Supply chain practice and information sharing

Journal of Operations Management 25 (2007) 1348–1365 www.elsevier.com/locate/jom

Supply chain practice and information sharing Honggeng Zhou a,1, W.C. Benton Jr.b,* a

Department of Decision Sciences, Whittemore School of Business and Economics, University of New Hampshire, 15 College Road, Durham, NH 03824, United States b Department of Management Sciences, Fisher College of Business, The Ohio State University, 2100 Neil Avenue, Columbus, OH 43210, United States Available online 17 January 2007

Abstract Effective supply chain practice and information sharing enhances the current supply chain management environment. The purpose of this study is to investigate the integration of information sharing and supply chain practice in supply chain management. Data from 125 North American manufacturing firms were collected. The results show that (1) effective information sharing significantly enhances effective supply chain practice; (2) supply chain dynamism has significant positive influence on effective information sharing as well as effective supply chain practice. Supply chain dynamism has more influence on information sharing than supply chain practice; (3) and effective supply chain practice becomes more important when the level of information sharing increases. The findings show that both effective information sharing and effective supply chain practice are critical in achieving good supply chain performance. # 2007 Elsevier B.V. All rights reserved. Keywords: Supply chain management; Information sharing; Supply chain practice

1. Introduction During the past 10 years, supply chain management and information technology management have attracted much attention from both practitioners and researchers. As information technology evolves, firms tend to become more integrated. Therefore, integrating effective supply chain practice with effective information sharing becomes critical for improving supply chain performance. Supply chain practice focuses on material movement (Chopra and Meindl, 2001), while informa-

* Corresponding author. E-mail addresses: [email protected] (H. Zhou), [email protected] (W.C. Benton Jr.). 1 Tel.: +1 603 862 0869. 0272-6963/$ – see front matter # 2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jom.2007.01.009

tion sharing focuses on information flow (Premkumar and William, 1994). In this study, three categories of supply chain practice are considered: supply chain planning, just-intime (JIT) production, and delivery practice. A group of supply chain practice is regarded as effective supply chain practice if the selected best practices have been implemented. Information sharing is another focus of this study. Information technology investment in Corporate America has increased significantly. It is estimated that the US information technology (IT) spending will reach $497 by 2008 (http://www.itfacts.biz). Information technology has had an impressive impact on supply chain practice. This study focuses on three aspects of information sharing: information sharing support technology, information content, and information quality.

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Effective supply chain practice and effective information sharing are two sources of supply chain improvement. While some companies emphasize improving supply chain practice, others emphasize leveraging the information sharing among supply chain partners. Since these two major approaches are not independent, firms must work on both supply chain practice and information sharing simultaneously. As an example, Toyota, a world class in supply chain practice, began to implement SAP in late 1990s. The purpose of this study is to investigate (1) the relationship between information sharing and supply chain practice, (2) the influence of supply chain dynamism on information sharing and supply chain practice, and (3) the impact of information sharing and supply chain practice on delivery performance. In the next section, we review the literature on supply chain practice, information sharing, supply chain dynamism, and delivery performance. In Section 3, the relationships among these variables are examined and the research hypotheses are presented. In Section 4, the research methodology and measurement scale development are presented. In Section 5, the model and the results of testing the relationship among supply chain dynamism, supply chain practice, and information sharing are given. The model and the results of testing the relationship among supply chain practice, information sharing, and delivery performance are in Section 6. Section 7 provides the managerial implications. Finally, Section 8 concludes the study.

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2.1.1. Supply chain planning Supply chain planning practices are used to process information from suppliers, customers, and internal operations. Supply chain planning is driven by two objectives: (1) make a good forecast of future demand, and (2) coordinate various functions within a firm and its suppliers and customers. The importance of supply chain demand forecast has been well documented (Lee et al., 1997; Aviv, 2001). The impact of supply chain forecast on delivery performance has also been well researched (Cook and Rogowski, 1996; Krajewski and Wei, 2001). Inter-functional coordination within a firm is important because the alignment among the functions is necessary to achieve a firm’s goal (Hodge et al., 1996; Womack et al., 1990). Several researchers have shown the value of the inter-firm cooperation and sharing information among supply chain partners (Hill, 1994; Mentzer, 2001; Gavirneni et al., 1999; Lee et al., 2000). This study measures the effectiveness of supply chain planning from the extent to which effective supply chain demand forecast and coordination practices are implemented.

2.1. Supply chain practice

2.1.2. JIT production JIT production includes five practices: pull system, cycle time reduction, cellular manufacturing, agile manufacturing strategy, and bottleneck removal (Flynn et al., 1995; Powell, 1995; MacDuffie et al., 1996). In a pull system, production is driven by customer demand. The objective is to meet the customer’s demand in a precise and timely manner. Reductions in cycle time allow for running smaller batches, which in turn improves the quality and timeliness of feedback. Cellular manufacturing identifies similar products or similar processes and groups them together. It can reduce throughout time. An agile manufacturing strategy allows production systems to cope with rapid demand changes, which enhances effective supply chain management. Bottleneck removal balances resources and maximizes output of production. Overall, JIT production practices improve the responsiveness and efficiency of supply chains.

In this study, we consider three categories of supply chain practice (supply chain planning, JIT production, and delivery practice), because they have been shown to be closely related to delivery performance (Schroeder and Flynn, 2001; Supply Chain Council, 2002). Since other categories of supply chain practice such as sourcing and product return are not expected to have significant influence on delivery performance, they are not considered in this study.

2.1.3. Delivery practice The extant literature and anecdotal evidence have shown that effective delivery practices have had significant impact on supply chain performance (Supply Chain Council, 2000). Delivery is clearly a competitive weapon for Dell Computers. Ford recently partnered with the UPS logistics group to develop and implement an Internet-based delivery process (Gurin, 2000). WalMart uses the cross docking technique, in which its

2. Literature review In this section, we review the literature on supply chain practice, information sharing, supply chain dynamism, and delivery performance. The literature review provides the theoretical foundation for this research. The literature taxonomy is provided in Table 1.

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Table 1 Literature review taxonomy Authors

Aviv (2001) Boyer et al. (1997) Boyer and Pagell (2000) Chopra and Meindl (2001) Cook and Rogowski (1996) Dess and Davis (1984) Fine (1998) Fisher (1997) Flynn et al. (1995) Gavirneni et al. (1999) Gurin (2000) Handfield and Nichols (1999) Hill (1994) Hodge et al. (1996) Lee et al. (1997) Lee et al. (2000) Liker and Yu (2000) Krajewski and Wei (2001) MacDuffie et al. (1996) Mendelson and Pillai (1998) Mentzer (2001) McCormack (1998) McGowan (1998) Miller and Friesen (1983) Neumann and Segev (1979) Petersen (1999) Powell (1995) Ramdas and Spekman (2000) Schroeder and Flynn (2001) Seddon (1997) Stalk et al. (1992) Supply Chain Council (2000) Sum et al. (1995) Vijayasarathy and Robey (1997) Womack et al. (1990)

Supply chain practice

Information sharing

Plan

Information sharing support technology

JIT production

Delivery practice

Information content

Information quality

*

*

Supply chain dynamism

Delivery performance

* * *

* * * * * * * * * * * * * * * * * *

*

* * * * * * * * * *

*

* * *

*

* means the particular topic was discussed in the particular article. For example, the first asterisk means supply chain planning was discussed in Aviv (2001).

warehouse serves as a switching station rather than a stocking place (Stalk et al., 1992). Given the importance of delivery practice, a formal measurement scale has yet to be developed. Thus one contribution of this study is the development, validation, and testing of a reliable measurement scale for delivery practice. 2.2. Information sharing This study considers three aspects of information sharing: information sharing support technology, information content, and information quality. Information sharing support technology includes the hardware

and software needed to support information sharing. Information content refers to the information shared between manufacturers and customers. Information quality measures the quality of information shared between manufacturers and customers. In sum, the three aspects of information sharing measure the technologies used to support information sharing, the scope of information shared, and the quality of information shared, respectively. 2.2.1. Information quality Information quality measures the degree to which the information exchanged between organizations meets

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the needs of the organizations (Petersen, 1999). A number of researchers have identified several important characteristics of information quality (Neumann and Segev, 1979; Mendelson and Pillai, 1998). Neumann and Segev (1979) studied four information characteristics: content, accuracy, recency, and frequency. McCormack (1998) measured information by accuracy, frequency, credibility, and availability of forecast. Petersen (1999) measured information quality by currency, accuracy, and completeness. Vijayasarathy and Robey (1997) measured information intensity and quality. Information quality is an important determinant of the usefulness of an information system. Sum et al. (1995) found that data accuracy is critical in affecting operating efficiency and customer service. McGowan (1998) argued that the information system is perceived useful when the information is high quality, readily accessible, accurate and relevant. In this study, there are nine aspects of information quality: accuracy; availability; timeliness; internal connectivity; external connectivity; completeness; relevance; accessibility; and frequently updated information. 2.2.2. Information content Many managers mistakenly concentrate their information sharing on only the hardware and software, ignoring the decision-making in the information sharing process (Davenport, 1994). Schroeder and Flynn (2001) suggested that what makes the performance difference is how information is used. For example, high performing firms had a higher percentage of information exchanged via EDI with customers and suppliers. Their results demonstrated that information technology investment alone is not enough. Only when management teams both emphasize technology investment and choose the appropriate information to share, can a firm achieve effective firm performance. Information content can be classified as supplier information, manufacturer information, customer information, distribution information, and retailer information (Handfield and Nichols, 1999; Chopra and Meindl, 2001). This study measures two information flows: the information that manufacturers share with their customers (manufacturer information), and the information that customers share with their manufacturers (customer information). Since the two information flows are completely different, two latent variables will be used to measure the two information flows. As a result, information sharing in this study has four latent variables: information sharing support technology, customer information, manufacturer information, and information quality.

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2.2.3. Information sharing support technology Since this research focuses on manufacturing environments, information sharing support technology focuses on advanced manufacturing technology and emerging supply chain management IT applications. Besides advanced manufacturing technology (Boyer et al., 1997; Boyer and Pagell, 2000), many supply chain management (SCM) IT applications have emerged and become widely adopted in supply chain management in recent years. The SCM ITapplications can be categorized into three categories based on the length of the planning periods (Supply Chain Council, 2002). The first category is supply chain execution, which focuses on short term daily activities such as warehouse management, transportation management, and collaborative manufacturing. The second category is supply chain planning, which focuses on medium to long term activities. The third category is supply chain execution management, which bridges the first two categories as a supporting tool. 2.3. Supply chain dynamism The business environmental dynamism is defined as the unpredictable changes in products, technologies, and demand for products in the market (Miller and Friesen, 1983; Dess and Davis, 1984). Fine (1998) measured three environmental clock speeds: product clock speed, process clock speed, and organization clock speed. All three clock speeds measure the pace of the changes in business environment and are shown to have a significant impact on operations. Fisher (1997) suggested that supply chains facing different environmental dynamism (e.g. volatile demand versus stable demand) should use different supply chain practices. In this study, supply chain dynamism measures the pace of changes in both products and processes. 2.4. Delivery performance Delivery performance is a key performance measurement criterion in supply chain management. McCormack (1998) measured delivery performance versus committed date and delivery performance versus quoted order lead-time. Ramdas and Spekman (2000) measured order fulfillment. The Performance Measurement Group benchmarks supply chain performance along three dimensions, which are delivery performance to request, order fulfillment lead-time, and order fill rate. The Supply Chain Council uses on-timedelivery-to-schedule as one of its supply chain performance measures. Liker and Yu (2000) used the percentage of late deliveries as a performance measure.

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In this study, the delivery performance includes on-time delivery, perfect order fulfillment rate, and delivery reliability/dependability. 3. Conceptual model and hypotheses As can be seen from the literature review and the taxonomy given in Table 1, there have been no scholarly research attempts that link supply chain practice and information sharing. The research herein will test the linkages among supply chain dynamism, information sharing, supply chain practice, and delivery performance. We use a structural equation model (as shown in Fig. 1) to test the influence of supply chain dynamism on information sharing and supply chain practice followed by a regression model to test the impact of information sharing and supply chain practice on delivery performance. 3.1. Supply chain practice and information sharing Effective information sharing between supply chain partners enhances most supply chain initiatives, including vendor managed inventory, continuous replenishment program, collaborative forecasting and replenishment, and efficient customer response (Chen and Chen, 1997; Lummus and Vokurka, 1999; Chen, 2002; Lee and Whang, 2000). Shaw (2000) found that

emerging manufacturing technologies have an influence on supply chain activities and supply chain structures. It is also found that those web-based emerging manufacturing technologies make information transmission among the supply chain partners much easier. Spring and Sweeting (2002) synthesized a number of existing and emerging themes in supply chain management, information and customer relationships. They showed that the use of enterprise resource planning software profoundly changes the supply chain partner relationships. Two anecdotal examples of information sharing and supply chain practice are shown below. 3.1.1. Dell Dell is a good example of using information sharing to improve its supply chain practices. Dell receives customer order information directly from its website. At the same time component availability information is shared with its customers. As an example, the component feature price is lower for components with high inventory levels. The Dell web site also allows customers to customize their orders. The interaction between Dell and its customers makes the pull production system more effective and enables the supply chain planning. Dell also shares information with its suppliers. Once it receives the order information, it transmits the information directly to appropriate suppliers. Suppliers also have backlog and inventory information. Sharing information with

Fig. 1. Supply chain practice, information sharing, and supply chain dynamism (conceptual model).

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suppliers (especially long lead time suppliers) improve the supply chain planning capability. Specifically, Sony’s logistics information system is linked directly to Dell’s information system. In some instances, Sony ships its monitors directly to the Dell’s customers. Finally, Dell outsources its warranty and repair service systems. When Dell receives service request, it forwards the request directly to its service and parts providers. This information sharing feature improves customer service and makes the supply chain more responsive. Overall, Dell’s information system interacts with its customers and suppliers to improve its supply chain planning, JIT production, and delivery practices. 3.1.2. Cisco Cisco also uses information sharing to enhance supply chain practices. Cisco outsources more than 50% of its production capacity. This virtual manufacturing model is driven by information sharing. Cisco’s manufacturing model allows it to focus on its core competencies and innovation. Information sharing drives the Cisco’s supply chain practices in the following ways: 1. A significant number of Cisco’s orders originate from online customer interfaces. 2. Cisco shares its order information electronically with its component suppliers. Information sharing allows Cisco to coordinate its supply chain in real time and respond to demand changes. Cisco also shares production schedule, inventory, quality, performance and capacity information with its suppliers. 3. Cisco’s logistics system is also driven by information sharing with its suppliers. Many products are shipped from its suppliers directly to its customers. Through the virtual manufacturing model, suppliers are also involved in Cisco’s product design process. 4. Information sharing with suppliers significantly improves Cisco’s ability to rapidly respond to the demand changes in the supply chain. Overall, Cisco shares information with its customers to enhance supply chain management. Cisco also has significant two-way information sharing with its suppliers, which enhances supply chain planning, JIT production, and delivery practices. Based on the literature and anecdotal evidence, information sharing is critical for managing the esupply chain and effective supply chain practices. To date, the importance of information sharing in supply chain management has not been comprehensively investigated. The previous studies focus on the

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influence of one aspect of information sharing on one particular group of supply chain practice (Schroeder and Flynn, 2001; Chen, 2002). This study fills the research gap by considering various groups of supply chain practice and various aspects of information sharing simultaneously. Please see the literature taxonomy in Table 1. Based on the previous research studies (Lee and Whang, 2000; Schroeder and Flynn, 2001; Chen, 2002) and anecdotal evidence (Brunn and Mefford, 2004; Supply Chain Council, 2002), the following hypothesis is developed: Hypothesis 1. Effective information sharing enhances effective supply chain practice. 3.2. Influence of supply chain dynamism on supply chain practice and information sharing Information processing theory supports the influence of supply chain dynamism on information sharing and supply chain practice (Galbraith, 1973; Tushman and Nadler, 1978; Daft and Lengel, 1986; Forster, 2000). As supply chain dynamism increases, information processing capacity needs to increase in order to achieve superior firm performance. In the information processing model described in Galbraith (1973), information systems are suggested as an effective approach to increase information processing capability. In supply chains, sharing information among supply chain members is one way to increase information processing capacity. Galbraith (1973) also suggested that appropriate structures such as lateral relations and self-contained tasks can increase information processing capability. Effective supply chain practices are the ‘‘structures’’ that can increase information processing capacity. For instance, effective supply chain planning and delivery practices can improve lateral relationships in supply chains. An effective production system usually consists of selfcontained tasks. Recently, Forster (2000) extended the information processing theory into the supply chain environment. Since it has been suggested that supply chain dynamism has a positive influence on supply chain practice and information sharing (Galbraith, 1973; Forster, 2000), the research hypotheses are proposed as follows: Hypothesis 2. Supply chain dynamism has significant positive influence on information sharing. Hypothesis 3. Supply chain dynamism has significant positive influence on supply chain practice.

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3.3. Supply chain practice, information sharing and delivery performance Since delivery performance is one of the key supply chain measures, this study uses a regression model to test the impact of supply chain practice and information sharing on delivery performance. The literature review for the three groups of supply chain practices suggests that effective supply chain practices have a positive influence on delivery performance. Supply chain planning can provide an accurate customer demand forecast and thus meet the varying customer demand in a timely manner through intra-firm and inter-firm coordination (Supply Chain Council, 2002; Mentzer, 2001). JIT production uses practices such as pull system and agile manufacturing strategy to meet the customer demand in a timely fashion (Schroeder and Flynn, 2001). Effective delivery practices directly impact the supply chain delivery performance (Gurin, 2000; Supply Chain Council, 2002). Thus, given the literature and anecdotal evidence, we hypothesize the following: H4a. Effective supply chain planning practice has positive impact on delivery performance. H4b. Effective JIT production practice has positive impact on delivery performance. H4c. Effective delivery practice has positive impact on delivery performance. In addition, studies also have shown that information sharing has positive influence on delivery performance. Bourland et al. (1996) demonstrated that sharing timely demand information may result in improvement in delivery performance. Gurin (2000) revealed how Ford and UPS leverage information sharing to improve Ford’s delivery performance. Ahmad and Schroeder (2001) showed the impact of EDI system on delivery performance. Because information sharing can facilitate the information exchange between customers and manufacturers, it is expected to have a positive influence on delivery performance. Higher information quality (Seddon, 1997; McGowan, 1998) and higher level of investment in information sharing support technology (Boyer et al., 1997; Supply Chain Council, 2002) are also expected to improve delivery performance. Thus, the following hypotheses are developed: H5a. Increases in the information quality improves delivery performance.

H5b. Increases in the level of information sharing support technology investment improves delivery performance. H5c. Increases in the level of sharing customer information improves delivery performance. H5d. Increases in the level of sharing manufacturer information improves delivery performance. 4. Research design and methodology The purpose of this study is to investigate (1) the relationship between information sharing and supply chain practice, (2) the influence of supply chain dynamism on information sharing and supply chain practice, and (3) the impact of information sharing and supply chain practice on delivery performance. The primary research instrument for the study is a rigorously validated questionnaire. A summary of the survey questions is shown with the summary statistics in Table 2. 4.1. Instrument design and data collection The study involves two data collection stages: pilot survey and formal survey. The pilot survey is designed to test the viability of the study and purify the data collection instrument. Four academic researchers and three industry executives critiqued the research instrument for relevance and clarity. The questionnaire for the main study was refined based on feedback from the pilot study. The study includes a wide variety of manufacturing industries. A total of 745 surveys were mailed. Of the 134 returned questionnaires, 125 were usable. The response rate was approximately 18%. The data analysis is based on the 125 useable questionnaires. 4.2. The sample list The sample list consisted of individuals at decisionmaking levels, and in strategically oriented positions. The targeted respondents were senior executives (i.e. CEO, President, Vice President, Director, and Plant Manager). The average number of employees in the respondents’ firms was about 5000. Eight companies had more than 10,000 employees. The median annual sales value, as reported by the respondents, was between $100 million and $500 million dollars. Five companies had annual sales of more than $5 billion. Regarding the manufacturing process, 41% of the companies used make-to-stock strategy. Twenty-eight percent of the companies used make-to-order strategy. Six percent of

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Table 2 Survey questions and descriptive statistics Survey question

Mean

S.D.

Assess your firm’s information system capability in the following dimensions: [1 = not capable, 7 = highly capable] IA1. Information accuracy IA2. Information availability IA3. Real-time information IA4. Internal connectivity IA5. External connectivity IA6. Updating information frequently IA7. Information completeness IA8. Information relevance IA9. Information accessibility

5.62 5.36 4.88 5.33 4.75 5.27 5.24 5.48 5.02

1.23 1.32 1.55 1.49 1.60 1.22 1.23 1.09 1.40

How often does your major customer electronically provide your firm with its information in the following dimensions [1 = never, 2 = annually, 3 = semi-annually, 4 = quarterly, 5 = monthly, 6 = weekly, 7 = daily] IB1. Changes in purchase order information 5.23 2.48 IB2. Planned order information 4.39 2.35 IB3. Inventory level information 3.36 2.55 IB4. Product design specifications 2.66 2.16 IB5. Performance evaluation information 3.12 2.01 IB6. Future demand forecasting information 3.78 2.12 IB7. Production planning information 3.42 2.39 How often does your firm electronically provide your major customer with your firm’s information in the following dimensions [1 = never, 2 = annually, 3 = semi-annually, 4 = quarterly, 5 = monthly, 6 = weekly, 7 = daily] IC1. Production capacity information 2.43 2.12 IC2. Order status information 5.10 2.53 IC3. Delivery schedule information 5.03 2.64 IC4. Changes in delivery schedule 4.95 2.68 IC5. Lead time information for products 3.28 2.47 What percentage of the information in the following dimensions does your major customer provide your firm in an electronical format [1 = 0–10%, 2 = 10–25%, 3 = 25–40%, 4 = 40–60%, 5 = 60–75%, 6 = 75–90%, 7 = 90–100%] ID1. Changes in purchase order information 4.93 2.50 ID2. Planned order information 4.52 2.68 ID3. Inventory level information 3.07 2.59 ID4. Product design specifications 3.16 2.58 ID5. Performance evaluation information 3.82 2.71 ID6. Future demand forecasting information 4.13 2.60 ID7. Production planning information 3.49 2.73 What percentage of the information in the following dimensions does your firm provide to your major customer in an electronical format [1 = 0– 10%, 2 = 10–25%, 3 = 25–40%, 4 = 40–60%, 5 = 60–75%, 6 = 75–90%, 7 = 90–100%] IE1. Production capacity information 2.86 2.53 IE2. Order status information 4.64 2.62 IE3. Delivery schedule information 4.77 2.58 IE4. Changes in delivery schedule 4.45 2.64 IE5. Lead time information for products 3.19 2.56 The following questions are designed to measure the use of information system support technologies including both hardware and software in your company. Please indicate the amount of investment your company has in the following activities [1 = no investment, 4 = moderate investment, 7 = heavy investment] II1. Advanced planning and scheduling software 4.28 2.04 II2. Bar coding/automatic identification system 4.26 1.98 II3. Electronic data interchange (EDI) capability 4.88 1.94 II4. Enterprise resource planning systems (ERP) system 4.61 2.35 II5. E-procurement system 3.30 2.05 II6. Forecast/demand-management software 3.66 2.17 II7. Manufacturing Execution Systems (MES) 2.16 1.67 II8. Transportation/warehouse management software (WMS) 3.21 2.20 To what extent have the following planning practices been implemented in your company [1 = not implemented, 7 = extensively implemented] IIIA1. The use of historical data in the development of forecasts 5.00 1.86

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Table 2 (Continued ) Survey question IIIA2. IIIA3. IIIA4. IIIA5. IIIA6.

‘‘What-if’’ analysis has been implemented for supply/demand balancing A change in the demand information instantaneously ‘‘reconfigures’’ the production and supply plans Online visibility of supply-chain demand requirements The designation of a supply chain planning team Both marketing and manufacturing functions are involved in supply chain planning process

Mean

S.D.

3.41 3.21 3.35 3.65 3.70

1.98 2.18 2.05 2.15 2.08

To what extent have the following production practices been implemented in your company [1 = not implemented, 7 = extensively implemented] IIIB1. Pull system 3.97 2.11 IIIB2. Cellular manufacturing 3.42 2.25 IIIB3. Cycle time reduction 4.40 1.96 IIIB4. Agile manufacturing strategy 3.10 2.04 IIIB5. Bottleneck/constraint removal 4.02 1.83 To what extent have the following delivery practices been practiced in your company [1 = not practiced, 7 = extensively IIIC1. We deliver products to our major customer on a just-in-time basis IIIC2. We have a single point of contact for all order inquiries IIIC3. We have real time visibilities of order tracking IIIC4. We consolidate orders by customers, sources, carriers, etc. IIIC5. We use automatic identification during the delivery process to track order status

practiced] 4.82 5.12 4.41 4.59 3.26

2.07 1.82 2.17 2.03 2.19

Please indicate whether you agree or disagree with the following statements about your business environment [1 = strongly disagree, 7 = strongly agree] IV1. New products account for a high fraction of total revenue 4.04 1.74 IV2. Products and services are innovated frequently 4.64 1.53 IV3. The innovation rate of operating processes is high 3.64 1.55 The following questions relate to the performance of your firm. Compared to your competitors, please indicate your position on the following dimensions [1 = significantly lower, 4 = equal, 7 = significantly higher] V1. On-time delivery 5.12 1.41 V2. Perfect order fulfillment rate 4.96 1.36 V3. Delivery reliability/dependability 5.30 1.23

the companies used assemble-to-order strategy. Fifteen percent are engineer-to-order companies. The remaining 10% were hybrid systems. The study resulted in a missing value rate of 2%. The missing values were replaced with the mean of each item. To test the nonresponse bias, the responses of those who returned early were compared with those who returned late to determine if there are any statistical differences (Lessler and Kalsbeek, 1992). There were no statistical differences between the early and late responses. 4.3. Measurement scales Descriptive statistics for each survey statement are presented in Table 2. Each statement required responses based on a 7-point Likert scale. There are 11 latent variables: information sharing support technology, manufacturer information, customer information, information quality, supply chain planning, JIT production, delivery practice, supply chain dynamism, delivery performance, supply chain practice, and information sharing. For customer information, questions IB1 to IB7 given in Table 2 measure how often customer information is

provided to manufacturers electronically (i.e. the frequency of information sharing). Questions ID1 to ID7 measure the percentage of electronically provided customer information (i.e. the scope of information sharing). In this study, the product of the information sharing frequency and information sharing scope was used to measure the level of electronic customer information sharing. Only when both information sharing frequency and information sharing scope have high scores will the level of customer information sharing be high. The indicators for customer information are generated as follows: MB1 = (IB1  1)(ID1  1)/7, . . ., MB7 = (IB7  1)(ID7  1)/7. Variables MB1 to MB7 measure the level of customer information shared in electronic format. Similarly, questions IC1 to IC5 measure how often manufacturer information is provided to customers electronically. Questions IE1 to IE5 measure the percentage of manufacturer information that is provided electronically. The indicators for manufacturer information are generated as follows: MC1 = (IC1  1)(IE1  1)/7, . . ., MC5 = (IC5  1)(IE5  1)/7. Variables MC1 to MC5 measure the level of manufacturer information shared electronically.

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Table 3 Results of measurement validation Scale name

Variable name

CITC

Factor loading

Scale statistics

Information quality

IA1 IA2 IA3 IA4 IA5 IA6 IA7 IA8 IA9

0.63 0.75 0.69 0.57 0.48 0.74 0.76 0.60 0.70

0.72 0.82 0.76 0.64 0.56 0.80 0.84 0.70 0.79

Cronbach’s alpha: 0.89, largest eigenvalue (variance explained): 4.99 (55%), second largest eigenvalue (variance explained): 0.99 (11%)

Customer information

MB1 MB2 MB3 MB4 MB5 MB6 MB7

0.54 0.77 0.54 0.46 0.65 0.73 0.74

0.66 0.86 0.67 0.58 0.75 0.83 0.84

Cronbach’s alpha: 0.86, largest eigenvalue (variance explained): 3.89 (55.6%), second largest eigenvalue (variance explained): 0.88 (12.6%)

Manufacturer information

MC1 MC2 MC3 MC4 MC5

0.52 0.62 0.76 0.75 0.53

0.68 0.76 0.86 0.85 0.70

Cronbach’s alpha: 0.83, largest eigenvalue (variance explained): 2.99 (60.0%), second largest eigenvalue (variance explained): 0.94 (18.8%)

Information sharing support technology

II1 II2 II3 II4 II5 II6 II7 II8

0.64 0.59 0.63 0.50 0.69 0.70 0.47 0.62

0.75 0.70 0.73 0.61 0.78 0.80 0.58 0.73

Cronbach’s alpha: 0.86, largest eigenvalue (variance explained): 4.06 (50.8%), second largest eigenvalue (variance explained): 0.89 (11.1%)

Plan

IIIA1 IIIA2 IIIA3 IIIA4 IIIA5 IIIA6

0.43 0.63 0.59 0.54 0.63 0.59

0.58 0.77 0.74 0.69 0.78 0.74

Cronbach’s alpha: 0.81, largest eigenvalue (variance explained): 3.10 (51.6%), second largest eigenvalue (variance explained): 0.93 (15.5%)

JIT production

IIIB1 IIIB2 IIIB3 IIIB4 IIIB5

0.41 0.66 0.74 0.59 0.71

0.57 0.79 0.86 0.77 0.84

Cronbach’s alpha: 0.82, largest eigenvalue (variance explained): 2.99 (59.8%), second largest eigenvalue (variance explained): 0.87 (17.4%)

Delivery practice

IIIC1 IIIC2 IIIC3 IIIC4 IIIC5

0.39 0.46 0.63 0.53 0.48

0.58 0.67 0.81 0.74 0.68

Cronbach’s alpha: 0.74, largest eigenvalue (variance explained): 2.45 (48.9%), second largest eigenvalue (variance explained): 1.04 (20.8%)

Supply chain dynamism

IV1 IV2 IV3

0.60 0.60 0.47

0.84 0.84 0.74

Cronbach’s alpha: 0.73, largest eigenvalue (variance explained): 1.96 (65.2%), second largest eigenvalue (variance explained): 0.63 (21.0%)

Delivery performance

V1 V2 V3

0.53 0.72 0.65

0.76 0.90 0.86

Cronbach’s alpha: 0.79, largest eigenvalue (variance explained): 2.12 (70.7%), second largest eigenvalue (variance explained): 0.59 (19.7%)

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Table 3 (Continued ) Scale name

Variable name

CITC

Factor loading

Scale statistics

Information sharing

Information sharing support technology Customer information Manufacturer information Information quality

0.52

0.76

0.49 0.58 0.37

0.73 0.81 0.60

Cronbach’s alpha: 0.70, largest eigenvalue (variance explained): 2.13 (53.2%), second largest eigenvalue (variance explained): 0.78 (19.4%)

Supply chain planning JIT production Delivery practice

0.51 0.55 0.43

0.80 0.82 0.72

Supply chain practice

4.3.1. Validity and reliability of measurement scales The validation process for the survey instrument had three steps: content validity; construct validity, and reliability. The literature review and in-depth interviews conducted with business executives and researchers established the basis of content validity for the survey instrument. The purpose of construct validity is to show that the items measure what they purport to measure. Unidimensionality was established with exploratory factor analysis, where 0.30 is generally considered to be the lowest significant factor loading to define the construct (Hair et al., 1998). Two approaches were used to measure reliability. First, the internal consistency in this study is measured by Cronbach’s alpha. The lower limit of 0.6 is considered acceptable for newly developed scales and 0.7 for established scales (Nunnally, 1994). Cronbach’s coefficient alphas were calculated for the items of each survey construct. Second, the Corrected Item-Total Correlation (CITC) reliability test is used (Kerlinger, 1986). In this test all items for the same construct should be closely related to the underlying latent variable. 0.30 is considered as the lowest acceptable value. 4.3.2. The results of the measurement scales The results of the measurement scales are shown in Table 3. Table 3 shows that all factor loadings meet the criterion of larger than 0.3 and all constructs satisfy the unidimensionality requirement. For all construct scales except ‘‘Delivery Practice’’, only one eigenvalue is larger than 1.00. The variance explained by the largest eigenvalue is larger than 40%. For the construct ‘‘Delivery Practice’’, the second largest eigenvalue is slightly larger than 1.00. The largest eigenvalue explains more than 40% of the variance. The scree test also suggests that one factor is the most appropriate

Cronbach’s alpha: 0.68, largest eigenvalue (variance explained): 1.84 (61.3%), second largest eigenvalue (variance explained): 0.67 (22.3%)

for this set of items. Thus, ‘‘Delivery Practice’’ is determined to be unidimensional. All scales have Cronbach’s alpha values of 0.70 or higher except ‘‘Supply Chain Practice’’. ‘‘Supply Chain Practice’’ construct is a newly developed construct for this study. Its Cronbach’s alpha is well above 0.6, which is acceptable for a newly developed scale (Hair et al., 1998). In addition, the CITC for all items are larger than 0.30. Since all Cronbach’s alpha and CITC measures are supported, all measurement scales are deemed reliable (Hair et al., 1998). 5. Structural equation model testing and results Structural equation modeling is used to test the influence of supply chain dynamism on supply chain practice and information sharing. The RAMONA program is used for this study. The root mean square error of approximation (RMSEA) measures the sample discrepancy function value per degree of freedom. An absolute RMSEA value of less than 0.05 suggests a good fit, and a RMSEAvalue between 0.05 and 0.08 indicates a reasonable fit (Browne and Cudeck, 1993). The normed chi-square, which divides chi-square statistics by the degree of freedom, is also used as a fit statistic (Joreskog, 1969). A normed chi-square below 1 indicates that the Table 4 Fit measures of overall model in Fig. 2 Sample discrepancy function value Population discrepancy function value—biased adjusted point estimate Root mean square error of approximation (RMSEA)—point estimate RMSEA—90% confidence interval p-Value H0: close fit (RMSEA  0.05) Chi-square test statistic Degrees of freedom Normed chi-square (chi-square/degree of freedom)

0.382 0.128 0.063 (0.017, 0.099) 0.26 47.879 32 1.50

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Table 5 Estimations of measurement model parameters in Fig. 2 Scale name

Effect indicator

Path coefficient estimate (t value)

Supply chain practice

Plan JIT production Delivery practice

0.753 (14.61) 0.610 (9.18) 0.541 (7.40)

Information sharing

Information sharing support technology Customer information Manufacturer information Information quality

0.734 0.544 0.614 0.455

Supply chain dynamism

New products account for a high fraction of total revenue (IV1) Products and services are innovated frequently (IV2) The innovation rate of operating processes is high (IV3)

0.729 (10.23) 0.765 (10.84) 0.576 (7.46)

model is over fitted (Joreskog, 1969), while a value larger than 3.0 (Carmines and McIver, 1981) to 5.0 (Wheaton et al., 1977) indicates that a model does not adequately fit the data. Both fit statistics adjust the sample discrepancy function by the degree of freedom. The results of the structural model fit statistics are presented in Table 4. The RMSEA of the model is 0.063, which is smaller than the desirable cut-off value of 0.08. The normed

(13.25) (7.37) (9.13) (5.60)

chi-square is 1.50, which is within the desirable range. The p-value of the close fit test is 0.26, which implies a good model fit. From these fit statistics, it is concluded that the overall model has a good fit. In addition to a good fit of the structural model, a good structural equation model needs to have a good measurement model (i.e. the path coefficients of all indicators to the related latent variables are significant at

Fig. 2. Supply chain practice, information sharing, and supply chain dynamism. ** indicates significance at p < 0.05 and * indicate significance at p > 0.05. # please see Table 2 for the survey questions related to the variables IV1, IV2, and IV3. Please see Table 5 for the path estimates li (i = 1, . . ., 10).

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0.05 level). As shown in Table 5, all path coefficients are significant at 0.05 level and the t-values are larger than 2.0. 5.1. Findings related to Hypothesis 1 We hypothesized that effective information sharing enhances effective supply chain practice (Hypothesis 1). The results of the study suggest that Hypothesis 1 is strongly supported (i.e. effective information sharing enhances effective supply chain practice) as shown by the standardized coefficient of 0.937 for b1 in Fig. 2, and a significant t-statistic of 23.77 in Table 6. This result provides empirical evidence for the enabling effect of information sharing on supply chain practice, and corroborates the findings in previous literature and industry anecdotes (Supply Chain Council, 2002). This significant empirical finding suggests that effective information sharing enhances effective supply chain practices, such as supply chain planning, JIT production and delivery practices. As a result, supply chain transparency may be enhanced and forecast errors may be reduced. 5.2. Findings related to Hypothesis 2 We hypothesized that supply chain dynamism has significant positive influence on information sharing (Hypothesis 2). The results of the study show that Hypothesis 2 is strongly supported (i.e. supply chain dynamism has significant positive influence on information sharing) as shown by the standardized coefficient of 0.251 for g1 in Fig. 2, and a significant t-statistic of 2.14 in Table 6. Hypothesis 2 is supported at the 5% significance level, i.e. supply chain dynamism has significant positive influence on information sharing ( p < 0.05). The result of Hypothesis 2 empirically supports the information processing theory proposed by Galbraith (1973). Furthermore, the acceptance of Hypothesis 2 extends the information processing theory to the supply Table 6 Summary of statistical tests for the hypotheses in Fig. 2 Paths in the structural model

Point estimate

t-Value

Information sharing ! supply chain practice (H1) Supply chain dynamism ! information sharing (H2) Supply chain dynamism ! supply chain practice (H3)

0.937

23.77

0.251

2.14

0.186

1.90

chain environment. The basic proposition of the information processing theory is that ‘‘the degree to which information-processing requirements and information-processing capabilities are appropriately matched determines the quality of outcomes for the firm’’ (Galbraith, 1973). The original theory was proposed in the context of a firm instead of a supply chain. Therefore, this research adds the generality of the information processing theory in the context of supply chains (i.e. the information-processing requirement is determined by the supply chain dynamism). Effective information sharing provides the information processing capabilities needed. This study shows that higher supply chain dynamism leads to a higher level of information sharing in supply chains. The match between the degree of supply chain dynamism and the degree of information sharing is critical, which is exactly what the information processing theory predicts. 5.3. Findings related to Hypothesis 3 We hypothesized that supply chain dynamism has significant positive influence on supply chain practice (Hypothesis 3). The results of the study show that supply chain dynamism does have positive influence on supply chain practice, but not as much as on information sharing as shown by the standardized coefficient of 0.186 for g2 in Fig. 2, and a t-statistic of 1.90 in Table 6. Hypothesis 3 is supported at the 10% significance level, but not at the 5% significance level. It indicates that supply chain dynamism does have positive influence on supply chain practice, but not as much as on information sharing. The coefficient of the path from supply chain dynamism to information sharing is larger than the coefficient of the path from supply chain dynamism to supply chain practice. Meanwhile, effective information sharing mediates the impact of supply chain dynamism on supply chain practice. It suggests that firms need to have some mechanisms to collect supply chain dynamics information and use the information to guide effective supply chain practice. One mechanism is effective information sharing. 6. Supply chain practice, information sharing and delivery performance: regression model and results Regression analysis is used to test the influence of information sharing and supply chain practice on delivery performance. The reason to use regression method is that it allows us to test the influence of

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Table 7 Supply chain practice, information sharing, and delivery performance (regression results) Variable name

Hypothesis

Standardized coefficient

t-Value

p value

Hypothesis supported?

Supply chain planning JIT production Delivery practice Information quality Information sharing support technology Customer information Manufacturer information

H4a H4b H4c H5a H5b H5c H5d

0.137 0.032 0.215 0.245 0.135 0.292 0.143

0.87 0.24 1.78 2.14 0.87 2.26 1.08

0.388 0.811 0.079* 0.036** 0.386 0.026** 0.284

No No Yes Yes No No No

**

Indicates significance at p < 0.05 and * indicate significance at 0.05 < p < 0.10. Dependent variable: delivery performance; R2 is 0.175 for this regression model.

information sharing and supply chain practice on delivery performance at a more detail level, i.e. each of the three supply chain practice variables and four information sharing variables are tested separately. Considering the possible influence of firm size on delivery performance, we first ran an ANOVA test on delivery performance according to firm size. We measured the firm size by sales revenue. The respondents were asked to choose one of the eight categories: (1) <$5 million, (2) $5–20 million, (3) $20– 50 million, (4) $50–100 million, (5) $100–500 million, (6) $500 million–$1 billion, (7) $1–5 billion, and (8) >$5 billion. The F statistics of the ANOVA test about firm size is 1.366, which is not significant at 0.10 levels. Therefore, the firm size is not entered into regression as a control variable. For the regression model, the delivery performance variable is entered as the dependent variable. The three supply chain practice variables (supply chain planning, JIT production, and delivery practice) and four information sharing variables (information quality, customer information, manufacturer information, and information sharing support technology) are simultaneously entered as independent variables. The Fstatistic for the overall regression model is 0.026, which

indicates that information sharing and supply chain practice have a significant impact on delivery performance. The R2 value is 0.175, which is fairly high compared to other studies such as Boyer et al. (1997) and Droge et al. (2004). The coefficient and t-value for each independent variable are shown in Table 7. The results show that information quality has significant positive influence on delivery performance ( p < 0.05). The coefficient is 0.245. The coefficient of delivery practice is 0.215, which is significant at the 10% level. Surprisingly, customer information has a significant negative impact on delivery performance at the 5% significance level. A possible explanation is that when the customer shares information with the manufacturer more frequently, the customer’s delivery requirement might change more often. As a result of the more frequent delivery requirement changes, it is more difficult for the manufacturer to meet the delivery requirement. Therefore, increases in sharing customer information lead to adverse delivery performance. As can be seen in Table 7, the remaining four independent variables are not significant at the 10% level. Therefore, H4c and H5a are supported. H4a, H4b, H5b, H5c, and H5d are not supported.

Table 8 Correlations of the information sharing and supply chain practice variables

IQ CI MI ISST SCP JIT DP

IQ

CI

MI

ISST

SCP

JIT

DP

1 0.271** 0.289** 0.288** 0.365** 0.332** 0.343**

1 0.477** 0.373** 0.341** 0.398** 0.283**

1 0.481** 0.436** 0.359** 0.382**

1 0.663** 0.429** 0.458**

1 0.480** 0.371**

1 0.362**

1

IQ: information quality; CI: customer information; MI: manufacturer information; ISST: information sharing support technology; SCP: supply chain planning; JIT: JIT production; DP: delivery practice. ** Indicates significance at p < 0.05.

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Table 9 Hierarchical regression for delivery performance Step

R2

Variables

B

Supply chain planning JIT production Manufacturer information Information sharing support technology

0.216 0.032 0.143 0.162

1

0.037

2 Delivery practice

0.238

Information quality

0.238

3 4 Customer information *

p < 0.10;

DR 2

F

F change

0.797

0.797

0.078

0.041

1.391

3.662*

0.123

0.044

1.886*

4.101**

0.175

0.053

2.432**

5.128**

0.292

**

p < 0.05; values shown for F change are for each step; B is the standardized coefficient of the variable.

Considering the potential multicollinearity issue of the seven independent variables, a correlation analysis was run. The results presented in Table 8 show that the independent variables are highly correlated. Thus, a hierarchical regression analysis to confirm the results in Table 7 was conducted. First, the four insignificant variables in Table 7 (supply chain planning, JIT production, manufacturer information, and information sharing support technology) are entered. Second, delivery practice is entered. Third, information quality is entered. Finally, customer information is entered. The results of the hierarchical regression are shown in Table 9. In order to confirm the impact of those significant independent variables in Table 7, the less significant independent variables are first entered into the hierarchical regression. By doing so, the changes in R2 and F statistics in each step can only be attributed to the additional variable. The standardized coefficients in Table 9 are the coefficients from each step. For example, 0.216 is the coefficient for the supply chain planning variable when the first four variables were entered into the regression in the first step. After the delivery practice variable was entered into the regression, the coefficient for delivery practice was 0.238 (i.e. when there are five independent variables in the second step). As expected, the results of the hierarchical regression shown in Table 9 confirm that (1) the delivery practice variable has significant positive influence on delivery performance at the 0.10 level, (2) information quality variable has significant positive influence on delivery performance at the 0.05 level, and (3) the customer information variable has significant negative influence on delivery performance at the 0.05 level. Since the customer information variable has a negative impact on delivery performance and the delivery practice has a positive impact, we further

explore the relationship among delivery performance, delivery practice, and customer information. We categorize customer information and delivery practice into two categories according to their factor scores as shown in Table 10. The higher score of customer information reflects more frequent sharing of customer information. The higher score of delivery practice reflects a higher level of effective delivery practice implemented. The mean delivery performance score for each combination is calculated. A higher value of delivery performance is preferred. The results in Table 10 show that as the frequency of sharing customer information increases, the delivery performance decreases. However, effective delivery practice mitigates the effect of frequent sharing of customer information. When the level of sharing customer information is high, effective delivery practice can improve the delivery performance significantly. When the level of sharing customer information is low, effective delivery practice does not improve delivery performance as much as when the level of sharing customer information is high. This finding suggests that when the level of sharing customer information is high, there is a need to implement a high level of effective Table 10 Customer information, delivery practice, and delivery performance Delivery practice/ customer information

High

Low

High Low

0.045 (34)a 0.238 (28)

0.316 (28) 0.106 (35)

a The value in the cell is the mean of the delivery performance factor score in each category. The number in the parenthesis is the number of companies in each category. For example, 0.238 is the mean of delivery performance when customer information is low and delivery practice is high.

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supply chain practice in order to leverage the effective information sharing. 6.1. Findings related to Hypotheses 4a, 4b, 4c, 5a, 5b, 5c, and 5d (the influence of information sharing and supply chain practice on delivery performance) As shown in Tables 7 and 9, information quality and delivery practice have significant positive influence on delivery performance, while customer information has a significant negative influence. The remaining variables are not significant. This corroborates several extant studies such as Schroeder and Flynn (2001). Schroeder and Flynn (2001) found that the difference in advanced manufacturing technology investment does not have impact on firm performance. The information quality and the type of information shared are important. The finding that information quality has a significant positive influence on delivery performance in this study confirms the results in Schroeder and Flynn (2001). Among the three groups of supply chain practice, only delivery practice has a significant positive influence on delivery performance. Supply chain planning and JIT production do not have a significant direct impact on delivery performance. The counterintuitive result that customer information has a significant negative impact on delivery performance, led to further exploration of the relationship between information sharing and supply chain practice. The result shown in Table 10 suggests that effective supply chain practice is critical for leveraging effective information sharing when the level of information sharing is high. Together with the results of supply chain dynamism, this study shows that effective supply chain practice becomes more important when supply chain dynamism is high. A higher level of supply chain dynamism, leading to a higher level of information sharing, enhances effective supply chain practice in order to utilize the information shared and maintain a high level of performance. 7. Managerial implications This study offers practitioners several managerial insights about the role of information sharing and supply chain practices in supply chain management. First, effective supply chain practices and information sharing play different roles in managing supply chains. To improve supply chain performance, executives often choose to implement either effective information sharing or effective supply chain practice because limited resources usually prevent firms from pursuing

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both simultaneously. Effective supply chain practice standardizes the supply chain processes and exploits the efficiency from the standardization. The standardization of supply chain processes tends to help companies better leverage the information shared among supply chain partners. Information sharing is a means to capture the supply chain dynamics and thus reduce uncertainty in external and internal environments. When coupled with the standardization inherent in effective supply chain practice, this uncertainty reduction allows performance improvement. Without standardization, however, uncertainty reduction is less valuable, because the processes themselves are too uncertain to control effectively. Clearly, managers should seek to achieve supply chain performance improvement by exploiting opportunities to implement both effective supply chain practice and effective information sharing. Dell is a good example of using information technology to get information from its customers and share the information with its suppliers. It uses effective supply chain practices to standardize the supply chain processes and reduce process uncertainties. Second, the importance of effective supply chain practices increases as the level of information sharing increases. Fisher (1997) categorizes supply chains into efficient supply chains and responsive supply chains. In efficient supply chains, products are standardized and firms tend to deploy more effective supply chain practices because effective supply chain practices tend to standardize processes. In responsive supply chains, firms tend to emphasize on flexibility and buffering rather than standardization. However, this study suggests that effective supply chain practices that standardize the processes have greater value in responsive supply chains. Looking at the auto industry, the Japanese automakers such as Toyota and Honda have more competitive advantage than the US automakers such as General Motor and Ford these days when the needs for product variety and information sharing increase. The findings in this study suggest that the effective supply chain practices that Toyota and Honda have are giving them more competitive advantage than their major US competitors in today’s more dynamic world. Third, this study suggests that firms do not have to excel in all dimensions of supply chain processes in order to achieve superior delivery performance. The regression analysis shows that only delivery practices (not the supply chain planning or JIT production) have significant positive influence on delivery performance. This suggests that firms need to be clear about the

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