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Multistage Production for Stochastic Seasonal Demand

Author

Listed:
  • Wallace B. Crowston

    (York University)

  • Warren H. Hausman

    (Massachusetts Institute of Technology)

  • William R. Kampe, II

    (Hewlett-Packard Company, Santa Clara, California)

Abstract

We consider the problem of production planning for a seasonal good which is produced in a multistage manner (e.g., when one or more components must be produced or purchased with a lead time that is long compared to the sales season). During the selling season, lost Bales occur if demand cannot be satisfied; at the end of the season, leftover inventory incurs the usual overage cost. As the season progresses, the forecast of total demand is revised in light of current sales. The problem is to determine production quantities of the various components and assemblies at each period to minimize expected costs of underage and overage. If delivery is not required until the end of the selling (or "order-taking") season, then a dynamic programming formulation can produce the optimal decision rule. However, for the case in which delivery is required during the season, the associated dynamic programming formulation is computationally infeasible. The paper explores four heuristics for the latter problem and compares their cost performance in a numerical example. The most sophisticated heuristic produces expected profits which range from 3.2% to 5.5% of an upper bound on expected profit.

Suggested Citation

  • Wallace B. Crowston & Warren H. Hausman & William R. Kampe, II, 1973. "Multistage Production for Stochastic Seasonal Demand," Management Science, INFORMS, vol. 19(8), pages 924-935, April.
    • Handle: RePEc:inm:ormnsc:v:19:y:1973:i:8:p:924-935
    DOI: 10.1287/mnsc.19.8.924
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    Citations

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    Cited by:

    1. James D. Hess & Marilyn T. Lucas, 2004. "Doing the Right Thing or Doing the Thing Right: Allocating Resources Between Marketing Research and Manufacturing," Management Science, INFORMS, vol. 50(4), pages 521-526, April.
    2. Nilsen, Jeffrey, 2013. "Delayed production and raw materials inventory under uncertainty," International Journal of Production Economics, Elsevier, vol. 146(1), pages 337-345.
    3. Kaijie Zhu & Ulrich W. Thonemann, 2004. "An adaptive forecasting algorithm and inventory policy for products with short life cycles," Naval Research Logistics (NRL), John Wiley & Sons, vol. 51(5), pages 633-653, August.
    4. Nicholas C. Petruzzi & Maqbool Dada, 2002. "Dynamic pricing and inventory control with learning," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(3), pages 303-325, April.
    5. Bitran, Gabriel R. & Wadhwa, Hitendra K. S. (Hitendra Kumar Singh), 1996. "A methodology for demand learning with an application to the optimal pricing of seasonal products," Working papers 3898-96., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    6. Lee, Chih-Ming, 2008. "A Bayesian approach to determine the value of information in the newsboy problem," International Journal of Production Economics, Elsevier, vol. 112(1), pages 391-402, March.
    7. Nagaraja, C.H. & Thavaneswaran, A. & Appadoo, S.S., 2015. "Measuring the bullwhip effect for supply chains with seasonal demand components," European Journal of Operational Research, Elsevier, vol. 242(2), pages 445-454.
    8. Joseph M. Milner & Meir J. Rosenblatt, 2002. "Flexible supply contracts for short life‐cycle goods: The buyer's perspective," Naval Research Logistics (NRL), John Wiley & Sons, vol. 49(1), pages 25-45, February.
    9. Karen L. Donohue, 2000. "Efficient Supply Contracts for Fashion Goods with Forecast Updating and Two Production Modes," Management Science, INFORMS, vol. 46(11), pages 1397-1411, November.
    10. Dina Smirnov & Yale T. Herer & Assaf Avrahami, 2021. "Two‐Phase Newsvendor with Optimally Timed Additional Replenishment: Model, Algorithm, Case Study," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 2871-2889, September.
    11. Gel, Esma S. & Salman, F. Sibel, 2022. "Dynamic ordering decisions with approximate learning of supply yield uncertainty," International Journal of Production Economics, Elsevier, vol. 243(C).

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