Authors: Alexandre Dolgui, Anatoly Pashkevich, Maksim Pashkevich, Frederic Grimaud
Addresses: Division for Industrial Engineering and Computer Sciences, Ecole des Mines de Saint Etienne, 158, Cours Fauriel, Saint Etienne 42023, France. ' Robotic Laboratory, Department of Automatic Control, Belarusian State University of Informatics and Radioelectronics, 6 P.Brovka St., Minsk 220027, Belarus. ' CISNET Modeling Team, Stanford University, 1201 Welch Road, Stanford, CA 94305-5488, USA. ' Division for Industrial Engineering and Computer Sciences, Ecole des Mines de Saint Etienne, 158, Cours Fauriel, Saint Etienne 42023, France
Abstract: This paper focuses on the forecasting risk analysis in supply chains with intermittent demand, which is typical for the inventory management of the |slow-moving items|, such as service parts or high-priced capital goods. The adopted demand model is based on the Generalised Beta-Binomial Distribution (GBBD), which is capable of incorporating the additive distortions in the demand historical records as parameters. For this setting, there are proposed explicit expressions for forecasting risk and the prediction function, which minimises the error impact on the risk. The efficiency of the proposed approach is confirmed by computer simulation and is illustrated by an application example for forecasting of the intermittent demand values for car spare parts.
Keywords: inventory control; demand modelling; risk forecasting; robust prediction; error correction; supply chain management; SCM; intermittent demand; risk assessment; inventory management; automotive spare parts.
International Journal of Risk Assessment and Management, 2008 Vol.9 No.3, pp.213 - 224
Available online: 29 Jul 2008 *Full-text access for editors Access for subscribers Purchase this article Comment on this article