Fuzzy decision modeling for supply chain management

Managing a supply chain (SC) is very difficult, since various sources of uncertainty and complex interrelationships between various entities exist in the SC. Moreover, the reducing product life cycle and the heightened expectations of customers have also made the SC even harder to manage, especially for innovative products. Although the innovative products can enable a company to achieve higher profit margins, they make demands for them unpredictable, because no historical data is available. This paper develops a fuzzy decision methodology that provides an alternative framework to handle SC uncertainties and to determine SC inventory strategies, while there is lack of certainty in data or even lack of available historical data. Fuzzy set theory is used to model SC uncertainty. A fuzzy SC model based on possibility theory is developed to evaluate SC performances. Based on the proposed fuzzy SC model, a genetic algorithm approach is developed to determine the order-up-to levels of stock-keeping units in the SC to minimize the SC inventory cost subject to the restriction of fulfilling the target fill rate of the finished product. The proposed model allows decision makers to express their risk attitudes and to analyze the trade-off between customer service level and inventory investment in the SC and better SC inventory strategies can be made. A simulation approach is used to validate the concept developed.