A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization
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 Moni...
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| Format: | Article |
| Language: | English |
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University of New Mexico
2025-07-01
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| Series: | Neutrosophic Sets and Systems |
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| Online Access: | https://fs.unm.edu/NSS/26DigitalTechnologies.pdf |
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| author | Nabil M. AbdelAziz Dina Mohamed Hasnaa Soliman |
| author_facet | Nabil M. AbdelAziz Dina Mohamed Hasnaa Soliman |
| author_sort | Nabil M. AbdelAziz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-780da36c4bf34cd28e55e46ca8b278bf |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-780da36c4bf34cd28e55e46ca8b278bf2025-08-20T03:44:18ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-07-018737943310.5281/zenodo.15691241A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain OptimizationNabil M. AbdelAzizDina Mohamed Hasnaa SolimanThe 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. https://fs.unm.edu/NSS/26DigitalTechnologies.pdfmulti-criteria decision-making (mcdm)supply chain optimizationsustainabilitytriangular fuzzy neutrosophic environment |
| spellingShingle | Nabil M. AbdelAziz Dina Mohamed Hasnaa Soliman A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization Neutrosophic Sets and Systems multi-criteria decision-making (mcdm) supply chain optimization sustainability triangular fuzzy neutrosophic environment |
| title | A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization |
| title_full | A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization |
| title_fullStr | A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization |
| title_full_unstemmed | A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization |
| title_short | A Multi-Criteria Decision-Making Framework for Evaluating Emerging Digital Technologies in Supply Chain Optimization |
| title_sort | multi criteria decision making framework for evaluating emerging digital technologies in supply chain optimization |
| topic | multi-criteria decision-making (mcdm) supply chain optimization sustainability triangular fuzzy neutrosophic environment |
| url | https://fs.unm.edu/NSS/26DigitalTechnologies.pdf |
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