Improving supply chain risk assessment with artificial neural network predictions

Operational excellence serves as a cornerstone for the success of businesses,and effective risk management is key for minimizing disruptions, and ensuring business continuity. This paper proposes an innovative methodology that harnesses the power of machine learning in supply chain risk assessment to enhance the ability of organizations to identify, predict, and mitigate various risks that can impact their efficiency, effectiveness, and resilience. This study addresses the inherent subjectivity in human assessment which presents a significant challenges and potential biases in the evaluation process. Auditors, who play a crucial role in identifying and assessing risks within an organization's operations, often rely on subjective judgments influenced by their experiences, expertise, and personal biases. To mitigate this issue, we employ a deconstruction approach, breaking down risk factors into sub-factors, and leverage an Artificial Neural Network model as a predictive tool for accurate risk level predictions and enhanced assessment objectivity. Real-world data from a global automotive company specializing in wiring harnesses are utilized to train the Neural Network model, on a dataset of 2100 samples, exhibits good performance of risk prediction as evaluated by appropriate metrics such as Determination Coefficients and Mean Square Error. Overall, this research contributes to the advancement of risk management practices addressing the challenges of subjectivity in human assessment, to more objective by providing a reliable and data-driven framework that supports managers in strategic decision-making and fortifies supply chain operations through an early risk alarm, empowering organizations to proactively manage risks and achieve autonomy in effective risk management.