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Enriching demand forecasts with managerial information to improve inventory replenishment decisions : exploiting judgment and fostering learning

Author

Listed:
  • Yacine Rekik

    (EM - EMLyon Business School)

  • Christoph H. Glock
  • Aris Syntetos

Abstract

This paper is concerned with analyzing and modelling the effects of judgmental adjustments to replenishment order quantities. Judgmentally adjusting replenishment quantities suggested by specialized (statistical) software packages is the norm in industry. Yet, to date, no studies have attempted to either analytically model this situation or practically characterize its implications in terms of ‘learning'. We consider a newsvendor setting where information available to managers is reflected in the form of a signal that may or may not be correct, and which may or may not be trusted. We show the analytical equivalence of adjusting an order quantity and deriving an entirely new one in light of a necessary update of the estimated demand distribution. Further, we assess the system's behavior through a simulation experiment on theoretically generated data and we study how to foster learning to efficiently utilize managerial information. Judgmental adjustments are found to be beneficial even when the probability of a correct signal is not known. More generally, some interesting insights emerge into the practice of judgmentally adjusting order quantities.

Suggested Citation

  • Yacine Rekik & Christoph H. Glock & Aris Syntetos, 2017. "Enriching demand forecasts with managerial information to improve inventory replenishment decisions : exploiting judgment and fostering learning," Post-Print hal-02311980, HAL.
    • Handle: RePEc:hal:journl:hal-02311980
    as

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    Cited by:

    1. Katsagounos, Ilias & Thomakos, Dimitrios D. & Litsiou, Konstantia & Nikolopoulos, Konstantinos, 2021. "Superforecasting reality check: Evidence from a small pool of experts and expedited identification," European Journal of Operational Research, Elsevier, vol. 289(1), pages 107-117.
    2. Boutselis, Petros & McNaught, Ken, 2019. "Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context," International Journal of Production Economics, Elsevier, vol. 209(C), pages 325-333.
    3. Dominguez, Roberto & Cannella, Salvatore & Ponte, Borja & Framinan, Jose M., 2020. "On the dynamics of closed-loop supply chains under remanufacturing lead time variability," Omega, Elsevier, vol. 97(C).
    4. Dominguez, Roberto & Cannella, Salvatore & Framinan, Jose M., 2021. "Remanufacturing configuration in complex supply chains," Omega, Elsevier, vol. 101(C).

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