The COVID-19 pandemic has caused a change in the supply chain landscape, requiring a shift toward resilience and flexibility. This study is motivated by the profound changes in supply chain dynamics brought about by the COVID-19 epidemic. This disruption has led to an increased emphasis on resilience and flexibility within supply chains, thereby creating an urgent need for organizations to explore generative AI (Artificial Intelligence) solutions. The study seeks to illuminate the critical factors organizations must consider when evaluating these solutions, emphasizing the importance of understanding both benefits and costs. Additionally, it aims to identify the most appropriate applications of generative AI across supply chain functions, including planning, sourcing, manufacturing, and logistics, ultimately assisting organizations in navigating the complexities of modern supply chain management. The study aims to provide valuable insights into the factors to consider when evaluating generative AI solutions, the relative importance of benefits and costs, and the most appropriate applications of generative AI in supply chain planning, sourcing, manufacturing, and logistics by addressing three main research questions. The research methodology combines the hesitant fuzzy DEMATEL (The Decision Making Trial and Evaluation Laboratory) and ARAS (Additive Ratio ASsessment) methods to investigate the complicated interrelationships between the indicated components comprehensively. The findings show that practitioners should prioritize staff resistance, cost savings, decision support, sustainability, green efforts, and ethical concerns when investing in generative AI solutions. This study assists organizations in navigating the opportunities and challenges posed by disruptive innovations by introducing a structured and actionable framework and a novel decision-support tool for evaluating generative AI solutions and by offering practitioners a practical tool to guide decision-making.
| [1] |
AccentureSupply Chain Networks in the Age of Generative AI: Turning Promise into Performance, 2024
|
| [2] |
Ahmed T, Karmaker C L, Nasir S B, Moktadir M A, Paul S KModeling the artificial intelligence-based imperatives of Industry 5.0 towards resilient supply chains: A post-COVID-19 pandemic perspective. Computers & Industrial Engineering, 2023, 177: 109055.
|
| [3] |
Alvarez & MarsalGenerative AI in Supply Chain: Building More Resilient Supply Chains, 2024
|
| [4] |
ASCMTop 10 Supply Chain Trends, 2024
|
| [5] |
Beg I, Rashid TTOPSIS for hesitant fuzzy linguistic term sets. International Journal of Intelligent Systems, 2013, 28(12): 1162-1171.
|
| [6] |
Büyüközkan G, Güler MSmart watch evaluation with integrated hesitant fuzzy linguistic SAW-ARAS technique. Measurement, 2020, 153107353.
|
| [7] |
Büyüközkan G, Güler M, Mukul EAssessing the maturity for digital education with HFL DEMATEL-HFL ARAS techniques. Central European Conference on Information and Intelligent Systems, 2021303-307
|
| [8] |
Chen C T, Chen S C, Khan A, Lim M K, Tseng M LAntecedents of big data analytics and artificial intelligence adoption on operational performance: The Chat-GPT platform. Industrial Management & Data Systems, 2024, 124(7): 2388-2413.
|
| [9] |
Divsalar M, Ahmadi M, Nemati YA SCOR-based model to evaluate LARG supply chain performance using a hybrid MADM method. IEEE Transactions on Engineering Management, 2022, 69(4): 1101-1120.
|
| [10] |
Economist Intelligence Unit (EIU)AI: From Experimentation to Implementation?, 2024
|
| [11] |
Fosso Wamba S, Guthrie C, Queiroz M M, Minner SChatGPT and generative artificial intelligence: An exploratory study of key benefits and challenges in operations and supply chain management. International Journal of Production Research, 2024, 62(16): 5676-5696.
|
| [12] |
Fosso Wamba S, Queiroz M M, Chiappetta Jabbour C J, Shi C VAre both generative AI and ChatGPT game changers for 21st-century operations and supply chain excellence?. International Journal of Production Economics, 2023, 265: 109015.
|
| [13] |
Future Today Strategy Group (FTSG)2025 Tech Trends Report, 2025
|
| [14] |
Haddud AChatGPT in supply chains: Exploring potential applications, benefits and challenges. Journal of Manufacturing Technology Management, 2024, 35(7): 1293-1312.
|
| [15] |
Hou Q, Xie LResearch on supplier evaluation in a green supply chain. Discrete Dynamics in Nature and Society, 2019, 20192601301.
|
| [16] |
Hui X, Kexin ZDynamic evaluation of a post-pandemic agricultural traceability system, based on the HFLTS-DEMATEL method. International Journal of Quality & Reliability Management, 2023, 40(8): 1963-1982.
|
| [17] |
Jackson I, Ivanov D, Dolgui A, Namdar JGenerative artificial intelligence in supply chain and operations management: A capability-based framework for analysis and implementation. International Journal of Production Research, 2024, 62(17): 6120-6145.
|
| [18] |
Jackson I, Jesus Saenz M, Ivanov DFrom natural language to simulations: Applying AI to automate simulation modelling of logistics systems. International Journal of Production Research, 2024, 62(4): 1434-1457.
|
| [19] |
Kassa A, Kitaw D, Stache U, Beshah B, Degefu GArtificial intelligence techniques for enhancing supply chain resilience: A systematic literature review, holistic framework, and future research. Computers & Industrial Engineering, 2023, 186109714.
|
| [20] |
KPMGGenerative AI in Supply Chain: A Path to Better Returns, 2023
|
| [21] |
Kumar A, Moktadir M A, Khan S A R, Garza-Reyes J A, Tyagi M, Kazançoğlu YBehavioral factors on the adoption of sustainable supply chain practices. Resources, Conservation and Recycling, 2020, 158104818.
|
| [22] |
Li P, Liu J, Wei CFactor relation analysis for sustainable recycling partner evaluation using probabilistic linguistic DEMATEL. Fuzzy Optimization and Decision Making, 2020, 19(4): 471-497.
|
| [23] |
Liao H, Wen Z, Liu LIntegrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section. Technological and Economic Development of Economy, 2019, 25(6): 1188-1212.
|
| [24] |
Liu H, Rodríguez R MA fuzzy envelope for hesitant fuzzy linguistic term set and its application to multicriteria decision making. Information Sciences, 2014, 258220-238.
|
| [25] |
LTIMindtreeInnovating Manufacturing Supply Chain Management with Generative AI, 2024
|
| [26] |
Ma P, Tsai S, He Y, Jia X, Zhen D, Yu N, Wang Q, Ahuja J K C, Wei C ILarge language models in food science: Innovations, applications, and future. Trends in Food Science & Technology, 2024, 148: 104488.
|
| [27] |
Pandey S, Sharma SA comparative study of retrieval-based and generative-based chatbots using deep learning and machine learning. Healthcare Analytics, 2023, 3100198.
|
| [28] |
Quayson M, Bai C, Sarkis J, Hossin M AEvaluating barriers to blockchain technology for sustainable agricultural supply chain: A fuzzy hierarchical group DEMATEL approach. Operations Management Research, 2024, 17(2): 728-753.
|
| [29] |
Richey R G, Chowdhury S, Davis-Sramek B, Giannakis M, Dwivedi Y KArtificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 2023, 44(4): 532-549.
|
| [30] |
Rodríguez R M, Martínez L, Herrera FHesitant fuzzy linguistic term sets for decision making. IEEE Transactions on Fuzzy Systems, 2012, 20(1): 109-119.
|
| [31] |
S&P GlobalLook Forward: Supply Chain 2024, 2024
|
| [32] |
Sai S, Sai R, Chamola VGenerative AI for Industry 5.0: Analyzing the impact of ChatGPT, DALL-E, and other models. IEEE Open Journal of the Communications Society, 2025, 63056-3066.
|
| [33] |
Shahidzadeh M H, Shokouhyar SToward the closed-loop sustainability development model: A reverse logistics multi-criteria decision-making analysis. Environment, Development and Sustainability, 2023, 25(5): 4597-4689.
|
| [34] |
Sharifani K, Amini MMachine learning and deep learning: A review of methods and applications. World Information Technology and Engineering Journal, 2023, 10(6): 3897-3904
|
| [35] |
Susithra S, Vasantha SThe adoption of green supply chain practices using artificial intelligence (AI) for smart global value chain. 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), 202418
|
| [36] |
Tsai J F, Tran D H, Nguyen P H, Lin M HInterval-valued hesitant fuzzy DEMATEL-based blockchain technology adoption barriers evaluation methodology in agricultural supply chain management. Sustainability, 2023, 15(5): 4686.
|
| [37] |
Vafadarnikjoo A, Tavana M, Chalvatzis K, Botelho TA socio-economic and environmental vulnerability assessment model with causal relationships in electric power supply chains. Socio-Economic Planning Sciences, 2022, 80101156.
|
| [38] |
van Erp T, Goncalves R, Rytter N G MDesign of matrix production systems: A skill-based systems engineering approach. Procedia CIRP, 2023, 1201173-1178.
|
| [39] |
Wang F LThe application of intelligent information systems driven by 6G big data in product sales traceability. Wireless Personal Communications, 2024Advance online publication
|
| [40] |
Wang M, Wu P, Luo QConstruction of software supply chain threat portrait based on chain perspective. Mathematics, 2023, 11(23):4856.
|
| [41] |
Wang Z, Ye C, Guo JRobust optimization of multi-objective multi-cycle remanufacturing supply chain network considering incentive compatibility theory under improved algorithm. Computers & Industrial Engineering, 2024, 187109777.
|
Rights & permissions
Systems Engineering Society of China and Springer-Verlag GmbH Germany
PDF
