environmental determinants of vmi adoption: an exploratory analysis

Transportation Research Part E 43 (2007) 355–369

www.elsevier.com/locate/tre

Environmental determinants of VMI adoption:An exploratory analysis

Yan Dong a,1, Kefeng Xu b,2, Martin Dresner c,*

a Department of Marketing and Logistics Management, University of Minnesota, The Carlson School of Management,

Minneapolis, MN 55455, United Statesb Department of Management Science and Statistics, University of Texas, San Antonio, College of Business,

6900 North Loop 1604 West, San Antonio, TX 78249, United Statesc Department of Logistics, Business and Public Policy, Robert H. Smith School of Business, University of Maryland,

College Park, MD 20742, United States

Received 31 January 2005; received in revised form 5 July 2005; accepted 10 January 2006

Abstract

This study empirically examines the determinants of adoption of Vendor Managed Inventory programs that haverecently gained popularity in many industries. To achieve this goal, survey scales are adapted and developed for buyerand supplier market competitiveness, product demand, buyer operational uncertainty, and buyer–supplier cooperation.Based on the analysis of responses from purchasing managers in three industries, structural equation modeling results sug-gest that the competitiveness of the supplier’s market and buyer–supplier cooperation are positively associated with VMIadoption, while operational uncertainty for the buyer is negatively associated with VMI adoption. Managerial implicationsand limitations of the study are also noted.� 2006 Elsevier Ltd. All rights reserved.

Keywords: Vendor managed inventory adoption; Supply chain integration; Market competitiveness; Operational uncertainty; Empiricaltest; Structural equation modeling

1. Introduction

In the years since vendor managed inventory (VMI) was pioneered in the early 1980s by firms such as Wal-Mart and Proctor & Gamble, researchers have attempted to determine how this policy delivers benefits to par-ticipants. VMI is a supply chain system whereby a supplier (often a manufacturer) assumes responsibility formaintaining inventory levels and determining order quantities for its customers (often distributors or retail-

1366-5545/$ - see front matter � 2006 Elsevier Ltd. All rights reserved.

doi:10.1016/j.tre.2006.01.004

* Corresponding author. Tel.: +1 301 405 2204; fax: +1 301 314 1381.E-mail addresses: [email protected] (Y. Dong), [email protected] (K. Xu), [email protected] (M. Dresner).

1 Tel.: +1 612 625 2903.2 Tel.: +1 210 458 5388; fax: +1 253 679 7607.

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356 Y. Dong et al. / Transportation Research Part E 43 (2007) 355–369

ers). Implementation of VMI generally results in increased frequency of replenishments, similar to what mightbe expected under a continuous replenishment program (CRP).

A number of researchers have found significant benefits from VMI adoption. Waller et al. (1999) simulatedthe effects of VMI on supply chain inventories and concluded that VMI leads to a reduction in inventories,mainly resulting from more frequent inventory reviews, shorter order intervals, and more frequent deliveries.These results were largely confirmed by Cachon and Fisher (2000), who focused on information sharing.Cheung and Lee (2002) found that the value of VMI is realized, in part, by the ability of the supplier touse downstream demand information from several buyers to coordinate shipments. By coordinating ship-ments, VMI suppliers were able to deliver to their customers at higher frequencies without increasing trans-portation costs. In a more recent study, Kulp et al. (2004) showed that VMI had a significant impact on (thesupplier’s) profit margin.

These benefits are supported by industry accounts. For instance, to streamline its supply chain operations,Herman Miller, a leader in the industrial furniture industry, has significantly benefited from employing a sup-ply center for components, managed by the component suppliers (Sucher and McManus, 2002). Furthermore,a 2004 AMR survey of VMI indicates that VMI adopters observe an average 53% inventory reduction, 30–50% lead-time improvement, and retail in-stock improvement of 2–3% (http://www.glscs.com/archives/02.04.vmi.htm?adcode = 10).

The acceptance of VMI benefits in the trade literature is, however, not universally positive. For example,Spartan Stores, a co-op grocery chain in Michigan, decided to terminate its VMI program because of higherdelivery frequencies, inadequate forecasting capabilities by vendors, and inefficient coordination in promo-tions planning under VMI. Cooke (1998) states that VMI could stand for ‘‘very mixed impact’’, and describesa number of firms that have abandoned VMI. According to one grocery products retailer, VMI could impairvisibility in the supply chain by transferring decision making authority to suppliers. As the retailer states(Cooke, 1998, p. 1): ‘‘The farther you go up the supply chain, the harder it is to see what’s going on.’’ Bruceand Ireland (2002) state that data inaccuracies and poor data integrity limit the benefits that can be realizedfrom VMI, as do differences in technologies and systems employed by suppliers and buyers. As well, VMI maynot function because some firms in the supply chain may realize benefits from implementing this process, whileothers may experience costs. A firm that believes VMI is a cost burden may be reluctant to make the invest-ments required to make the process function. For example, Air Products and Chemicals (AP&C) was facedwith customers that wanted a VMI program to reduce their inventory levels. However, AP&C’s view was thatVMI would increase the firm’s administrative costs and consume the firm’s working capital (Gamble, 1994).

In this paper, we take a different tack from most of the previous research into VMI, and instead follow thetechnology adoption literature (e.g., Williams, 1994; Walton, 1994; Germain and Droge, 1995; Williams et al.,1998) by examining the environmental determinants of VMI based on a mail survey; that is, the conditionsunder which VMI is likely to be adopted. The environmental conditions we examine include the competitive-ness of the buyer and supplier markets, uncertainty levels in the buyer and supplier industries, and the coop-eration levels between the buyer and supplier. Using the results from a structural equation analysis of 137buying organizations, we find that a buyer adopts VMI to a greater extent when it has a large degree of coop-eration with its supplier, when it faces a low degree of uncertainty in its operations, and when the supplier’sindustry is characterized by a high degree of competitiveness.

Our research adds to the literature in two important ways. First, we empirically test determinants of adop-tion of VMI, rather than testing adoption of other supply chain initiatives. VMI differs from other supplychain technologies and processes, such as Just-In-Time (JIT), in that VMI requires the buyer to relinquish con-trol of an important aspect of its business, namely inventory replenishment. VMI also requires the implemen-tation of an information sharing process across the supply chain using a communication technology, such aselectronic data interchange (EDI) that is conducted either through traditional electronic means (telephoneline) or over the Internet. Although other supply chain technology programs, such as JIT, may be adoptedwith EDI (Germain and Droge, 1995, for example), EDI may not be critical to their implementation. Second,we investigate operational uncertainty and the competitiveness of both the buyer and seller markets as deter-minants of VMI adoption. These measures of operational uncertainty and market competitiveness, as adaptedfrom existing supply chain literature, characterize the operating environment of a firm and thus are shown tocritically influence the firm’s choice of VMI strategy.

http://www.glscs.com/archives/02.04.vmi.htm?adcode=10




Y. Dong et al. / Transportation Research Part E 43 (2007) 355–369 357

The rest of our paper is structured as follows: The next section provides a brief review of the literaturerelated to VMI and the adoption of technologies to facilitate supply chain management, presents the theoret-ical framework for the paper, and discusses our hypotheses. Section 3 provides a discussion of our researchmethodology, data, and results. Finally, conclusions, managerial implications, and limitations are discussedin Section 4.

2. Vendor managed inventory and technology adoption

2.1. Elements of VMI

The main feature of a VMI program is the transfer of inventory decision making from the downstream sup-ply chain firm (i.e., the buyer) to the upstream supply chain firm (i.e., the supplier). A VMI program typicallyinvolves the use of a software platform, the sharing of demand forecasts and/or cost information, timely com-munications, and common goal sharing between the buyer and the supplier (Waller et al., 1999; Daughertyet al., 1999; Dong and Xu, 2002). With VMI, a buyer must work with its supplier to set sound inventory man-agement goals, liability levels, and risk-sharing parameters (Baljko, 2002). VMI can be viewed as a SCM tech-nology, often adopted in conjunction with other SCM components, such as electronic data interchange.

Although the distinct feature of VMI is the transfer of decision making over inventory and orders, animportant element of VMI is the sharing of information (demand, inventory, etc.) between members of thesupply chain. As one analyst states, in order for VMI to function retailers must, ‘‘Give our suppliers allthe necessary information they need, give it to them real-time, and let them be an extension of us and helpus manage our component inventory’’ (Inventory Management Report, 2004). Echoing this view, Caputo(1998) argues that information flows must be continuous in VMI to enable manufacturers to formulate real-istic order proposals and make reliable provisions. Using the example of the household electrical appliancesindustry, De Toni and Zamolo (2005) state that the exchange and sharing of data and information throughoutthe whole supply chain is a key element of VMI implementation.

Goal sharing is also cited as an important element of a VMI relationship. Schenck and McInerney (1998)find that joint metrics are set by the retailer and the supplier in VMI agreements in the apparel industry, andthat both parties must have buy-in to the process. In the VMI implementation between Boeing and Alcoa,Micheau (2005) emphasizes that key success factors include representation of every interest advanced by eachfirm, and the sponsorship and support by top management in both companies. The goals of both partnersmust be transparent and shared.

In his study of the electronics industry, Kuk (2004) indicates that VMI initiatives are information intensiveand require effective database linkages among supply chain partners to facilitate information flows. He arguesthat the process of organizational change must be addressed before an IT investment such as VMI becomessuccessful. Pohlen and Goldsby (2003) suggest that collaborative relationships, such as those involving VMI,are unlikely to succeed without an established relationship built on trust and mutual benefit, since VMIrequires the alignment of functional performance with processes spanning multiple companies.

2.2. Supply chain technology adoption

A number of papers examine factors leading to the adoption of supply chain programs. Most of these focuson the adoption of electronic data interchange technologies. An important research stream in this veininvolves the sociological and behavioral dimensions leading to technology adoption. Key issues includecoercion from other supply chain members, internal needs, and top management support (Premkumar andRamamurthy, 1995). Williams (1994), Williams et al. (1998), Daugherty et al. (1995), and Walton (1994)examine the variables likely to lead to EDI adoption. Using surveys of Council of Logistics Management(CLM) members, the four papers find that a number of organizational variables, such as firm size and struc-ture, the decentralization of EDI adoption decisions, and formal benchmarking procedures, influence EDIadoption. Competitive variables, including the desire to stay competitive, reduce costs, influence channelmembers, and increase customer service, as well as external variables, such as industry competitiveness anddemand uncertainty, are found to affect adoption.


Suzuki and Williams (1998) examine factors influencing the resistance of firms to adopting EDI. Using asurvey of CLM members, the authors find that high levels of technological uncertainty, a low diffusion rateof EDI formats, and little improvement in processing time due to the use of EDI, are factors that mostincreased the resistance of firms to EDI adoption.

2.3. Research focus

The technology adoption literature outlines the conditions leading to the adoption and implementation ofsupply chain technologies. Most of these papers, however, focus specifically on the adoption of EDI. Since thetransfer of inventory control from buyer to supplier is an integral part of VMI, VMI is significantly differentfrom EDI and other supply chain technologies. Therefore, the conditions that lead to VMI adoption may besignificantly different from those that lead to the adoption of the other supply chain technologies. The mainintention of this research paper is to add to the technology adoption literature by linking key organizational,environmental, and competitive conditions to VMI adoption.

2.4. Hypotheses development

We have identified five factors that may be associated with the adoption of VMI by buyers. These factorsare as follows: Buyer’s Market Competitiveness, Supplier’s Market Competitiveness, Product Demand Uncer-tainty, Buyer Operational Uncertainty, and Buyer–Supplier Cooperation. Hypotheses for these factors aredeveloped below and outlined in Fig. 1.

2.5. Buyer’s market competitiveness

Competition has long been known as a major factor in the technology adoption literature. As an innovativesupply chain program that calls for reengineering efforts in both information technologies and supply chainprocesses, VMI can be adopted to respond to market pressure and competition. Gatignon and Robertson(1989) indicate that market competition increases the awareness of firms to competitor actions, which maylead to quick adoption of innovative technologies (also see Levin et al., 1987). In making early enterpriseresource planning (ERP) adoption decisions, firms tend to respond positively when they face strong compe-tition; later adopters however are more likely to respond to internal pressure (Waarts et al., 2002). Accordingto Rai and Bajwa (1997), competition can ‘‘promote the transition from a state of non-adoption to adoption’’of Executive Information Systems (EIS). As components of ‘‘environmental uncertainty’’, competition factorsare shown to have significant impact on adoption of telecommunications technologies (Grover and Goslar,1993) and supply chain technologies (Patterson et al., 2003).

Based on a survey of CLM members, Williams (1994) finds empirical evidence that industry competitive-ness significantly explains rates of Electronic Data Interchange adoption. Since EDI (either through a privatenetwork or web-based) is an integral part of VMI, it may be that the same relationship will hold for VMIadoption. The more competitive a market, the more likely a firm is to seek ways to improve efficiency, forexample, through VMI (Christensen, 2000). On the other hand, the ‘‘leakage effect’’ of information sharingmay discourage downstream firms in a competitive industry from sharing data through VMI with their supplychain partners (Li, 2002).

Supplier Buyer Customers

Supplier’s Market Buyer’s Market

VMI

Fig. 1. Supply chain structure and VMI.


The literature, therefore, indicates that the competitiveness of the buyer’s market may have opposing effectson the adoption of VMI. On one hand, increased competition encourages firms to improve their efficiency,thus pointing to a positive relationship between competitiveness in the buyer’s market and VMI adoption.On the other hand, firms in competitive markets may be reluctant to share information with suppliers, fearinga loss in their competitive standing should the information be leaked. On balance, we feel that the efficiencygoal may be stronger, in that firms that choose not to adopt efficient supply chain practices can find their com-petitive positions seriously eroded. In the competitive retail sector, we do see that cost efficient firms, mostnotably Wal-Mart, have been leaders in the adoption of VMI. Thus, our first hypothesis is as follows:

H1: Greater competitiveness in the buyer’s market is associated with a higher degree of adoption of VMI.

2.6. Supplier’s market competitiveness

The same efficiency argument made for the buyer’s market conditions can also be made for the supplier’smarket conditions. A competitive market will provide the incentives to the supplier to invest in technology toimplement a VMI program. As Fulcher (2002) states, by adopting VMI, suppliers gain visibility beyond theirorganizations, allowing them to minimize inventory costs while maintaining service levels. From a long-termperspective, a competitive supplier’s market may make suppliers more willing to invest in the technology tosupport VMI. Investing in VMI, in turn, makes connections between suppliers and buyers more ‘‘asset spe-cific’’, thereby cementing the buyer–seller relationship.

A competitive supplier’s market also may afford a buyer stronger channel power relatively to a supplier,and make it easy for the buyer to enforce a system or technology that is more advantageous to its own oper-ations, as found in an empirical study of EDI adoption behavior (Williams, 1994). Large retail buyers, such asWal-Mart, have used their relative dominance over their suppliers to enforce the adoption of supply chaintechnologies and systems, such as VMI. Robertson and Gatignon (1986) identify supply side competitivenessas an important factor affecting adoption of innovative technologies. Waarts et al. (2002) further show thatsupply side competitive activities have a positive and significant impact on both early and late adopters ofERP. This impact is even stronger than the impact of the competitiveness facing the (buyer’s) market. There-fore, our second hypothesis is as follows:

H2: Greater competitiveness in the supplier’s market is associated with a higher degree of adoption of VMI.

2.7. Product demand uncertainty

There are two counteracting effects arising from demand uncertainty. On one hand, a supplier might bebetter able to use VMI to optimize a supply chain system (e.g., by coordinating orders, planning production,and/or consolidating distribution, across customers) when facing relatively stable demand. For instance, sim-ulation results in Yang et al. (2003), or case studies results in Clark and Hammond (1997), support the findingthat VMI is most appropriate for products with stable, predictable, demand patterns. If VMI is to be imple-mented for products with high demand variability, there clearly has to be some effort by the vendor to mitigatethe stockout costs at the retail level.

On the other hand, one of the major advantages of programs, such as VMI, is that they allow for consumerdemand information to be disseminated up the supply chain, thus reducing upstream demand fluctuations dueto the bullwhip effect (Lee et al., 1997). The greater the product demand uncertainty in the consumer market,the greater the need for VMI to reduce this uncertainty for upstream firms in the supply chain.

This argument, that firms will integrate their operations with other firms in the supply chain to reduceuncertainties, fits nicely into the Transaction Cost Economics (TCE) framework (Coase, 1937; Williamson,1975), as firms seek to organize in ways that reduce transaction costs. Using the TCE framework, Walkerand Weber (1987) state that firms are more likely to vertically integrate as uncertainty increases so as to reducethese uncertainties. In their empirical study, they find, however, no clear relationship between uncertainty and


vertical integration, indicating that other factors, such as the competitiveness of the industry, may influencethis relationship.

John and Weitz (1988) use TCE to compare the use of direct and indirect distribution channels. Theyhypothesize that as downstream environmental uncertainties increase, manufacturers will more likely relyon direct distribution channels in order to internalize these uncertainties. They also define ‘‘behavioral uncer-tainties’’ as uncertainties arising in the context of the exchange relationship between a buyer and a supplier; forexample contractual difficulties. They posit that there should be a positive relationship between behavioraluncertainties and direct distribution as well. In their empirical analysis of 87 industrial firms, they find supportfor both of these hypotheses.

MacMillan et al. (1986) also use the TCE framework to examine the relationship between uncertainty andintegration but posit a hypothesis opposite to those proposed by Walker and Weber (1987) and John andWeitz (1988). The authors state that sales volatility will discourage firms from integrating backwards into theirsupplier industries. Firms can avoid uncertainty by refusing to integrate into a volatile industry. However, aswas the case with Walker and Weber (1987), the results indicate the relationship between integration anduncertainty may be complex. The authors find a negative relationship between integration and sales volatilityamong the consumer products firms, but the opposite relationship among capital goods and component sup-pliers businesses.

Using the TCE framework, Lieberman (1991), too, examines the relationship between supply chain integra-tion and uncertainty and finds a complex relationship. He posits that a buying firm is more likely to integratebackwards into the supplying industry when the demand for inputs is highly variable. However, he also positsthat a buying firm is less likely to integrate backwards if it faces large fluctuations in its own market demand.Using a sample of 34 chemical producers, Lieberman (1991) finds support only for the first of these twohypotheses; i.e., that firms integrate backwards when faced with a volatile input market.

In a broad sense, Droge and Germain (1998) define environmental uncertainty to include such demand sidecomponents as ‘‘fluctuating prices, unpredictable competitor actions, volatile levels of demand, and/or quickproduct obsolescence.’’ Patterson et al. (2003) propose that firms operating in an uncertain environment tendto adopt new technologies to respond to the changing conditions. One of the major uncertainties found toaffect EDI and delivery performance, according to Ahmad and Schroeder (2001), is unpredictability ofdemand. Flint and Mentzer (2000) propose that environmental uncertainty may predict a firm’s reliance onradical process innovation, such as VMI.

Given the mixed results, it is difficult to posit a hypothesis between uncertainty in product market demandand the adoption of VMI. However, we believe that the preponderance of evidence supports the view thatVMI may be employed to overcome these uncertainties. Therefore, Hypothesis 3 is as follows:

H3: Greater product demand uncertainty in the buyer’s market is associated with a higher degree of adoptionof VMI.

2.8. Buyer operational uncertainty

Logistics managers are concerned with the effects of operational uncertainty on technology adoption andorganization design. For instance, the environmental uncertainty factors that would affect the design of alogistics organization in Droge and Germain (1998) include such operational uncertainties as unreliabilityof inbound supplies and rapid change in production processes. Ahmad and Schroeder (2001) also identifyuncertainty factors that affect EDI and delivery, which include unreliable suppliers, machine breakdowns, pro-duction schedule changes, inefficient inventory management, etc. As a buyer’s operations become leanerthrough the elimination of waste in its procurement process, an uncontrolled or unstable re-supply scheduleposes great risks and potential costs.

As was the case with product demand uncertainty, the TCE framework can be used to analyze the likelyrelationship between buyer operational uncertainty and VMI usage. As noted above, both John and Weitz(1988) and Lieberman (1991) found a positive relationship between supply chain integration and measuresakin to buyer operational uncertainty. John and Weitz (1988) found that behavioral uncertainties between


the upstream and downstream industries lead upstream firms to forward integrate into the distribution indus-try. Lieberman found that buyer firms integrate backwards when faced with a volatile input market.

However, there is a significant difference between a VMI operational relationship and vertical integration.Whereas a firm may ‘‘solve’’ a relational difficulty by integrating forward or backward in the supply chain, afirm may complicate a relational difficulty by entering into a VMI arrangement. If a supplier observes a highlyunstable pattern in the buyer’s operations, the supplier may hesitate to implement a VMI program, as inte-gration through VMI may transfer this instability into the supplier’s own logistics/production processes(Taylor, 1999). Cetinkaya and Lee (2000) find that VMI allows for a more coordinated transportation systemby consolidating shipments under a dispatch time. Uncertainty in lead times and in the transportation system,however, makes such coordination difficult. In addition, the buyer organization faces uncertainties due to fluc-tuations in replenishment times, materials quality, and other operational factors, largely determined by sup-plier capabilities. These operational uncertainties will significantly affect a buyer’s ability to satisfy its owncustomer demand on a timely and reliable basis. If these operational uncertainties are high, a buyer organi-zation may be reluctant to release control of its inbound logistics processes to a supplier that may be the maincause of these uncertainties.

Therefore, our fourth hypothesis is as follows:

H4: Greater levels of buyer operational uncertainty are associated with a lower degree of adoption of VMI.

2.9. Buyer–supplier cooperation

It is clear that cooperation in other areas (e.g., cross-firm team building, shared-cost programs, etc.) canindicate that a trusting relationship has developed between the buyer and supplier leading to conditions thatare conducive to the adoption of VMI (Hart and Saunders, 1997). A buyer that has collaborated with a sup-plier in multi-functional areas, such as product design (R&D), joint-team problem solving (operations), andthe sharing of joint-cost savings (financial management), has demonstrated a great degree of trust and engage-ment with the supplier. Hausman and Stock (2003) show that when adopting innovative technologies, such asEDI, firms need to develop long-term coordination in their relationships. Cachon (2001) demonstrates thatinventory cooperation, via VMI, for example, may lead to supply chain efficiency. It would be a natural stepto extend this level of trust and engagement to the logistics management area. Therefore, it is expected thatfirms that have greater degrees of cooperation in general, would be more likely to employ VMI. Our fifthhypothesis, therefore, is as follows:

H5: Greater levels of buyer–supplier cooperation are associated with a greater degree of adoption of VMI.

Fig. 2 summarizes our model including the direction of the hypotheses.

3. Methodology and results

3.1. Sampling

A survey method was used to gather data for this paper. The development of the survey and the samplingprocedures followed Dillman (2000) and Churchill (1979). Seven-point Likert scales were used to measure sur-vey responses. Respondents were asked to what extent (from 1 = ‘‘to no extent’’ to 7 = ‘‘to a great extent’’)they believed each statement in the questionnaire, or to what extent they agreed or disagreed with each state-ment in the questionnaire (1 = ‘‘strongly disagree’’ to 7 = ‘‘strongly agree’’). A pretest was conducted beforethe questionnaire was finalized to reduce measurement error (Churchill, 1979). The preliminary questionnairewas pre-tested both with logistics managers working for major manufacturing companies and with logisticsprofessors. Modifications were made to the questionnaire before the final survey was mailed.

The research sample consisted of purchasing managers selected from the subscriber list of Purchasing mag-azine. Purchasing managers are in positions that span the boundaries between their organizations and their

VMI adoption

Buyer’s MarketCompetitiveness

Supplier’s MarketCompetitiveness

Buyer-SupplierCooperation

Product Demand Uncertainty

Buyer Operational Uncertainty

H1 (+)

H2 (+)

H3 (+)

H4 (-)

H5 (+)

Fig. 2. VMI adoption structural model.


suppliers (Webster, 1992). Thus, purchasing personnel are likely well informed about their firm’s inboundlogistics activities, as well as the processes of their suppliers, particularly given the significant expansion ofthe purchasing professional’s job content over the past several years (Tracey et al., 1995; Pooley and Dunn,1994).

The industries selected for the sample were Industrial Machinery and Equipment (SIC 35), Electronic andOther Electrical Equipment (SIC 36), and Transportation Equipment (SIC 37). In choosing these industries,we were able to obtain a good sample of firms that are heavy users of VMI and, as a control group, a sufficientsample of firms that use little or no VMI. Furthermore, by investigating the product categories for which thepurchasing managers in these industries are most likely responsible, we determined that common purchasesincluded electronic components. Thus, in assessing their supply chain relationships, purchasing managers wereasked to focus their answers on a firm supplying ‘‘. . . critical electrical/electronic components or supplies thatare used in your firm’s major end product(s).’’

The survey and a follow-up postcard were mailed following Dillman’s (2000) approach to 2305 individuals.Companies included in the sample had at least 250 employees. Only one person per plant site was sampled inorder to obtain independent observations from the respondents, and to prevent over-sampling from largecompanies and plants (due to multiple responses). A total of 159 questionnaires were returned by respondents,although 35 were not usable due to a large number of incomplete responses. Thirty-seven questionnaires werereturned as undeliverable, resulting in a final response rate of 7.0%.3

The low response rate was likely due to a number of factors, including the reluctance of busy managers tocomplete surveys (survey burn-out!), the length of the survey, the size of the database, the national approach(instead of regional approach), and the size of our budget. In approaching this survey, we had two distinctoptions, given our budget. We could either send the survey to a relatively small number of individuals andconduct substantial follow-up contacts, or, send the survey to a large number of individuals with fewer fol-low-up notices. Neither of the options is, per se, better than the other. We decided to use the latter approach

3 Low response rates have become a more common phenomenon recently. After analyzing 15 years worth of published research inlogistics, Larson (2005) pointed out that response rates in two major logistics journals, International Journal of Physical Distribution and

Logistics Management and Journal of Business Logistics, are in the range of 2.5–97.7% and 4.3–93.3%, respectively. In particular, he foundthat the more surveys that are sent out for a particular project, the lower the response rate, and that the average response rate decreased ata rate of about one point per year from 1989 to 2003. This decline coincides with the increasing use of survey research among logisticsresearchers. Other published research using large scale mail-surveys in the related Operations Management area have also obtained lowresponse rates, such as Griffin (1997) – 2.7%, Koufteros et al. (2001) – 10% (even with the co-sponsorship of a professional organization ina survey of its members), Hong et al. (2005) – 9.1%. Our response rate, though relatively low, is in line with these recent research projects.


since that was likely to produce the greater sample size so that we would be better able to conduct structuralequation modeling.

Given the low response rate, precautions were taken to test for the presence of non-response bias. Two testswere conducted. In the first test, comparisons were made between survey answers from early and late respon-dents (i.e., those that responded after the reminder notice was sent out) (Armstrong and Overton, 1977). Amultivariate t-test yielded no significant mean difference between the early and late respondents at the 0.05significance level. In the second test, 20 surveys were mailed to a randomly selected group of the non-respon-dents, of which 13 were returned. A multivariate t-test was employed to test for any overall difference amongthe study’s major variables between the respondents and the 13 ‘‘non-respondents’’. No significant differenceswere found between the two samples, suggesting the absence of non-response bias. Thus, the 13 questionnaireswere merged with the earlier responses, providing a total of 137 surveys in our sample.

3.2. Constructs

Appendix A lists the questions used to form the six constructs in our model. The construct for VMI adop-tion includes a direct question related to the extent that inventory is managed by the supplier, and three otherquestions corresponding to information and goal sharing common with VMI programs.4 As indicated above(section on Elements of VMI), this construct is consistent with the typical definition of VMI cited in theliterature.

Competitiveness in the buyer and supplier markets is assessed by questions related to the number and typeof firms competing for business (e.g., the existence of dominant firms). Product demand uncertainty is mea-sured by the instability or unpredictable nature of demand, demand forecasts, and market prices. Operationaluncertainty is assessed by the instability or unpredictable nature of lead times, order cycle times, the inboundproduct inspection process, and materials and service quality. Finally, buyer–supplier cooperation is measuredby supplier involvement in a buyer’s product or system design process, the existence of multifunctional buyer–supplier teams, and the sharing of joint cost savings between buyers and suppliers.

Confirmatory factor analysis was used to evaluate the reliability and validity of the study’s constructs(Anderson and Gerbing, 1988). Factor loadings and reliability measures are shown in Appendix A while theoverall Fit statistics are reported in Appendix B. The Covariance Analysis and Linear Structural Equations(CALIS) procedure in SAS 8.2 was used to conduct the confirmatory factor analysis (CFA) and structuralequation modeling (SEM). The Bentler’s (1989) Comparative Fit Index (CFI), and Bentler and Bonett’s(1980) Non-normed Fit Index (NNFI) were both above the 0.90 recommended levels at 0.9707 and 0.9632,respectively. The Goodness of Fit Index (GFI) measure was only marginally below the 0.9 level at 0.8948.All factor loadings were large and significant (P < 0.0001), providing evidence of convergent validity (Gerbingand Anderson, 1988). All constructs displayed composite reliability values (Fornell and Larcker, 1981) in excessof the 0.60 minimum for exploratory studies (Churchill, 1979; Flynn et al., 1990; Van de Ven and Ferry, 1978).

The chi-square difference test between the standard measurement model and a fixed measurement modelwas used to assess discriminant validity among the constructs (Anderson and Gerbing, 1988; Bagozzi andPhillips, 1982). This test indicated that discriminant validity exists between the constructs, and thus the factorscan be modeled separately. Finally, there were no large, standardized residuals. Together, these findings sug-gest that the scale items displayed in Appendix A are reliable and valid indicators of the study’s constructs.

3.3. Hypotheses testing

The results for the full latent structural model are displayed in Fig. 3. The R-squared for the predictivemodel is 0.5154, indicating that the explanatory variables accounted for just over 50% of the variation in

4 Alternate constructs for VMI adoption were also tested. One alternate construct included only two of the four survey questions – ‘‘Aninventory system managed by this supplier’’ and ‘‘Information sharing with this supplier’’, while a second alternate construct used threequestions, adding ‘‘Timely communications between you and this supplier’’ to the two questions, above. Structural equation modelingresults with the alternate constructs for VMI adoption were largely similar to those reported, but construct reliability measures fell belowacceptable levels.

VMI adoption

Buyer’s MarketCompetitiveness

Supplier’s MarketCompetitiveness

Buyer-SupplierCooperation

Product Demand Uncertainty

Buyer Operational Uncertainty

-0.1034

0.2111**

0.1372

-0.2205**

0.6163***

Fig. 3. Results from the structural model. Note: **p < 0.05; ***p < 0.01. N/S = not significant.


VMI adoption. Fig. 3 indicates that of the five hypotheses tested, three are significant in the direction, as pre-dicted, while two are not significant. The three hypotheses that are supported by our model (H2, H4, and H5)suggest that the competitiveness of the supplier’s market and buyer–supplier cooperation are both associatedwith higher VMI adoption rates, while operational uncertainty for the buyer is associated with lower VMIadoption rates.

3.4. Discussion

In this paper, we do not measure the impact of VMI on performance. This has been done in a number ofother studies (e.g., Clark and Hammond, 1997; Raghunathan and Yeh, 2001; Cachon, 2001). Instead, weexamine the conditions under which VMI is adopted. To the extent that adoption is a revealed preference,then our findings can provide an indication as to the conditions under which managers, using their better judg-ment, view VMI as appropriate.

The decision to adopt VMI is risky, both for suppliers and their customers. The decision is risky for sup-pliers because they take on additional costs and responsibilities by managing customer inventories. Unlessthere are sufficient cost savings that can be derived from employing VMI, the suppliers could end up losingmoney. The decision is especially risky for buyers since they transfer managerial control over the replenish-ment process to their suppliers. Unless the suppliers provide inventory in sufficient quantities to satisfy thedemands at the customer locations, the service levels at the downstream firms could deteriorate resulting inlost business and market share.

An important question becomes, therefore, under what environmental conditions it makes most sense toadopt VMI. Transaction Cost Economics argues that VMI should be employed if its adoption reduces uncer-tainties in the exchange process and makes the organization of the process more efficient. However, based onthe TCE literature reviewed, there does not appear to be a clear association between the adoption of a pro-gram such as VMI and its effects on uncertainties (e.g., MacMillan et al., 1986; Walker and Weber, 1987;Lieberman, 1991). As well, there may be other environmental and operational conditions that make the adop-tion of VMI attractive to firms.

We hypothesize that increased competitiveness in both the buyer and supplier markets will increase theextent to which VMI is adopted, given that increased competition may drive firms to programs, such asVMI, in order to remain competitive. We found that VMI adoption is only associated with increased compet-itiveness in the supplier’s market. A number of authors have shown that suppliers may be more likely tobenefit from VMI than buyers. For example, Waller et al. (1999) state that VMI benefits are likely to besignificant for manufacturers (suppliers) even under low VMI adoption rates. Manufacturers could expect


to experience increased capacity utilization and production smoothing under VMI. A VMI supplier in a highlycompetitive environment could gain better knowledge about its customer requirements and ‘‘lock in’’ its cus-tomers more firmly than can its competitors that do not adopt VMI. These results may explain the greaterassociation between VMI adoption and competitiveness in the supplier’s market, as opposed to the buyer’smarket.

Our third and fourth hypotheses are that increased demand uncertainty in the product (end consumer) mar-ket will be associated with greater VMI adoption (H3) while operational uncertainties will be associated withlower VMI adoption (H4). Our findings add to the mixed results reported in previous studies on the connec-tions between uncertainties and integrative relationships (e.g., MacMillan et al., 1986; Walker and Weber,1987; Lieberman, 1991). H3 was not significant while H4 was supported. Since a company facing large uncer-tainties in its inbound logistics process (often as a result of the poor performance of a supplier) is not likely torelinquish control of this process to a supplier, the findings regarding H4 are not surprising. On the otherhand, the insignificant results from H3 seem to be consistent with the simulation findings in Waller et al.(1999) that demand volatility does not play a significant role in determining the likely benefits of VMI. In addi-tion, there may also be supplier resistance to the implementation of a VMI program under high buyer demanduncertainties. A supplier may see a much larger inventory burden associated with high demand fluctuations inthe buyer’s market if it takes control of the buyer’s inbound operations.

Finally, H5 predicts that higher levels of buyer–supplier multifunctional cooperation in areas such as prod-uct/system design, service support, or joint-cost savings, will be associated with a higher degree of adoption ofVMI. This relationship was supported. VMI is a form of buyer–supplier cooperation that requires a degree oftrust between the implementing firms. It only stands to reason that firms establishing VMI systems also haveother cooperative agreements in place. In other words, existing collaborations between the two companies inother business areas would certainly facilitate the adoption of a VMI program.

4. Conclusions, managerial implications, and limitations

VMI is a supply chain system that has been employed by firms for over 20 years. However, the success ofVMI in industry is mixed (Lapide, 2002) and the extent of adoption varies significantly across firms and indus-tries. In this paper, we use a structural equation model to determine the environmental conditions under whichVMI is most likely to be adopted by buyer organizations. Based on responses from purchasing managers in137 firms, we find that the competitiveness of the supplier’s market and buyer–supplier cooperation are pos-itively associated with VMI adoption, while operational uncertainty for the buyer is negatively associated withVMI adoption. In conducting this research, we adapt and develop scales for buyer and supplier market com-petitiveness, product demand, buyer operational uncertainty, and buyer–supplier cooperation. A number oftests conducted using confirmatory factor analysis supported the validity and reliability of our constructs.

Determining the conditions under which VMI is adopted can be an important consideration for managers.Although adoption does not necessarily equate to success, it does provide an indication as to whether man-agers see some reason to employ a system. Our study shows that VMI is most often adopted within the contextof an existing cooperative relationship between a buyer and a seller. Therefore, if a current buyer–seller rela-tionship is more adversarial than cooperative, then this could be an indication that VMI adoption may not besuccessful.

Given the scale development required for this research project, and the limited response rate to our survey,the results from this work should be viewed as exploratory and might not be fully generalizable to other indus-tries. Further studies using similar scale items need to be conducted to confirm our results. The cross-sectionalmethod used for this study only allows for the confirmation of associative relationships. In the future, a lon-gitudinal study, if employed, may help determine causal relationships among the constructs – in particular,whether and to what extent VMI adoption leads to performance improvement. Finally, this study examinesVMI adoption from the purchasing organization’s perspective. Future research could develop the associativerelationships between the constructs and VMI adoption by suppliers, as well as compare and contrast thedeterminants of VMI adoption by buyers and suppliers. In the future, research could also investigate VMIadoption in the retailing sector, an early adopter of VMI. The research on the retail sector could offer insights,from a longer term perspective, into conditions that may facilitate VMI adoption.

Appendix A. Results of confirmatory factor analysis (Standardized factor loadings in second column)

Factor and item descriptiona Standardizedloading

Compositereliability

Buyer’s Market Competitivenessb

(How would you describe thecompetitive environment of your

company? Please indicate the extentof agreement with the following item.)

0.83

Many firms compete directly with you 0.9747Your industry has several dominant firms 0.7008

Supplier’s market competitivenessb

(How would you describe the competitiveenvironment of this particular supplier?Please indicate the extent of agreementwith the following item.)

0.80

This supplier has many competitors 0.6273There are a few big firms in this supplier’s industry 0.9747

Product demand uncertaintyc

(To what extent do you think the followingfactors are unpredictable or unstable?)

0.78

Product demand for your major product(s) 0.7948Demand forecasting for your major product(s) 0.8329Patterns of market price changes of your major product(s) 0.5544

Operational uncertaintyc (To what extent do youthink the following factors are unpredictable or unstable?)

0.80

Your lead times for inbound deliveries 0.7495Your purchase order cycle times 0.8809Your incoming product inspection processes 0.6514Your materials and/or service quality 0.5240

Buyer–supplier cooperation (To what extent do youand this supplier have thefollowing agreements and programs?)

0.78

Supplier involvement in your product/system design 0.8093Multifunctional teams with this supplier 0.8055Sharing joint cost savings with this supplier 0.5802

VMI adoption (To what extent do you and thissupplier have the following logisticsagreements and programs?)

0.70

An inventory system managed by this supplier 0.5500Information sharing (e.g. demand forecasts and costs) with this supplier 0.7030Timely communications between you and this supplier 0.5907The same goals shared with this supplier 0.5797

a All scale items were measured using seven-point Likert scales. Respondents were asked to what extent (1: to no extent and 7: to a greatextent) each statement in the questionnaire was true, or to what extent they agreed or disagreed with each statement (1: strongly disagreeand 7: strongly agree).

b Adapted from Williams (1994).c Adapted from, for example, Germain and Droge (1995).



Appendix B. Overall statistics for the measurement model

Fit statistics for the overall measurement model

Goodness of fit index (GFI)
0.8948 Chi-square 143.0580 Chi-square DF 122 RMSEA estimate 0.0356 Bentler’s comparative fit index 0.9707 Bentler and Bonett’s (1980) non-normed index 0.9632
Correlations among the structural model’s latent constructs

Construct
1 2 3 4 5 6
1. Buyer’s market competitiveness
1.0 2. Supplier’s market competitiveness 0.2958 1.0 3. Product demand uncertainty 0.1914 0.0871 1.0 4. Buyer operational uncertainty �0.0030 �0.0818 0.2751 1.0 5. Buyer–supplier cooperation �0.1260 �0.1428 �0.1018 �0.2710 1.0 6. VMI adoption �0.0917 0.1226 0.0125 �0.3667 0.6450 1.0
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environmental determinants of vmi adoption: an exploratory analysis

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