A global supply chain framework

A global supply chain framework

Industrial Marketing Management 33 (2004) 37 – 44 A global supply chain framework David J. Clossa,*, Diane A. Mollenkopf b a Department of Marketing...

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Industrial Marketing Management 33 (2004) 37 – 44

A global supply chain framework David J. Clossa,*, Diane A. Mollenkopf b a

Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University, N370 North Business Complex, East Lansing, MI 48824-1122, USA b Department of Marketing and Supply Chain Management, Eli Broad College of Business, Michigan State University, East Lansing, MI 48824-1122, USA

Abstract The relationship between supply chain competencies and performance has been somewhat elusive. The 21st Century Logistics framework, developed at Michigan State University is currently assessed as to its global relevance, particularly relating to performance. A sample of U.S. firms is compared to a sample of Australian and New Zealand firms to assess the robustness of the framework across different business environments as well as to better understand the supply chain competencies/performance relationship. Results suggest that the framework is reasonably robust across environments, although some improvements in future versions of the framework are suggested. Additionally, results confirm that supply chain competencies do lead to improved performance. Interestingly, supply chain competencies appear to be employed in different ways to create different performance advantages across the various business environments. D 2003 Elsevier Inc. All rights reserved. Keywords: Supply chain competencies; Performance; Global supply chain framework

1. Introduction The 21st Century Logistics framework, developed at Michigan State University and introduced in 1999, builds upon more than 15 years of research exploring leading logistics practices. While prior research had included international considerations, lending support to the 21st Century Logistics framework, the 21st Century Logistics framework was constructed based on domestic (U.S.A.) data and interviews (Bowersox, Closs, & Stank, 1999). Since its introduction, however, many authors have applied the framework to international environments. For example, Mollenkopf and Dapiran (1999) used the framework and survey instrument to benchmark the logistics capabilities and competencies of firms in Australia and New Zealand. Carranza, Maltz, and Antun (2002) used the framework to discuss and compare the logistics strategy of Argentinean firms. Morash and Lynch (2002) investigated the relationship between public policy and supply chain capabilities and performance in three global regions: North America, Europe, and the Pacific Basin. The 21st Century Logistics framework allows managers to identify and implement the competencies and capabilities * Corresponding author. Tel.: +1-517-353-6381; fax: +1-517-432-1112. E-mail address: [email protected] (D.J. Closs). 0019-8501/$ – see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/j.indmarman.2003.08.008

characteristic of leading logistics and supply chain organizations. While much has been written in recent years about the importance of supply chain management, its link to superior operational and financial performance has remained somewhat elusive. Even though it is intuitive that superior supply chain performance should lead to improved financial and competitive performance, ‘‘proof’’ has been primarily anecdotal. However, the 21st Century Logistics research made a contribution by confirming the relationship between logistics best practice and firm performance for the U.S. sample. Additional research since the initial framework publication further substantiates the performance benefits of supply chain logistical integration (Stank, Keller, & Closs, 2001). However, such investigations have remained primarily U.S. based and focused. Therefore, the purpose of this research is to investigate the 21st Century Logistics framework in a global context, using a specific international sample to compare and assess its international relevance.

2. Background The 21st Century Logistics framework (see Fig. 1) identifies six firm competencies critical for logistics and supply chain management. Each competency is composed

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D.J. Closs, D.A. Mollenkopf / Industrial Marketing Management 33 (2004) 37–44

Fig. 1. The 21st Century Logistics framework.

of multiple underlying capabilities, which guide philosophies and processes to complete specific logistics and supply chain activities. The competencies leading to high supply chain performance can be grouped into operational, planning, and behavioral processes. Within the operational process, firm competencies include customer integration, internal integration, and supplier integration (whether material or service suppliers). Customer integration builds lasting distinctiveness with customers of choice. Internal integration links internally performed work to support customer requirements, and supplier integration links externally performed work into a seamless congruency with internal work processes. The planning process includes competencies of technology & planning integration and measurement integration. Technology & planning integration refers to information systems capable of supporting the wide variety of operational configurations needed to serve diverse market segments. Measurement integration refers to the development of measurement systems that facilitate segmental strategies and processes. Finally, in the behavioral process, relationship integration refers to the ability to develop and maintain a shared mental framework with customers and suppliers regarding interenterprise dependency and principles of collaboration. Table 1 lists the capabilities and definitions for each competency. A major challenge to empirically demonstrate the relationship concerns how to measure firm ‘‘success.’’ Firm performance must certainly incorporate financial measures, but should also include broader measures. The 21st Century Logistics framework was developed using a measurement model that considers both firm and supply chain performance using 13 logistics and supply chain variables repre-

senting five key performance areas. Customer service focuses on the customer value-added including customer satisfaction, product flexibility, and delivery speed. Cost management focuses on the functional and integrated logistics and supply chain cost components. A single, comprehensive measure of total landed logistics cost is used. Quality reflects broader service measures used to enhance customer loyalty, based on the logic that superior service attracts and keeps key customers. The four quality measures include delivery dependability, responsiveness, order flexibility, and delivery flexibility. Productivity reflects how effectively material and labor resources are used to provide service, and includes information systems support, order fill capacity, and advanced shipment notification. Finally, asset management indicates how well a firm uses fixed assets and working capital. This research includes two specific asset utilization measures inventory turnover and return on assets (ROA). Although these five categories and the individual items can be measured quantitatively, the focus of this research uses performance relative to competition as the basis of cross-industry comparisons.

3. The current study In the current study, the U.S. data gathered during the 21st Century Logistics research is compared with data collected by Mollenkopf and Dapiran (1999) in Australia and New Zealand (ANZ). The U.S. sample includes 284 responses from Council of Logistics Management (CLM). The ANZ sample includes 193 responses from executives in Australian and New Zealand firms. Details of the data collection methodologies for both studies can be reviewed

D.J. Closs, D.A. Mollenkopf / Industrial Marketing Management 33 (2004) 37–44 Table 1 Competency and capability definitions Customer integration Segmental focus Relevancy Responsiveness Flexibility

Internal integration Cross-functional unification Standardization Simplification Compliance Structural adaptation

Development of customer specific programs designed to generate maximum customer success. Maintenance and modification of customer focus to continuously match changing expectations. Accommodation of unique and/or unplanned customer requirements. Adaptation to unexpected operational circumstances.

Operationalization of potentially synergistic activities into manageable operational processes. Establishment of cross-functional policies and procedures to facilitate synchronous operations. Identification, adoption, implementation, and continuous improvement of best practice. Adherence to established operational and administrative policies and procedures. Extent to which the network structure and deployment of physical assets has been modified to facilitate integration.

Material/service supplier integration Strategic alignment Development of a common vision of the total value creation process and planning clarity concerning shared responsibility. Operational fusion Linkage of systems and operational interfaces to reduce duplication, redundancy, and dwell while maintaining operational synchronization. Financial linkage Willingness to structure joint financial ventures with suppliers to solidify goal attainment. Supplier management Extended management to include hierarchical structure of suppliers’ suppliers. Technology and planning integration Information Commitment and capability to facilitate supply management chain resource allocation through seamless transactions across the total order-to-delivery cycle. Internal Capability to exchange information across internal communication functional boundaries in a timely, responsive, and usable format. Connectivity Capability to exchange information with external supply chain partners in a timely, responsive, and usable format. Collaborative Customer collaboration to develop shared visions forecasting and and mutual commitment to jointly generated planning action plans. Measurement integration Functional The development of comprehensive functional assessment performance measurement capability. Activity-based and Adoption and commitment to activity-based total cost costing, budgeting, and measurement of methodology comprehensive identification of cost/revenue contribution of a specific entity such as a product. Comprehensive Establishment of cross-enterprise and overall metrics supply chain performance standards and measures. Financial impact Direct linkage of supply chain performance to financial measurement such as EVA, RONA, etc.

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Table 1 (continued) Relationship integration Role specificity Clarity concerning leadership process and establishment of shared versus individual enterprise responsibility. Guidelines Rules, policies, and procedures to facilitate interenterprise collaboration, leverage, and conflict resolution. Information sharing Willingness to exchange key technical, financial, operational, and strategic information. Gain/risk sharing Framework and willingness to apportion fair share reward and penalty. Source: Bowersox et al. (1999).

in the original publications (Bowersox et al., 1999; Mollenkopf & Dapiran, 1999). While the 21st Century Logistics research provided evidence of a positive relationship between supply chain competency and firm performance, which was further elaborated by Stank et al. (2001), the current research compares the U.S. results with ANZ results to determine the robustness across international boundaries. While many other countries could be used, it was felt that the use of ANZ firms offered some unique advantages although there are some limitations. The primary advantages include a common language, similar legal systems and cultures, and the geographies are distant enough to not have extensive cultural interchange, as would be the case of the United States and Canada. One of the primary limitations, although this too presents some interesting comparisons, is that the firms in the U.S. tend to be significantly larger than those in ANZ and often represent corporate headquarters where ANZ firms often represent divisions. First, reliabilities, Table 2 summarizes the item-to-total correlations and principal component scores for the two sample groups. While Stank et al. (2001) validated the scales for the U.S., study, it is useful to reevaluate the scales for the two samples. While the principal component scores vary across the two samples, the scores meet minimal levels of 0.30 and above in all cases (Hair, Anderson, Tatham, & Black, 1995). Thus, all of the scales reflect unidimensional characteristics. Construct reliabilities are also satisfactory as coefficient alphas meet or exceed 0.70 in all but one instance (Nunnally, 1978). Interestingly the coefficient alphas for the ANZ sample tend to be lower than for the U.S. sample. This suggests that perhaps the items did not resonate as clearly with the ANZ respondents as with U.S. respondents. Item-to-total correlations exceed 0.30 (Dunn, Seaker, & Waller, 1994) in all cases, although once again the ANZ sample scores tend to be somewhat lower than the U.S. sample. The general conclusion is that while the scales were developed using the U.S. sample, the scales also work in ANZ but the fit could be improved through some refinement. The first analysis focuses on comparing firm competencies in each region with relative performance measures. The six competencies are used as independent variables in a

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Table 2 Reliabilities, item-to-total correlation, and principal components scores Items

Customer integration Segmental focus Relevance Responsiveness Flexibility Internal integration Cross-functional unification Standardization Simplification Compliance Structural adaptation Supplier integration Strategic alignment Operational fusion Financial linkage Supplier management Technology and planning Information management Internal communication Connectivity Collaborative forecasting and planning Measurement integration Functional assessment Activity-based and total cost methodology Comprehensive metrics Financial impact Relationship integration Role specificity Guidelines Information sharing Gain/risk sharing Overall logistics performance Advanced shipping notification Customer satisfaction Delivery dependability Delivery speed Delivery time flexibility Inventory turns Information systems support Low logistics costs Order fill capacity Order flexibility Product flexibility (customization) Responsiveness to key customers Return on assets (ROA)

Principal component scores

Item-to-total correlation

.87 .86 .85 .81

.80 .82 .80 .72

.75 .74 .72 .67

.60 .63 .62 .52

.77 .81 .76 .83 .70

.73 .67 .77 .83 .72

.63 .68 .62 .70 .55

.54 .50 .60 .68 .56

.83 .87 .70 .85

.65 .63 .75 .81

.67 .73 .52 .70

.44 .39 .49 .57

.86 .87 .87 .77

.84 .82 .85 .61

.74 .76 .61 .74

.64 .63 .66 .42

.76 .84

.77 .82

.68 .57

.56 .65

.85 .71

.75 .77

.52 .69

.55 .57

.86 .85 .82 .78

.71 .76 .80 .76

.73 .71 .67 .62

.49 .55 .60 .53

.42 .76 .74 .66 .67 .44 .47 .47 .61 .56 .46

.56 .63 .70 .64 .63 .53 .47 .60 .76 .62 .59

.35 .65 .61 .53 .54 .38 .40 .40 .48 .45 .34

.48 .54 .60 .53 .54 .47 .38 .51 .67 .52 .49

.64

.72

.49

.63

.34

.53

.29

.45

Cronbach alpha .87

.78

.84

.79

.83

.68

.86

.77

.80

.78

.85

.74

.82

.86

Key: U.S. scores/ANZ scores

series of regression models with each performance measure treated as a dependent variable. An overall logistics performance measure—the combination of the 13 performance measures—is also used in the analysis. Table 3 reports the results of all regression models. Standardized beta coefficients are shown in the table for all significant ( P < .05) variables. Model significance is also reported in the R2 column. All R2 values are significant at P < .001.

In both the U.S. and ANZ models, 30% or more of the variation in overall logistics performance is explained by each model. For the U.S. sample, both customer integration and internal integration explain substantial overall logistics performance, whereas only customer integration explains substantial logistics performance for the ANZ sample. In both regions it is clear that firms that focus on serving customers with unique and profitable logistics offerings gain advantages in performance throughout the supply chain. This suggests that firms that develop and apply logistics and supply chain capabilities to meet the specific needs of key customers achieve higher performance in both the U.S. and ANZ. U.S. firms also clearly gain by linking internal activities, such as reducing duplication and increasing alignment. Neither sample revealed a significant statistical association between supplier, technology/planning, measurement nor relationship integration with overall logistics performance. As discussed by Stank et al. (2001), this could be because these competencies do not influence a firm’s overall logistics performance. More likely, however, is the explanation that these competencies are not substantial differentiators of logistics and supply chain performance, at least based on current measures. This observation might suggest one of two conclusions. The first is that internal integration is not a necessary competency in ANZ. A more likely interpretation is that ANZ firms are smaller and have historically achieved internal integration through internal relationships that are not possible due to the size and geographic spread of U. S. firms. To provide a clearer picture of the role of the six competencies in affecting logistics performance, Table 3 also reports the multiple regression results for both samples when using each performance measure individually as a dependent variable. Each of the models is statistically significant. For the U.S. sample, supply chain integration competencies explain 10% or more of the performance variance related to customer satisfaction, delivery speed, logistics cost, delivery dependability, responsiveness, delivery flexibility, order fill capability, advanced shipment notification, and inventory turns. For the ANZ sample, supply chain integration competencies explain 10% or more of the performance variance for all 13 measures of logistics performance. This is true even though in five of the analyses no individual competency is statistically significant. This suggests that the regression model is unable to partition the explained variance across the six competencies in the ANZ sample as clearly as it does in the U.S. sample. For both samples, customer integration is the most common significant predictor variable relative to the other logistics competencies. For both samples it is a significant predictor for product customization, responsiveness, order flexibility, and delivery flexibility. In the U.S. sample, customer integration is also a significant predictor of customer satisfaction and delivery speed, whereas it is a significant predictor for advanced shipment notification in the ANZ sample.

D.J. Closs, D.A. Mollenkopf / Industrial Marketing Management 33 (2004) 37–44

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Table 3 Multiple regression results for U.S. and ANZ samples Dependent variables

Overall performance Customer Service . Customer satisfaction . Product customization . Delivery speed Cost management . Total landed logistics cost Quality . Delivery dependability . Responsiveness . Order Flexibility . Delivery flexibility Productivity . Information systems support . Order fill capacity . Advanced ship notification Asset management . Inventory turns . ROA

Supply chain logistics competency Customer integration

Internal integration

.31

.31

.28

.26 .43 .33

.28

Material/service supplier integration

Technology and planning integration

Measurement integration

Relationship integration

.30 .36 .18 .30

.16 .11 .09 .13 .10 .14

.20

.41

.16 .17

.50 .48 .24 .34

.20

.37 .28 .29

.26

.21

.61

.25

.46

.31

.13 .12 .05 .11

.13 .17 .16 .11

.36 .19 .12 .19 .10 .20

.35

.32

R2

.26

.42 .25

.10 .17 .06 .12

Cell values represent standardized beta coefficients and indicate the statistically significant relative influence of that competency variable on the performance measure. R2 value is significant at P=.05. Numbers on the left of each column represent U.S. results; numbers on the right represent ANZ results.

Internal integration is the second most dominant predictor for both samples. For U.S. firms, it is a significant predictor of logistics cost, delivery dependability, order fill capacity and inventory turns; but is only significant in predicting product customization and inventory turns in the ANZ sample. As reported by Stank et al. (2001), the U.S. regression results related to relationship, measurement, and supplier integration indicate significant negative associations with certain individual performance measures. Analyses of the slope of the bivariate relationships between the variables in question indicate that these competencies individually have a slight positive impact on firm performance but when combined with another dominant competency, the weak relationships may be moderated such that the magnitude or direction of its effect on the dependent variable is reversed. Further analysis and theory development is needed to establish the reasons behind such moderation. No such effect is noticed in the ANZ sample, but again, five of the models fail to identify any individual competency as a significant predictor of performance. This suggests that theory development is needed to further clarify the underlying causes of logistics performance. Since the 21st Century Logistics research identified five performance categories using 13 measures, a path analysis approach next investigated the relative impact of the competencies on the performance measure. This approach illustrates more fully than regression the relationships between logistics competencies and performance. Fig. 2 presents three sets of models for the categories where reasonably

good-fitting models were obtained: customer service, productivity, and asset management. For the customer service measures, customer integration and internal integration play a role for both U.S. and ANZ firms. For the United States, customer integration significantly predicts product flexibility (customization), while internal integration significantly predicts delivery speed. In contrast, for the ANZ firms, customer integration and internal integration are both predictors of product flexibility. The significant covariation between the independent variables suggests the interrelated nature of the two types of integration. Firms that focus on creating customer integration competencies to provide high levels of customer service seem to need to develop high levels of internal integration in order to deliver on the customer service promise. The productivity models illustrate interesting differences between the U.S. and ANZ firms. For U.S. firms, customer integration, internal integration and technology/planning integration are jointly involved in improved productivity. For the ANZ firms, customer integration is replaced by relationship integration, to work with internal integration and technology/planning integration in creating more productive firms. For both groups, a significant amount of variation in information systems support is explained by the combination of three independent variables. Order fill capacity is also partially explained in the U.S. model, while advance ship notification is partially explained in the ANZ model. Once again, the significant covariance levels between the independent variables suggest the interrelated nature of developing logistics competencies.

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Fig. 2. Comparative path analyses. Standardized path coefficients are presented, P < .05. Coefficients of determination are presented above each dependent variable. Various measures of fit are presented with each model.

The final model presented in Fig. 2 focuses on the asset management variables. In the United States, internal and relationship integration are both facilitate improved asset management. The role of relationship integration in this model suggests that it contributes to improved ROA not just at the firm level, but also across the supply chain. While the ANZ model does not provide a particularly good-fit of the data, it is included for comparison purposes with the U.S. model. For ANZ firms, internal integration appears to be the sole contributor to a firm’s improved performance in asset management. The lack of relationship integration in this model is perhaps indicative of the stage of supply chain

development or evolution amongst ANZ firms, as compared to U.S. firms.

4. Implications The differences between the U.S. and ANZ results should not be interpreted as failings of the 21st Century Logistics framework. On the contrary, the differences provide substantial insight into the different logistical capabilities and performance realizations across different business environments. Understanding these differences can provide

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some meaningful guidance for decision makers operating globally as well as offering suggestions for the refinement of model competencies and capabilities. Interesting comparisons can be made across several of the competencies. First, customer integration plays a significant role in firm performance for both U.S. and ANZ firms. This is exhibited in the initial regression analysis for overall performance. More interesting, however, are the specific regression results that demonstrate the role of customer service in six U.S. and five ANZ different performance measures, which tend to be grouped into customer service and quality measures. For the U.S. firms, customer integration leads to improved customer service as measured by customer satisfaction, product customization, and delivery speed. For the ANZ firms, customer integration is significantly related only to product customization. This is probably a reflection of the niche marketing approach that many antipodean firms take in serving overseas markets, as well as their distance from most markets, thus, making delivery speed a relatively unimportant performance objective. Similarities across the two samples are most apparent in the performance measures relating to quality. Analysis reveals that for both the U.S. and ANZ firms, customer integration is a significant predictor of responsiveness, order flexibility, and delivery flexibility. Second, while internal integration is very important to U.S. firms, it does not seem to be as important a predictor of performance for ANZ firms. This can be somewhat explained by the size differences between firms in the two countries, as suggested earlier. Because ANZ firms tend to be smaller than their U.S. counterparts, internal integration may present less of a hurdle for firms to overcome in improving their logistics and supply chain performance. Because of their small size, they tend to be market niche players in the global arena, thus, explaining their focus on customer integration. Also, because of their small size, they tend to have fewer production or distribution sites than their U.S. counterparts. This allows a substantial degree of internal integration to be achieved through personal and geographic proximity. So, while internal integration is critical for larger firms and geographies, it is not as apparent a factor in smaller firms and geographies. However, attributing the difference between U.S. and ANZ firms solely to the size factor is probably naı¨ve and simplistic. ANZ firms have traditionally been very commodity focused, and are ‘‘younger’’ than many of their corresponding U.S. firms in terms of logistics and supply chain evolution. Environmental differences in the two regions are certain to play a role also. The geography and population dispersion of the antipodean countries are very different from the U.S., making for different configurations of firms and supply chain networks. These results suggest that customer integration is important in both environments but that there should be a mediating consideration for internal integration in terms of size of firm or market. Third, customer integration, internal integration, technology/planning integration, and relationship integration all

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contribute to improved productivity across the two samples. While these differences are detected in the individual regression analyses, they become more evident in the path analyses. Most interesting to observe is how companies in the two samples use the capabilities to create different results. For example, U.S. companies use internal integration to improve order fill capacity, whereas ANZ firms use internal integration to facilitate improved information systems support. Another interesting observation is to note how interrelated the competencies are in improving firm performance. These interrelationships are most evident in the productivity path analysis. This suggests that these competencies and underlying capabilities cannot be developed in a vacuum. Improved logistics and supply chain performance is very dependent on a firm’s ability to create competencies across the three contexts (operational, planning, and relational).

5. Conclusion The common lesson to be gleaned from the comparison of U.S. and ANZ firms is a reaffirmation of the logistics competencies/performance relationships. High levels of logistics competencies do lead to superior logistics performance. The differences across these two samples suggest that firms in different operating environments will focus on different capabilities to improve their logistics performance. Interestingly, customer integration seems to be the most important competency between both sets of firms. This confirms the supply chain emphasis of a customer-focus. The relevance of the 21st Century Logistics framework has been demonstrated in a limited international environment through this comparison of U.S. and ANZ firms. While the magnitude and/or relative importance of specific competencies to particular performance measures may vary across settings, the model has repeatedly supported the logistics competency/performance relationship. Managers seeking to leverage supply chain processes to enhance performance need to understand the relative importance of the various competencies in each particular operating arena. The needs of key customers may vary across international borders, and the means to developing an effective fulfillment and replenishment process may also vary across international locations. From an academic standpoint, the 21st Century Logistics framework has been shown to be robust across international samples. It appears to be robust across at least two relatively different cultures. Equally important, the model appears to be robust across size and business scale differences, suggesting that it can be applied in a variety of environments to gain knowledge and understanding of how firms develop and employ their logistics competencies to create performance advantages in their respective marketplaces. The research does demonstrate that while customer and internal integration are critical for high performance supply chains, the

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internal integration in smaller firms can be achieved through relationships. The model, however, is not without its limitations. The measures employed in assessing firm capabilities need to be refined to better transcend size differences in firms, and also to transcend supply chain ‘‘language’’ differences. This issue came out during the research phase in Australia and New Zealand, when many willing participants reported that they did not understand the technical language used in the survey instrument. Language was not a problem for U.S. respondents. In addition, additional measures will need to incorporate notions of organizational complexity and even a firm’s supply chain complexity. The explanations throughout this paper suggest that these organizational issues may vary substantially across business environments, and act as moderators in the competencies/performance relationships. The challenge for future researchers will be to further refine and clarify the model and its measures so as to provide clearer insights into the competency/performance relationships across more operating and cultural settings.

References Bowersox, D. J., Closs, D. J., & Stank, T. P. (1999). 21st century logistics: Making supply chain integration a reality. Oak Brook, IL: Council of Logistics Management. Carranza, O., Maltz, A., & Antun, J. P. (2002). Linking logistics to strategy in Argentina. International Journal of Physical Distribution & Logistics Management, 32(6), 480 – 496.

Dunn, S. C., Seaker, R. F., & Waller, M. A. (1994). Latent variables in business logistics research: Scale development and validation. Journal of Business Logistics, 15(2), 145 – 172. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1995). Multivariate data analysis. Englewood Cliffs, NJ: Prentice Hall. Mollenkopf, D. A., & Dapiran, G. P. (1999). World class logistics: How well do Australian/New Zealand firms perform? Council of Logistics Management Annual Conference, Toronto, Canada. Morash, E. A., & Lynch, D. F. (2002). Public policy and global supply chain capabilities and performance: A resource-based view. Journal of International Marketing, 10(1), 25 – 51. Nunnally, J. C. (1978). Psychometric theory (2nd ed.). New York: McGraw-Hill. Stank, T. P., Keller, S. B., & Closs, D. J. (2001). Performance benefits of supply chain logistical integration. Transportation Journal, 41(2/3), 32 – 46.

Dr. David Closs is the John H. McConnell Professor of Business Administration in the Department of Marketing and Supply Chain Management at Michigan State University. Dr. Closs has numerous publications in the logistics and supply chain area, and is a coauthor of 21st Century Logistics: Making Supply Chain Integration a Reality, and Supply Chain Logistics Management. Dr. Diane Mollenkopf is Assistant Professor in the Department of Marketing and Supply Chain Management at Michigan State University. She has many years of industry experience in the logistics field, and has worked extensively overseas. Her research interests include logistics and supply chain integration, and environmentally responsible logistics. She has consulted and researched with numerous corporations, and her work has appeared in business logistics journals and many overseas publications.