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The Use of a Neural Factory to Investigate the Effect of Product Line Width on Manufacturing Performance

Listed author(s):
  • Paul M. Swamidass

    (Thomas Walter Center for Technology Management, Tiger Drive, Auburn University, Auburn, Alabama 36849-5358)

  • Satish S. Nair

    (Mechanical and Aerospace Engineering, University of Missouri, Columbia, Missouri 65211)

  • Sanjay I. Mistry

    (John Deere Products Engineering Center, P.O. Box 8000, Waterloo, Iowa 50704)

Registered author(s):

    The dual goals of this study are: (1) to develop an empirically valid neural model of U.S. factories in a range of industries producing discrete products, and (2) to use the model to test the effect of changes in product line width on plant performance variables. Accordingly, a neural factory was developed using 59 input and 5 output/performance variables, and was trained using field data collected from 385 U.S. manufacturing plants. The model was validated using a holdout sample before conducting sensitivity tests. The study demonstrates that, through the use of parametric sensitivity analysis, the neural factory could be used to investigate the relationship between inputs and performance of a factory. While the focused factory principle would favor a smaller product line, economies of scope theory would favor a larger product line for the good of the factory; this implies a rather complex relationship between product line width (PLW) and plant performance. The neural factory was used to study the sensitivity of output/performance variables when product line width was varied over a range extending from 10% to 200% of the average values. The sensitivity analysis of the neural factory shows that, as the product line increases, it (1) does not affect cost-of-goods-sold (COGS), (2) decreases return on investment, (3) has a negative effect on the top management's perception of manufacturing performance, (4) increases inventory turns, and (5) increases sales per employee. The explanations for these findings show how complex and intertwined the relationships between PLW and performance variables are. They enhance our understanding of PLW and provide some new directions for future empirical research.

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    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 45 (1999)
    Issue (Month): 11 (November)
    Pages: 1524-1538

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    Handle: RePEc:inm:ormnsc:v:45:y:1999:i:11:p:1524-1538
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    1. Paul M. Swamidass & William T. Newell, 1987. "Manufacturing Strategy, Environmental Uncertainty and Performance: A Path Analytic Model," Management Science, INFORMS, vol. 33(4), pages 509-524, April.
    2. Kar Yan Tam & Melody Y. Kiang, 1992. "Managerial Applications of Neural Networks: The Case of Bank Failure Predictions," Management Science, INFORMS, vol. 38(7), pages 926-947, July.
    3. Lee J. Krajewski & Barry E. King & Larry P. Ritzman & Danny S. Wong, 1987. "Kanban, MRP, and Shaping the Manufacturing Environment," Management Science, INFORMS, vol. 33(1), pages 39-57, January.
    4. Sunder Kekre & Kannan Srinivasan, 1990. "Broader Product Line: A Necessity to Achieve Success?," Management Science, INFORMS, vol. 36(10), pages 1216-1232, October.
    5. Baumol, William J & Braunstein, Yale M, 1977. "Empirical Study of Scale Economies and Production Complementarity: The Case of Journal Publication," Journal of Political Economy, University of Chicago Press, vol. 85(5), pages 1037-1048, October.
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