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

Yacine Rekik, Christoph H. Glock and Aris A. Syntetos

European Journal of Operational Research, 2017, vol. 261, issue 1, 182-194

Abstract: This paper is concerned with analysing 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 behaviour 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.

Keywords: Inventory; Judgement; Judgmental adjustments; Newsvendor model; Learning (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:261:y:2017:i:1:p:182-194

DOI: 10.1016/j.ejor.2017.02.001

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