Designing a Hybrid DEA–ANN Model for Predicting and Optimizing Risk Management in the Digital Supply Chain of the Steel Industry
Keywords:
Digital transformation, digital supply chain, risk management, artificial neural networkAbstract
This study aims to design and validate an intelligent hybrid DEA–ANN model to predict and optimize risk management in the digital supply chain of the steel industry. This applied mixed-method research identified 160 digital supply chain risks through a systematic literature review and expert interviews. Risk indicators including probability, severity, and detectability were quantified using questionnaires. Risk Priority Numbers were calculated, optimized via Data Envelopment Analysis with cross-efficiency, and subsequently used to train an Artificial Neural Network for predictive modeling. The results indicate that the hybrid DEA–ANN model, with an optimized two-hidden-layer architecture, demonstrates strong explanatory power and accurately predicts variations in risk efficiency, showing low prediction error and a satisfactory coefficient of determination. The proposed model provides an effective intelligent tool for risk prediction and decision support in digital steel supply chains and represents a robust alternative to conventional risk assessment approaches.
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