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Search Theory and the Manufacturing Progress Function

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Author Info

  • John F. Muth

    (School of Business, Indiana University, Bloomington, Indiana 47405)

Abstract

A theory based upon random search within a fixed population of technological possibilities is used to explain the manufacturing progress function. The theory is consistent with the power function relation between unit costs and cumulative output that has frequently been observed. It is also consistent with initial rates of improvement smaller than those predicted later by the power function relation, the eventual cessation of cost reduction, and an irregularity of improvements. Existing theories in the literature either fail to agree with the main empirical phenomena or else assume precisely what they attempt to explain.

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File URL: http://dx.doi.org/10.1287/mnsc.32.8.948
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Bibliographic Info

Article provided by INFORMS in its journal Management Science.

Volume (Year): 32 (1986)
Issue (Month): 8 (August)
Pages: 948-962

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Handle: RePEc:inm:ormnsc:v:32:y:1986:i:8:p:948-962

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Related research

Keywords: production/scheduling: work studies; learning; search and surveillance; technology;

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Citations

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Cited by:
  1. Kauffman, Stuart & Lobo, Jose & Macready, William G., 2000. "Optimal search on a technology landscape," Journal of Economic Behavior & Organization, Elsevier, vol. 43(2), pages 141-166, October.
  2. Kim, Bowon, 1996. "Learning-induced control model to allocate managerial resources for production technology development," International Journal of Production Economics, Elsevier, vol. 43(2-3), pages 267-282, June.
  3. Stuart Kauffman & Jose Lobo & William G. Macready, 1998. "Optimal Search on a Technology Landscape," Research in Economics 98-10-091e, Santa Fe Institute.
  4. Philip Auerswald, 2010. "Entry and Schumpeterian profits," Journal of Evolutionary Economics, Springer, vol. 20(4), pages 553-582, August.
  5. Demeester, Lieven L. & Qi, Mei, 2005. "Managing learning resources for consecutive product generations," International Journal of Production Economics, Elsevier, vol. 95(2), pages 265-283, February.
  6. Carlos Ocaña Pérez de Tudela, 1993. "Modelos dinámicos de competencia estratégica y cambio técnico: una panorámica," Investigaciones Economicas, Fundación SEPI, vol. 17(1), pages 43-63, January.
  7. Carlos Ocana & Eitan Zemel, 1990. "Learning from Mistakes: A Note on Just-in-Time Systems," Discussion Papers 874, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
  8. Natarajan Balasubramanian & Marvin Lieberman, 2006. "Industry Learning Environments and the Heterogeneity of Firm Performance," Working Papers 06-29, Center for Economic Studies, U.S. Census Bureau.
  9. Fioretti, Guido, 2007. "The organizational learning curve," European Journal of Operational Research, Elsevier, vol. 177(3), pages 1375-1384, March.
  10. Kobos, Peter H. & Erickson, Jon D. & Drennen, Thomas E., 2006. "Technological learning and renewable energy costs: implications for US renewable energy policy," Energy Policy, Elsevier, vol. 34(13), pages 1645-1658, September.
  11. Guido Fioretti, 2002. "A Cognitive Model of the Learning Curve," Industrial Organization 0207010, EconWPA.
  12. Vits, Jeroen & Gelders, Ludo, 2002. "Performance improvement theory," International Journal of Production Economics, Elsevier, vol. 77(3), pages 285-298, June.
  13. Peter Thompson, 2008. "Learning by Doing," Working Papers 0806, Florida International University, Department of Economics.
  14. Auerswald, Philip & Kauffman, Stuart & Lobo, Jose & Shell, Karl, 2000. "The production recipes approach to modeling technological innovation: An application to learning by doing," Journal of Economic Dynamics and Control, Elsevier, vol. 24(3), pages 389-450, March.
  15. Schoots, K. & Kramer, G.J. & van der Zwaan, B.C.C., 2010. "Technology learning for fuel cells: An assessment of past and potential cost reductions," Energy Policy, Elsevier, vol. 38(6), pages 2887-2897, June.
  16. Daniel Johnson, 2002. ""Learning-by-Licensing": R&D and Technology Licensing in Brazilian Invention," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 11(3), pages 163-177.
  17. Mazzola, Joseph B. & Neebe, Alan W. & Rump, Christopher M., 1998. "Multiproduct production planning in the presence of work-force learning," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 336-356, April.
  18. McNerney, James & Doyne Farmer, J. & Trancik, Jessika E., 2011. "Historical costs of coal-fired electricity and implications for the future," Energy Policy, Elsevier, vol. 39(6), pages 3042-3054, June.
  19. Sáenz-Royo, Carlos & Salas-Fumás, Vicente, 2013. "Learning to learn and productivity growth: Evidence from a new car-assembly plant," Omega, Elsevier, vol. 41(2), pages 336-344.
  20. Peter Thompson, 2003. "How Much Did The Liberty Shipbuilders Forget?," Working Papers 0301, Florida International University, Department of Economics.
  21. Wang, F. -K. & Lee, W., 2001. "Learning curve analysis in total productive maintenance," Omega, Elsevier, vol. 29(6), pages 491-499, December.

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