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An adaptive forecasting algorithm and inventory policy for products with short life cycles

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  • Kaijie Zhu
  • Ulrich W. Thonemann

Abstract

Products with short life cycles are becoming increasingly common in many industries, such as the personal computer (PC) and mobile phone industries. Traditional forecasting methods and inventory policies can be inappropriate for forecasting demand and managing inventory for a product with a short life cycle because they usually do not take into account the characteristics of the product life cycle. This can result in inaccurate forecasts, high inventory cost, and low service levels. Besides, many forecasting methods require a significant demand history, which is available only after the product has been sold for some time. In this paper, we present an adaptive forecasting algorithm with two characteristics. First, it uses structural knowledge on the product life cycle to model the demand. Second, it combines knowledge on the demand that is available prior to the launch of the product with actual demand data that become available after the introduction of the product to generate and update demand forecasts. Based on the forecasting algorithm, we develop an optimal inventory policy. Since the optimal inventory policy is computationally expensive, we propose three heuristics and show in a numerical study that one of the heuristics generates near‐optimal solutions. The evaluation of our approach is based on demand data from a leading PC manufacturer in the United States, where the forecasting algorithm has been implemented. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

Suggested Citation

  • 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.
    • Handle: RePEc:wly:navres:v:51:y:2004:i:5:p:633-653
    DOI: 10.1002/nav.10124
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    1. Samuel Karlin, 1960. "Dynamic Inventory Policy with Varying Stochastic Demands," Management Science, INFORMS, vol. 6(3), pages 231-258, April.
    2. Katy S. Azoury, 1985. "Bayes Solution to Dynamic Inventory Models Under Unknown Demand Distribution," Management Science, INFORMS, vol. 31(9), pages 1150-1160, September.
    3. David C. Schmittlein & Vijay Mahajan, 1982. "Maximum Likelihood Estimation for an Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 1(1), pages 57-78.
    4. 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.
    5. Gary D. Eppen & Ananth. V. Iyer, 1997. "Improved Fashion Buying with Bayesian Updates," Operations Research, INFORMS, vol. 45(6), pages 805-819, December.
    6. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    7. Herbert E. Scarf, 1960. "Some remarks on bayes solutions to the inventory problem," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 7(4), pages 591-596, December.
    8. Gary L. Lilien & Ambar G. Rao & Shlomo Kalish, 1981. "Bayesian Estimation and Control of Detailing Effort in a Repeat Purchase Diffusion Environment," Management Science, INFORMS, vol. 27(5), pages 493-506, May.
    9. Abbas A. Kurawarwala & Hirofumi Matsuo, 1996. "Forecasting and Inventory Management of Short Life-Cycle Products," Operations Research, INFORMS, vol. 44(1), pages 131-150, February.
    10. V. Srinivasan & Charlotte H. Mason, 1986. "Technical Note—Nonlinear Least Squares Estimation of New Product Diffusion Models," Marketing Science, INFORMS, vol. 5(2), pages 169-178.
    11. George R. Murray, Jr. & Edward A. Silver, 1966. "A Bayesian Analysis of the Style Goods Inventory Problem," Management Science, INFORMS, vol. 12(11), pages 785-797, July.
    12. Donald L. Iglehart, 1964. "The Dynamic Inventory Problem with Unknown Demand Distribution," Management Science, INFORMS, vol. 10(3), pages 429-440, April.
    13. Ananth. V. Iyer & Mark E. Bergen, 1997. "Quick Response in Manufacturer-Retailer Channels," Management Science, INFORMS, vol. 43(4), pages 559-570, April.
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    2. B Aytac & S D Wu, 2011. "Modelling high-tech product life cycles with short-term demand information: a case study," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(3), pages 425-432, March.
    3. Berrin Aytac & S. Wu, 2013. "Characterization of demand for short life-cycle technology products," Annals of Operations Research, Springer, vol. 203(1), pages 255-277, March.
    4. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.
    5. Kejia Hu & Jason Acimovic & Francisco Erize & Douglas J. Thomas & Jan A. Van Mieghem, 2019. "Forecasting New Product Life Cycle Curves: Practical Approach and Empirical Analysis," Service Science, INFORMS, vol. 21(1), pages 66-85, January.

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