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A Bayesian approach to determine the value of information in the newsboy problem

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  • Lee, Chih-Ming

Abstract

The potential benefits of demand forecasting in a newsboy problem are to decrease the risk of overstocking or shortage, but forecasting is usually not free. In this paper, a model to help the decision-maker in a newsboy problem to assess the value of information is presented. First, two cases on available demand information are considered to develop an upper bound of the cost that the decision-maker would spend on forecasting. Then, a Bayesian approach to forecasting is proposed and EVAI, the expected value of additional information, is computed to help the decision-maker in deciding whether to use the extra information. Finally, the relationship between the EVAI and model parameters is discussed.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:proeco:v:112:y:2008:i:1:p:391-402
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    Cited by:

    1. Langenberg, Kerstin U. & Seifert, Ralf W. & Tancrez, Jean-Sébastien, 2012. "Aligning supply chain portfolios with product portfolios," International Journal of Production Economics, Elsevier, vol. 135(1), pages 500-513.
    2. Xu, Xun & Jackson, Jonathan E., 2019. "Examining customer channel selection intention in the omni-channel retail environment," International Journal of Production Economics, Elsevier, vol. 208(C), pages 434-445.
    3. Rossi, Roberto & Prestwich, Steven & Tarim, S. Armagan & Hnich, Brahim, 2014. "Confidence-based optimisation for the newsvendor problem under binomial, Poisson and exponential demand," European Journal of Operational Research, Elsevier, vol. 239(3), pages 674-684.
    4. Celik, Nurcin & Son, Young-Jun, 2011. "State estimation of a shop floor using improved resampling rules for particle filtering," International Journal of Production Economics, Elsevier, vol. 134(1), pages 224-237, November.
    5. 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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