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Estimation of Forecast Errors for Seasonal-Style-Goods Sales

Author

Listed:
  • Sang Hoon Chang

    (The Goodyear Tire & Rubber Company, Akron, Ohio)

  • David E. Fyffe

    (Georgia Institute of Technology)

Abstract

Seasonal-style-goods inventories are characterized by the substantial losses associated with those merchandise items which are unsold at the end of a season. If the probability distribution for sales for the entire season were known, the decision problem would reduce to determining the optimal lot size, as in the classical newsboy problem. In reality, however, it may be possible to consider sales estimation as a multi-period inventory problem because there may be opportunities during the course of the season to review the earlier sales performance to reestimate forecast errors made earlier in the season, and to take appropriate action based on the new estimates. In this paper Kalman-Shaw linear feedback filtering is used to estimate previous forecast errors. The model is formulated in such a way that, given an initial forecast, the subsequent reestimation of the initial forecast error requires only the actual sales experience to date.

Suggested Citation

  • Sang Hoon Chang & David E. Fyffe, 1971. "Estimation of Forecast Errors for Seasonal-Style-Goods Sales," Management Science, INFORMS, vol. 18(2), pages 89-96, October.
    • Handle: RePEc:inm:ormnsc:v:18:y:1971:i:2:p:b89-b96
    DOI: 10.1287/mnsc.18.2.B89
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    Citations

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    Cited by:

    1. Sen, Alper & Zhang, Alex X., 2009. "Style goods pricing with demand learning," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1058-1075, August.
    2. 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.
    3. Mostard, Julien & Teunter, Ruud & de Koster, René, 2011. "Forecasting demand for single-period products: A case study in the apparel industry," European Journal of Operational Research, Elsevier, vol. 211(1), pages 139-147, May.
    4. Huang, Kwei-Long & Kuo, Chia-Wei & Lu, Ming-Lun, 2014. "Wholesale price rebate vs. capacity expansion: The optimal strategy for seasonal products in a supply chain," European Journal of Operational Research, Elsevier, vol. 234(1), pages 77-85.
    5. Woonghee Tim Huh & Paat Rusmevichientong, 2009. "A Nonparametric Asymptotic Analysis of Inventory Planning with Censored Demand," Mathematics of Operations Research, INFORMS, vol. 34(1), pages 103-123, February.
    6. 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.
    7. Vlachos, Dimitrios & Dekker, Rommert, 2003. "Return handling options and order quantities for single period products," European Journal of Operational Research, Elsevier, vol. 151(1), pages 38-52, November.
    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. 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.
    10. Yelland, Phillip M., 2006. "Stable seasonal pattern models for forecast revision: A comparative study," International Journal of Forecasting, Elsevier, vol. 22(4), pages 799-818.

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