Developing an Intelligent Inventory Control Model, Applying Fuzzy Logic and Association Rule Mining

The key issue of inventory management is the problem of safety stock control. The existence of imprecise data makes this control complex. Fuzzy logic (FL) is widely used to develop expert system, due to its ability in representing imprecise data. Therefore, in this study a fuzzy logic system and theory have been used that incorporate the linguistic variable more practically and also help in eliminating the imprecision and vagueness of the system. In addition to it, weighted association rule has been applied to extract related target items as inputs to Fuzzy Inference System (FIS) according to their significance in the dataset rather than their frequency alone. Three inputs to FIS are: 1proposed index, i.e., WFSN(WeightedFast Slow-Non moving) which take both the item movement in inventory and value into account, computed based on support in association rule.2-WC(Weighted cost), extracted from association rule 3Leadtime and output is Safety stock ,based on interview with experts. In this paper we propose integration of weighted association rule and FIS to develop an intelligent model of inventory control system. Then a calculation example is presented in MATLAB7.8.0 (R.2009.a) to test the feasibility of the model.