AI and Machine Learning Integration in Project Management for Mitigating Supply Chain Disruptions
Keywords:
Artificial Intelligence, Machine Learning, Project Management, Supply Chain DisruptionsAbstract
The globalized and interconnected nature of modern supply chains renders them highly susceptible to a multitude of disruptions, ranging from geopolitical events and natural disasters to cybersecurity threats. Traditional reactive approaches to managing these disruptions often result in significant financial losses, reputational damage, and operational inefficiencies. This article explores the strategic integration of Artificial Intelligence (AI) and Machine Learning (ML) within project management frameworks to foster proactive supply chain disruption mitigation and enhance resilience. We delineate methodologies for leveraging AI/ML in areas such as predictive analytics, real-time monitoring, intelligent risk assessment, and optimized decision-making. Empirical findings suggest that this integration leads to improved forecasting accuracy, earlier detection of potential disruptions, and greater supply chain agility. While challenges related to data quality, model interpretability, and ethical considerations exist, the judicious application of AI and ML offers a transformative pathway towards building more robust, responsive, and sustainable supply chains.
References
Govindan, K., Kannan, D., Jørgensen, T. B., & Nielsen, T. S. (2022). Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence. Transportation Research Part E: Logistics and Transportation Review, 164, 102725. https://doi.org/10.1016/j.tre.2022.102725
Guo, Y., Liu, F., Song, J., & Wang, S. (2024). Supply chain resilience: A review from the inventory management perspective. Fundamental Research. https://doi.org/10.1016/j.fmre.2024.08.002
Bednarski, L., Roscoe, S., Blome, C., & Schleper, M. C. (2023). Geopolitical disruptions in global supply chains: a state-of-the-art literature review. Production Planning & Control, 36(4), 536–562. https://doi.org/10.1080/09537287.2023.2286283
Wu, L., Huang, J., Wang, M., & Kumar, A. (2024). Unleashing supply chain agility: Leveraging data network effects for digital transformation. International Journal of Production Economics, 277, 109402. https://doi.org/10.1016/j.ijpe.2024.109402
Bentahar, O., & Belhadi, A. (2024). Integrating project management and supply chain management for resilient and sustainable operations in a VUCA world. Supply Chain Forum: An International Journal, 26(1), 1–6. https://doi.org/10.1080/16258312.2024.2447201
Durugbo, C. M., & Al-Balushi, Z. (2022). Supply chain management in times of crisis: a systematic review. Management Review Quarterly, 73(3), 1179–1235. https://doi.org/10.1007/s11301-022-00272-x
Taskan, B., Junça-Silva, A. and Caetano, A. (2022), "Clarifying the conceptual map of VUCA: a systematic review", International Journal of Organizational Analysis, Vol. 30 No. 7, pp. 196-217. https://doi.org/10.1108/IJOA-02-2022-3136
Ginikanwa, N. F. (2022). A Comparative Analysis of Agile and Traditional Project Management Methodologies and their Impact on Information System Development Projects: A Case Study of Nigerian Banking System. International Journal of Science and Research (IJSR). DOI: https://dx.doi.org/10.21275/SR24826210929
Daraojimba, E. C., Nwasike, C. N., Adegbite, A. O., Ezeigweneme, C. A., Gidiagba, J. O. (2024). Comprehensive Review of Agile Methodologies in Project Management. Computer Science & IT Research Journal. DOI: 10.51594/csitrj.v5i.717
Riad, M., Naimi, M., & Okar, C. (2024). Enhancing Supply Chain Resilience through Artificial Intelligence: Developing a Comprehensive Conceptual Framework for AI Implementation and Supply Chain Optimization. Logistics, 8(4), 111. https://doi.org/10.3390/logistics8040111
Nenni, M. E., De Felice, F., De Luca, C., & Forcina, A. (2024). How artificial intelligence will transform project management in the age of digitization: a systematic literature review. Management Review Quarterly. https://doi.org/10.1007/s11301-024-00418-z
Culot, G., Podrecca, M., & Nassimbeni, G. (2024). Artificial intelligence in supply chain management: A systematic literature review of empirical studies and research directions. Computers in Industry, 162, 104132. https://doi.org/10.1016/j.compind.2024.104132
Katsaliaki, K., Galetsi, P., & Kumar, S. (2021). Supply chain disruptions and resilience: a major review and future research agenda. Annals of Operations Research, 319(1), 965–1002. https://doi.org/10.1007/s10479-020-03912-1
Gao, B., & Xu, Z. (2024). Adjustment of supply chain investment by transnational corporations in the Ukrainian crisis and geopolitical risks – Taking the three levels as the analytical framework. Transnational Corporations Review, 16(3), 200076. https://doi.org/10.1016/j.tncr.2024.200076
Matsuo, H. (2014). Implications of the Tohoku earthquake for Toyota׳s coordination mechanism: Supply chain disruption of automotive semi. International Journal of Production Economics. DOI: https://doi.org/10.1016/j.ijpe.2014.07.010
Haraguchi, M. & Lall, U. (2015). Flood risks and impacts: A case study of Thailand’s floods in 2011 and research questions for supply chain decision making. International Journal of Disaster Risk Reduction. DOI: https://doi.org/10.1016/j.ijdrr.2014.09.005
Mohsendokht, M., Li, H., Kontovas, C., Chang, C., Qu, Z., & Yang, Z. (2024). Decoding dependencies among the risk factors influencing maritime cybersecurity: Lessons learned from historical incidents in the past two decades. Ocean Engineering, 312, 119078. https://doi.org/10.1016/j.oceaneng.2024.119078
Golmohammadi, A., and Hassini, E. (2020). Review of supplier diversification and pricing strategies under random supply and demand. International Journal of Production Research. 58. 1-33.10.1080/00207543.2019.1705419. DOI: https://doi.org/10.1080/00207543.2019.1705419
Choudhary, N. A., Singh, S., Schoenherr, T., & Ramkumar, M. (2022). Risk assessment in supply chains: a state-of-the-art review of methodologies and their applications. Annals of Operations Research, 322(2), 565–607. https://doi.org/10.1007/s10479-022-04700-9
Kalogiannidis, S., Kalfas, D., Papaevangelou, O., Giannarakis, G., &Chatzitheodoridis, F. (2024). The Role of Artificial Intelligence Technology in Predictive Risk Assessment for Business Continuity: A Case Study of Greece. Risks, 12(2), 19. https://doi.org/10.3390/risks12020019
Farzadmehr, M., Carlan, V., & Vanelslander, T. (2023). Contemporary challenges and AI solutions in port operations: Applying Gale–Shapley algorithm to find best matches. Journal of Shipping and Trade, 8(1), 1-44. https://doi.org/10.1186/s41072-023-00155-8
Gardas, R., & Narwane, S. (2024). An analysis of critical factors for adopting machine learning in manufacturing supply chains. Decision Analytics Journal, 10, 100377. https://doi.org/10.1016/j.dajour.2023.100377
Kumar, P., Choubey, D., Amosu, O. R. and Ogunsuji, Y. M (2024a) AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand. World Journal of Advanced Research and Reviews, 23(01), 1931–1944. DOI: https://doi.org/10.30574/wjarr.2024.23.1.2173
Desani, N. R. (2022) Enhancing Data Governance through AI-Driven Data Quality Management and Automated Data Contracts. International Journal of Science and Research (IJSR). DOI: https://dx.doi.org/10.21275/ES23812104904
Esmaeilzadeh, P. (2024). Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artificial Intelligence in Medicine, 151, 102861. https://doi.org/10.1016/j.artmed.2024.102861
Siddique, S., Haque, M. A., George, R., Gupta, K. D., Gupta, D., & Faruk, M. J. (2024). Survey on Machine Learning Biases and Mitigation Techniques. Digital, 4(1), 1-68. https://doi.org/10.3390/digital4010001
Aldoseri, A., N., K., & Hamouda, A. M. (2023). AI-Powered Innovation in Digital Transformation: Key Pillars and Industry Impact. Sustainability, 16(5), 1790. https://doi.org/10.3390/su16051790
Luo, J. (2023). Application of Machine Learning in Supply Chain Management. DOI: 10.2991/978-94-6463-124-1_58
Aljohani, A. (2022). Predictive Analytics and Machine Learning for Real-Time Supply Chain Risk Mitigation and Agility. Sustainability, 15(20), 15088. https://doi.org/10.3390/su152015088
Quayson, M., Bai, C., Effah, D., & Ofori, K. S. (2024). Machine learning and supply chain management. In Springer eBooks (pp. 1327–1355). https://doi.org/10.1007/978-3-031-19884-7_92
Kumar, P., Kant, K. Mishra, N. Babu, V. Chander, N (2024b). AI for Optimizing Supply Chain Management. Ijraset Journal for Research in Applied Science and Engineering Technology. DOI Link: https://doi.org/10.22214/ijraset.2024.65059
Binhammad, M., Alqaydi, S., Othman, A., & Abuljadayel, L. H. (2024). The role of AI in Cyber Security: Safeguarding Digital identity. Journal of Information Security, 15(02), 245–278. https://doi.org/10.4236/jis.2024.152015
Yang, M., Lim, M. K., Qu, Y., Ni, D., & Xiao, Z. (2022). Supply chain risk management with machine learning technology: A literature review and future research directions. Computers & Industrial Engineering, 175, 108859. https://doi.org/10.1016/j.cie.2022.108859
Suddala, S. (2021) Exploring the Ethical Implications of Biased Datasets on Decision-Making. Journal of Scientific and Engineering Research, 8(1):241-245.
Tamasiga, P., Ouassou, E. H., Onyeaka, H., Bakwena, M., Happonen, A., & Molala, M. (2023). Forecasting disruptions in global food value chains to tackle food insecurity: The role of AI and big data analytics – A bibliometric and scientometric analysis. Journal of Agriculture and Food Research, 14, 100819. https://doi.org/10.1016/j.jafr.2023.100819
Li, Z. (2024) Review of Application of AI in Amazon Warehouse Management. Proceedings of ICFTBA 2024 Workshop: Finance's Role in the Just Transition DOI: 10.54254/2754-1169/144/2024.GA18980
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