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Theoretical Foundation for a Learning Rate Budget

Author

Listed:
  • Paul B. Kantor

    (Tantalus, Inc., 3257 Omond Road, Cleveland, Ohio 44118)

  • Willard I. Zangwill

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

A conceptualization of production learning is proposed, which resolves costs into groups characterized by the rate at which the costs can be reduced. These groups may correspond to learning in process, materials or technology. This approach suggests a new budget methodology, the Learning Rate Budget (LRB), which combines activities with similar learning rates to facilitate planning, forecasting, bidding, accountability, and management control. This new conceptualization of cost progress rests on three postulates concerning the budget, the technology and a finite basis for the learning curve. This paper explores these postulates, presents the LRB, and summarizes the empirical study that led to this concept.

Suggested Citation

  • Paul B. Kantor & Willard I. Zangwill, 1991. "Theoretical Foundation for a Learning Rate Budget," Management Science, INFORMS, vol. 37(3), pages 315-330, March.
    • Handle: RePEc:inm:ormnsc:v:37:y:1991:i:3:p:315-330
    DOI: 10.1287/mnsc.37.3.315
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    Cited by:

    1. Wang, Weijia & Plante, Robert D. & Tang, Jen, 2013. "Minimum cost allocation of quality improvement targets under supplier process disruption," European Journal of Operational Research, Elsevier, vol. 228(2), pages 388-396.
    2. Cooper, William W. & Sinha, Kingshuk K. & Sullivan, Robert S., 1995. "Accounting for complexity in costing high technology manufacturing," European Journal of Operational Research, Elsevier, vol. 85(2), pages 316-326, September.
    3. Keumseok Kang & Jungpil Hahn & Prabuddha De, 2017. "Learning Effects of Domain, Technology, and Customer Knowledge in Information Systems Development: An Empirical Study," Information Systems Research, INFORMS, vol. 28(4), pages 797-811, December.
    4. Bradley R. Staats & Francesca Gino, 2012. "Specialization and Variety in Repetitive Tasks: Evidence from a Japanese Bank," Management Science, INFORMS, vol. 58(6), pages 1141-1159, June.
    5. Thomas Boucher & Yuchen Li, 2016. "Technical note: systematic bias in stochastic learning," International Journal of Production Research, Taylor & Francis Journals, vol. 54(11), pages 3452-3463, June.
    6. Clemens Werkmeister, 2003. "Lerneffekte in einer prozessorientierten Variantenkalkulation," Schmalenbach Journal of Business Research, Springer, vol. 55(4), pages 382-400, June.
    7. Andrew G. Loerch, 1999. "Incorporating learning curve costs in acquisition strategy optimization," Naval Research Logistics (NRL), John Wiley & Sons, vol. 46(3), pages 255-271, April.
    8. Linda Argote & Sunkee Lee & Jisoo Park, 2021. "Organizational Learning Processes and Outcomes: Major Findings and Future Research Directions," Management Science, INFORMS, vol. 67(9), pages 5399-5429, September.

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