Manufacturing and Service Supply Chain Performance: A Comparative Analysis

 

To compete in today's global markets, organizations strive to deliver their products and services in both an efficient and effective manner. A critical component in this effort is the design and coordination of the supply and distribution networks--supply chain management (SCM). SCM

was originally limited to traditional areas like purchasing, distribution and logistics. Over time, SCM has evolved as a field of inquiry and has expanded to include a myriad of areas, including supplier relationships, supply network structure and supplier collaboration. The majority of the SCM research has focused on the manufacturing sector even though the private services-producing sector leads the U.S. economic expansion and outpaces the goods-producing sector. In 2004, the private services-producing sector accounted for almost 70 percent of the current-dollar GDP (Strassner and Howells 2005). As the prominence of the service sector increases, attention is being directed to help understand the issues and complexities within "service" supply chains.

It is important to highlight the differences between service supply chains and the more traditional manufacturing supply chains. In service supply chains, human labor forms a significant component of the value delivery process and while, physical handling of a product leads to standardized and centralized procedures and controls in manufacturing supply chains, in services this is not entirely possible as many of the decisions are taken locally and the variation and uncertainties in outputs are higher because of the human involvement. In addition, the focus of efficiencies in service supply chains is on management of capacity, flexibility of resources, information flows, service performance and cash flow management. These issues are quite different from manufacturing supply chains and hence extensive examination of service supply chains is required to further understand these issues (Ellram, Tate and Billington 2004). However, there are also many areas where there are similarities. For instance, demand management, customer relationship management and supplier relationship management are critical factors in manufacturing supply chains that remain equally important in service supply chains.

Although these similarities and differences are well known, little research has been conducted to explore the implications for practicing supply chain managers. To address this gap, this paper analyzes specific supply chain-related strategies and the corresponding organizational performance in both manufacturing and service sectors. The results include an analysis of the relative prevalence of specific strategies and tactics within each sector and the differing impact on firm performance. The results provide supply chain managers in manufacturing and service sectors benchmarking data for deciding if various strategic SCM initiatives may enhance organizational performance. The following sections examine relevant literature, describe and test the research framework and summarize key findings.

LITERATURE REVIEW

With increasing levels of global competition, many accept the notion that organizations no longer compete directly, but rather compete based on their respective supply chains. In the manufacturing sector, the breadth of SCM is perhaps best illustrated by Croom, Romano and Giannakis (2000), who identify 11 different streams of research literature that have converged on the topic--including purchasing and supply, logistics and transportation, marketing, organizational behavior, contingency theory, institutional sociology, systems engineering, networks, best practices, strategic management and economic development. They also identify three primary levels of analysis (dyadic, chain and network) and four categories of exchange considered (assets, information, knowledge and relationships) in extant research. Ho, Au and Newton (2002) highlight some major weaknesses of the existing literature and propose that conceptual SCM models be developed based on a context-practices-performance framework. Cigolini, Cozzi and Perona (2004) propose a new conceptual framework for SCM strategies and introduce a set of corresponding management techniques and tools. Chen and Paulraj (2004a, b) emphasize the origins of SCM from a number of fields including purchasing, logistics, operations, organizational theory, information systems and strategic management. Lejeune and Yakova (2005) propose a typology of supply chain configurations to provide linkages between terms that have previously been used to disjointedly describe various supply networks. Finally, Li, Rao, Ragu-Nathan and Ragu-Nathan (2005) conceptualizes, develops and validates six dimensions of SCM practices including strategic supplier partnership, customer relationship, information sharing, information quality, internal lean practices and postponement.

Recent SCM research in the manufacturing sector indicates that researchers have started to move beyond the initial classification and theory development stage to the theory validation stage. However, the transferability of the manufacturing SCM principles to the service sector organizations is in its infancy and is only now receiving increased attention from practitioners and academics. So far only a limited number of studies have been conducted that specifically emphasize service supply chains.

Ellram et al. (2004) highlight the rising importance of the services sector and the service supply chain. Their study focuses on the professional service supply chain and the purchasing of professional services. They specifically incorporate the concept of service performance and capacity to help distinguish between services and manufacturing. Service performance helps ensure the customer receives the expected service with capacity serving as a substitute for inventory. For a service organization, the strategic use of capacity facilitates operational agility. They propose a supply chain framework appropriate for a services supply chain by comparing and contrasting three well-known product-based manufacturing models--Global Supply Chain Forum Framework, SCOR and Hewlett-Packard's Supply Chain Management Model--and they conclude with a call for more service supply chain-related research.

Frohlich and Westbrook (2002) investigate the relationship between Internet-enabled supply chain integration strategies and performance in manufacturing and services. Their findings showed that while higher levels of integration with a company's supply chain partners typically lead to a higher level of performance for the company, service sector companies lag manufacturing companies in terms of adoption of integration strategies. These findings have important implications for theory as well as for manufacturing and service companies interested in improving their performance. Boyer and Hult (2005) examine the supply chain operations affiliated with four online grocery operations. Their investigation of the relationships between marketing, operations and customer satisfaction provide insights into Internet or multichannel retailing. Sampson (2000) discusses the practical and managerial implications of customer-supplier duality as it relates to service supply chains. In customer-supplier duality, the resulting service supply chain is bidirectional, which implies that the customers are also the suppliers. This arrangement is more complex than a traditional manufacturing supply chain, where a unidirectional flow of materials from suppliers to customers is met by a unidirectional flow of demand information in the other direction. Youngdahl and Loomba (2000) broaden the concept of factory-based services to global supply chains. They review the concept of the service factory and expand the conceptualization of the service factory concept to global supply chains and information technology.

Owing to the vast number of potential subissues in SCM, many of the prior SCM studies have used a narrowly defined and focused analysis. In contrast, this study attempts to establish generalizable rules across sectors to assess the influence of specific SCM strategic initiatives on organizational operational and financial performance. This study specifically examines the effects of eight SCM strategic initiatives on the organizational performance metrics and compares manufacturing with services to understand whether there are differences across sectors or within sectors. Figure 1 illustrates the research framework. The specific SCM initiatives and the performance metrics are discussed in the following sections.

SCM Initiatives

Based on prior SCM literature, this study considered some of the more widely advocated SCM strategies as they relate to improving organizational performance. The intent is not to provide an exhaustive list, but rather to highlight recognized SCM practices and the perceived priorities of the firms' managers. While all items were captured at the same point in time, a distinction is made between supply chain strategies corresponding to current practices and those that relate to future priorities. The specific items categorized under current practices relate to those tasks that are presently being pursued by an organization, while items categorized under future priorities represent items that managers perceive as important to their organization's future competitive success. The distinction between the categories is minor, but was necessitated by the structure of the research data set. The only substantive distinction is that the items used to construct the current factors are purely descriptive, while the future items may include insights on the responding managers' perception of their firms' current operations. The perceived relative importance of future investment opportunities may provide insight on the manager's perception of deficiency in their current supply chain to compete in the long term.

The current SCM strategies include sharing information, level of product and service customization, building long-term relationships and hedging methods. These initiatives form the core of topical coverage related to SCM, which is typically taught in North American business schools (Chase, Jacobs and Aquilano 2006). Sharing information with supply chain partners (SHARE) is considered important because of its impact on enhanced coordination. Most related research focuses on information sharing related to inventory, forecasting, orders and production plans (Lee and Wang 1999; Li, Yan, Wang and Xia 2005; Zhao, Xie and Zhang 2002). Similar to the multi-industry context considered in Frohlich and West-brook (2001), this study adopts a more robust description of information sharing that includes the extent to which firms share information concerning inventory levels, demand forecasts and pricing information. The level of product and service customization (PRODUCT) is often cited as a key factor in determining the required flexibility of a supply chain. Fisher (1997) proposes that SCM performance can be attributed to a match or mismatch between the type of product or service supplied and the design of the supply chain. The PRODUCT factor includes both products and services to cover respondents from both manufacturing and service sectors. Building long-term relationships (RELATION) with supply chain partners often results in improved collaboration and enhanced administrative efficiency. This represents an opportunity for greater coordination in business decisions (Hahn, Pinto and Bragg 1983; Choi and Hartley 1996; De Toni and Nassimbeni 1999). A firm's strategy for hedging risk (HEDGE)--including maintaining multiple suppliers, holding finished good inventories and maintaining a reserve capacity--are techniques for preventing supply chain disruptions (Shanahan 2004; Kawtummachai and Nguyen 2005; Sheffi and Rice 2005). By leveraging the supply chain partners, a company may reduce its exposure to the risks associated with producing and delivering products to the marketplace.

The future SCM strategic initiatives include using advanced planning systems, leveraging the Internet, supply network structure and distribution network structure. Advanced planning systems (PLAN) are commonly used in the manufacturing sector to enhance supply-related communication and transparency. Their application in the service sector is growing, but is less well understood. However, the typical implementation of advanced planning systems in both sectors represents a large investment of both capital and managerial focus--thus justifying examination of this factor (Zuckerman 2005). Another technological development supporting increased collaboration and coordination among supply chain partners has been the evolution of the Internet (INTERNET). In a similar research, Frohlich and West-brook (2002) examined this factor and they also differentiated between manufacturing and service-oriented organizations. In this research, a similar definition is adopted to examine the effect of the Internet usage on organizational performance.

The supply network structure (SNET) includes the upstream supply chain for a company, including a variety of decisions related to outsourcing, supplier certification and rationalization of the supply base. Typically, the supply network structure implies the number of suppliers and the number of stages in the supply chain (Frohlich and Westbrook 2001; Li et al. 2005). While Frohlich and Westbrook (2001) consider the extent of integration with suppliers and customers, they do not consider factors that may affect the nature and extent of integration. In this research, the supply network structure only focuses on the upstream portion of the supply chain. Specifically, the paper examines whether a high level of focus on the supply side issues of a company's supply chain leads to a higher level of performance for such companies.

While the SNET factor focuses on the upstream portion of the supply chain, the distribution network structure (DNET) factor focuses on the movement of materials including where to hold inventory and locate facilities as well as methods of transportation. Similar to the case of the SNET factor, the analysis of the distribution network structure in prior studies has been limited (Frohlich and Westbrook 2001; Li et al. 2005). Therefore, the importance of developing and sustaining a network of distributors as a future priority and its effect on organizational performance is considered.

Performance Metrics

Prior research and industry practices clearly indicate that organizational performance is affected by the adoption of specific supply chain strategies. Tan (2002) investigated SCM and supplier evaluation practices that influence firm performance. Tan, Kannan, Handfield and Ghosh (1999) present survey results to assess the impact of quality management, supply base management and customer relations practices on corporate performance. Their results suggest that all three components of the supply chain (manufacturer, suppliers and customer) must be effectively integrated to achieve financial and growth objectives.

Other research studies have used a variety of performance measures to assess the effectiveness of SCM strategies. For example, Li et al. (2005) used delivery dependability and time to market to evaluate the predictive validity of their six SCM constructs. The six constructs analyzed in their research included strategic supplier partnership, customer relationship, information sharing, information quality, internal lean practices and postponement. Chen and Paulraj (2004a, b) used supplier performance and buyer performance to assess the links between strategic purchasing, supply management, customer responsiveness and financial performance of the buying firm. Vickery, Jayaram, Droge and Calantone (2003) investigated the performance implications of an integrated supply chain strategy, with customer service performance followed by financial performance as performance constructs. Narasimhan and Kim (2002) examined the effect of SCM relationships between integration, diversification and firm performance defined by sales growth, market share growth and profitability. Tan (2002) used overall product quality, competitive position and customer service levels as the associated performance measures.

This study defines organizational performance through operational results and financial results as measured by the respondents' perceived performance. The operational performance metric measures a company's relative performance with its main competitors on the three competitive priorities of speed, delivery and quality--items commonly used to represent the operational excellence of an organization. Financial performance is measured by the company's relative cost and profit-related performance compared with its direct competitors. The performance measures in this study were chosen for their applicability across a broad spectrum of industrial classifications. Given the multi-industry context of this study, the dependent variables were designed to capture evidence of an organization's perceived performance relative to its direct competitors in order to avoid confounding the results with disparate interindustry standards of performance. Although any self-reported, perceptual measure is subject to bias, similar methods have been used by several other studies (Tan et al. 1999; Gunasekaran, Patel and Tirtiroglu 2001; Sanders and Premus 2002; Tan 2002; Lockamy and McCormack 2004). The common use of this type of metric in cross-industry research is necessitated in part by a lack of alternatives. For example, Tan (2002) argues that due to a lack of consensus regarding a valid cross-industry measure of corporate performance, performance can be operationalized by management's perceptions of a firm's performance in comparison with that of major competitors. Furthermore, any bias in reported performance should be relatively consistent, unidirectional and independent of the research questions.

For both sectors, manufacturing and service, research hypotheses are tested based on the eight SCM strategic initiatives (SHARE, PRODUCT, RELATION, HEDGE, PLAN, INTERNET, SNET or DNET) and the two performance metrics (operational or financial). Specifically, the hypotheses evaluate if companies within an particular sector with a high level of adoption of an SCM strategic initiative exhibit the same levels of organizational performance as compared with companies with a low level of adoption of the same strategy.

RESEARCH METHODOLOGY

This study is part of a larger research project exploring supply chain-related practices, their relevance to managers, and their impact on firm performance. The overall study examined differences in perceptions of common SCM practices between academicians and practitioners. This arm of the study compares common SCM initiatives among practicing managers across manufacturing and service sectors. Respondents were drawn from the non-academic, North American membership of the Institute for Supply Management[TM] (ISM). Data was collected using a traditional mail survey to capture several items of interest, including the SCM strategies, and the operational and financial performance metrics. Each strategy and metric was constructed using multiple survey items measured on a seven-point Likert scale with higher scores indicating a higher level of respondent agreement that the item accurately describes their organization.

The content validity of each construct was ensured through pretesting of the questionnaire and structured interviews with managers and academic experts in the field. A two-step process was used to develop and refine the survey instrument. In the first step, a panel of SCM experts examined the questionnaire items to check for relevancy or possible ambiguity in the wording of specific items. In the next step, a panel of SCM professionals completed the survey. Subsequent interviews determined whether the professionals found any nonrelevant or ambiguous items. Feedback from this two-step process resulted in no significant changes to the instrument.

The initial survey instrument was directed to 666 managers and included a cover letter stating the purpose of the overall study. Several steps were taken to maximize the response rate, including the inclusion of a postage-paid business reply envelope, a financial incentive to complete the survey, and the use of a follow-up letter and second instrument to nonrespondents (Frohlich 2002). Approximately 15 percent of the targeted recipients replied within the first 4 weeks. After the second mailing, a combined total of 161 useable surveys were received, out of which 16 surveys were eliminated due to incomplete responses, leaving 145 responses and an effective response rate of 21.8 percent. The possibility of nonresponse bias was investigated through a series of t-tests comparing the responses from the first and second mailing. The t-tests yielded no statistically significant differences between the two groups, suggesting that nonresponse bias was not an area of concern in this study (Armstrong and Overton 1977; Lambert and Harrington 1990; Tan 2002).

RESPONDENTS' PROFILE

Table I shows the profile of the respondents across manufacturing and services sectors by the number of full-time employees (FTE) and annual sales volume. The respondents are equally represented in the manufacturing and the services sector. While the 18 industries in the service sectors are diverse, it is common to combine them under one category. For instance, Frohlich and Westbrook (2002) and Tsikriktsis, Lanzolla and Frohlich (2004) include goods-oriented segments, such as retail and merchandising under services. Ellram et al. (2004) also define the service-producing sector to include: transportation, communication and utilities; wholesale trade; retail trade; finance, insurance and real estate; public administration; and finally, services. Based on these extant research studies, and the desire to determine whether SCM strategies developed in the manufacturing sector were generalizable beyond the realm of manufacturing supply chains, we adopted a similar methodology to include all industries other than manufacturing under services.

STATISTICAL ANALYSIS

Exploratory Factor Analysis

Although the survey items are oriented toward measuring the key SCM factors, we used factor analysis to identify parsimonious, mutually exclusive and unitary research constructs. This method serves to both simplify the data analysis and add clarity to the interpretation of the results. In order to determine the consistency of the items specific to the key factors, an exploratory factor analysis was conducted using the extraction method of principal component analysis followed by a Varimax rotation (Tan 2002). The results for the current and future SCM strategies are displayed in Tables II and III, respectively. In each category, the specific strategies identified have a total variance explained greater than 60 percent. In addition, factor loadings (eigenvalues) for each of the included variables is at least 0.45. This threshold value corresponds to a 0.05 level of significance, with an 80 percent power level for a sample size of 150, which is close to the sample size of 145 in this study (Hair, Tatham, Anderson and Black 1998). In exploratory research, such as this study, the reliability coefficient, Cronbach's [alpha], has a minimum threshold value of at least 0.5 (Hair et al. 1998). For all subsequent analysis, each respondent's individual survey items under each extracted factor are summed and the resulting factors are used as the independent variables.

Correlation Analysis

A correlation analysis was conducted to examine the effect of each SCM factor on operational and financial performance by industry sector. The Spearman's non-parametric correlation coefficient is used to test the strength of relationship between the variables. Table IV displays the two-tailed significance levels at the 0.01 and 0.05 levels.

The results show that several correlations are significant. For instance, in the manufacturing sector, the correlations for the hedging strategy (HEDGE) factor is significant with the operational metric while the correlations for information sharing (SHARE), long-term relationship (RELATION), hedging strategy (HEDGE), use of advanced planning systems (PLAN) and distribution network structure (DNET) are significant with the financial metric. Likewise, in the Services sector, the information sharing (SHARE), product customization (PRODUCT), advanced planning systems (PLAN) and distribution network structure (DNET) factors have significant correlations with the operational metric while the information sharing (SHARE) and distribution network structure (DNET) factors have significant correlations with the financial metric.

The correlations highlight some of the major similarities and differences across the manufacturing and service sectors. First, the factors INTERNET and SNET do not appear to significantly affect the operational or financial metric of either the manufacturing or service sectors. However, several factors illustrate differences across the sectors. For example, only HEDGE displays a significant correlation with the operational metric in the manufacturing sector. However, in the service sector, SHARE, PRODUCT, PLAN and DNET are all significantly correlated with operational performance. This disparity might imply that manufacturing companies may have generally reached a level of performance with respect to these factors and thus they do not significantly influence their operational performance. Further, the lower importance assigned to inventory management in the service sector may be reflected in the lack of a significant correlation between operational performance and HEDGE for service firms.

Upon further examination of the financial metric for manufacturing companies it is evident that several factors influence their associated financial performance. It seems that as many as five of the eight factors--SHARE, RELATION, HEDGE, PLAN and DNET--affect the financial performance of manufacturing companies. However, only two of these--SHARE and DNET--affect the financial outcomes of the service companies. Based on these observations, one may conclude there are significant differences across the two sectors and across the two performance metrics concerning the relative impact of specific SCM strategies. To further examine these differences, a step-wise regression analysis was performed to identify the most significant relationships between SCM strategies and sector performance.

Regression Analysis

The two-step process of a correlation followed by regression mitigates the common pitfalls of correlation coefficients. Correlation is symmetrical and does not provide evidence of causation. If other variables also cause the dependent variable, then any covariance they share with the given independent variable in a correlation may falsely be attributed to that independent variable. This is addressed in the regression analysis where the effects of multicollinearity among the independent variables are examined. In addition, correlation will understate the relationship to the extent that there is a nonlinear relationship between the two variables being correlated. This may also be addressed through the regression analysis by testing for the presence of nonlinear relationships.

A stepwise multiple linear regression method was used to ensure that only those independent variables that significantly affect the dependent variable will be present in the final model. The dependent variables are the operational and financial performance metrics. The independent variables are represented by the eight SCM strategies. A significant [R.sup.2] implies that the independent variables that enter the final model predict the value of the dependent variable and the magnitude of the [R.sup.2] shows the percentage of variation in the dependent variable that is explained by the independent variables in the model. Table V displays the four multiple linear regression models which represent the two performance metrics for each sector, the associated coefficient of determination ([R.sup.2]), and the Durbin-Watson statistics. The [R.sup.2] values for all models are significant and the values are similar to those reported in other SCM research studies (Tan 2002; Lockamy and McCormack 2004). The Durbin-Watson statistics for the models confirm that the residuals are independent, normally distributed and not auto-correlated and are similar to values reported in other research (Tan 2002).

Based on the regression results for the manufacturing sector, the hypothesis for the operational performance metric is rejected for the HEDGE factor, while the hypotheses for the financial performance metric is rejected for the RELATION and SNET factors. For the second set of hypotheses, related to the services sector, the hypotheses related to operational performance metric are rejected for the SHARE and PRODUCT factors, while the hypotheses for the financial performance metric is rejected for the DNET and PRODUCT factors. The resulting implications are discussed in the following section.

The operational regression model for the manufacturing sector illustrates that as a company increases its level of hedging (HEDGE), the company's operational performance metric improves. The HEDGE factor includes strategies such as holding finished goods inventory, reserve capacity and multiple suppliers. Therefore, if a manufacturing organization increases its application of these types of practices, its associated operational performance with respect to speed, delivery and quality will increase.

The financial regression model for the manufacturing sector demonstrate that as a company increases the emphasis on long-term relationships (RELATION) and the supply network structure (SNET), the resulting financial performance of the company will improve. An examination of the items affiliated with these factors show that RELATION and SNET correspond to long-term relationships, supplier selection and rationalization of the supplier base. The positive relationships of both RELATION and SNET to the financial metric demonstrate that these factors have the same positive direction of influence on the financial performance of a company. The implication is that by emphasizing the level of importance on the supplier portion of the supply chains, manufacturing organizations will improve their financial performance.

The operational regression model for the service sector illustrates that as a company utilizes higher levels of information sharing (SHARE) and increases the level of product and service customization (PRODUCT) relative to its competitors the operational performance will improve. The SHARE factor includes strategies such as sharing various information including forecasts, promotions and capacity levels across the supply chain. The PRODUCT factor includes strategies related to design, delivery and customization of products and services. Therefore, if a service organization increases its application of these types of practices its associated operational performance with respect to speed, delivery and quality will increase.

The financial regression model for the service sector demonstrate that as a company increases its level of product and service customization (PRODUCT) the resulting impact is negative and the associated financial performance decreases. Although the increased product and service customization may improve the service operational performance, the cost to achieve this customization appears to exceed the benefit. The result emphasizes the associated cost implication of providing a more customized service versus a standard service offering. Finally, the service financial regression model also shows that an increased relative level of importance on the distribution network structure (DNET) improves financial performance. The DNET factor includes strategies such facility location, distribution modes and inventory staging. These items may be expected to influence sales volume through proximity to customers, transaction costs and firm overhead regardless of volume fluctuations. Therefore, an increased level interest on managing the DNET items should improve the financial performance of service companies.

MANAGERIAL IMPLICATIONS

The preceding analysis establishes the effect of specific SCM strategies on operational and financial performance for manufacturing and service sector organizations. The results highlight the differences between the two sectors and lead to several managerial implications. First, the specific strategies for predicting performance vary within each sector. For example, in the manufacturing sector, operational performance is predicted through a firm's strategies for hedging risk. However, financial performance is predicted by the strength of long-term relationships and attention to their supply network.

Second, the impact of specific SCM strategies on performance also varies across the manufacturing and service sectors. For instance, operational performance in the service sector is more closely associated with the firm's approach to sharing information with supplier chain partners and the level of product customization relative to their competitors, while operational performance for manufacturing companies is predicted only by their strategy toward hedging risk. Therefore, managers must use caution when attempting to generalize across sectors. It is important for manufacturing and service sector organizations to consider the impact of sector-specific considerations when benchmarking techniques from other sectors.

Further, some factors previously thought to have a significant influence on organizational performance may no longer be significant as a means to differentiate an organization from its competitors. Two factors, INTERNET and SNET, are interesting to note in this regard. For example, it is now common for both manufacturing and service organizations to use the Internet to conduct many types of business transactions. However, this application of technology does not appear to explain either operational or financial performance outcomes. This may mean that Internet usage is so widespread that it does not contribute to superior performance when compared to direct competitors. The SNET factor also does not affect operational performance for either manufacturing or service companies. This is interesting since it shows that strategies related to selection and certification of suppliers and rationalizing the supply base does not significantly affect organizational performance. Similarly, the level of product and service customization relative to their competitors does not seem to affect the performance of manufacturing companies, although it does influence service sector performance. This may be analogous to the concept of "order qualifiers" and "order winners" in manufacturing strategy (Hill 2000). The results suggest that for manufacturing organizations the application of the Internet and product customization may have transitioned from order winners to become basic qualifiers to do business.

As previously noted research into service supply chains is still relatively new. This study provides an exploratory analysis into the strategies and the relations of the strategies to company performance in the services sector. Several important implications may be drawn for the service sector organizations. First, operational performance--speed, delivery and quality--is positively affected by greater information sharing among supply chain partners. This result was expected since information sharing is one of main tenets of SCM. Higher levels of collaboration and transparency concerning inventory, forecasts, price and retail promotions should improve operational performance and reduce disruptions in the network.

Second, with regard to financial performance, a greater strategic importance of the distribution network seems to affect performance positively. This might be expected since many service companies are geographically dispersed. Therefore, it is logical that greater attention to facility location, distribution and transportation modes would improve financial performance in such companies.

Third, with regard to operational performance, a higher degree of product customization relative to their competitors is associated with better operational results. Presumably, in order to customize their offerings in the marketplace, service companies will have been in communication with their customers about the nature, quantity and time of demand. Therefore, a greater level of customization would lead to improved performance for the on-time delivery and customer satisfaction (quality) dimensions of the services delivered. As a result, the increased attention to product and service customization should lead to higher operational performance.

However, the higher degree of product and service customization is also associated with lower financial performance. When interpreting this inconsistent result, it is important to recall that this metric considers both lower operating costs and higher profits. If a service company emphasizes a higher level of customization compared with those of its competitors, one would expect that the company's operating and associated supply chain costs would also be higher than those of its competitors. Offsetting this to a degree should be an increase in either the market share for such companies, which in turn should lead to economies of scale and ultimately to greater profits. Alternatively, firms with more customized services should have the ability to obtain a premium price for their more customized offerings. Since the product customization factor has a negative coefficient on financial performance, it appears that service companies may not have been able to obtain enough of a price premium to offset the higher operating costs associated with customization. For example, Dell is commonly noted as a leader in the application of "mass customization" in the computer industry--rapidly producing units to individual customer specifications, while maintaining profitability under commodity-like pricing (Holzner 2006). Although many organizations strive to reach a similar standard, few have been successful. Therefore, managers in service industries need to carefully weigh the costs and benefits associated with product customization. In addition to the increased direct costs of customized services, they should explore the additional complications in the supply chain created by the customization and the level of incremental profits that such customization offers over more standardized offerings.

LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH

In this study, every attempt was made to ensure a reliable and valid design. However, several limitations of this study should be discussed. Organizations in the study were broadly classified as either in the manufacturing or services sectors. As shown in Table I, there are industry-based classifications within the service sector that may be used to examine the effect of the relative importance of the factors and the influence on the competitive priorities. In this study, the main objective was to study the differences between manufacturing and services. Under such a scenario, it was necessary to treat all non-manufacturing industries as services. In addition, the limited sample size prohibited any analysis within the specific industry classifications under the broader category of services. However, as an extension to this study, it is necessary to undertake an analysis based on the industry segments within services. Such an analysis would provide a deeper understanding of the supply chain issues in the various "service" industries. The results from this study can therefore be treated as a first step toward under-standing the differences across manufacturing and services. A larger study focused on the various service sector industries is suggested as a follow-up research study.

The proportion of large companies in the sample was slightly higher than small companies. Based on the criterion used for the Malcolm Baldrige Awards where a company with less than 500 FTEs is deemed as a small company and any company with more than 500 FTEs is deemed as a large company, Table I shows that, for manufacturing, there are 34 small companies and 39 large companies, whereas for the services sector, the numbers are 31 and 41, respectively. Therefore, while the sample is skewed toward the larger companies, it is not severely skewed and therefore, we believe the results are not influenced by these proportions. A follow-up study may be conducted to further analyze the impact of organizational size.

While this study considered eight specific SCM strategies, other factors are equally as important to the supply chain perspective. Further research is required to investigate additional factors that may predict the level of performance on the competitive priorities. In particular, additional research should examine the service supply chain practices. As found in this study, the factors that are relevant in service supply chains may differ from factors in the traditional manufacturing-oriented supply chains.

A longitudinal change in the effects of the factors on company performance may be relevant for a number of companies. In such a setting, the relative importance of certain strategies can change with time concerning how these strategies affect performance. A longitudinal study is required to understand how such supply chain-related strategies evolve over time.

Finally, SCM implies various levels of integration with a company's suppliers and distributors. Relative importance and prevalence of certain SCM strategies within a company would be affected not only by the company's own strategy but also by those of its supply chain partners. How this affects the performance level within the company is a potential area of research. The interactions with a company's partners and the different perspective on how these interactions differ across industries and sectors would be a fruitful research challenge.

CONCLUSIONS

SCM literature suggests that various companies have different levels of adoption of specific SCM strategies, which result in varying levels of organizational performance. This article has empirically analyzed the status of eight SCM strategies and their effects on specific operational and financial performance. The results indicate there are several differences in the relative importance of the strategies across the industry sectors and have disparate impacts on perceived performance. Finally, this article provides insights to the similarities and differences between the manufacturing and service supply chains that may help companies make informed decisions on which strategies to pursue in an effort to positively affect their organizational performance.

REFERENCES

Armstrong, J.S. and T.S. Overton. "Estimating Non-Response Bias in Mail Surveys," Journal of Marketing Research, (14:3), 1977, pp. 396-402.

Boyer, K.K. and G.T. Hult. "Extending the Supply Chain: Integrating Operations and Marketing in the Online Grocery Industry," Journal of Operations Management, (23:6), 2005, pp. 642-661.

Chase, R., F.R. Jacobs and N. Aquilano. Operations Management for Competitive Advantage, 11th ed., Irwin McGraw-Hill, 2006.

Chen, I.J. and A. Paulraj. "Towards a Theory of Supply Chain Management: The Constructs and Measurements," Journal of Operations Management, (22:2), 2004a, pp. 119-150.

Chen, I.J. and A. Paulraj. "Understanding Supply Chain Management: Critical Research and a Theoretical Framework," International Journal of Production Research, (42:1), 2004b, pp. 131-163.

Choi, T.Y. and J.L. Hartley. "An Exploration of Supplier Selection Practices Across the Supply Chain," Journal of Operations Management, (14:4), 1996, pp. 333-343.

Cigolini, R., M. Cozzi and M. Perona. "A New Framework for Supply Chain Management Conceptual Model and Empirical Test," International Journal of Operations & Production Management, (24:1), 2004, pp. 7-41.

Croom, S., P. Romano and M. Giannakis. "Supply Chain Management: An Analytical Framework for Critical Literature Review," European Journal of Purchasing and Supply Management, (6), 2000, pp. 67-83.

De Toni, A. and G. Nassimbeni. "Buyer-Supplier Operational Practices, Sourcing Policies and Plant Performance: Results of an Empirical Research," International Journal of Production Research, (37:3), 1999, pp. 597-619.

Ellram, L.M., W.L. Tate and C. Billington. "Understanding and Managing the Services Supply Chain," Journal of Supply Chain Management, (40:4), Fall 2004, pp. 17-32.

Fisher, M.L. "What Is the Right Supply Chain for Your Product?" Harvard Business Review, (75:2), 1997, pp. 105-116.

Frohlich, M.T. "Techniques for Improving Response Rates in OM Survey Research," Journal of Operations Management, (20:1), 2002, pp. 53-63.

Frohlich, M.T. and R. Westbrook. "Arcs of Integration: An International Study of Supply Chain Strategies," Journal of Operations Management, (19:2), 2001, pp. 185-200.

Frohlich, M.T. and R. Westbrook. "Demand Chain Management in Manufacturing and Services: Web-based Integration, Drivers and Performance," Journal of Operations Management, (20:6), 2002, pp. 729-745.

Gunasekaran, A., C. Patel and E. Tirtiroglu. "Performance Measures and Metrics in a Supply Chain Environment," International Journal of Operations & Production Management, (21:1), 2001, pp. 71-87.

Hahn, C.K., P.A. Pinto and D.J. Bragg. "Just-in-Time Production and Purchasing," Journal of Purchasing & Materials Management, (19:3), 1983, pp. 2-10.

Hair, J.F., R.L. Tatham, R.E. Anderson and W. Black. Multivariate Data Analysis, 5th ed., Prentice-Hall, New York, 1998.

Hill, T.J. Manufacturing Strategy--Text and Cases, Irwin/McGraw-Hill, Burr Ridge, IL, 2000.

Ho, D.C.K., K.F. Au and E. Newton. "Empirical Research on Supply Chain Management: A Critical Review and Recommendations," International Journal of Production Research, (40:17), 2002, pp. 4415-4430.

Holzner, S. How Dell Does It, McGraw-Hill, New York, 2006.

Kawtummachai, R. and V.H. Nguyen. "Order Allocation in a Multiple-Supplier Environment," International Journal of Production Economics, (93-94), 2005, pp. 231-238.

Lambert, D.M. and T.C. Harrington. "Measuring Non-Response Bias in Mail Surveys," Journal of Business Logistics, (11:2), 1990, pp. 5-25.

Lee, H.L. and S. Whang. Information sharing in a supply chain, research paper, Stanford University, CA, 1999.

Lejeune, M.A. and N. Yakova. "On Characterizing the 4 C's in Supply Chain Management," Journal of Operations Management, (23:1), 2005, pp. 81-100.

Li, G., H. Yan, S. Wang and Y. Xia. "Comparative Analysis on Value of Information Sharing in Supply Chains," Supply Chain Management: An International Journal, (10:1), 2005, pp. 34-46.

Li, S., S.S. Rao, T.S. Ragu-Nathan and B. Ragu-Nathan. "Development and Validation of a Measurement Instrument for Studying Supply Chain Management, (23:6), 2005, pp. 618-641.

Lockamy, A. III. and K. McCormack. "Linking SCOR Planning Practices to Supply Chain Performance: An Exploratory Study," International Journal of Operations & Production Management, (24:12), 2004, pp. 1192-1218.

Narasimhan, R. and S.W. Kim. "Effect of Supply Chain Integration on the Relationship Between Diversification and Performance: Evidence from Japanese and Korean firms," Journal of Operations Management, (20:3), 2002, pp. 303-323.

Sampson, S. "Customer-Supplier Duality and Bidirectional Supply Chains in Service Organizations," International Journal of Service Industry Management, (11:4), 2000, pp. 348-364.

Sanders, N.R. and R. Premus. "IT Applications in Supply Chain Organizations: A Link Between Competitive Priorities and Organizational Benefits," Journal of Business Logistics, (23:1), 2002, pp. 65-83.

Shanahan, J. "Marking Distribution More Convenient," Logistics Management, (43:2), 2004, p. 57.

Sheffi, Y. and J.B. Rice. "A Supply Chain View of the Resilient Enterprise," MIT Sloan Management Review, (47:1), 2005, pp. 41-48.

Strassner, E.H. and T.F. Howells. "Annual Industry Accounts: Advance Estimates for 2004." Survey of Current Business, U.S. Department of Commerce, Bureau of Economic Analysis, May 2005, pp. 7-19.

Tan, K.C. "Supply Chain Management: Practices, Concerns and Performance Issues," The Journal of Supply Chain Management, (38:1), 2002, pp. 42-53.

Tan, K.C. V. Kannan, R. Handfield and S. Ghosh. "Supply Chain Management: An Empirical Study of Its Impact on Performance," International Journal of Operations & Production Management, (19:10), 1999, pp. 1034-1052.

Tsikriktsis, N., G. Lanzolla and M. Frohlich. "Adoption of e-Processes by Service Firms: An Empirical Study of Antecedents," Production and Operations Management, (13:3), 2004, pp. 216-229.

Vickery, S.K., J. Jayaram, C. Droge and R. Calantone. "The Effects of An Integrative Supply Chain Strategy on Customer Service and Financial Performance: An Analysis of Direct Versus Indirect Relationships," Journal of Operations Management, (21:5), 2003, pp. 523-539.

Youngdahl, W.E. and A. Loomba. "Service-Driven Global Supply Chains," International Journal of Service Industry Management, (11:4), 2000, pp. 329-347.

Zhao, X., J. Xie and W.J. Zhang. "The Impact of Information Sharing and Ordering Coordination on Supply Chain Performance," Supply Chain Management: An International Journal, (7:1), 2002, pp. 24-40.

Zuckerman, A. "What's Working (and What Isn't) in Integrated Supply Chain Technology," World Trade, (18:6), 2005, pp. 50-55.

AUTHORS

Kaushik Sengupta is an assistant professor in the Department of Management, Entrepreneurship and General Business, Frank G. Zarb School of Business, Hofstra University in Hempstead, New York.

Daniel R. Heiser is an associate professor in the Department of Management, DePaul University in Chicago, Illinois.

Lori S. Cook is an associate professor in the Department of Management, DePaul University in Chicago, Illinois.

The authors' names appear in reverse alphabetical order. All authors contributed equally to this article.

This research was generously supported through grants from DePaul University Research Council and a Summer Research Grant, Frank G. Zarb School of Business, Hofstra University.

Table I RESPONDENTS' PROFILE

                       Manufacturing  Services       Total
                       Count  %       Count  %       Count  %

Number of full-time employees (FTE)
  Under 100            11      15.07   8      11.11   19     13.10
  101-500              23      31.51  23      31.94   46     31.72
  501-1,000            11      15.07  10      13.89   21     14.48
  1,001-2,500           9      12.33   4       5.56   13      8.97
  2,501-5,000           7       9.59   7       9.72   14      9.66
  More than 5,000      12      16.44  20      27.78   32     22.07
  Total                73     100     72     100     145    100
Annual sales volume
  Under $1 million      7       9.59   8      11.11   15     10.34
  $1 million to $10    17      23.29  15      20.83   32     22.07
    million
  $10 million to $50   24      32.88  14      19.44   38     26.21
    million
  $50 million to $250  10      13.70  13      18.06   23     15.86
    million
  $250 million to $1   15      20.55  22      30.56   37     25.52
    billion
  Total                73     100     72     100     145    100

Industry Sector                                         Count  %

Industry sectors for service companies
  Agricultural, Forestry, Fishing and Hunting            1       1.39
  Mining and oil & gas extraction                        1       1.39
  Utilities (e.g. communications and public utilities)  12      16.67
  Construction                                           5       6.94
  Wholesale trade                                        5       6.94
  Retail trade                                           5       6.94
  Transportation and warehousing                         4       5.56
  Information (publishing, broadcasting and data         4       5.56
    processing)
  Finance and insurance                                  4       5.56
  Real estate and rental and leasing                     1       1.39
  Professional, scientific and technical services        5       6.94
  Management of companies and enterprises                1       1.39
  Education services                                     6       8.33
  Healthcare and social assistance                       5       6.94
  Arts, entertainment and recreation                     3       4.17
  Accommodation and food services                        2       2.78
  Public administration                                  3       4.17
  Other services                                         5       6.94
  Total                                                 72     100

Table II FACTOR ANALYSIS FOR CURRENT SUPPLY CHAIN MANAGEMENT STRATEGIES

                                                    Factor    Cronbach's
Factors      Survey Statements                      Loadings  [alpha]

Information sharing (SHARE)
  SHARE1     We share information on inventory      0.678     0.795
               levels with our supply chain
               partners
  SHARE2     We share forecasts of customer demand  0.777
               with our supply chain partners
  SHARE3     We share information on price          0.756
               promotions with our supply chain
               partners
  SHARE4     We share information electronically    0.615
               with our supply chain partners
Product and service customization (PRODUCT)
  PRODUCT1   Demand for our products and services   0.484     0.664
               varies greatly over time
  PRODUCT2   Obsolescence of product or service     0.600
               design is a major concern for us
  PRODUCT3   Our products and services are highly   0.770
               customized
  PRODUCT4   Our products and services are more     0.819
               customized than our competitors'
Long-term relationships (RELATION)
  RELATION1  We choose suppliers based upon their   0.525     0.608
               flexibility and speed of delivery
  RELATION2  We build long-term, mutually           0.715
               beneficial relationships with key
               suppliers
  RELATION3  We negotiate long-term contracts with  0.746
               our suppliers
  RELATION4  We negotiate long-term contracts with  0.464
               our customers
Hedging strategy (HEDGE)
  HEDGE1     We always have multiple suppliers for  0.495     0.505
               key items
  HEDGE2     We maintain excess service/production  0.734
               capacity to hedge against
               fluctuations in customer demand
  HEDGE3     We maintain finished goods             0.798
               inventories to hedge against
               fluctuations in customer demand

Table III FACTOR ANALYSIS FOR FUTURE SUPPLY CHAIN MANAGEMENT STRATEGIES

                                                    Factor    Cronbach's
Factors      Survey Statements                      Loadings  [alpha]

Advanced planning systems (PLAN)
  PLAN1      Managing raw material and finished     0.708     0.878
               good inventories
  PLAN2      Managing work-in-process inventories   0.813
  PLAN3      Using material requirements planning   0.805
               (MRP) systems
  PLAN4      Using enterprise resources planning    0.716
               (ERP) systems
  PLAN5      Using collaborative planning,          0.615
               forecasting and replenishment
               (CPFR)
  PLAN6      Using activity-based costing (ABC)     0.605
               accounting methods
Leveraging the Internet (INTERNET)
  INTERNET1  Sharing information over the Internet  0.729     0.792
               with supply chain partners
  INTERNET2  Purchasing material and services via   0.819
               the Internet
  INTERNET3  Selling products and services via the  0.667
               Internet
Supply network structure (SNET)
  SNET1      Deciding whether, and how much, to     0.605     0.776
               outsource
  SNET2      Selecting and certifying suppliers     0.806
  SNET3      Rationalizing the supply base (e.g.:   0.713
               strategic partnering, vertical
               integration, single source supply)
Distribution network structure (DNET)
  DNET1      Deciding where to locate facilities    0.656     0.692
  DNET2      Deciding where to hold inventory in a  0.678
               distribution network
  DNET3      Choosing between different             0.642
               transportation and distribution
               modes

Table IV CORRELATIONS BY SECTOR BETWEEN SCM STRATEGIES AND OPERATIONAL
AND FINANCIAL METRICS

                          Performance Metric
                  Operational Metric      Financial Metric
SCM Strategies  Manufacturing  Service  Manufacturing  Service

SHARE            0.167         0.339*    0.245*         0.233*
PRODUCT         -0.077         0.388*   -0.022         -0.148
RELATION         0.178         0.135     0.342**       -0.087
HEDGE            0.248*        0.177     0.311**       -0.056
PLAN             0.144         0.242*    0.230*         0.217
INTERNET         0.058         0.217     0.157          0.035
DNET             0.113         0.318**   0.243*         0.292*
SNET             0.031         0.160     0.193          0.027

*0.05 significance level.
**0.01 significance level.
SCM, supply chain management.

Table V REGRESSION ANALYSIS BY SECTOR

                                                             Durbin-
                                                             Watson
Regression Models                                 [R.sup.2]  Test

Manufacturing sector
  Operational = 12.330 + 0.255 x HEDGE            0.092      2.039
  Financial = 0.067 + 0.239 x RELATION + 0.219 x  0.174      1.992
  SNET
Services sector
  Operational = 6.932 + 0.245 x SHARE + 0.221 x   0.229      1.857
  PRODUCT
  Financial = 8.014 + 0.236 x DNET - 0.139 x      0.141      1.683
  PRODUCT

HEDGE, hedging strategy; RELATION, long-term relationships; SNET, supply
network structure; SHARE, information sharing; PRODUCT, product and
service customization; DNET, distribution network structure.