Extended beta-binomial model for demand forecasting of multiple slow-moving inventory items

Authors:
Alexandre Dolgui;Maksim Pashkevich
Affiliations:
Centre for Industrial Engineering and Computer Science, Saint Etienne, France;CISNET Modeling Team, Stanford University, Stanford, USA
Venue:
International Journal of Systems Science - Production Coordination and Inventory Policies
Year:
2008

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Abstract

This article considers the problem of modelling the lead-time demand for multiple slow-moving inventory items in the case when the available demand history is very short, and a large percentage of items has only zero records. The Bayesian approach is used to overcome these problems with the past demand data: it is proposed to use the beta-binomial model to predict the lead-time demand probability distribution for each item. Further, an extension of this model is developed which incorporates the prior information regarding the maximum expected probability of demand per period. Parameter estimation and Bayesian forecasting routines are derived for the new model. The performance of the proposed techniques is evaluated by a simulation study.