Principal Investigators: Whitefoot, Kate
University: Carnegie Mellon University
Industry Partners: Westinghouse, Panasonic
Recent supply chain disruptions have caused billions of dollars of economic losses for manufacturers and consumers. Similar disruptions are likely to reoccur in the future due to geopolitical tensions and increased risks of natural disasters. We propose to develop the capabilities for a digital twin tool that combines real-time Machine Learning and Multimedia Analysis (ML-MA) of news media and other sources that predict the likelihood of future disruptions with supply chain tracking. This tool will provide recommendations for manufacturing actions that can preemptively mitigate the effects of the disruptions on production. By anticipating early indicators of global events that would disrupt the supply chain, the tool can re-optimize a manufacturer’s operations for ML-MA forecasted disruptions by reconfiguring production lines to accommodate alternative inputs. We will demonstrate the capabilities of electric vehicle battery cell manufacturing, in which critical materials supplies are vulnerable to disruptions from geopolitical disputes, labor strikes, and natural disasters.