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A Distributed Parameter Cohort Personnel Planning Model That Uses Cross-Sectional Data

Author

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  • Cheryl Gaimon

    (Academic Faculty of Management Sciences, the Ohio State University, Columbus, Ohio 43210)

  • Gerald L. Thompson

    (Graduate School of Industrial Administration, Carnegie-Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

The two types of mathematical manpower planning models that appear in the literature involve either longitudinal or cross-sectional formulations. Despite the high degree of realism achieved, the use of longitudinal models is limited because the implementation requires the knowledge of a large amount of historical personnel data that is often unavailable. The value of cross-sectional models requiring a minimal amount of data is diminished due to (1) the difficulty in transferring cross-sectional results into cohort information, and (2) an assumption implicit in the structure of these models stating that the movement of an individual from one grade in the organization to another is independent of that person's organizational age. In this paper, we present a cohort (longitudinal) personnel planning model solved using distributed parameter optimal control theory that requires only cross-sectional data. We derive the optimal hiring, promotion, separation and retirement policies of an organization as functions of time and a person's organizational age and grade. In response to changing goal levels of manpower, we observe changes in the optimal policies and their subsequent effect on the career paths of cohort groups over time.

Suggested Citation

  • Cheryl Gaimon & Gerald L. Thompson, 1984. "A Distributed Parameter Cohort Personnel Planning Model That Uses Cross-Sectional Data," Management Science, INFORMS, vol. 30(6), pages 750-764, June.
  • Handle: RePEc:inm:ormnsc:v:30:y:1984:i:6:p:750-764
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    File URL: http://dx.doi.org/10.1287/mnsc.30.6.750
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    Cited by:

    1. John W. Boudreau, 2004. "50th Anniversary Article: Organizational Behavior, Strategy, Performance, and Design in Management Science," Management Science, INFORMS, vol. 50(11), pages 1463-1476, November.
    2. Edward G. Anderson, Jr., 2001. "The Nonstationary Staff-Planning Problem with Business Cycle and Learning Effects," Management Science, INFORMS, vol. 47(6), pages 817-832, June.
    3. Anderson, Edward G., 2001. "Managing the impact of high market growth and learning on knowledge worker productivity and service quality," European Journal of Operational Research, Elsevier, vol. 134(3), pages 508-524, November.
    4. Edieal J. Pinker & Robert A. Shumsky, 2000. "The Efficiency-Quality Trade-Off of Cross-Trained Workers," Manufacturing & Service Operations Management, INFORMS, vol. 2(1), pages 32-48, July.
    5. Sanjeev Bordoloi, 2006. "A control rule for recruitment planning in engineering consultancy," Journal of Productivity Analysis, Springer, vol. 26(2), pages 147-163, October.
    6. White, Sheneeta W. & Badinelli, Ralph D., 2012. "A model for efficiency-based resource integration in services," European Journal of Operational Research, Elsevier, vol. 217(2), pages 439-447.

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