Decision making using a fuzzy induced linguistic ordered weighted averaging approach for evaluating risk in a supply chain.

Parts backorder reduction has become increasingly important in supply chain management. When it is difficult or time consuming to acquire parts, the operational availability of a system is reduced. This study describes a technique that helps planners proactively identify potential problem spare parts to better manage spare parts acquisition in military weapon systems. The paper reports on the results of fuzzy induced linguistic ordered weighted averaging for group decision support evaluation of backorder risk triggers to ensure that equipment is available and fully operational when needed. Risk factors were identified, the impact importance and probability metric performance ratings were determined via induced linguistic ordered weighted averaging, and a risk mitigation strategy was used to identify and predict supply chain backorder risk triggers (SCBORT). L-Project is an illustrative example that utilizes Petri nets and Pareto charts to present the evaluation hierarchy of supply chain risk. Cluster analysis is utilized to determine that there are five triggers of engineering, obsolescence, demand, NSN unique sustainment, and cataloging in light of the five SCBORT probability performance metric dimensions of quality, cost, lead time, service level, and information availability. There are also four aggregated SCBORT impact clusters of market sensitiveness, process integration, information drivers, and flexibility. Supply chain risk is presented as a probability/impact matrix. [ABSTRACT FROM AUTHOR]