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JIT Manufacturing: A Survey of Implementations in Small and Large U.S. Manufacturers


  • Richard E. White

    (College of Business Administration, University of North Texas, Denton, Texas 76203-1160)

  • John N. Pearson

    (Department of Business Administration, College of Business, Arizona State University, Tempe, Arizona 85287-3706)

  • Jeffrey R. Wilson

    (Department of Economics, College of Business, Arizona State University, Tempe, Arizona 85287-3806)


Since the early 1980s, the diffusion of Just-In-Time (JIT) manufacturing from Japanese manufacturers to U.S. manufacturers has progressed at an accelerated rate. At this stage of the diffusion process, JIT implementations are more common and more advanced in large U.S. manufacturers than in small; consequently, U.S. businessmen's understanding of issues associated with JIT implementations in large manufacturers is more developed than that of small manufacturers. When small manufacturers represent about 96 percent of all U.S. manufacturers, investigation of JIT implementations in small, as well as large, manufacturers is warranted. This survey study investigates JIT implementation differences between small and large U.S. manufacturers. Ten management practices that constitute the JIT concept are used to examine implementation of JIT manufacturing systems. Odds ratio were constructed to determine if an association exists between implemented versus not implemented and manufacturer size for each JIT practice. Ten changes in performance attributed to JIT implementation are also assessed and examined in the study. Logistic regression models are used to examine the relationships between implementation status of each of the JIT practices and of each of the changes in performance in small and large manufacturers. The results of the study show that the frequencies of the 10 JIT management practices implemented differ between the two groups of manufacturer size, and an association exists between the JIT practices implemented and manufacturer size. Moreover, the changes in performance attributed to JIT implementation vary, depending on implementation status of specific JIT management practices and manufacturer size.

Suggested Citation

  • Richard E. White & John N. Pearson & Jeffrey R. Wilson, 1999. "JIT Manufacturing: A Survey of Implementations in Small and Large U.S. Manufacturers," Management Science, INFORMS, vol. 45(1), pages 1-15, January.
  • Handle: RePEc:inm:ormnsc:v:45:y:1999:i:1:p:1-15

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    3. Chavez, Roberto & Yu, Wantao & Jacobs, Mark & Fynes, Brian & Wiengarten, Frank & Lecuna, Antonio, 2015. "Internal lean practices and performance: The role of technological turbulence," International Journal of Production Economics, Elsevier, vol. 160(C), pages 157-171.
    4. Tomas Bonavia & Juan A. Marin-Garcia, 2011. "Integrating human resource management into lean production and their impact on organizational performance," International Journal of Manpower, Emerald Group Publishing, vol. 32(8), pages 923-938, November.
    5. Fullerton, Rosemary R. & McWatters, Cheryl S., 2002. "The role of performance measures and incentive systems in relation to the degree of JIT implementation," Accounting, Organizations and Society, Elsevier, vol. 27(8), pages 711-735, November.
    6. Mabert, Vincent A. & Soni, Ashok & Venkataramanan, M. A., 2003. "Enterprise resource planning: Managing the implementation process," European Journal of Operational Research, Elsevier, vol. 146(2), pages 302-314, April.
    7. Alexopoulos, Michelle & Tombe, Trevor, 2012. "Management matters," Journal of Monetary Economics, Elsevier, vol. 59(3), pages 269-285.
    8. Akkermans, Henk A. & Bogerd, Paul & Yucesan, Enver & van Wassenhove, Luk N., 2003. "The impact of ERP on supply chain management: Exploratory findings from a European Delphi study," European Journal of Operational Research, Elsevier, vol. 146(2), pages 284-301, April.
    9. Zand, Fardad & Van Beers, Cees & Van Leeuwen, George, 2011. "Information technology, organizational change and firm productivity: A panel study of complementarity effects and clustering patterns in Manufacturing and Services," MPRA Paper 46469, University Library of Munich, Germany.
    10. M. Thürer & Y. H. Pan & T. Qu & H. Luo & C. D. Li & G. Q. Huang, 0. "Internet of Things (IoT) driven kanban system for reverse logistics: solid waste collection," Journal of Intelligent Manufacturing, Springer, vol. 0, pages 1-10.
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    12. Swamidass, Paul M., 2003. "Modeling the adoption rates of manufacturing technology innovations by small US manufacturers: a longitudinal investigation," Research Policy, Elsevier, vol. 32(3), pages 351-366, March.
    13. Anand Nair & Cigdem Ataseven & Marco Habermann & David Dreyfus, 2016. "Toward a continuum of measurement scales in Just-in-Time (JIT) research – an examination of the predictive validity of single-item and multiple-item measures," Operations Management Research, Springer, vol. 9(1), pages 35-48, June.
    14. Roberto Chavez, 2014. "Supplier Partnership and Customer Relationship: The Role of Lean Practices," Working Papers 56, Facultad de Economía y Empresa, Universidad Diego Portales.
    15. Demeter, Krisztina & Matyusz, Zsolt, 2011. "The impact of lean practices on inventory turnover," International Journal of Production Economics, Elsevier, vol. 133(1), pages 154-163, September.
    16. Sapovadia, Vrajlal, 2015. "Indian Manufacturing Sector: Competitiveness at Stake," MPRA Paper 68777, University Library of Munich, Germany.
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