Journal of Supply Chain Management Copyright © February 1999, by the Institute for Supply Management, Inc.

Author(s):

Thomas Y. Choi
Thomas Y. Choi is Associate Professor of Operations Management and Supply Chain Management at Arizona State University. He earned his Ph.D. degree from the University of Michigan. Dr. Choi's research interests include the supply network and complexity theory, organizational learning, and the dynamics of total quality management from the framework of market process economics.

Manus Rungtusanatham
Manus Rungtusanatham is Assistant Professor of Management at Arizona State University. He earned his Ph.D. degree from the University of Minnesota. Dr. Rungtusanatham's research interests include total quality management and its applicability across organizations, industries, and national cultures; and the cultural barriers to the adoption of total quality management across multiple countries.

SUMMARY

This article reports the findings of an exploratory study that compares the quality management practices of manufacturing firms at different levels of the supply chain and across different industries. Firms are first separated into three levels in the supply chain - final assemblers, top-tier suppliers, and tertiary-tier suppliers. The study also separates firms into three industry groups - automotive, electronics, and others. A mailed survey was used to collect the data. The analysis found no statistical difference in the level of quality management practices across the supply chain. This contradicts the general speculation that tertiary-tier suppliers have fallen behind final assemblers and top-tier suppliers in quality management practices. However, the results did reveal an industry effect regarding strategic quality planning. The manufacturers in the automotive industry were more active in strategic quality planning than their counterparts in the electronics industry.

NOTE: Some graphics may not be available

BACKGROUND

In recent years, manufacturing firms have increased their reliance on suppliers. Top-tier suppliers are now engaged much more in design activities of the subassemblies and in the management of the quality and delivery of parts that merge into these subassemblies (Ballew and Schnorbus 1994; Purchasing 1995; Raia 1994; Spekman 1988). Such reliance on suppliers has created a critical need for firms to better understand the dynamics across the supply chain. Therefore, developing and maintaining strong relationships between firms and their suppliers, as well as among suppliers at different tiers of the supply chain, has become an important strategic issue. Many have suggested that supply chain management can lead to faster product development, decreased production leadtimes, reduced cost, and increased quality (Battaglia 1994; Billington 1994; Davis 1993; Hines 1994). With efficient supply chains as "one of the strongest barriers to entry for competitors . . ." (Martin Christopher, as quoted in Hastings 1994), many firms have made supply chain management an integral part of corporate strategy (Cavinato 1992; Ellram 1991).

Although supply chain management has gained significant approval from managers and has captured the interests of many scholars (Dobler and Burt 1996), the scientific knowledge on this topic is still emerging. Much of what we know about supply chain management has been based largely on case records of a few leading-edge companies such as Hewlett-Packard (Billington 1994; Davis 1993; Lee and Billington 1992, 1993). In fact, there have been few empirical studies that have compared management practices across the supply chain - a few noted exceptions are Choi and Hartley (1996) and Nishiguchi (1994). What goes on within the manufacturing environments of suppliers that exist upstream to the top-tier suppliers, therefore, remains very much a mystery (Bamford 1994).

This article hopes to shed some understanding about quality management practices across the supply chain. A primary goal of this study was to investigate the quality management practices of not only final assemblers and their top-tier suppliers but also those suppliers that exist beyond the top-tier level (referred to as tertiary-tier suppliers). The focus is on quality management practices because many final assemblers (e.g., Chrysler and General Motors) have singled out quality management practices of their suppliers throughout the supply chain as a key strategic focus (Bamford 1994; Purchasing 1995). Yet very little is known about the extent to which the "quality movement," initiated a decade ago by final assemblers, has been adopted by suppliers across the supply chain. This study adopted the four quality management elements prescribed in the Malcolm Baldrige National Quality framework (Black and Porter 1996; Dean and Bowen 1994; Ross 1995):

• Management of process quality
• Human resources development and management
• Strategic quality planning
• Information and analysis

A second goal of this study was to compare quality management practices across the supply chain in different industries. In particular, included in this study are the automotive and electronics industries, since firms in these two industries have been most profoundly affected by the quality movement (Abo 1994). These two industries, for example, have had strong representation in the winner's circle of the Malcolm Baldrige Quality Award (National Institute of Standards and Technology 1997). In addition, a control group constituting a sample of organizations in industries related to the first two (referred to as "others") was included in the study.

LITERATURE REVIEW

Final assemblers (e.g., Chrysler, IBM) are increasingly working closely with their top-tier suppliers to practice quality management (Bamford 1994). In doing so, they have conducted extensive evaluations of their suppliers and have reduced the size of their supplier base to facilitate more extensive, long-term relationships (Bamford 1994; Purchasing 1995). As these relationships are developed, final assemblers have also increased the responsibilities of their top-tier suppliers. Many top-tier suppliers, for example, are being invited to participate in the final assemblers' new product design activities and being asked to produce finished subassemblies (Fleischer 1996).

As result, top-tier suppliers are expected to proactively manage the quality and delivery of parts that eventually go into the final assemblers' products (Ballew and Schnorbus 1994; Raia 1994; Spekman 1998). This approach essentially requires top-tier suppliers, in turn, to work more closely with their suppliers (i.e., the final assemblers' tertiary-tier suppliers). Florida and Kenney (1991) posited that top-tier suppliers would eventually assume a large share of control over quality, delivery, and cost issues that were traditionally managed by final assemblers.

This shift in responsibility does not mean that final assemblers would merely assume a passive role in how their top-tier suppliers would manage their own suppliers. In fact, final assemblers appear to show a keen interest in actively managing the tertiary-level suppliers as well (Cavinato 1992; Ellram 1991). For instance, desiring to improve the management of upstream supply chains, Chrysler and General Electric have begin to pay closer attention to their tertiary-tier suppliers (Van de Vliet 1996). General Motors has gone as far as to become involved in the selection of tertiary-tier suppliers for electronic parts (Winter 1996).

In the automotive industry, besides being the driving force behind reducing, consolidating, and tiering their supplier bases (Fitzgerald 1996; Winter 1996), final assemblers have also been actively influencing the implementation of quality management practices across the supply chain, within both top-tier suppliers as well as tertiary-tier suppliers (see Figure 1). For example, Chrysler expects its top-tier suppliers to stress quality management practices to their suppliers (i.e., tertiary-tier suppliers to Chrysler), much as Chrysler is stressing quality management to them (Purchasing 1995). Such exertion of influence has been well argued by many who see quality management as important for top- and tertiary-tier suppliers (e.g., Bamford 1994), especially since the quality of the final product is largely determined by what occurs upstream in the supply chain.

Preliminary evidence appears to suggest that the efforts expended by final assemblers are producing the desired results. Top-tier suppliers in the automotive industry are in fact keeping pace with final assemblers' lead in improving quality management practices. For instance, ITT Automotive, a top-tier supplier, recently hosted a supplier conference to discuss quality, cost, technology, and supply chain management (Purchasing 1995). Some top-tier suppliers even conduct their own supplier development programs, after participating in supplier development programs offered by final assemblers (Hartley and Choi 1996). On the contrary, some industry experts have speculated that tertiary-tier suppliers are not as far along as final assemblers and top-tier suppliers with respect to quality management practices (Bamford 1994; Purchasing 1995). James Womack, co-author of The Machine that Changed the World, for example, claimed that many tertiary-tier suppliers may not have been familiar with lean manufacturing or quality management practices (as quoted in Bamford 1994). One question addressed in this study can, therefore, be stated as follows:

Question 1: Do the levels of quality management practices differ across the supply chain from final assemblers to top-tier suppliers to tertiary-tier suppliers? If so, in what ways do quality management practices differ across the supply chain?

Although the electronics industry, like the automotive industry, has had many Malcolm Baldrige National Quality Award winners, these winners have primarily been at the level of final assemblers (Baker 1994). Consequently, unlike the automotive industry, what we know about quality management practices in the electronics industry comes almost exclusively from final assemblers. The quality literature in this industry has focused more on how final assemblers have benefited from implementing quality management practices in such areas as product design (Lockamy and Khurana 1995) and research and development projects (Auer, Karjalainen, and Seppanen 1996) and less on how suppliers are managed. Even less information is available on quality management practices at the level of tertiary-tier suppliers. One implication of this dearth of information is that quality management practices at the final assembler level may be more advanced than at other levels in the supply chain. Another question addressed is:

Question 2: Across the supply chain, do firms in the automotive industry possess more active quality management practices than firms in the electronics industry?

RESEARCH METHOD

This section discusses the development of the measurement instrument and the content validity of the measurement scales for each construct. Data collection and analysis are also described.

Questionnaire Design and Content Validity
To create the survey questionnaire, a list of activities related to quality management was first extracted from the existing literature (e.g., Deming 1986; Ross 1995; Sashkin and Kiser 1991). These activities included, for example, workers' use of problem-solving techniques, team-based rewards, employee awareness of a firm's mission, and the use of statistical process control data. The list was eventually sorted into the four elements of a quality management system consistent with the Malcolm Baldrige National Quality Award framework:

• Management of process quality
• Human resource development and management
• Strategic quality planning
• Information and analysis (see Ross 1995, for more detailed discussion)

Practices related to the management of process quality entail monitoring work processes and reducing operational variations (Anderson, Rungtusanatham, and Schroeder 1994; Dean and Bowen 1994; Deming 1986; Feigenbaum 1951; Juran 1951). Human resource development and management practices focus on the behavior and compensation of workers on the shop floor, making them the foci in improvement efforts expended within a plant. In these practices, workers' intellect, development, and skill levels are cultivated (Dean and Bowen 1994; Hackman and Wageman 1995; Waldman 1994). Strategic quality planning practices facilitate the design of internal functions to reflect an organization's mission (Dean and Bowen 1994; Juran 1989) and to keep the organization focused on chosen objectives. Information and analysis practices emphasize the importance of data-based, factual decision-making (Dean and Bowen 1994; Hackman and Wageman 1995).

Operations management faculty, executive MBA students, and managers from the targeted industries were subsequently asked to review the activities and their categorization as an assessment of content validity (i.e., an examination of how well the created measurement items represent the different quality management elements).

Sample and Respondents
The 1994 Ohio Manufacturer's List provided the sampling frame for this study. Based on industry descriptions provided at the four-digit SIC code level, 55 classifications for the automotive, electronics, and other related manufacturing industries (e.g., motor vehicles and car bodies - 3711, computer peripheral equipment - 3577, and metal coating and allied services - 3479) were identified. Firms listed within these classifications were included in the sample.

Plant managers were chosen to respond to the survey questionnaire. Plant managers were appropriate because they would be most familiar with quality management practices implemented within their plants, as well as most knowledgeable about what tier (i.e., final assembler, top-tier, or tertiary-tier) their plants fall into along a supply chain.

Measurement and Statistical Analyses
To ascertain the quality of the measurement scales, an internal consistency reliability assessment was also conducted such that items that did not show consistency with other items in the same measurement scale were dropped from further consideration. The convergent and discriminant validity of the measurement scales for the four quality management elements were also examined.

Multivariate analysis of variance (MANOVA) was used to simultaneously compare the levels of quality management practices across the three levels of the supply chain and the three industries. When significant differences were noted in the MANOVA results, a post hoc analysis using Tukey's studentized range test for pairwise comparisons of means was conducted to identify the differences.

RESULTS

A total of 1,679 surveys were mailed, 67 of which were returned as undeliverable. Within two months of the mailing, 339 completed survey questionnaires were received, corresponding to a 21 percent return rate. Of the 339 survey questionnaires, 49 plant managers chose the "don't know"option on the specific question asking for the firm's position on the supply chain. Therefore, only 290 responses were used in the analyses.

Measurement Analyses
The internal consistency of the measurement scales was examined by calculating Cronbach's alpha. Table I lists the number of respondents, the alpha score, and the measurement items that belong to each measurement scale. As noted, several measurement items were designed to be reverse-scored. The four measurement scales all exceeded the minimum alpha criterion of 0.6 (Flynn, Schroeder, and Sakakibara 1994).

Convergent validity and discriminant validity were established using confirmatory factor analysis. The confirmatory factor analysis results showed that all measurement indicators loaded significantly at the 0.05 level onto their a priori specified factors, indicating convergent validity (Bagozzi, Yi, and Phillips 1991). Factor loadings ranged from 0.29 to 0.86 with the corresponding t-values ranging from 5.09 to 19.58. The six inter-factor correlations among the four quality management elements were also found to be significantly different from 1.0 at the 0.05 significance level based on the Chi-square difference test, indicating discriminant validity (Anderson and Gerbing 1988; Bagozzi, Yi, and Phillips 1991). The Chi-square of the unconstrained model was 1196.57 (degrees of freedom=428), while Chi-squares of the six constrained models ranged from 1244.85 to 1399.16 (degrees of freedom=429).

Based on the results of the measurement analyses, the measurement items listed in Table I appear to be reliable and valid indicators for each of the four quality management elements.

MANOVA Analysis
MANOVA was used to compare simultaneously the quality management practices across the three tier levels of the supply chain and across the three industries. Table II lists means and standard deviations for the four quality management practices within each tier and by industry. A quick scan reveals a decreasing trend in the means of the four quality management elements from final assemblers to tertiary-tier suppliers.

Table III shows the MANOVA results for Tier, Industry, and Tier-Industry interaction effects (independent variables) on each of the four quality management practices (dependent variables). Since none of the F-values for the Tier effect in Table III are statistically significant, there does not appear to be any Tier effects on the four quality management elements. In fact, the only statistically significant effect in Table III is the Industry effect (F-value=4.34, p-value=0.01) on strategic quality planning, with the overall model being statistically significant at the 0.10 significance level (F-value=0.94, p-value=0.069). Neither the Tier effect (F-value=0.36, p-value=0.70) nor the Tier-Industry interaction effect (F-value=1.45, p-value=0.22) on strategic quality planning has a statistically significant F-value and corresponding p-value. Based on these results, it appears that at least one pair of industries has levels of strategic quality planning that are statistically different.

Post-Hoc Analysis
In order to determine which industries are reporting statistically significant differences, a post-hoc analysis was conducted to identify the source of the Industry effect. More specifically, a Tukey's studentized range test was performed for simultaneous pairwise comparisons of means, with the results summarized in Table IV. The post-hoc analytical results in Table IV indicate that, with respect to strategic quality planning, there is a statistically significant difference between automotive plants and electronics plants, across the three tiers of the supply chain.

DISCUSSION

This study attempted to determine whether the levels of quality management practices varied across the supply chain and across industries. Because final assemblers have taken the leadership role in urging and/or mandating the implementation of quality management practices across the supply chain, the study attempted to capture the extent of the final assemblers' influence. Employing a survey questionnaire with perceptual measures answered by plant managers, the study probed for the extent to which four quality management elements (consistent with the Malcolm Baldrige Quality Award) had been implemented at the plant level. Based on the statistical analyses, three key results were found:

1. Without segregating by industries, no statistical differences in the levels of quality management practices across the three levels of the supply chain (i.e., across final assemblers, top-tier suppliers, and tertiary-tier suppliers) were detected.
2. Without segregating by tiers in the supply chain, no statistical differences in the levels of three out of the four quality management practices - management of process quality, human resource development and management, and information and analysis were found.
3. However, a statistical industry difference was found for the strategic quality planning between plants in the automotive industry and their counterparts in the electronics industry. More specifically, plants in the automotive industry, taken together across the supply chain, showed a higher level of activity in this area than plants in the electronics industry.

Hence, contrary to the initial conjecture that the quality management practices of tertiary-tier suppliers would lag behind those of final assemblers and top-tier suppliers, the first summary result suggests that the levels of quality management practices are, in fact, not statistically different across the supply chain. One explanation for this result is that the quality movement, initiated and led by final assemblers, has had the desired impact on suppliers, suppliers' suppliers, and so on up the supply chain. In other words, quality management practices, at least for the automotive and electronics industries, may have now become institutionalized throughout the whole industry. So, independent of their location on the supply chain, all firms may have, at the very least, been exposed to quality management practices from various sources (i.e., conferences, customers, and media) and have institutionalized quality management practices into the way their business is conducted.

With respect to the remaining two summary results, it is interesting to note that, on the one hand, the three quality management elements that do not vary statistically across industries all pertain to more operational aspects on the plant floor. These three quality management elements (management of process quality, human resource development and management, and information and analysis) address such daily activities as solving problems, rewarding quality improvement, and collecting and analyzing data to support decisions. Because the constituent activities in these three elements have immediate implications for such operational goals as making quality parts, keeping workers motivated, and meeting delivery schedules, it appears that plants across the industries have readily embraced these practices.

On the other hand, the one quality management element that does vary statistically across industries (strategic quality planning) is concerned with a more long-term orientation of developing a mission, making commitments, and planning for quality. The post-hoc analysis indicated that plants in the automotive industry appear to be more active in this regard than plants in the electronics industry. One explanation for this is that the automotive industry is in a more mature market with relatively longer product life cycles than the electronics industry. The volatility and increasingly shorter product life cycles in the electronics industry make long-term strategic planning more difficult. When the planning horizon exceeds the time duration of product life cycles, it becomes difficult to project and plan for quality requirements. In fact, it may not make sense to plan for quality for the time horizon that reaches beyond the life span of the current product line. In no way does this suggest that electronics plants should not or do not engage in strategic quality planning. Rather, the compression of time in the electronics industry may render strategic quality planning unfeasible or undesirable.

It is important to qualify the implications of these statistical results by noting a limitation of the study. Despite the overall sample size being relatively large, the data were exclusively collected from plants in Ohio. In other words, the results may lack the generalizability to be extended to a larger population of plants beyond Ohio. However, based on the 1995 Harris Ohio Industrial Directory, Ohio ranks as the third leading manufacturing state in the United States. Furthermore, 27.4 percent of Ohio's gross state product can be attributed to manufacturing versus 18.6 percent of the gross national product (estimates provided by the State Science and Technology Institute Web page). Therefore, data from Ohio may be more representative of manufacturing than data from other states with less of a manufacturing emphasis.

CONCLUSION

In summary, this study is one of the few empirical studies that attempt to measure and understand quality management practices across the supply chain. It offers a rare glimpse of the dynamics that take place across the supply chain and across different industries. The study is exploratory in nature, since theoretical explanations for why quality management practices differ across tiers and industries did not drive the analyses and since the sample is restricted to one geographical area. Replication studies in different industries should be encouraged in order to build on the results reported herein, as well as theoretical research to advance conceptualization of the dynamics that exist across the supply chain and across various industries operating in different competitive environments.

Table I
QUALITY MANAGEMENT (QM) FACTORS

"Box page 23"

Quality Management Factors	N*	Cronbach's alpha	Activity Items**
Management of maintenance Process Quality	310	0.69	Workers' involvement in machine Workers' use of problem-solving techniques Standards continuously updated Quality data just to show to customers (-) Problem solving techniques primarily used by the technical people (-)
Human ResourceDevelopment and Management	279	0.78	Workers rewarded for quality Individuals encouraged to learn new skills Worker involvement in suggestion program Timely feedback on suggested ideas Profit sharing with workers Team-based rewards Performance data sharing with workers Financial data sharing with workers
Strategic Quality Planning	304	0.92	Quality improvement as top priority Top management's commitment to quality Annual objectives based on long-term focus Consistent support to improve quality Awareness of the mission and vision Establishment of short- and long-term goals for quality performance Everyone's understanding of three to five year objectives
Information and Analysis	250	0.71	Workers' use of statistical process control (SPC) data SPC data used for machine maintenance Easy access to organization's database Decision making based on factual information Use of customer input for quality improvements Customer satisfaction tracked and analyzed Interpretation of quality as hitting the target

*Number of respondents.

**Measurement scale reflects the level of implementation ranging from 1 to 5 in an increasing order. Items with (-) indicate a reverse scaling.

Table II
MEANS AND STANDARD DEVIATIONS (SD) FOR QUALITY PRACTICES

"Box page 24"

Final Assemblers (N=108)	Automotive (N=63)Mean* SD(N=8)	Electronics (N=167)Mean SD (N=73)	Other (N=60)Mean SD (N=27)
Management of Process Quality	3.65 0.68	3.47 0.65	3.69 0.60
Human Resources Development and Management	3.39 0.90	3.39 0.47	3.13 0.66
Strategic Quality Planning	3.66 0.81	3.41 0.71	3.27 1.00
Information and Analysis	3.41 0.49	3.36 0.58	3.15 0.63
Top-Tier Suppliers (N=92)	(N=42)	(N=42)	(N=8)
Management of Process Quality	3.47 0.72	3.22 0.80	3.48 0.77
Human Resources Development and Management	3.50 0.69	3.20 0.84	3.30 0.66
Strategic Quality Planning	3.67 0.72	3.13 1.02	3.41 0.92
Information and Analysis	3.38 0.65	3.10 0.66	3.23 0.60
Tertiary-Tier Suppliers (N=90)	(N=13)	(N=52)	(N=25)
Management of Process Quality	3.14 0.72	3.58 0.33	3.67 0.74
Human Resources Development and Management	3.26 0.88	3.05 0.46	3.47 0.80
Strategic Quality Planning	3.45 1.03	3.04 0.39	3.58 1.00
Information and Analysis	3.32 0.73	3.09 0.84	3.34 0.74

*Scale reflects the level of implementation ranging from 1 to 5 in an increasing order.

Table III
MANOVA RESULTS

"Box page 25"

Quality Management Factors	Tier*	Industry	Tier x Industry	Overall Model
Management of Process Quality	1.55 (0.21)	1.89 (0.15)	1.68 (0.15)	1.70 (0.10)
Human Resources Development and Management	0.08 (0.92)	1.20 (0.30)	2.12 (0.08)	1.38 (0.20)
Strategic Quality Planning	0.36 (0.70)	4.34 (0.01)**	1.45 (0.22)	1.90 (0.06)
Information and Analysis	0.73 (0.48)	1.48 (0.23)	0.77 (0.54)	0.94 (0.49)

*The first number represents F-values, and the second number in parentheses represents p-values. **Significant at p=0.01.

Table IV
TUKEY'S STUDENTIZED RANGE TEST FOR "STRATEGIC QUALITY PLANNING"

"Box page 25"

Tukey	Grouping	Means*	N	Industry
	A	3.61	107	Automotive
B	A	3.46	89	Other
B		3.25	90	Electronics

*Means with the same letter are not significantly different at p=0.05.

CAPS

"Box page 27"

Mission Statement: CAPS contributes competitive advantage to organizations by providing leading-edge research to support the evolution of strategic purchasing/supply management.

Goals and Objectives: CAPS, the result of an affiliation agreement between NAPM and Arizona State University's College of Business, seeks to provide research that identifies trends, analyzes issues, and suggests solutions. The CAPS 26-member Board of Trustees, key researchers, and other top purchasing professionals who review reports ensure that the Center is attuned to the issues reshaping the purchasing and supply management profession.

CAPS Research Activities: CAPS research falls into four main program areas:

• Focus Studies - 36 research studies have been published by CAPS (60 to 100 pages long), which focus on issues of critical importance to purchasing executives. Topics range from target costing to consortium buying to ISO 14000.
• Benchmarking - CAPS has published benchmarking reports on purchasing performance on 28 industries/sectors to date.
• Practix, Best Practices - CAPS publishes a quarterly report, after identifying best practices through its benchmarking process, on how companies achieved their industry-leading results.
• Executive Purchasing Roundtables - CAPS invites the senior purchasing/supply management executives from contributing companies to annual two-and-one-half-day forums in North America, Europe, Asia, and South Africa, to share best practices, prioritize the research agenda, network, and benchmark informally with each other.

REFERENCES

Abo, T. Hybrid Factory (New York: Oxford Press, 1994).

Anderson, J.C. and D.W. Gerbing, "Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach," Psychological Bulletin, vol. 103 (1998), pp. 411-423.

Anderson, J.C., M. Rungtusanatham, and R.G. Schroeder, "A Theory of Quality Management Underlying the Deming Management Method," Academy of Management Review, vol. 19 (1994), pp. 472-509.

Auer, A., J. Karjalainen, and V. Seppanen, "Improving R and D Processes by an ISO 9001-Based Quality Management System," Journal of Systems Architecture, vol. 42, no. 3 (1996), pp. 235-244.

Baker, W.H., "In Pursuit of Benchmarking Excellence: The Texas Instruments Story," National Productivity Review, vol. 14 (1994), pp. 63-72.

Ballew, P.D. and R.H. Schnorbus, "Realigning in the Auto Supplier Industry: The Rippling Effects of Big Three Restructuring," Economic Perspectives, vol. 18, no. 1 (1994), pp. 2-9.

Bamford, J. "Driving America to Tiers," Financial World, vol. 163, no. 23 (1994), pp. 24-27.

Battaglia, A.J., "Beyond Logistics: Supply Chain Management," Chief Executive (November/December 1994), pp. 48-49.

Bagozzi, R.P., Y. Yi, and L.W. Phillips, "Assessing Construct Validity in Organizational Research," Administrative Science Quarterly, vol. 36 (1991), pp. 421-458.

Billington, C. "Strategic Supply Chain Management," OR/MS Today, vol. 21, no. 2 (1994), pp. 22-27.

Black, S.A. and L.J. Porter, "Identification of the Critical Factors of TQM," Decision Sciences, vol. 27 (1996), pp. 1-22.

Bounds, G.M., Cases in Quality (Chicago: Irwin Publishing, 1995). Cavinato, J.L., "A Total Cost/Value Model for Supply Chain Competitiveness," Journal of Business Logistics, vol. 13, no. 2 (1992), pp. 285-301.

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

Davis, T., "Effective Supply Chain Management," Sloan Management Review, vol. 34, no. 4 (1993), pp. 35-46.

Dean, J.W. and D.E. Bowen, "Management Theory and Total Quality: Improving Research and Practice through Theory Development," Academy of Management Journal, vol. 19 (1994), pp. 392-418.

Demming, W.E., Out of the Crisis (Cambridge, MA: MIT Center for Advanced Engineering Study, 1986).

Dobler, D.W. and D.N. Burt, Purchasing and Supply Management Text and Cases (New York: McGraw-Hill, 1996).

Ellram, L., "Supply Chain Management - The Industrial Organization Perspective," International Journal of Physical Distribution and Logistics Management, vol. 21, no. 1 (1991), pp. 12-22.

Feigenbaum, A.V., Total Quality Control: Engineering and Management (New York: McGraw-Hill, 1951).

Fitzgerald, C., "One-stop Shopping," Ward's Auto World, vol. 32, no. 7 (July 1996), p. 85.

Fleischer, M., "Supply Chains R Us," Automotive Production, vol. 108, no. 1 (January 1996), p. 14.

Florida, R. and M. Kenney, "Transplanted Organizations: The Transfer of Japanese Industrial Organizations to the U.S.," American Sociological Review, vol. 56 (1991), pp. 381-398.

Flynn, B.B., R.G. Schroeder, and S. Sakakibara, "A Framework for Quality Management Research and an Associated Measurement Instrument," Journal of Operations Management, vol. 11 (1994), pp. 339-366.

Hackman, J.R. and Wageman, "Total Quality Management: Empirical, Conceptual, and Practical Issues," Administrative Science Quarterly, vol. 40 (1995), pp. 309-342.

Hartley, J. and T.Y. Choi, "Supplier Development: Customers as a Catalyst of Process Change," Business Horizons, vol. 39, no. 4 (1996), pp. 37-44.

Hastings, P., "Capabilities will Supersede Brands," Purchasing and Supply Management, Logistics Supplement (December 1994), pp. 4-5.

Hines, P., "Can You Create Your Own World Class Supply Chain?" Purchasing and Supply Management (September 1994), pp. 30-32.

Juran, J.M., Quality Control Handbook (New York: McGraw-Hill, 1951).

Juran, J.M., Juran on Leadership for Quality (New York: Free Press, 1989).

Lee, H. and C. Billington, "Managing Supply Chain/Inventory: Pitfalls and Opportunities," Sloan Management Review, vol. 33, no. 3 (1992), pp. 65-73.

Lee, H. and C. Billington, "Material Management in Decentralized Supply Chains," Operations Research, vol. 41 (1993), pp. 835-847.

Lockamy, A. and A. Khurana, "Quality Function Deployment: Total Quality Management for New Product Design," International Journal of Quality and Reliability Management, vol. 12 (1995), pp. 73-84.

National Institute of Standards and Technology, "Malcolm Baldrige National Quality Award Winners 1988-1995," http://www.nist.gov/public_affairs/results/res-appd.html.

Nishiguchi, T., Strategic Industrial Sourcing: The Japanese Advantage (New York: Oxford Press, 1994).

Purchasing, "Chrysler Pushes Quality Down the Supply Chain," vol. 119, no. 1 (1995), pp. 125-128.

Raia, E., "Teaming in Detroit," Purchasing, vol. 116, no. 3 (March 3, 1994), pp. 40-45.

Ross, J.E., Total Quality Management (Delray Beach, Florida: St. Lucie Press, 1995).

Sashkin, M. and K.J. Kiser, Total Quality Management (Seabrook, Maryland: Ducohon Press, 1991).

Spekman, R.E., "Strategic Supplier Selection: Understanding Long-Term Buyer Relationships," Business Horizons, vol. 31, no. 4 (1988), pp. 75-81.

Van de Vliet, A., "When the Haggling Has to Stop," Management Today (June 1996), pp. 56-60.

Waldman, D.A., "The Contributions of Total Quality Management Practices: A Look at Employee Attitudes, Customer Satisfaction, and Bottom-Line Consequences," Human Resource Planning, vol. 14 (1994), pp. 105-115.

Winter, D., "Supplier Tiering Creates Purchasing Pickle," Ward's Auto World, vol. 32, no. 7 (July 1996), pp. 45-48.

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