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Bayesian forecasting of parts demand

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  • Yelland, Phillip M.

Abstract

As supply chains for high technology products increase in complexity, and as the performance expectations of these supply chains also increase, forecasts of parts demands have become indispensable to effective operations management in these markets. Unfortunately, rapid technological change and an abundance of product configurations mean that the demand for parts in high-tech products is frequently volatile and hard to forecast. The paper describes a Bayesian statistical model which was developed to forecast the parts demand for Sun Microsystems, Inc., a major vendor of enterprise computer products. The model embodies a parametric description of the part's life cycle, allowing it to anticipate changes in demand over time. Furthermore, using hierarchical priors, the model is able to pool demand patterns for a collection of parts, producing calibrated forecasts for new parts with little or no demand history. The paper discusses the problem addressed by the model, the model itself, and a procedure for calibrating it, then compares its forecast performance with those of alternatives.

Suggested Citation

  • Yelland, Phillip M., 2010. "Bayesian forecasting of parts demand," International Journal of Forecasting, Elsevier, vol. 26(2), pages 374-396, April.
  • Handle: RePEc:eee:intfor:v:26:y::i:2:p:374-396
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    Cited by:

    1. Babai, Zied & Boylan, John E. & Kolassa, Stephan & Nikolopoulos, Konstantinos, 2016. "Supply chain forecasting: Theory, practice, their gap and the futureAuthor-Name: Syntetos, Aris A," European Journal of Operational Research, Elsevier, vol. 252(1), pages 1-26.
    2. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    3. Panudet Saengseedam & Nantachai Kantanantha, 2017. "Spatio-temporal model for crop yield forecasting," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(3), pages 427-440, February.
    4. Goodwin, Paul & Meeran, Sheik & Dyussekeneva, Karima, 2014. "The challenges of pre-launch forecasting of adoption time series for new durable products," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1082-1097.
    5. Goodwin, Paul & Gönül, M. Sinan & Önkal, Dilek, 2019. "When providing optimistic and pessimistic scenarios can be detrimental to judgmental demand forecasts and production decisions," European Journal of Operational Research, Elsevier, vol. 273(3), pages 992-1004.
    6. Prak, Dennis & Teunter, Ruud, 2019. "A general method for addressing forecasting uncertainty in inventory models," International Journal of Forecasting, Elsevier, vol. 35(1), pages 224-238.
    7. Babai, M.Z. & Chen, H. & Syntetos, A.A. & Lengu, D., 2021. "A compound-Poisson Bayesian approach for spare parts inventory forecasting," International Journal of Production Economics, Elsevier, vol. 232(C).

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