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The impact of learning, forgetting and capacity profiles on the acquisition of advanced technology

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  • Gagnon, Roger J.
  • Sheu, Chwen

Abstract

Many technology researchers and managers have stressed the importance of developing long-term, corporate technology strategies for acquiring advanced technological capabilities (in R&D, engineering, production, etc.) necessary to gain competitive advantage. In addition technology managers are under increasing pressure to produce better results more productively. A resulting trend is greater use of external relationships and resources to achieve the needed technological accomplishments with greater efficiency. There are innumerable alternatives for combining internal and external corporate resources (primarily personnel and equipment) to obtain the needed advanced technologies. Also the decision on how to acquire advanced soft technologies, typically labor intensive, may depend on the learning and forgetting inherent in the personnel using the technologies, the amount of technology capacity needed per period, and the resulting cost implications. We report on a research effort designed to aid technology researchers and managers make more accurate advanced technology sourcing and usage decisions. We present a mixed integer, nonlinear programming model, which can determine the lowest cost strategy for obtaining the needed long-term, advanced technology capability, while considering nonlinear performance improvement (learning), and forgetting characteristics for personnel and equipment performance decay. Comprehensive and realistically-based example problems are provided and the resulting insights discussed. Numerous future research extensions are offered.

Suggested Citation

  • Gagnon, Roger J. & Sheu, Chwen, 2000. "The impact of learning, forgetting and capacity profiles on the acquisition of advanced technology," Omega, Elsevier, vol. 28(1), pages 51-76, February.
    • Handle: RePEc:eee:jomega:v:28:y:2000:i:1:p:51-76
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    1. Carnero Moya, M. Carmen, 2004. "The control of the setting up of a predictive maintenance programme using a system of indicators," Omega, Elsevier, vol. 32(1), pages 57-75, February.
    2. V. Heinrich Amavilah, 2003. "The Ebbinghaus Effect and the Implications of Net Learning for the Performance of Production Systems, with Some Experimental Results," Experimental 0307002, University Library of Munich, Germany.
    3. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.

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