Abstract
Virtually every segment of the economy relies on some form of inventory for their operations. The ubiquitous nature of inventory has motivated to carry out this study wherein a decision support system (DSS) could be formulated to assist the decision-makers for effective monitoring of inventory levels and to ensure continuous availability of goods. The DSS needs be designed in a manner which can communicate its information to its user that is comprehensible and useful within the context of the decision situation. However, while dealing with the parameters of the system it is often seen that they are uncertain, imprecise and vague. Fuzzy-based approaches are best suited for such situations but these cannot provide automated decision support unless combined with learning systems like artificial neural network (ANN). When ANN and fuzzy are combined, fuzzy neural system (FNS) and neuro-fuzzy system (NFS) are created. The model of DSS developed in this study is based on a new framework using a system called adaptive neuro-fuzzy inference system. The model established has the advantage of the ANFIS for the DSS for use as part of inventory control.
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