Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method
This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operat...
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| Format: | Article |
| Language: | English |
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Riga Technical University Press
2025-07-01
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| Series: | Complex Systems Informatics and Modeling Quarterly |
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| Online Access: | https://csimq-journals.rtu.lv/csimq/article/view/329 |
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| _version_ | 1839602593125367808 |
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| author | Fadoua Tamtam Amina Tourabi |
| author_facet | Fadoua Tamtam Amina Tourabi |
| author_sort | Fadoua Tamtam |
| collection | DOAJ |
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This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operational efficiency. A structured multi-criteria decision-making approach is applied using the ELECTRE III method, leveraging quantitative data from DHL’s operational records (2022–2025). The evaluation is conducted with a panel of 18 industry experts, including logistics professionals and AI specialists, who participated in structured interviews and expert assessments to establish weighting criteria and performance metrics. Findings indicate that IoT-driven real-time tracking and predictive analytics for maintenance rank highest in enhancing supply chain resilience, improving operational responsiveness, and reducing downtime. Additionally, blockchain-supported security mechanisms reinforce data integrity and transparency, strengthening logistics security. Conversely, OCR-based automation and NLP-powered logistics systems demonstrate comparatively lower impact, emphasizing the need for targeted AI adoption strategies. This study contributes to structured AI evaluation methodologies by establishing a repeatable decision-making framework, ensuring scalability beyond DHL’s logistics operations. Limitations include the reliance on industry-specific datasets, which require further validation across diverse supply chain environments.
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| format | Article |
| id | doaj-art-0b8cdccfcdab47d0b64eb62ecec6b439 |
| institution | Matheson Library |
| issn | 2255-9922 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Riga Technical University Press |
| record_format | Article |
| series | Complex Systems Informatics and Modeling Quarterly |
| spelling | doaj-art-0b8cdccfcdab47d0b64eb62ecec6b4392025-08-02T07:42:24ZengRiga Technical University PressComplex Systems Informatics and Modeling Quarterly2255-99222025-07-014310.7250/csimq.2025-43.02Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III MethodFadoua Tamtam0https://orcid.org/0000-0002-7720-1674Amina Tourabi1https://orcid.org/0000-0002-7739-0321National School of Applied Sciences, Systems Engineering and Decision Support Laboratory (LISAD), IBN ZOHR University, Agadir, 80000, MoroccoNational School of Applied Sciences, Systems Engineering and Decision Support Laboratory (LISAD), IBN ZOHR University, Agadir, 80000, Morocco This study evaluates the role of Generative AI in optimizing digital supply chain performance, focusing on IoT integration, predictive analytics, and blockchain security. The primary objective is to determine which AI-driven initiatives offer the greatest benefits in enhancing resilience and operational efficiency. A structured multi-criteria decision-making approach is applied using the ELECTRE III method, leveraging quantitative data from DHL’s operational records (2022–2025). The evaluation is conducted with a panel of 18 industry experts, including logistics professionals and AI specialists, who participated in structured interviews and expert assessments to establish weighting criteria and performance metrics. Findings indicate that IoT-driven real-time tracking and predictive analytics for maintenance rank highest in enhancing supply chain resilience, improving operational responsiveness, and reducing downtime. Additionally, blockchain-supported security mechanisms reinforce data integrity and transparency, strengthening logistics security. Conversely, OCR-based automation and NLP-powered logistics systems demonstrate comparatively lower impact, emphasizing the need for targeted AI adoption strategies. This study contributes to structured AI evaluation methodologies by establishing a repeatable decision-making framework, ensuring scalability beyond DHL’s logistics operations. Limitations include the reliance on industry-specific datasets, which require further validation across diverse supply chain environments. https://csimq-journals.rtu.lv/csimq/article/view/329Generative AIELECTRE IIIDigital Supply ChainMulti-Criteria Decision-MakingIoT-Driven Real-Time TrackingPredictive Analytics |
| spellingShingle | Fadoua Tamtam Amina Tourabi Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method Complex Systems Informatics and Modeling Quarterly Generative AI ELECTRE III Digital Supply Chain Multi-Criteria Decision-Making IoT-Driven Real-Time Tracking Predictive Analytics |
| title | Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method |
| title_full | Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method |
| title_fullStr | Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method |
| title_full_unstemmed | Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method |
| title_short | Advancing Digital Supply Chains through Generative AI: A Strategic Framework with the ELECTRE III Method |
| title_sort | advancing digital supply chains through generative ai a strategic framework with the electre iii method |
| topic | Generative AI ELECTRE III Digital Supply Chain Multi-Criteria Decision-Making IoT-Driven Real-Time Tracking Predictive Analytics |
| url | https://csimq-journals.rtu.lv/csimq/article/view/329 |
| work_keys_str_mv | AT fadouatamtam advancingdigitalsupplychainsthroughgenerativeaiastrategicframeworkwiththeelectreiiimethod AT aminatourabi advancingdigitalsupplychainsthroughgenerativeaiastrategicframeworkwiththeelectreiiimethod |