| Market trends that are characterized by changing customer behavior, uncertainty, continuously changing business environment, globalization, and security issues cause many changes which result in supply chain disruptions. Research literature indicates that disruption handling is one of the primary issues in supply chain management. To mitigate the risk of supply chain disruptions, there is a need to have an early warning system so that response to supply chain disruptions can be faster. One approach adopted by companies for quick response is using different sourcing strategies such as single vs. multiple sourcing which leads to the need to explore alternate sources of supplies. This research leverages sense and respond framework (SaR) for adaptive supply chain management. The combination of Kalman Filtering (KF) technique and discrete event simulation is applied to proactively sense disruptions in a supply chain. In addition, a new algorithm based on Rough Set Theory (RST), called Min-Min-Roughness (MMR) is developed to cluster categorical data sets such as supplier dataset. MMR is used to create alternate sources of supply. A Genetic Algorithm (GA) based Heuristic Response Model (HRM) is developed to explore the business impact of different response strategies. |
