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Managing technology development for safety-critical systems

Author

Listed:
  • Sachon, Marc

    (IESE Business School)

  • Paté-Cornell, Elisabeth

    (IESE Business School)

Abstract

This paper presents a model that determines the optimal budget allocation strategy for the development of new technologies for safety-critical systems over multiple decision periods. The case of the development of a hypersonic passenger airplane is used as an illustration. The model takes into account both the probability of technology development success as a function of the allocated budget, and the probability of operational performance of the final system. It assumes that the strategy is to consider (and possibly fund) several approaches to the development of each technology to maximize the probability of development success. The model thus decomposes the system's development process into multiple technology development modules (one for each technology needed), each involving a number of alternative projects. There is a tradeoff between development speed and operational reliability when the budget must be allocated among alternative technology projects with different probabilities of development success and operational reliability (e.g., an easily and quickly developed technology may have little robustness). The probabilities of development and operational failures are balanced by a risk analysis approach which allows the decision maker to optimize the budget allocation among different projects in the development program at the beginning of each budget period. The model indicates that by considering reliability in the R&D management process, the decision maker can make better decisions, optimizing the balance between development time, cost, and robustness of safety-critical systems.

Suggested Citation

  • Sachon, Marc & Paté-Cornell, Elisabeth, 2002. "Managing technology development for safety-critical systems," IESE Research Papers D/465, IESE Business School.
  • Handle: RePEc:ebg:iesewp:d-0465
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    File URL: http://www.iese.edu/research/pdfs/DI-0465-E.pdf
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    References listed on IDEAS

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    1. C. Derman & G. J. Lieberman & S. M. Ross, 1976. "Optimal System Allocations with Penalty Costs," Management Science, INFORMS, vol. 23(4), pages 399-403, December.
    2. Jensen, Elizabeth J, 1987. "Research Expenditures and the Discovery of New Drugs," Journal of Industrial Economics, Wiley Blackwell, vol. 36(1), pages 83-95, September.
    3. James E. Smith & Robert F. Nau, 1995. "Valuing Risky Projects: Option Pricing Theory and Decision Analysis," Management Science, INFORMS, vol. 41(5), pages 795-816, May.
    4. Aoki, Reiko, 1991. "R&D Competition for Product Innovation: An Endless Race," American Economic Review, American Economic Association, vol. 81(2), pages 252-256, May.
    5. Richard R. Nelson, 1959. "The Simple Economics of Basic Scientific Research," Journal of Political Economy, University of Chicago Press, vol. 67(3), pages 297-297.
    6. Abdul Ali & Manohar U. Kalwani & Dan Kovenock, 1993. "Selecting Product Development Projects: Pioneering versus Incremental Innovation Strategies," Management Science, INFORMS, vol. 39(3), pages 255-274, March.
    7. Avinash K. Dixit & Robert S. Pindyck, 1994. "Investment under Uncertainty," Economics Books, Princeton University Press, edition 1, number 5474.
    8. Paul D. Childs & Alexander J. Triantis, 1999. "Dynamic R&D Investment Policies," Management Science, INFORMS, vol. 45(10), pages 1359-1377, October.
    9. Matthew J. Liberatore & George J. Titus, 1983. "The Practice of Management Science in R&D Project Management," Management Science, INFORMS, vol. 29(8), pages 962-974, August.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Technology development; system reliability; risk analysis; project management;
    All these keywords.

    JEL classification:

    • M10 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - General
    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management

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