A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization

Abstract

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, cost
effectiveness, 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.

DOI: 10.5281/zenodo.15691241