AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration

References

[1] Adadi, A., & Berrada, M. (2018). Peeking inside the black-box: A survey on explainable artificial intelligence (XAI). IEEE Access, 6, 52138–52160.

[2] Adamashvili, N., Zhizhilashvili, N., &Tricase, C. (2024). The integration of the Internet of Things, artificial intelligence, and blockchain technology for advancing the wine supply chain. Computers, 13(3), 72. https://doi.org/10.3390/computers13030072

[3] Alatalo, J., Heilimo, E., Rantonen, M., Väänänen, O., & Sipola, T. (2025). Reducing emissions using artificial intelligence in the energy sector: A scoping review. Applied Sciences, 15(2), 999. https://doi.org/10.3390/app15020999

[4] Amosu, O. R., Kumar, P., Ogunsuji, Y. M., &Faworaja, O. (2024). AI-driven demand forecasting: Enhancing inventory management and customer satisfaction. World Journal of Advanced Research and Reviews, 23(2), 708-719. https://doi.org/10.30574/wjarr.2024.23.2.2394

[5] Belhadi, A., Mani, V., Kamble, S. S., Khan, S. A. R., & Verma, S. (2024). Artificial intelligence-driven innovation for enhancing supply chain resilience and performance under the effect of supply chain dynamism: An empirical investigation. Annals of Operations Research, 333, 627–652. https://doi.org/10.1007/s10479-021-03956-x

[6] Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency, 149–159.

[7] Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. Proceedings of the Conference on Fairness, Accountability, and Transparency, 77–91.

[8] Burgess, P. R., Sunmola, F. T., & Wertheim-Heck, S. (2023). A review of supply chain quality management practices in sustainable food networks. Heliyon, 9(11), e21179. https://doi.org/10.1016/j.heliyon.2023.e21179

[9] Luo, S. (2024). Intelligent supply chain demand forecasting and inventory management strategies. Transactions on Economics Business and Management Research, 12, 38-44. https://doi.org/10.62051/ejb42e55

[10] Mohsen, B. (2023). Impact of artificial intelligence on supply chain management performance. Journal of Service Science and Management, 16, 44-58. https://doi.org/10.4236/jssm.2023.161004.

[11] Molnar, C. (2020). Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Leanpub.

[12] Nwangwu, J. C., Ohanyere, C. P., Nwangwu, H. U., &Atueyi, C. L. (2025). Artificial intelligence (AI) and decision-making in supply chain management of lubricant firms in Anambra State, Nigeria. International Journal of Business & Law Research, 13(1), 242-254.

[13] Nweje, U., & Taiwo, M. (2025). Leveraging artificial intelligence for predictive supply chain management: Focus on how AI-driven tools are revolutionizing demand forecasting and inventory optimization. International Journal of Science and Research Archive, 14(1). https://doi.org/10.30574/ijsra.2025.14.1.0027

[14] O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing Group.

[15] Oyewole, A., Okoye, C. C., Ofodile, O. C., &Ejairu, E. (2024). Reviewing predictive analytics in supply chain management: Applications and benefits. World Journal of Advanced Research and Reviews, 21(3), 568- 574. https://doi.org/10.30574/wjarr.2024.21.3.0673

[16] 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

[17] Richey, R. G., Jr., Chowdhury, S., Davis-Sramek, B., Giannakis, M., & Dwivedi, Y. K. (2023). Artificial intelligence in logistics and supply chain management: A primer and roadmap for research. Journal of Business Logistics, 44(4), 532-549. https://doi.org/10.1111/jbl.12364

[18] Ronoh, R. (2016). Benefits of supply chain management in the manufacturing sector. International Journal of Science and Research (IJSR), 5(11), 1967-1970. https://doi.org/10.21275/ART20162219

[19] Samuels, A. (2025). Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1477044

[20] Shamsuddoha, M., Khan, E. A., Chowdhury, M. M. H., & Nasir, T. (2025). Revolutionizing supply chains: Unleashing the power of AI-driven intelligent automation and real-time information flow. Information, 16(1), 26. https://doi.org/10.3390/info16010026

[21] Shekarian, E., Ijadi, B., Zare, A., &Majava, J. (2022). Sustainable supply chain management: A comprehensive systematic review of industrial practices. Sustainability, 14(13), 7892. https://doi.org/10.3390/su14137892

[22] Zamani, E. D., Smyth, C., Gupta, S., & Dennehy, D. (2023). Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Annals of Operations Research, 327, 605–632. https://doi.org/10.1007/s10479-022-04983-y.

[23] Zhong, R. Y., Xu, X., & Wang, L. (2017). IoT-enabled smart manufacturing and real-time data analytics. Journal of Manufacturing Systems, 43

[24] Chen, J., Lin, L., & Zhang, S. (2020). Addressing bias in artificial intelligence: Strategies and perspectives. Journal of Artificial Intelligence Research, 67, 1-25. https://doi.org/10.1613/jair.1.12179

[25] Chen, W., Men, Y., Fuster, N., Osorio, C., & Juan, A. A. (2024). Artificial intelligence in logistics optimization with sustainable criteria: A review. Sustainability, 16(21), 9145. https://doi.org/10.3390/su16219145

[26] Choi, T. M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1884.

[27] 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

[28] Daios, A., Kladovasilakis, N., Kelemis, A., &Kostavelis, I. (2025). AI applications in supply chain management: A survey. Applied Sciences, 15(5), 2775. https://doi.org/10.3390/app15052775

[29] Dastin, J. (2018). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. Retrieved from https://www.reuters.com

[30] Daugherty, P. R., & Wilson, H. J. (2018). Human + Machine: Reimagining work in the age of AI. Harvard Business Press.

[31] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.

[32] European Commission. (2020). Ethics guidelines for trustworthy AI. Retrieved from https://ec.europa.eu/digital- strategy

[33] Hajian, S., Bonchi, F., & Castillo, C. (2022). Algorithmic bias: A survey. ACM Computing Surveys, 54(3), 1– 35.

[34] Holstein, K., Wortman Vaughan, J., Daumé III, H., Dudik, M., & Wallach, H. (2019). Improving fairness in AI- assisted decision making: What do stakeholders need? Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–14.

[35] Hussain, J., Lee, C.-C., & Lev, B. (2025). Optimizing AI-based emission reduction efficiency and subsidies in supply chain management: A sensitivity-based approach with duopoly game dynamics. Journal of Cleaner Production, 494, 144991. https://doi.org/10.1016/j.jclepro.2025.144991

[36] Islam, M., Monjur, M., & Akon, T. (2023). Supply chain management and logistics: How important interconnection is for business success. Open Journal of Business and Management, 11, 2505-2524. https://doi.org/10.4236/ojbm.2023.115139

[37] Ivanov, D., &Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. International Journal of Production Research, 58(10), 2904–2915.

[38] Ivanov, D., Dolgui, A., & Sokolov, B. (2019). The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics. International Journal of Production Research, 57(3), 829-846. https://doi.org/10.1080/00207543.2018.1488086

[39] Jerlan, D. G., Angelo, B. A., Cleofe, M. B., Jessie, T. G., Bruce, B. T., John, H. C., Ria, B. C. (2025). The Nexus Between AI Self-Efficacy and Attitude Towards AI of University Students in Davao City as Moderated by Sex

[40] Jodlbauer, H., Brunner, M., Bachmann, N., Tripathi, S., &Thürer, M. (2023). Supply chain management: A structured narrative review of current challenges and recommendations for action. Logistics, 7(4), 70. https://doi.org/10.3390/logistics7040070

[41] Kurrahman, T., Tsai, F. M., Lim, M. K., Sethanan, K., & Tseng, M. L. (2025). Generative AI capabilities for green supply chain management improvement: Extended dynamic capabilities view. International Journal of Logistics Research and Applications, 1–28. https://doi.org/10.1080/13675567.2025.2479006

[42] Laldin Ismaeil, M. K., & Fad Lalla, A. (2024). The role and impact of artificial intelligence on supply chain management: Efficiency, challenges, and strategic implementation. Journal of Ecohumanism, 3(4), 89-106. https://doi.org/10.62754/joe.v3i4.3461

[43] Li, C., & Chen, H. (2025). Machine learning-based supply chain risk prediction and management. In ICCSMT 2024: 2024 5th International Conference on Computer Science and Management Technology. https://doi.org/10.1145/3708036.3708177

[44] Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276. https://doi.org/10.1016/j.jik.2022.100276

[45] Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.

[46] Adesola O., Taiwo I., David, D. A., Ezenwa, H. N., & Quddus, A. A. (2025). Utilizing AI and machine learning algorithms to optimize supplier relationship management and risk mitigation in global supply chains. International ournal of Science and Research Archive, 2025, 14(02), 219-228

[47] Olawale, A., Ajoke, O., &Adeusi, C. (2020).Quality assessment and monitoring of networks using passive.

[48] A. A. Bello, D. A. Oduro, E. O. Manu, A. D. Bello, A. O. Leo, C. E. Ukatu, and N. Okika, "Enhancing Know Your Customer (KYC) and Anti-Money Laundering (AML) Compliance Using Blockchain: A Business Analysis Approach," Iconic Research and Engineering Journals, vol. 8, no. 9, pp. 297–305, Mar. 2025

[49] David, A. A., & Edoise, A. (2025). Cloud computing and Machine Learning for Scalable Predictive Analytics and Automation: A Framework for Solving Real-world Problem.

[50] Ÿ ¡±²´ÉÊÜÝÞßá4 5 6 V W X © ª ñäÙÏöϫϢ™Ž€wl^wP€wGh=°6�OJQJh91Úh91Ú6�H*OJQJh^~ h91Ú6�H*OJQJh91Ú6�H*OJQJh91Ú6�OJQJh^~ h^~ 6�H*OJQJh91Úh^~ OJQJh91ÚH*OJQJh4H*OJQJh4H*OJPJQJh^~ h4OJPJQJh^~ h4H*OJQJh4OJPJQJh^~ h^~ OJQJh4h^~ CJ(OJQJhzhÓhzhÓCJ(OJPJQJ ¡ß4 V ª « ¬ ­ •–—õèÚõõõÅ°¢‘‰‰‰‰$a$gd4$„ñÿdì¤:]„ñÿa$gd4 [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

[51] [Some characters in this reference could not be displayed correctly — please refer to the published PDF for the full reference.]

@article{1708194,
      author = {Achu Obinna, Elijah Kayode Adejumo, Samuel Yaw Larbi},
      title = {AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {11},
      pages = {77-94},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708194.pdf},
      abstract = {This paper stems from the revolutionary changes that are currently observed regarding the integration of artificial intelligence (AI) into manufacturing supply chains as measures to enhance efficiency, ures to optimizing logistics, and mitigate risks. This study throws light on AI-driven risk management, especially its applications in predictive analytics, real-time risk detection, and logistics automation. It examines the challenges associated with AI adoption, including data bias, lack of transparency, and resistance to automation. Case studies of successful AI implementations, such as predictive maintenance and warehouse automation, are presented alongside instances of AI failures, while lessons learned are also presented and analysed. Strategies for mitigating bias, improving explainability, and fostering human-AI collaboration are discussed, with a focus on regulatory and ethical considerations. The study also identifies emerging AI technologies that will shape the future of supply chain management. Findings suggest that while AI enhances resilience and efficiency, addressing bias, ensuring transparency, and fostering human oversight are critical for sustainable AI adoption. The study recommends policy interventions, workforce training, and further research on AI fairness and explainability to maximize AI?s potential in supply chain management.},
      keywords = {Algorithmic transparency, AI-driven supply chains, human-AI collaboration, predictive analytics,  risk management},
      month = {May},
  }