Supply Chain Innovations in Automotive Risk Assessment With AI Algorithms

This research looks at new strategies for risk assessment in automotive supply chains through the application of Digital Twin Technology in conjunction with Artificial Intelligence analysis using Mindsphere from Siemens. The real-time data monitoring coupled with the predictive model and detailed scenario at plan check also demonstrates how supply chain risk exposure could be managed. Digital Twin Technology means that one can model the supply chain and then use it to experiment with failures and make improvements. We were able to identify risk at 90% accuracy using predictive analytics and saw a 95% success rate with our use of scenario testing, resulting in improved readiness for the unexpected. This concept supports economic savings including operational cost reduction of 15% and overall 30% reduction in unplanned disruption. This method is valuable in that it promotes the use of decision making, and enhanced anti-risk capacity and provides the solid foundation for the present automotive supply chain risk management.

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