Providing decision support for replenishment operations using a genetic algorithms based fuzzy system



Owing to the shockwaves brought by the recent financial tsunami, most enterprises are facing tremendous challenges in maintaining the good liquidity of their own companies. In order to sustain a desirable level of cash flow for expanding business, inventory needs to be well organized because unnecessary inventory that ties up the capital in the business would prevent the enterprises from making investments. Because the existing approaches to replenishment are inflexible and unsophisticated, a new customer-based responsive replenishment system embracing online analytical processing, fuzzy logic and genetic algorithm is proposed in this paper. This system could determine accurate and realistic order quantities based on all possible and relevant variables that affect the order quantity for each item that needs to be replenished. Once the quantity has been accurately identified, the company can increase the level of customer satisfaction while minimizing stocks. Furthermore, rather than static rule repositioning, the proposed dynamic rule refining ability makes the replenishment system self-ameliorating by using genetic algorithm to investigate the possible fuzzy rule candidates for a more accurate inventory management model. A study has been conducted in a case company for the validation of the feasibility of the proposed system. After performing a spatial analysis, the results obtained indicate that the proposed responsive replenishment system is capable of ensuring improved inventory control performance in the case company.