A Neural Network Application to Supplier Selection Considering Disruption Risk

Supply chain disruptions have attracted attention of both practitioners and scholars due to both their tremendous effect on supply chain and their recent incidence. Supply chains are nowadays large scale and globally distributed, suppliers are located in more risky regions because of their low supply cost which makes global corporations more exposed to unexpected events and disasters. In this study, the disruption in supply-side of supply chain and its effect on supplier selection is investigated. In order to fortify supply chain, suppliers are assessed in terms of their ability to confront disasters. This assessment is conducted by means of two well-known artificial intelligence techniques, namely ANFIS and BPL. These networks rate suppliers according to risk criterion. The networks outcome is then introduced to a framework in which suppliers are ranking according to their risk.