A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization - Nabil M. AbdelAziz; Dina Mohamed; Hasnaa Soliman (Neutrosophic Sets and Systems)
The digital transformation of supply chains has accelerated the need for robust evaluation frameworks to guide the selection of emerging technologies. This study proposes a comprehensive Multi-Criteria Decision-Making (MCDM) approach to assess four advanced supply chain solutions: Real-Time IoT Monitoring & Tracking, AI-Powered Predictive Maintenance, Blockchain for Transparent & Secure Supply Chain, and Digital Twins for Supply Chain Optimization. Ten critical attributes covering technical, economic, and environmental dimensions were identified through expert consultation and a review of relevant literature, including scalability, integration ease, performance benefit, costeffectiveness, environmental and social sustainability, data privacy, and supply chain resilience. The evaluation framework combines the Entropy method for determining objective attribute weights with the TOPSIS method for ranking alternatives. Results indicate that Blockchain for Transparent & Secure Supply Chain is the most favorable technology, followed by AI-Powered Predictive Maintenance, Digital Twins, and Real-Time IoT Monitoring & Tracking. A sensitivity analysis confirmed the robustness of these rankings against weight variations, while comparative validation using alternative MCDM methods (e.g., CODAS,COPRAS, EDAS, and SPOTIS) further supports the reliability of the findings. The study contributes to both academic research and practical decision-making by offering a replicable evaluation model for technology adoption in digitally enabled supply chains. Future research should explore dynamic integration with real-time analytics and AI-driven models to better reflect evolving industrial and economic conditions.