Abstract
This paper is concerned with the development of an intelligent inventory management system which aims at bridging the substantial gap between the theory and the practice of inventory management. The proposed system attempts to achieve this by providing automatic demand and lead time pattern identification and model selection facilities. The process of demand pattern identification together with the statistical tests used is discussed. The models incorporated cover deterministic demand models including: constant, quasi‐constant, trended and seasonal demand as well as stochastic demand models. This paper includes an empirical evaluation of the system on real data from the manufacturing and airline industries which shows that this system can lead to significant savings in inventory cost.
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