A supply chain application of fuzzy set theory to inventory control models  DRP system analysis
As competition abounds, the efficient solution on inventory control of a DRPs (Distribution Requirement Planning) supply chain management is a vital success factor for companies in todays business world. A stochastic program of market distribution and its deterministic equivalent control program is approximated by a multi-echelon lot-sizing model based on risk inflated effective demands. The DRP-decomposition of this approximate model, which can be used with allocation application of Fuzzy Set Theory, is then introduced. The aim of this paper is to find methods to address traditional DRPs weaknesses and to improve the performances of DRP systems. In this paper, the field of continuous review model will be focused in, and a new method on the model with triangular fuzzy numbers (input data) will be presented. By using the method, the maximum of order quantity under a minimum of total cost can be obtained. In many previous research, authors take a precise number approximately as the representative of a fuzzy number. But the precise number can not reflect the property of fuzzy inventory control number fully. Therefore, in a numerical example of this paper, in addition to providing a transformation for reducing a fuzzy number into a closed interval by introducing the interval mean value concept proposed by Dubios and Prude, this fuzzy system can be transformed into a more precise diagnosis system for channel members in the supply chain distribution organization.
supply chain fuzzy set inventory control drp multi-echelon distribution