. Two of the raters were researchers associated with the project and a third was a JD/ MBA student. The data collected from the contracts included a reference to the relevant contract page for easy verification of the source of the information. When the raters were unable to find the necessary data after a first read through the contract, they used Adobe Acrobat word searches to capture the relevant data. Any discrepancies in coding were thoroughly investigated by the raters by reviewing the contracts' terms and meeting as a group to ensure agreement and consistency. Furthermore, in addition to the assigned rater, one or both of raters who are members of our research team also coded the contracts from the 33 largest customers. The JD/MBA student coded the remainder of the contracts, with random verification provided by one of the researchers. Any discrepancies underwent another round of investigation and recommendations for corrections were made to the student orally as well as in writing. The minimum duration of the contracts we analyzed was twelve months, and some contracts were open-ended. While most contracts were written by customers, we estimate that over 20 percent were prepared by EMS. The contracts prepared by EMS were similarly organized from one customer to the next, although the terms of the contracts varied. 12
  15. 15. Additionally, the contracts prepared by EMS were with smaller customers. The length of the contracts varied from 5 to 185 pages. Based on theoretical support provided by agency theory and transaction cost economics, we selected variables to examine from the contracts. These variables, defined in detail below, included measures of relation-specific investment, collaboration, type of service, number of services, monitoring, contract duration, customer size, and uncertainty. Variable Definitions This section discusses how we define the variables used in our empirical analyses. Uncertainty: As mentioned earlier, we use uncertainty and monitoring as proxies for measurement difficulty. We examine the contracts to evaluate the extent of the uncertainty the services provided by EMS are faced with. We believe that EMS is faced with higher uncertainty when contracts include demand forecast, provisions for order reduction, and responsibility for unsold inventory. In other words, we propose that the presence of contractual clauses that address demand forecasts, order reduction, and responsibility for unsold inventory signifies that uncertainty is high. Conversely, absence of such clauses can be taken as a sign that uncertainty is low. We compute a factor score to capture the extent to which the services performed by EMS are exposed to uncertainty. We use factor analysis of the following variables extracted from the contract: specification of demand forecast (included or not included), protection from reduction of orders (i.e., downside) and from cancellation (included or not included), clauses specifying responsibility of customer for obsolete and excess inventory (specified or not specified). All five variables load on a single factor with the following factor weights: forecast 0.677, downside 13
  16. 16. 0.624, cancellation 0.64, obsolete 0.476, and excess 0.722 (Cronbach alpha = 0.62). This factor explains 40% of the total variance. Monitoring We examine the contracts to identify provisions for auditing and inspections. EMS customers will only be able to rely on the monitoring of services as a control mechanism if they are able to define, measure, and agree on the appropriate performance measures with which to monitor the contractual performance of those services. We thus assume that the presence of monitoring provisions implies that performance measures are available for use in the contracts; conversely, we assume that the absence of such provisions suggests that performance is ambiguous and difficult to define and, therefore, to measure. We compute a monitoring factor score to capture the extent to which the customer is allowed to audit and inspect the work and records of EMS. We use factor analysis of the following variables extracted from the contract: provisions for conducting a formal audit of EMS records by the customer or its representative (audit allowed or not specified), provisions for inspecting EMS records (inspection allowed or not specified), and provisions for inspecting the EMS production facility (inspection allowed or not specified). All three variables load on a single factor with the following factor weights: audit 0.712, inspection of records 0.802, and inspection of facility 0.663 (Cronbach alpha = 0.55). This factor explains 53% of the total variance. Collaboration: One of the purposes of our study is to examine the drivers of collaboration and the effect collaborative contracting has on the supplier’s decision to invest in relation-specific assets. Therefore, it is important that our measure of collaboration accurately captures the underlying construct. We use two methods to measure collaboration. The first is based on the 14
  17. 17. extent to which the contract language conveys the signal that the customer and the supplier have a collaborative relationship (i.e., collaborative tone). The second is based on a combination of collaborative tone and other important determinants of collaboration. Collaborative Tone of Agreement: We measure a contract's collaborative tone by examining its language and coding the contract based on the extent of collaborative tone used. Three raters coded a common set of six contracts and then compared the scores. The raters coded the tone of the contract agreement on a scale of one to ten, where one implies least collaborative and ten implies most collaborative. Examples of collaborative language include: • “collaborate in the development and execution of strategic business plans” • “align business strategies” • “operate based on mutual trust” • “jointly and openly work to reduce costs” • “good faith discussion regarding nature and extent of each party’s contribution” Examples of non-collaborative tone include: • “Penalties for ___” and substantial use of penalties • “Supplier shall reimburse opportunity costs of lost revenues” • Extensive use of the phrase “supplier will” and relatively small use of the phrase “customer will” • Substantial use of threatening language such as “Supplier will strictly adhere to these terms” Collaboration based on factor analysis: Based on the contracts, we identified a number of indicators that suggest that the supplier and the customer have a collaborative relation. 15
  18. 18. These include the following: the presence of mutual sharing of information (as opposed to the contract specifying that only EMS will provide information to the customer), the extent of information that was shared (none, limited, or extensive), whether EMS was the customer's preferred vendor, the extent to which EMS had the flexibility to use any vendor for inputs rather than only those vendors on the customer’s approved vendor list (AVL), and the tone of the contract as described above. We performed a factor analysis to examine whether these variables load on one distinct construct or have multiple constructs. Our factor analysis indicated the presence of one significant factor, and the following three variables loaded on the single factor: tone (0.756 factor score), mutual sharing (0.840 factor score), and extensive sharing (0.792 factor score). The other two variables, i.e., preferred vendor and flexibility in using the customer’s AVL, did not load on any single factor. We used the three variables that loaded on the single factor (tone, mutual sharing, and extensive sharing) to construct a weighted factor measure of collaboration. This factor explained 63.50% of the combined variance. Supplier Relation-Specific Investment: Relation-specific investments refer to those investments that, while essential to the success of a particular customer-supplier transaction, have lower or no value outside of that transaction. The EMS contracts provide information about investments made specifically for a particular customer-supplier relation. Contracts in which EMS made investments in assets unique to the customer are coded as one. Contracts in which relation- specific investments are either made by EMS and reimbursed by the customer or made by the customer are coded as zero. Type of Service: 16
  19. 19. The contracts cover four types of services including production, production modification, repair, and supply chain management. As described in Section 2, the degree of difficulty in identifying performance standards and the resulting challenge of measuring performance are likely to vary as a function of the type of service provided. Our discussions with EMS management and our analyses of their contracts suggest that production and production modification contracts are similar in that they both focus on product manufacturing and have low to moderate levels of performance measurement difficulty. Their similarities lead us to combine production and production modification under the umbrella of manufacturing. As a result, we use three indicator variables to identify the three possible types of service: Manufacturing, Repair, and Supply Chain Management. Note that while all contracts include manufacturing services, they vary in the extent to which they include repair and supply chain management services. Control Variables Customer Size: As discussed above, we observed that contracts with smaller customers are more likely to be prepared by EMS. Additionally, we noted that contracts prepared by EMS seem to offer EMS better protection against customer opportunism than contracts prepared by customers. Thus, we control for customer size using the revenue ranking of the customers provided to us by EMS. Larger customers might have greater negotiation power thus dominating the relationship and imposing contractual terms onto EMS. EMS identifies its 33 largest customers in terms of revenue. These 33 customers are coded as one. The other 54 customers are coded as zero. We include a control for customer size in all the models. Contract Duration: This variable captures the duration of the contract, measured in months. Joskow (1987, 169) proposes that “A long-term contract that specifies the terms and conditions for some set of future transactions ex ante, provides a vehicle for guarding against ex post 17
  20. 20. performance problems.” Thus, by encouraging repeated exchanges, long-term contracts offer EMS another type of protection against opportunistic behavior by the customer. Number of Services: This variable captures the number of services that the supplier provides the customer. The number of services provided is a proxy for the complexity of the relationship between EMS and its customer. Since services include manufacturing, production modification, repairs, and supply chain management, Number of Services takes values from 1 to 4. The Appendix provides examples of variables extracted from the contract. [Insert Appendix here] Empirical Models Hypothesis 1 examines whether collaborative contracts are more likely when the contracted services are difficult to measure, which is proxied by uncertainty and lack of ability to monitor performance. Based on the conversations we had in the field with EMS managers, it appeared that the type of service provided was an important driver of uncertainty. Therefore, in the first portion of our analysis, we explore whether uncertainty and monitoring are associated with the type of service provided. Discussions with EMS management and our own examination of the contracts suggest that specifying and measuring the contractual performance of supply chain management services is more complex for several reasons. First, supply chain management services have a longer lead-time. In other words, effort expended at a particular point in time is associated with returns at a different point in time. Second, the payoffs for increased effort in one part of the value chain may accrue at a different part of the value chain. Third, the range of acceptable outcomes is less clear because the activity is inherently more ambiguous. Fourth, supply chain management requires coordination from people in different parts of the supply chain as well as in different 18
  21. 21. functional areas such as engineering, marketing, and finance, each of which may have different measures of performance. Finally, since the outcome of services such as supply chain management depends on the performance of numerous suppliers as well as on the customer, attributing responsibility for the outcome can prove difficult. Hence, we expect performance to be easiest to measure with repair services and hardest with supply chain management services. In sum, the arguments presented above suggest that uncertainty and accordingly noise in performance measurement will be greater with supply chain management service than manufacturing, and that consequently because of difficulty in assessing performance, monitoring will be lower. We perform a multivariate analysis to determine whether the type of service is associated with uncertainty by estimating the following equation: Uncertainty(or Monitoring) = α + β1*(Supply Chain Management Service) + β2*(Repair Service) + β3*(Customer Size) + εi (1) In this model, the omitted variable is the Manufacturing Service variable since all contracts include the provision of manufacturing services. Based on our discussions with EMS managers, we expect β1 to be positive, implying that supply chain management services have higher uncertainty than repairs or production services. We include a control for customer size. To explore whether monitoring differs amongst the services, we perform the same estimation provided in equation 1 using monitoring as the dependent variable. We expect β1 to be negative, implying that supply chain management contracts have lower degree of monitoring due to the inability of contracting parties to ex-ante agree on measures to gauge performance of supply chain services. We use the following empirical model to test hypothesis 1: 19
  22. 22. Collaboration = α + β1*(Uncertainty) + β2*(Monitoring) + β3*(Supply Chain Management Service) + β4*(Repair Service) + β5*(Customer Size) + β6*(Number of Services) + β7*(Contract Duration) + εi (2) In this model also the omitted variable is the Manufacturing Service. We include Customer Size to control for the possibility that very large customers may dominate the relationship and thereby impose a less collaborative contract. We also include the Number of Services and Contract Duration to control for the possibility that customers who contract for multiple services or for longer duration may have higher levels of collaboration. Hypothesis 1 predicts that collaborative contracts are more likely when an activity’s performance is difficult to measure and assess. Since uncertainty increases the noise in performance measures and renders them less useful for contracting, a positive sign on β1 is supportive of Hypothesis 1. Similarly, a negative sign on β2 is supportive of Hypothesis 1 and implies that when an activity is difficult to monitor because of unavailability of good quality performance measures, firms are more likely to use collaborative contracting. It is also feasible that supply chain management proxies for a number of other transaction characteristics, such as task complexity and other interdependencies, such that performance standards are more difficult to establish and performance is more difficult to measure with supply chain management services than with repair or manufacturing services. In addition, these transaction characteristics may expose the contracting parties to a greater degree of risk and this risk may be partially mitigated by using collaborative contracts. If these other features of supply chain management drive the extent of collaboration then the coefficient β3 will be positive. By a similar logic, significance of β4 implies that features of repair services drive the extent of collaboration. 20
  23. 23. In summary, if collaboration is driven by service features other than uncertainty and monitoring, then β3 and β4 will be significant, but β1 and β2 will not be different from zero. Thus, if β1 and/or β2 are significant, then Hypothesis 1 is supported. If in addition, β3 and β4 are also significant, then it implies that there are other service-related features that drive collaboration in addition to uncertainty and monitoring. If on the other hand, β3 and β4 are not significant then it implies that variation in collaboration is driven primarily by variation in uncertainty and monitoring and not due to other service-level factors. Hypothesis 2 states that the supplier is more likely to invest in relation-specific assets when the relation between the supplier and the customer is collaborative. To test this proposition, we examine the extent to which collaboration is associated with the probability that the supplier will invest in the relation-specific asset. We expect that collaboration will influence the extent to which the supplier is willing to bear the risk arising from investing in relation-specific assets. We use the following model to test hypothesis 2: Supplier Relation-Specific Investment = α + β1*(Collaboration) + β2*(Contract Duration) + β3*(Customer Size)+ εi (3) A positive coefficient on β1 would support hypothesis 2, implying that a more collaborative contract increases the likelihood that the supplier will be willing to bear the risk of investing in relation-specific equipment. We include contract duration and customer size as control variables in equation 3. We are unable to predict the sign of the coefficients on these control variables: On the one hand, they might signify a closer customer-supplier relationship and may be associated with a greater willingness on the part of EMS to invest in relation-specific assets. On the other hand, some contracts may be renewed automatically, which would mean that short contract duration is not automatically indicative of a short-term relationship. In addition, 21
  24. 24. contracts prepared by EMS might offer better protection against hold-up than contracts prepared by the customers regardless of customer size. IV. RESULTS As described above, we collected data from 87 contracts between EMS and its customers. We estimate that these contracts represent between 80 and 90% of EMS sales revenue. Table 1, Panel A provides the descriptive statistics for the variables used in our analyses. [Insert Table 1 here] All the contracts included production; in addition, 21 percent of the contracts included production modification, 26 percent included repair, and 21 percent included supply chain management. Of all the contracts analyzed, only four included a combination of production modification, supply chain management, and repair services. On average, the collaborative tone was 6.2 (rated on a scale of 1 to 10). Examination of the uncertainty factor means reveals that supply chain management has the highest degree of uncertainty (mean = 0.470) followed by repairs (0.111), and finally production modification (0.013). A nonparametric Mann-Whitney test shows that the uncertainty factor is significantly higher for supply chain management than non supply chain services (p < 0.05). Thus, our results indicate that, consistent with our discussions with EMS, supply chain management contracts have the highest degree of uncertainty. We next examined the difference in the monitoring score by service type. Results reveal that supply chain management has a very low monitoring factor score (mean = 0.084) relative to repairs (mean = 0.465) and production modification (mean = 0.677). A nonparametric Mann- Whitney test indicates that the monitoring factor mean score for supply chain management is significantly lower than the monitoring factor mean score for repairs (p < 0.05). Thus, our 22
  25. 25. univariate results support our proposition that it is more difficult to measure the performance of supply chain management services relative to repair services. This finding is consistent with our proposition that supply chain management services are more difficult to specify and consequently to monitor. Table 1, Panel B provides details about the correlations between measures of supplier relation-specific investment, collaboration, uncertainty, monitoring, and controls for production modification, repair, and supply chain management services. Production modification and repair services are correlated with monitoring whereas supply chain management services are associated with measures of uncertainty and collaboration. Table 1, Panel C provides univariate correlations of these variables. Table 2 contains the results of estimating equation 1 which examines the association between uncertainty [monitoring] and the type of service. In Table 2, Panel A, results indicate that supply chain management has a positive coefficient indicating that supply chain management services are indeed associated with higher uncertainty as per our conjectures and consistent with our discussions with EMS managers. In Table 2, Panel B, supply chain management is not associated with monitoring, which is not consistent with our expectations. Customer size is positively associated with monitoring in Panel B. We speculate that larger customers use their bargaining power to include monitoring in contracts as a protection mechanism. [Insert Table 2 here.] Table 3 provides the results of estimating equation 2 associated with hypothesis 1, which examines the relation between performance measurement difficulty (i.e., uncertainty and monitoring) and collaboration. Panel A shows the results when collaboration is defined based on the collaborative tone, and Panel B when collaboration is constructed using factor analysis. In 23
  26. 26. both cases, there is a positive association between uncertainty and collaboration. Consistent with hypothesis 1, the results indicate that uncertainty increases the noise in performance measures and consequently increases contract incompleteness and risk. Hence uncertainty increases the probability of collaborative contracting. The coefficient on β2 is not significant indicating that monitoring is not associated with collaboration. As discussed above, larger customers are likely to use monitoring clauses to protect themselves. Thus, the presence of monitoring clauses in the contracts could be due to the bargaining power of the customer and bear no relationship with the collaborative nature of the contract. This conjecture is supported by the observation that the mean monitoring score of larger customers is significantly larger than the mean monitoring score of customers that do not belong in the top-33 list (t = -4.15, p < 0.001). Supply chain management does not have significant coefficient after controlling for uncertainty and monitoring, indicating that collaboration is primarily driven by uncertainty rather than other specific features of supply chain services. Repairs has a negative coefficient in Panel A indicating that repairs services are less collaborative than other types of services. Results also indicate that number of services is positively related to collaborative tone. Providing more than one service to a customer is likely to increase the complexity of the relationship between EMS and its customer. When relationships are complex, EMS and its customers are likely to use collaborative contracting to complement formal contractual protection. Customer size is negatively related to collaboration in Panel B. Importantly this measure of collaboration encompasses tone and the extent of mutual information sharing. Large customers are likely to demand more one-sided information sharing and rely less on collaboration than small customers. 24
  27. 27. [Insert Table 3 here.] We next examine the determinants of Supplier Relation-specific Investment in Table 4, which reports the results of estimating equation 3. In Panel A of Table 4, collaboration is defined as the collaborative tone of the contract, and in Panel B of Table 4, collaboration is measured using the collaboration factor. We use PROBIT estimation because the dependent variable (Supplier Relation-Specific Investment) is a dichotomous variable, which takes the value of one when the supplier owns the relation-specific asset and otherwise takes the value of zero. Contract Duration and Customer Size are included as control variables. [Insert Table 4 here.] Panel A of Table 4 indicate that Collaborative Tone is significantly positively associated with Supplier Relation-Specific Investment. This finding is consistent with hypothesis 2 and signifies that a more collaborative contracting relationship increases the likelihood that the supplier will invest in relation-specific assets. The results in Table 4, Panel B, where collaboration is constructed using factor analysis, are consistent with the results in Table 4, Panel A as well as with the predictions of hypothesis 2. Moreover, our discussions with EMS managers indicated that they believed our results were consistent with their decisions; namely, they use collaboration as a contractual safeguard to protect relation-specific investments. V. DISCUSSION AND CONCLUSIONS To manage competitive and technological uncertainties, firms are increasingly seeking collaborative partnerships with their suppliers. In a 2008 PricewaterhouseCoopers survey of CEOs, more than half the respondents indicated that collaborative networks such as alliances, joint ventures, and contractual partnerships are critical to their companies’ success (PricewaterhouseCoopers 2008, 46). Indeed, a number of firms, such as Boeing, General Motors, 25
  28. 28. Chrysler, and Xerox, have expressed their intent to work collaboratively with suppliers as a means of reducing uncertainty and gaining a competitive advantage in the marketplace (Sheth and Sharma 1997; Humphrey and Ashforth 2000). Speaking at the 2006 Procurement and Sourcing Conference, Norma Clayton, Boeing’s vice-president of Global Sourcing Effectiveness, noted that “collaboration with suppliers could be the single biggest differentiator in competitive management” (Geraint 2007). While collaborative inter-firm relationships have primarily been studied from the perspective of buyers, Clayton's comment indicates that both buyers and suppliers could benefit from collaboration. Some archival evidence also suggests that collaborative relationships may offer suppliers benefits such as higher returns on investment and increased growth. However, the evidence also points to disadvantages for suppliers, such as lower margins (Kalwani and Narayandas 1995). It is therefore important to gain a better understanding of the characteristics and outcomes of collaborative relationships from the perspective of suppliers. In this study, we examine whether performance measurement difficulty (i.e., uncertainty and monitoring) increases the probability that a contract will be collaborative. We also examine whether collaborative contracting increases the probability that the supplier will invest in relation-specific assets. Results using data from 87 contracts with customers of a leading provider of electronics manufacturing and integrated supply chain services indicate that uncertainty is associated with collaborative contracting. In addition, collaborative contracting is associated with an increased probability that the supplier will invest in relation-specific assets. The results of our study have implications for firms at both the supplier and customer ends of the value chain. On the supplier end, our results show that when relation-specific investments are necessary, suppliers can engage in collaborative contracting to protect 26
  29. 29. themselves from customers' opportunistic behavior. Our results also demonstrate that collaborative contracts are more likely to be used when an activity's performance is ambiguous and, accordingly, difficult to measure. On the customer end, our results show that collaborative contracting can encourage suppliers to invest in relation-specific assets, which, in turn, will likely increase the combined return on the investment. Future research might productively explore the link between collaboration and performance for the contracting parties. 27
  30. 30. REFERENCES 28
  31. 31. FIGURE 1 Conceptual Model Uncertainty + Collaborative Contracting Ability to Monitor + Measurement Difficulty Relation Specific Investments 29
  32. 32. TABLE 1 Descriptive Statistics and Correlations by Type of Service Provided Panel A: Mean (Standard Deviation) by Type of Service Production Repair Supply Chain modification Service Management Service Service Supplier Relation-Specific Investment 0.35 0.24 0.24 (0.493) (0.436) (0.437) Collaborative Tone 6.94 5.98 7.50 (2.175) (2.208) (1.372) Collaborative Factor 0.260 -0.108 0.535 (1.150) (1.054) (0.954) Monitoring Factor 0.677 0.465 0.084 (1.025) (0.904) (1.020) Uncertainty Factor 0.013 0.111 0.470 (1.280) (1.037) (0.566) Contract Duration 28.67 47.91 40.33 (23.01) (35.43) (37.40) Customer Size (Top-33) 0.722 0.783 0.556 (0.461) (0.422) (0.511) Number of Services 2.94 2.83 2.78 (0.725) (0.717) (0.808) N 18 23 18 Panel B: Correlations by Type of Service Production Repair Supply Chain Modification Service Management Service Service Supplier Relation-Specific Investment 0.280** 0.126 0.105 Collaborative Tone 0.204* -0.082 0.361** Collaboration Factor 0.133 -0.065 0.275** Monitoring Factor 0.348** 0.281** 0.043 Uncertainty Factor 0.007 0.067 0.242* Contract Duration -0.031 0.330** 0.158 Customer Size (Top-33) 0.361** 0.498** 0.186* Number of Services 0.726* 0.773* 0.621* ** Correlation is significant at the 0.01 level (one-tailed). * Correlation is significant at the 0.05 level (one-tailed). 30
  33. 33. Panel C: Pearson Correlations Supplier Collab. Collab. Monitoring Uncertainty Contract Customer Number of Relation- Tone Factor Factor Factor Duration Size Services Specific ** Correlation is Investment significant at the Supplier Relation-Specific 1.000 0.249* 0.160 0.190 0.154 0.013 0.231* 0.240* 0.01 level (one- Investment Collaborative Tone 1.000 0.756** 0.025 0.362** -0.070 -0.071 0.216* tailed). * Correlation is Collaborative Factor 1.000 0.099 0.369** -0.106 -0.114 0.154 significant at the 0.05 level (one- Monitoring Factor 1.000 0.224* 0.262** 0.410** 0.317** tailed). Uncertainty Factor 1.000 0.101 0.100 0.146 Contract Duration 1.000 0.248* 0.221* Customer Size (Top-33) 1.000 0.495** Number of Services 1.000 31
  34. 34. TABLE 2 Type of Service Provided, Uncertainty, and Monitoring Uncertainty[Monitoring] = α + β1*(Supply Chain Management Service) + β2*(Repair Service) + β3*(Customer Size) + εi Panel A: Type of Service and Uncertainty Predictor Coefficient (t value, p value) Supply Chain Management Service 0.571 (2.129, p < 0.04) Repairs Service -0.030 (-0.107, p = 0.915) Customer Size 0.130 (0.513, p = 0.609) Intercept -0.159 (-1.127, p = 0.263) N 87 Adjusted R2 0.03 F-statistic 1.82 (p<0.07) Panel B: Type of Service and Monitoring Predictor Coefficient (t value, p value) Supply Chain Management -0.116 (-0.463, p = -0.463) Repairs 0.245 (0.937, p = 0.351) Customer Size 0.748 (3.166, p < 0.01) Intercept -0.324 (-2.453, p < 0.02) N 87 Adjusted R2 0.15 F-statistic 8.573 (p<0.01) Notes: Coefficients that are significant at p < 0.10 or better are boldfaced. Manufacturing Services is the omitted indicator variable. Repairs takes the value of 1 if the contract specifies repair services. Supply Chain Management is defined similarly. The contracts included in the Panel A analyses offer either repair or supply chain services (in addition to manufacturing services). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. In Panel A, the dependent variable is Uncertainty, which is based on a factor analysis of the extent to which demand forecast, order modification, and inventory responsibility are specified. In Panel B, the dependent variable is Monitoring, which is based on a factor analysis of the extent to which the customer is allowed to audit and inspect the work of EMS. 32
  35. 35. TABLE 3 Uncertainty, Monitoring and Collaboration Collaboration = α + β1*(Uncertainty) + β2*(Monitoring) β3*(Supply Chain Management Service) + β4*(Repair Service) + β5*(Customer Size) + β6*(Number of Services) + β7*(Contract Duration)+ εi Panel A: Collaboration Defined as Collaborative Tone Predictor Coefficient (t value, p value) Uncertainty 0.592 (3.291, p<0.01) Monitoring -0.076 (-0.375, p = 0.709) Supply Chain Management Service 0.060 (0.085, p = 0.933) Repairs -2.100 (-2.663, p < 0.01) Customer Size -0.548 (-1.277, p = 0.21) Number of Services 1.318 (2.654, p<0.01) Contract Duration -0.002 (-0.306, p = 0.761) Intercept 4.819 (7.57, p < 0.01) N 87 Adjusted R2 0.256 F-statistic 5.233(p < 0.01) Panel B: Collaboration Defined Based on Factor Analysis Predictor Coefficient (t value, p value) Uncertainty 0.327 (3.18, p<0.01) Monitoring 0.103 (0.891, p=0.376) Supply Chain Management Service 0.183 (0.451, p = 0.654) Repairs -0.600 (-1.331, p = 0.187) Customer Size -0.459 (-1.872, p < 0.07) Number of Services 0.411 (1.449, p = 0.151) Contract Duration -0.004 (-1.065, p = 0.29) Intercept -0.284 (-0.779, p = 0.438) N 87 Adjusted R2 0.198 F-statistic 4.024 (p < 0.01) Notes: Coefficients that are significant at p < 0.10 or better are boldfaced. Manufacturing Services is the omitted indicator variable. In Panel A, Collaborative Tone is coded based on the tone of the contract from 1 to 10, where 1 implies least collaborative and 10 implies most collaborative. In Panel B, Collaboration is constructed based on a factor analysis and is a combination of the following three contract provisions: collaborative tone (1–10), sharing of information (one-sided or mutual), and extent of sharing (limited or extensive). Uncertainty is based on a factor analysis of the extent to which demand forecast, order modification, and inventory responsibility are specified. Monitoring is based on a factor analysis of the extent to which the customer is allowed to audit and inspect the work of EMS. Repairs takes the value of 1 if the contract specifies repair services. Supply Chain Management is defined similarly. The contracts included in the Panel A analyses offer either repair or supply chain services (in addition to manufacturing services). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. Number of Services indicates the number of services that the supplier provides the customer and takes values from 1 to 4. Duration is contract duration in months. 33
  36. 36. TABLE 4 Determinants of Supplier Relation-Specific Investment Supplier Relation-Specific Investment = α + β1*(Collaboration) + β2*(Contract Duration) + β3*(Customer Size) + εi Panel A: Collaboration Defined as Collaborative Tone Predictor Coefficient (t value, p value) Collaboration 0.240 (Z = 2.186, p < 0.03) Customer Size 0.875 (Z = 2.152, p < 0.04) Duration -0.003 (-0.435, p < 0.664) Intercept -2.872 (3.554, p < 0.01) N 87 Adjusted R2* 0.093 Chi-square (Pearson)** 73.124 (0.344) Panel B: Collaboration Defined Based on Factor Analysis Predictor Coefficient (t value, p value) Collaboration 0.353 (1.805, p < 0.08) Customer Size 0.920 (2.252, p < 0.03) Duration -0.001 (-0.182, p < 0.856) Intercept -1.434 (-4.374, p < 0.01) N 87 2* Adjusted R 0.064 ** 80.517 (0.162) Chi-square (Pearson) Notes: Coefficients that are significant at p < 0.10 or better are boldfaced. Supplier Relation-Specific Investment takes the value of 1 if the supplier owns the relation-specific asset. In Panel A, Collaborative Tone is coded based on the tone of the contract from 1 to 10, where 1 implies least collaborative and 10 implies most collaborative. In Panel B, Collaboration is constructed based on a factor analysis and is a combination of the following three contract provisions: collaborative tone (1–10), sharing of information (one-sided or mutual), and extent of sharing (limited or extensive). Customer Size is an indicator variable that indicates whether the customer is in the top 33 in terms of revenue. Duration is contract duration in months. *Based on results from OLS. OLS results are consistent with the results from the PROBIT. ** Pearson goodness-of-fit test. A well-fitting model has an insignificant Chi-square statistic. 34
  37. 37. APPENDIX Examples of Variables from Contracts Type of service This Exhibit describes Supplier’s contractual requirements for implementing [customer] direct customer fulfillment supply chain initiative solely with respect to business of [customer] anticipated to be conducted with Customer(s), as defined below. Supply Chain Management This section documents the extended supply chain planning process which addresses the coordination of [customer] forecasted demand and [customer] related material planning, capacity planning and collaborative supply commitment. At Company’s request, Supplier shall repair the Products listed in Schedule 1 and perform all the necessary tests specified by Company in order to verify compliance of the repair services being performed under this Attachment G. Supplier agrees to work with Company in support of Company’s maintenance agreements with its end customers concerning all required tests, ATP replacements and metrics. This Attachment addresses (a) the repair of Product by Supplier at Company’s request subject to Supplier’s obligations to company as set forth in Article 23 – WARRANTY and Article 24 – REPAIRS Repair NOT COVERED UNDER SUPPLIER’S WARRANTY of the Electronics Manufacturing Services Agreement No. _ made between Company and Supplier (the “Agreement”), of which this Attachment is made a part, and (b) other products manufactured by third parties. The Products subject to this Attachment are set forth in Schedule 1 hereto. The Supplier will be responsible to perform for the Customer testing, diagnostic and repair works of [customer] equipment [customer] on the territory of [customer] and return it back to the Customer. Production 1.2.8 provide design for manufacturability and technical support Modification services to [customer] new and existing Product development teams in accordance with [customer] then-current processes and procedures, which current processes and procedures are described in Attachment F (“Assembly/Test Requirements”), Attachment L (“Supplier Quality Requirements”) and Attachment P (“Development and Engineering Change Requirements”), as product ideas evolve into potential new Products or improvements to existing Products; 35
  38. 38. [Customer] may contract the development of new product and enhancements to existing Products. EMS shall work with the Product development teams in [customer] to assist in the design of new product and shall have design personnel co-located at [customer] facilities, as mutually agreed upon and set out in the applicable VSHA. Subject to Clauses 27.2 and 27.3 this Agreement will continue until terminated. 17.1 This Agreement becomes effective on the Effective Date and, unless terminated sooner in the accordance with Section 17.2, Contract duration 17.3, 17.4, 17.5 or 17.6, shall remain in effect for a period of five (5) years thereafter. This Agreement shall be effective on the Effective Date and shall remain in force for one (1) year. Uncertainty [Customer] shall provide EMS monthly with a non-binding 6 months rolling forecast (“Forecast”) for each Product. EMS understands and agrees that the Forecast is an estimation of quantities required. EMS will provide to [customer] the written production plan within 5 (five) working days (after EMS receives materials delivery confirmation from [customer]) after receiving each forecast. Company will use reasonable commercial efforts to provide to Supplier, at a minimum, a rolling 6-month forecast. The forecast will be updated periodically and tied to Company’s demand planning process for all Products required under this Agreement Forecast (the “Forecast”), and Supplier shall acknowledge delivery capability as called for in the forecast documents or firm Orders placed by Company. The parties will mutually agree as to the definition supplied within the Forecast (i.e. weekly or monthly schedules). Except as otherwise provided for in the terms of any Flexible Delivery Agreement agreed to by the parties, all Forecasts, whether for use by Supplier or Supplier’s Material vendors, are for planning purposes only and do not constitute a commitment to purchase by Company except for the liability set forth in Article 10 EXCESS FINISHED GOODS AND WORK IN PROCESS INVENTORY and Article 11 EXCESS UNIQUE MATERIAL INVENTORY. Downside Purchase orders are subject to a variation of plus or minus 20% 36
  39. 39. provided that if [customer] wishes to increase the volumes by more than 20%, Y shall use its best endeavor to meet [customer] requirements. [Customer] may vary the volumes for week 13 and thereafter by plus or minus 100%. [Customer] ’s sole liability and the sole remedy for Company with respect to a downward variance shall be payment by [customer] as set forth in this Section 9(a). Company may at any time terminate an Order for Services without cause, in the whole or in part, upon written notice to Supplier. In such case, Company’s liability shall be limited to payment of the amount due for the Services including any materials purchased or which have been ordered by Supplier and are non-cancelable or non-returnable up to and including the date of termination (which amount shall be substantiated with reasonable proof to Company) and no further Services pursuant to such terminated Order will be rendered by Supplier. Such payment by Company shall constitute a Cancellation full and complete release and discharge of Company’s obligations. In no event shall Company’s liability exceed the price identified in the applicable Order for the Services being terminated. Company may at any time terminate a Repair Order or Repair Orders in whole or in part, upon written notice to Supplier and no further repair of Products pursuant to such terminated Repair Order or Repair Orders will be rendered by Supplier. Any Product that has not been ordered within the past six (6) months and for which there is no forecasted demand in the Market Forecast shall be reviewed during the QBR process to determine the disposition of that Product. At the end of every calendar quarter, Supplier shall identify to Company, Unique Material in Supplier’s inventory that is in excess of 135 days of supply. Such inventory shall be defined as Obsolete Inventory “Excess Unique Material Inventory” provided that it was the result of either Company’s complete or partial termination without cause of an Order, a change in Specifications, an Engineering Change Order, discontinuance of a Product or a change in Forecast; and was purchased by Supplier consistent with the vendor’s lead-time and the delivery requirements of Company’s Orders and Flexible Delivery Arrangements. Excess Inventory Exceptional Excess Inventory shall be defined as Inventory that is Unique Inventory to [customer] including Work in Process and Finished Goods, and is on hand or on order but not within Time- to-Cancel, as a result of forecast reductions that, by Product 37
  40. 40. Family, exceed 50% over a two month period, in accordance with the methodology set out in Exhibit 20 (“Exceptional Excess Inventory”). Such FGI, as defined in this article 10.1, shall be considered “Excess FGI” provided that it was the result of Company’s complete or partial termination without cause of an Order, change in Specifications, Engineering Change Order, or change in Forecast and was manufactured consistent with Supplier’s manufacturing cycle times and the delivery requirements of Company’s Orders and Flexible Delivery Arrangements. Three weeks of supply as used in this Article 10.1 will be determined by adding the prior four weeks of dollar shipments by Product family and the next four weeks of forecasted demand and multiplying the total by three eighths (3/8). Monitoring factor Access to assembly Lines used to manufacture Products shall be limited to those Company employees who have a need for access, and no other party shall have access without [customer] prior written consent. [Customer] personnel shall have access to such Assembly Lines, subject to Company’s Standard plant security and safety rules, Confidential Information obligations, and upon reasonable advance notice. Supplier shall allow Company’s customer(s) to conduct onsite Facility inspection evaluations of Company’s Product, or allow for inspection of Company’s Product by Supplier or Company, given Company’s customer inspection requirements. [Customer] shall have the right to review EMS facilities, operations, and procedures as they relate to the Products at any reasonable time with adequate prior notice for purposes of determining compliance with the requirements of this Agreement. Record inspection EMS shall maintain, and provide [customer] request copies of or access to, appropriate records in a manner sufficient for the Parties to ascertain [customer] compliance with the requirements of this Agreement. [Customer] shall be entitled to review and inspect all relevant manufacturing records (including reasonable backup documentation to substantiate the charges payable under the quoted Bills of Materials and agreed Pricing Models between the parties), in order to verify that Manufacturer is in due compliance 38
  41. 41. with all of its contractual and legal obligations under this Agreement. Upon [customer] request and at [customer] cost, EMS shall cooperate in the audit of its records in the manner set forth below, for the purpose of confirming compliance with this Agreement. [Customer] may retain the services of a major, independent accounting firm, other than the accounting firm(s) employed as primary outside auditors of [customer] . Audit of records Company shall, at its cost and expense, have the right exercisable on a semi-annual basis upon reasonable notice to Supplier during Supplier’s normal business hours to have a nationally recognized accounting firm that has executed a non-disclosure agreement reasonably acceptable to Supplier, to examine and audit (“Audit”) the necessary records described in Article 33.1 to confirm conformance to the terms of this Agreement. Collaboration Factor Company and Supplier acknowledge that a strategic relationship is required in order to insure the ongoing continuity of supply and service to Company’s end customers. To that end, both parties agree to establish a Strategic Alliance Team, which will meet quarterly, coinciding with the Quarterly Performance Review Process, as described in Article 27 QUARTERLY PERFORMANCE REVIEW PROCESS. In addition, Supplier shall appoint a senior operations executive and the parties shall agree on a governance model for managing the relationship including accountability metrics that the senior operations executive shall meet for Company and Supplier. Extensive sharing of information The purpose of the ER is a senior level update of each Party’s corporate developments, roadmaps, strategies, etc; Top level review of Scorecard performance and issues; Highlight of any specific issues that the Parties collectively decide to focus on (including such matters as System Staging, Global Supply Chain, Systems Test Engineering etc). This is an opportunity for the respective Senior Management of the Parties to meet at least twice a year to ensure and maintain overall alignment in the relationship. The QMRs provide much of the input that is reviewed at these executive level reviews. Mutual sharing of In addition to all the above, [customer] recommends regular information executive communications between [customer] Executive Team 39
  42. 42. (typically GAE, and occasionally _, etc), with Y Executive Team, & major customers – through lunches, dinners, conference calls etc. This is a process that needs to be actively managed and encouraged by the GAM, to ensure the right executives stay in touch with each other in both formal and informal environments so as to enhance the overall relationship. Parties agree to seek best in class supply chain costs through a total cost of ownership business model that includes operating and capital expenses. Furthermore, both parties agree that it will be the responsibility of the Strategic Alliance Team to establish and document detailed process and information flows, procedures and guidelines applicable to the process management required to facilitate timely delivery of Products, Commercially Purchased Items and Services as described in this Agreement. For the Team of this Agreement, Supplier agrees to maintain in working condition any unique Tooling purchased by Supplier or consigned by Company performing all routine and other maintenance including reasonable calibration as may be required in order to maintain the unique Tooling at the same level of functionality as when Supplier purchased or Company consigned such unique Tooling. All Reserved Assets in EMS’s custody or control shall be held at EMS risk and be kept insured by EMS at EMS expense in Supplier relation- accordance with the provisions of Section 24.2, with loss payable specific investment to [customer] and EMS as their interests appear. EMS shall use such Reserved Assets solely in the performance of its obligations hereunder. For Supplier developed [customer] Tooling, Supplier shall provide complete tool design drawings to [customer] SE for approval prior to construction or [customer] tooling. Tool Approval/First Article Inspection. Supplier shall provide to [customer], for its approval, data obtained form a 100% inspection of all dimensions/specifications of the initial parts produced to evaluate the tooling and set-up. 40

×