NONE 20260414084903077 wseas/wseas content-registration-form+ja@crossref.org content-registration-form WSEAS TRANSACTIONS ON COMPUTERS 1109-2750 2224-2872 Safiye Turgay Department of Industrial Engineering Sakarya University 54187, Esentepe Campus Serdivan-Sakarya TURKEY Bilal Torkul Office Services and Secretarial Department, Çınarcık Vocational School, Yalova University, Yalova, TURKEY Recep Yilmaz Department of Business Faculty of Business Sakarya University 54187, Esentepe Campus Serdivan-Sakarya TURKEY Yunus Emre Torkul Sanayi, Teknopark Blv. No:1 D:9A, Pendik, Istanbul, 34906, TURKEY In modern, globally interconnected markets, supply chain agility, or the competency for rapid adaptation, has become increasingly important. Traditional approaches to enhancing supply chain agility are certain to fall short because none of them capture either the dynamic or decentralized nature of modern supply chains. This paper examines how Agent-Based Models can act as a transformational approach toward evolutionary enhancements in supply chain agility. ABMs can model independent entities, or agents, at each level in the supply chain-suppliers, manufacturers, distributors, and customers-operating independently according to their own behaviours, goals, and interactions. In fact, through the modelling of agents and their interactions, ABMs provide a potent platform for testing how local decisions and behavior lead to emergent global outcomes, such as responsiveness, resilience, and adaptability of the supply chain. The adaptive aspect comes in with the adaptive learning mechanisms within the agents that are able to evolve strategies over time, given changes in the environmental conditions and agent interactions. The agent-based model is now applied to a wide range of scenarios, from routine fluctuations in demand to large-scale disruptions, and provides valuable insights into the conditions under which agility can be enhanced. These findings confirm that the evolutionary improvements induced by ABM can be associated with substantial gains in performance relevant to supply chain speed and flexibility in response to unforeseen events. The final part of the paper discusses the practical implications of real-world deployment and further research in deploying ABMs on supply chains. 04 14 2026 04 14 2026 1 1 https://creativecommons.org/licenses/by/4.0/deed.en_US 10.37394/23205.2026.25.1 https://wseas.com/journals/computers/2026/a025105-001(2026).pdf Freije, I., Calle, A., Ugarte, J.V., Role of supply chain integration in the product innovation capability of servitized manufacturing companies, Technovation, Volume 118,2022,102216, doi.org/10.1016/j.technovation.2020.102216. Gunasekaran, A., Lai, K.H., Cheng, T.C.E., Responsive supply chain: A competitive strategy in a networked economy, Omega, Volume 36, Issue 4,2008,Pages 549- 564,doi.org/10.1016/j.omega.2006.12.002. Alomari, K.M., Salah, A.A., Mansour, A.M., Alshaketheep, K.I., Altarawneh, M.I., Jray, A.A.A., "Supply Chain Quality and Organizational Performance: Moderating Role of Competitive Advantages," WSEAS Transactions on Business and Economics, vol. 17, pp. 806-817, 2020, DOI:10.37394/23207.2020.17.79 Lee, N.C., Reconciling integration and reconfiguration management approaches in the supply chain, International Journal of Production Economics, Volume 242,2021,108288,doi.org/10.1016/j.ijpe.2021.10828 8. Si, W., Ma, J., Corporation in a Closed-Loop Supply Chain Based on Remanufacturing, WSEAS Transactions on Mathematics, vol. 12, pp. -, 2013, DOI: Turgay, S., Streamlined Supply Chain Operations: Leveraging Permutation-Based Genetic Algorithms for Production and Distribution, WSEAS Transactions on Information Science and Applications, Vol. 21, 2024, pp. 23-32, ISSN: 1790- 0832 Gunasekaran, A., Agile manufacturing: A framework for research and development, International Journal of Production Economics, Volume 62, Issues 1–2,1999,Pages 87- 105,doi.org/10.1016/S0925-5273(98)00222-9. Turgay, S., Bayesian Learning for Dynamic Agent Based Data Analysis. Authorea. April 10, 2023. DOI: 10.22541/au.168113706.65297590/v1 Madanaguli, A., Parida, V., Sjödin, D., Oghazi, P., Literature review on industrial digital platforms: A business model perspective and suggestions for future research, Technological Forecasting and Social Change, Volume 194,2023,122606,doi.org/10.1016/j.techfore.2023.1 22606. Handfield, R.B., Bechtel, C., The role of trust and relationship structure in improving supply chain responsiveness, Industrial Marketing Management, Volume 31, Issue 4,2002, Pages 367- 382,doi.org/10.1016/S0019-8501(01)00169-9. Rezaeian, M., Pinkse, J., Rigby, J., Transforming titans: The role of policy mixes in business model adaptation strategies for sustainability transitions, Energy Research & Social Science, Volume 112,2024,103499,doi.org/10.1016/j.erss.2024.10349 9. Chowdhury, M.H., Quaddus, M., Supply chain resilience: Conceptualization and scale development using dynamic capability theory, International Journal of Production Economics, Volume 188,2017,Pages 185- 204,doi.org/10.1016/j.ijpe.2017.03.020. Gao, R.X., Krüger, J., Merklein, M., Möhring, H.C., Váncza, J., Artificial Intelligence in manufacturing: State of the art, perspectives, and future directions, CIRP Annals, Volume 73, Issue 2,2024, Pages 723-749, doi.org/10.1016/j.cirp.2024.04.101. Beinabadi, H.Z., Baradaran, V., Komijan, A.R., Sustainable supply chain decision-making in the automotive industry: A data-driven approach, SocioEconomic Planning Sciences, Volume 95,2024,101908,doi.org/10.1016/j.seps.2024.10190 8. Bootaki, B., Zhang, G., A location-productionrouting problem for distributed manufacturing platforms: A neural genetic algorithm solution methodology,International Journal of Production Economics,Volume 275,2024,109325,doi.org/10.1016/j.ijpe.2024.10932 5. Pinho, C.R.A., Pinho, M.L.C.A., Deligonul, S.Z., Cavusgil, S.T., The agility construct in the literature: Conceptualization and bibliometric assessment, Journal of Business Research,Volume 153,2022,Pages 517- 532,doi.org/10.1016/j.jbusres.2022.08.011. Chou, Y.C., Cao, H., Cheng, H.H., A bioinspired mobile agent-based integrated system for flexible autonomic job shop scheduling,Journal of Manufacturing Systems,Volume 32, Issue 4,2013,Pages 752- 763,doi.org/10.1016/j.jmsy.2013.01.005. Tong, X., Bao, J., Tao, F., Co-evolutionary digital twins: A multidimensional dynamic approach to digital engineering, Advanced Engineering Informatics, Volume 61,2024,102554,doi.org/10.1016/j.aei.2024.102554. Spoladore, D., Pessot, E., An evaluation of agile Ontology Engineering Methodologies for the digital transformation of companies, Computers in Industry, Volume 140,2022,103690,doi.org/10.1016/j.compind.2022.1 03690. Popoola, O., Rodrigues, M., Marchang, J., Shenfield, A., Ikpehai, A., Popoola, J., A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: Problems, challenges and solutions, Blockchain: Research and Applications, Volume 5, Issue 2,2024,100178,doi.org/10.1016/j.bcra.2023.100178. Datta, S., Jauhar, S.K., Paul, S.K., Leveraging blockchain to improve nutraceutical supply chain resilience under post-pandemic disruptions, Computers & Industrial Engineering,Volume 183,2023,109475, doi.org/10.1016/j.cie.2023.109475. Repetto, M., Adaptive monitoring, detection, and response for agile digital service chains, Computers & Security, Volume 132,2023,103343,doi.org/10.1016/j.cose.2023.1033 43. Savaglio, C., Ganzha, M., Paprzycki, M., Bădică, C., Ivanović, M., Fortino, G., Agent-based Internet of Things: State-of-the-art and research challenges,Future Generation Computer Systems,Volume 102,2020,Pages 1038- 1053,doi.org/10.1016/j.future.2019.09.016. Turgay, S., Yaşar, O., Aydın, A., A Multiobjective Framework for Dairy Products Supply Chain Network with Benders Decomposition, Industrial Engineering and Innovation Management (2023), Vol. 6 Num. 5, DOI: 10.23977/ieim.2023.060509 ISSN 2522-6924 Mahmoodi, E., Fathi, M., Tavana, M., Ghobakhloo, M., Ng, A.H.C., Data-driven simulation-based decision support system for resource allocation in industry 4.0 and smart manufacturing,Journal of Manufacturing Systems,Volume 72,2024,Pages 287- 307,doi.org/10.1016/j.jmsy.2023.11.019. Lejeune, M.A., Yakova, N., On characterizing the 4 C's in supply chain management,Journal of Operations Management,Volume 23, Issue 1,2005,Pages 81- 100,doi.org/10.1016/j.jom.2004.09.004. Nakayama, R.S., Spínola, M.M., Silva, J.R., Towards I4.0: A comprehensive analysis of evolution from I3.0,Computers & Industrial Engineering,Volume 144,2020,106453, doi.org/10.1016/j.cie.2020.106453. Leng, J., Guo, J., Xie, J., Zhou, X., Liu, A., Gu, X., Mourtzis, D., Qi, Q., Liu, Q., Shen, W., Wang, L., Review of manufacturing system design in the interplay of Industry 4.0 and Industry 5.0 (Part I): Design thinking and modeling methods, Journal of Manufacturing Systems, Volume 76,2024,Pages 158-187,doi.org/10.1016/j.jmsy.2024.07.012. Chen, Z., Hammad, A.W.A., Mathematical modelling and simulation in construction supply chain management,Automation in Construction,Volume 156,2023,105147, doi.org/10.1016/j.autcon.2023.105147. Canpolat, O., Demir, H.İ., Erden,C., Metaheuristic algorithms for integrating manufacturing and supply chain functions,Computers & Industrial Engineering,Volume 192,2024,110240,doi.org/10.1016/j.cie.2024.110240