Multiple Normalization Rating Analysis (MUNRA) and its application to digital supplier selection in the textile industry

The rapid development of digital technologies – such as IoT, AI, blockchain, and digital twins – has transformed supply chains into interconnected ecosystems, making digital supplier selection both critical and complex. For the first time, this study proposes a novel multi-criteria decision-making (MCDM) method, Multiple Normalization Rating Analysis (MUNRA), for ranking alternatives. It integrates linear, vector, and non-linear normalization to improve robustness, reduce rank reversal, and enhance decision accuracy. A case study of digital supplier selection in the textile industry is considered for a real-life application of the method. Results highlight technology integration, flexibility, and technological capability as the most influential criteria for selecting digital suppliers. Moreover, the final ranking of the six digital suppliers is as follows: DS5, DS4, DS2, DS6, DS1, and DS3. Validation through comparative MCDM methods, Spearman correlation, and sensitivity analyses confirms the credibility of the method. It is also shown that it is free from the rank reversal phenomenon. The research presents a computationally efficient and rigorous method for evaluating digital suppliers, offering strategic insights for digital supply chain management. The application of MUNRA to a larger decision-making problem further illustrates its scalability and cross-domain applicability.