Journal of Optimization and Supply Chain Management

Journal of Optimization and Supply Chain Management

AI-Enabled Decision-Making and Governance Risk in Global Supply Chains: Frameworks for Accountability, Transparency, and Resilience

Document Type : Review Paper

Authors
1 Pompea College of Business, University of New Haven, West Haven, Connecticut, USA.
2 Barclays, Whippany, New Jersey, USA.
3 Independent Researcher, USA.
4 Harrisburg University of Science and Technology, Harrisburg, Pennsylvania, USA.
5 Kennesaw State University, Kennesaw, Georgia, USA.
6 Independent Researcher, Dallas, Texas, USA.
7 Kellstadt Graduate School of Business, DePaul University, Chicago, Illinois, USA.
Abstract
Objective: Artificial intelligence in global supply chains has moved from analytical aid to executing authority, selecting suppliers, rerouting freight, and screening counterparties with little human intervention. This review characterizes the resulting governance risk: the risk that an organization cannot explain, justify, contest, correct, or answer for a decision taken through its automated systems.
Methods: A structured narrative review with a disclosed protocol synthesized 119 sources, comprising 101 studies and instruments published between 2018 and 2026 and 18 earlier foundational works, identified through five databases, targeted regulatory retrieval, and snowballing. A governance framework was then developed by design synthesis.
Results: Accountability structures rarely track the actual locus of decision, and advisory systems with high acceptance rates function as delegated systems with advisory-grade protections. Seven mechanisms generate governance risk, notably epistemic opacity, accountability diffusion, algorithmic monoculture, and displaced coercion onto lower-tier suppliers. The proposed Governed Autonomy Framework has four layers (decision rights and authority tiering, evidentiary traceability, assurance and contestability, and resilience engineering), operationalized through a five-level maturity model, governance indicators, and a staged implementation path.
Conclusion: Governance risk is separable from model and operational risk and poorly addressed by prevailing compliance architectures. Supplier contestability is the most valuable and most neglected control, justified epistemically rather than ethically. Current regulatory deferrals do not justify delaying the evidentiary work that later compliance will require.
Keywords
Subjects

Abioye, R. F., Okojie, J. S., Filani, O. M., Ike, P. N., Idu, J. O. O., Nnabueze, S. B., Okojokwu-Idu, J. O., & Ihwughwavwe, S. I. (2023). Automated ESG reporting in energy projects using blockchain-driven smart compliance management systems. International Journal of Multidisciplinary Evolutionary Research, 4(2), 120–129. https://doi.org/10.54660/IJMER.2023.4.2.120-129
Agbabiaka, J., Okonkwo, C. S., Ogunwole, O., Mayo, W., & Okeke, O. T. (2019). Supply chain risk management model for EPC and gas processing projects. Iconic Research and Engineering Journals, 3(2), 968–980. https://doi.org/10.64388/IREV3I2-1713124
Ahiaeke Patrick, M. C., Okonkwo, C. S., Mayo, W., & Okeke, O. T. (2021). Model for data driven vendor evaluation and bid selection using geospatial intelligence. Shodhshauryam, International Scientific Refereed Research Journal, 4(4), 426–443. https://doi.org/10.32628/SHISRRJ214461
Aifuwa, S. E., Oshoba, T. O., Ogbuefi, E., Ike, P. N., Nnabueze, S. B., & Olatunde-Thorpe, J. (2020). Predictive analytics models enhancing supply chain demand forecasting accuracy and reducing inventory management inefficiencies. International Journal of Multidisciplinary Research and Growth Evaluation, 1(3), 171–181. https://doi.org/10.54660/.IJMRGE.2020.1.3.171-181
Akanbi, O., Ganiu, O. S., & Sunday, E. A. (2025). A multi-agent AI framework for swarm intelligence in autonomous mobile robot (AMR) fleet coordination. International Journal of Scientific Research in Humanities and Social Sciences, 2(1), 81–109.
Akanbi, O., & Sunday, E. A. (2024). An integrated path-planning and slotting optimization model for AMR-enabled high-density warehousing. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 3226–3243. https://doi.org/10.62225/2583049X.2024.4.6.6181
Akanbi, O., & Sunday, E. A. (2025). A systematic review of AI-driven autonomous mobile robots (AMR) in scalable micro-fulfillment centers. International Journal of Advanced Multidisciplinary Research and Studies, 5(6), 2363–2379. https://doi.org/10.62225/2583049X.2025.5.6.6182
Akin-Oluyomi, O. T., Atima, M. E., & Akinleye, O. K. (2023a). Green procurement strategies for balancing cost efficiency with long-term environmental responsibility. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2183–2193.
Akin-Oluyomi, O. T., Atima, M. E., & Akinleye, O. K. (2023b). Regulatory compliance and supplier risk assessment frameworks in international pharmaceutical procurement. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2194–2204.
Akin-Oluyomi, O. T., Atima, M. E., & Akinleye, O. K. (2024). Cross-border supplier relationship management frameworks for pharmaceutical and global construction sectors. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1709–1718.
Akin-Oluyomi, O. T., Atima, M. E., Akinleye, O. K., & Okoruwa, P. O. (2023). Predictive analytics approaches for improving demand forecasting accuracy in e-commerce procurement systems. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2173–2182.
Akin-Oluyomi, O. T., Okoruwa, P. O., Babatope, O. M., & Akokodaripon, D. A. (2023). Evaluating supplier sustainability metrics through data-driven procurement and supply chain frameworks. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2213–2223.
Akinlade, O. F., Filani, O. M., & Nwachukwu, P. S. (2024). Automation and digital twins framework reducing procurement errors and turnaround time. International Journal of Scientific Research in Humanities and Social Sciences, 1(1), 197–216.
Akinleye, O. K. (2024). A sustainable procurement and resilience framework for post-pandemic pharmaceutical operations. International Journal of Advanced Multidisciplinary Research and Studies, 4(1), 1602–1621.
Akinleye, O. K., & Adeyoyin, O. (2021). Process automation framework for enhancing procurement efficiency and transparency. Shodhshauryam, International Scientific Refereed Research Journal, 4(4), 356–387.
Akinleye, O. K., & Adeyoyin, O. (2022). Supplier relationship management framework for achieving strategic procurement objectives. Gyanshauryam, International Scientific Refereed Research Journal, 5(4), 565–598. https://doi.org/10.32628/GISRRJ225430
Akinleye, O. K., Okoruwa, P. O., Babatope, O. M., & Akokodaripon, D. A. (2023). Leveraging big data and business intelligence for optimization of manufacturing sector procurement. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2164–2172.
Aliliele, C., Mbonu, I. S., Uzoka, E., & Iwuanyanwu, U. (2025). A review of AI assisted continuous auditing systems in technology risk and cybersecurity oversight. Gyanshauryam, International Scientific Refereed Research Journal, 8(4), 210–250. https://doi.org/10.32628/GISRRJ258369
Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989. https://doi.org/10.1177/1461444816676645
Aneke, O. B., & Adesemoye, A. C. (2019). Toward an integrated conceptual framework for traceability and chain-of-custody integrity in pharmaceutical distribution. Iconic Research and Engineering Journals, 3(4), 655–672.
Aneke, O. B., Sydney, E., & Adesemoye, A. C. (2023). Data integrity and governance in regulated life sciences operations: A review of failures, controls, and remediation strategies. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(10), 538–568.
Annan, A. O. (2022). Data privacy governance models for cross-border digital platforms. International Journal of Multidisciplinary Research and Growth Evaluation, 3(6), 949–964.
Annan, A. O. (2024). Algorithmic accountability and trade secret protection in artificial intelligence. Shodhshauryam, International Scientific Refereed Research Journal, 7(5), 315–347.
Annan, A. O. (2025a). Automated decision-making and anti-discrimination compliance under U.S. law. International Journal of Advanced Multidisciplinary Research and Studies, 5(2), 2522–2540.
Annan, A. O. (2025b). Cybersecurity compliance as a source of competitive advantage in technology markets. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 11(5), 456–487.
Babatope, O. M., Akokodaripon, D. A., Akinleye, O. K., & Okoruwa, P. O. (2023). The role of data visualization in enhancing strategic procurement decision-making effectiveness. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2205–2212.
Baryannis, G., Validi, S., Dani, S., & Antoniou, G. (2019). Supply chain risk management and artificial intelligence: State of the art and future research directions. International Journal of Production Research, 57(7), 2179–2202. https://doi.org/10.1080/00207543.2018.1530476
Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468. https://doi.org/10.1111/j.1468-0386.2007.00378.x
Burrell, J. (2016). How the machine thinks: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
Choi, T.-M., Wallace, S. W., & Wang, Y. (2018). Big data analytics in operations management. Production and Operations Management, 27(10), 1868–1883. https://doi.org/10.1111/poms.12838
Christopher, M., & Peck, H. (2004). Building the resilient supply chain. The International Journal of Logistics Management, 15(2), 1–14. https://doi.org/10.1108/09574090410700275
Cobbe, J., Lee, M. S. A., & Singh, J. (2021). Reviewable automated decision-making: A framework for accountable algorithmic systems. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 598–609). ACM. https://doi.org/10.1145/3442188.3445921
Danielsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, Article 106290. https://doi.org/10.1016/j.jbankfin.2021.106290
Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56–62. https://doi.org/10.1145/2844110
Efobi, O. Z., Akinleye, O. K., & Fasawe, O. (2022). Conceptual model for data-driven lean supply chain optimization in manufacturing and retail operations. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 8(2), 703–734. https://doi.org/10.32628/CSEIT2541335
Efobi, O. Z., Akinleye, O. K., & Fasawe, O. (2023). Conceptual framework for developing a resilience index for post-pandemic supply chains. Shodhshauryam, International Scientific Refereed Research Journal, 6(2), 421–432.
Ekwunife, D. I., Precious, O. T., Rasul, O. A., Akinlade, O. F., Nwokoro, T. O., & Ikpe, V. I. (2024). Using blockchain technology to maximize supply chain and logistics management in North America. International Journal of Science and Research Archive, 12(2), 854–863.
Elish, M. C. (2019). Moral crumple zones: Cautionary tales in human-robot interaction. Engaging Science, Technology, and Society, 5, 40–60. https://doi.org/10.17351/ests2019.260
Essandoh, S., Sakyi, J. K., Ibrahim, A. K., Okafor, C. M., & Wedraogo, L. (2025). Artificial intelligence and the future of work: Impacts on employment and job roles. International Journal of Multidisciplinary Futuristic Development, 6(1), 31–41. https://doi.org/10.54660/IJMFD.2025.6.1.31-41
European Parliament & Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). Official Journal of the European Union, L 2024/1689. http://data.europa.eu/eli/reg/2024/1689/oj
European Parliament & Council of the European Union. (2026). Directive (EU) 2026/470 amending Directives 2013/34/EU, 2006/43/EC, (EU) 2022/2464 and (EU) 2024/1760 as regards corporate sustainability reporting and due diligence requirements. Official Journal of the European Union.
Eze, F. I., Anene, U. N., & Akinleye, O. K. (2023). Pharmaceutical supply chain governance, environmental risk management, and sustainable access to essential medicines in emerging economies: The PSCG-ERM framework. International Journal of Health and Pharmaceutical Research, 8(4), 192–241. https://doi.org/10.56201/ijhpr.v8.no4.2023.pg192.241
Filani, O. M., Nnabueze, S. B., Ike, P. N., & Wedraogo, L. (2022). Real-time risk assessment dashboards using machine learning in hospital supply chain management systems. International Journal of Multidisciplinary Evolutionary Research, 3(1), 65–76. https://doi.org/10.54660/IJMER.2022.3.1.65-76
Filani, O. M., Nnabueze, S. B., Sakyi, J. K., & Okojie, J. S. (2023). Scenario-based financial modelling for enhancing strategic decision-making and organizational long-term planning. Journal of Frontiers in Multidisciplinary Research, 4(2), 251–265. https://doi.org/10.54660/.JFMR.2023.4.2.251-265
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
Green, B. (2022). The flaws of policies requiring human oversight of government algorithms. Computer Law & Security Review, 45, Article 105681. https://doi.org/10.1016/j.clsr.2022.105681
Green, B., & Chen, Y. (2019). The principles and limits of algorithm-in-the-loop decision making. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW), Article 50. https://doi.org/10.1145/3359152
Huertos, F. J., Bañales, O., Alvarez, P., & Cabanes, I. (2026). Multi-agent reinforcement learning for cooperative manipulation in industrial robotics: A systematic review of trends, gaps and research drivers. Robotics, 15(8), Article 156. https://doi.org/10.3390/robotics15080156
Ike, P. N., Aifuwa, S. E., Nnabueze, S. B., Olatunde-Thorpe, J., Ogbuefi, E., Oshoba, T. O., & Akokodaripon, D. (2024). Quantitative risk architecture for public-private partnerships: A multi-layered model for allocating public and private risk. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2669–2682. https://doi.org/10.62225/2583049X.2024.4.6.5021
Ike, P. N., Ogbuefi, E., Nnabueze, S. B., Olatunde-Thorpe, J., Aifuwa, S. E., Oshoba, T. O., & Akokodaripon, D. (2021). Supplier relationship management strategies fostering innovation, collaboration, and resilience in global supply chain ecosystems. International Journal of Multidisciplinary Evolutionary Research, 2(2), 52–62. https://doi.org/10.54660/IJMER.2021.2.2.52-62
Ike, P. N., Ogbuefi, E., Nnabueze, S. B., Olatunde-Thorpe, J., Aifuwa, S. E., Oshoba, T. O., & Akokodaripon, D. (2022). Lean supply chain practices improving operational efficiency, reducing waste, and enhancing organizational competitiveness globally. Journal of Frontiers in Multidisciplinary Research, 3(2), 182–192. https://doi.org/10.54660/.IJFMR.2022.3.2.182-192
International Organization for Standardization. (2018). Risk management: Guidelines (ISO Standard No. 31000:2018). ISO. https://www.iso.org/standard/65694.html
International Organization for Standardization & International Electrotechnical Commission. (2023). Information technology: Artificial intelligence: Management system (ISO/IEC Standard No. 42001:2023). ISO/IEC. https://www.iso.org/standard/81230.html
Ivanov, D., & Dolgui, A. (2020). Viability of intertwined supply networks: Extending the supply chain resilience angles towards survivability. A position paper motivated by COVID-19 outbreak. International Journal of Production Research, 58(10), 2904–2915. https://doi.org/10.1080/00207543.2020.1750727
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
Kim, Y., Chen, Y.-S., & Linderman, K. (2015). Supply network disruption and resilience: A network structural perspective. Journal of Operations Management, 33–34, 43–59. https://doi.org/10.1016/j.jom.2014.10.006
Kleinberg, J., & Raghavan, M. (2021). Algorithmic monoculture and social welfare. Proceedings of the National Academy of Sciences, 118(22), Article e2018340118. https://doi.org/10.1073/pnas.2018340118
Kleindorfer, P. R., & Saad, G. H. (2005). Managing disruption risks in supply chains. Production and Operations Management, 14(1), 53–68. https://doi.org/10.1111/j.1937-5956.2005.tb00009.x
Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H. (2017). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–705. https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3
Kumar, R. J., Kumar, I. A., Eswaran, M., Mohan, M. B., Aseer, R. J., & Bahubalendruni, M. V. A. R. (2026). Autonomous mobile robots in intelligent warehousing: Workflow-centric integration for Industry 5.0 logistics. Advanced Engineering Informatics, 76(Part A), Article 104858. https://doi.org/10.1016/j.aei.2026.104858
Ladapo, O. O., Dosunmu, A. A., Jooda, D., & Abolaji, T. O. (2022). Human-in-the-loop machine learning: A state of the art. Journal of Frontiers in Multidisciplinary Research, 3(1), 656–669. https://doi.org/10.54660/.JFMR.2022.3.1.656-669
Ladapo, O. O., Jooda, D., Dosunmu, A. A., & Abolaji, T. O. (2024). Keeping humans in the loop: Human-centered automated annotation with generative AI. International Journal of Multidisciplinary Futuristic Development, 5(1), 81–95. https://doi.org/10.54660/IJMFD.2024.5.1.81-95
LeBaron, G., & Lister, J. (2015). Benchmarking global supply chains: The power of the ethical audit regime. Review of International Studies, 41(5), 905–924. https://doi.org/10.1017/S0260210515000388
Locke, R. M. (2013). The promise and limits of private power: Promoting labor standards in a global economy. Cambridge University Press. https://doi.org/10.1017/CBO9781139381840
Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021). Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 735–746). ACM. https://doi.org/10.1145/3442188.3445935
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data & Society, 3(2), 1–21. https://doi.org/10.1177/2053951716679679
National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
Nissenbaum, H. (1996). Accountability in a computerized society. Science and Engineering Ethics, 2(1), 25–42. https://doi.org/10.1007/BF02639315
Nnabueze, S. B., Filani, O. M., Sakyi, J. K., Okojie, J. S., & Okereke, M. (2023). Developing sustainable business models for large-scale service organizations using predictive and strategic insights. International Journal of Multidisciplinary Evolutionary Research, 4(2), 76–88. https://doi.org/10.54660/IJMER.2023.4.2.76-88
Nnabueze, S. B., Ike, P. N., Olatunde-Thorpe, J., Aifuwa, S. E., Oshoba, T. O., Ogbuefi, E., & Akokodaripon, D. (2021). End-to-end visibility frameworks improving transparency, compliance, and traceability across complex global supply chain operations. International Journal of Multidisciplinary Futuristic Development, 2(2), 50–60. https://doi.org/10.54660/IJMFD.2021.2.2.50-60
Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2024a). LLM-as-a-judge for automated model governance: A review of methods, biases, and release-gating practices. World Journal of Innovation and Modern Technology, 8(6), 185–216. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.216
Nwakamma, S., Ojukwu, J., & Oyesiji, S. O. (2024b). Securing agentic AI enterprise workflows against prompt injection, tool poisoning, memory manipulation, and excessive agency threats. World Journal of Innovation and Modern Technology, 8(6), 217–248. https://doi.org/10.56201/wjimt.v8.no6.2024.pg217.248
Ogundairo, K. M. (2025). Hardening the autonomous value chain: Lean Six Sigma guardrails and multi-enterprise ETL architecture for agentic supply chain orchestration. IIARD International Journal of Economics and Business Management, 11(12), 441–478. https://doi.org/10.56201/ijebm.vol.11.no12.2025.pg441.478
Ogunwole, O., Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2021). Supply chain resilience framework for critical infrastructure and gas processing plants. Shodhshauryam, International Scientific Refereed Research Journal, 4(4), 444–461. https://doi.org/10.32628/SHISRRJ214462
Okojie, J., Ike, P., Idu, J., Nnabueze, S. B., Filani, O., & Ihwughwavwe, S. (2023). Predictive analytics models for monitoring smart city emissions and infrastructure risk in urban ESG planning. International Journal of Multidisciplinary Futuristic Development, 4(1), 45–57. https://doi.org/10.54660/IJMFD.2023.4.1.45-57
Okojie, J. S., Filani, O. M., Ihwughwavwe, S. I., & Sakyi, J. K. (2020). Sustainable procurement practices: ESG-integrated supply chain models for corporate responsibility and environmental performance. Iconic Research and Engineering Journals, 4(2).
Okojie, J. S., Filani, O. M., Ike, P. N., Okojokwu-Idu, J. O., Nnabueze, S. B., & Ihwughwavwe, S. I. (2023). Integrating AI with ESG metrics in smart infrastructure auditing for high-impact urban development projects. International Journal of Multidisciplinary Futuristic Development, 4(1), 32–44. https://doi.org/10.54660/IJMFD.2023.4.1.32-44
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024a). Conceptual framework for digital supply chain governance in energy and infrastructure sectors. Gyanshauryam, International Scientific Refereed Research Journal, 7(4), 335–356. https://doi.org/10.32628/GISRRJ247423
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024b). Framework for secure and scalable supply chain systems supporting national energy reliability. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2816–2826. https://doi.org/10.62225/2583049X.2024.4.6.5494
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024c). Model for predictive procurement planning to sustain operational uptime. International Journal of Scientific Research in Humanities and Social Sciences, 1(2), 909–928. https://doi.org/10.32628/IJSRSSH242772
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024d). Review of advances in procurement strategy, ERP adoption, and logistics performance. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2827–2835. https://doi.org/10.62225/2583049X.2024.4.6.5495
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024e). Review of digital supply chain models for cost control and operational continuity. International Journal of Advanced Multidisciplinary Research and Studies, 4(6), 2836–2846. https://doi.org/10.62225/2583049X.2024.4.6.5496
Okonkwo, C. S., Agbabiaka, J., Mayo, W., & Okeke, O. T. (2024f). Supply chain automation framework using service management platforms. Shodhshauryam, International Scientific Refereed Research Journal, 7(2), 157–177. https://doi.org/10.32628/SHISRRJ2472157
Okonkwo, C. S., Agbabiaka, J., Okeke, O. T., & Mayo, W. (2025). Framework for national-scale supply chain optimization through integrated IT and procurement systems. Gulf Journal of Advance Business Research, 3(12), 1610–1625. https://doi.org/10.51594/gjabr.v3i12.189
Okonkwo, C. S., Ahiaeke Patrick, M. C., Okeke, O. T., & Mayo, W. (2023). Framework for integrating IT systems engineering with supply chain operations. International Journal of Advanced Multidisciplinary Research and Studies, 3(6), 2580–2589. https://doi.org/10.62225/2583049X.2023.3.6.5500
Okonkwo, C. S., Ogunwole, O., Mayo, W., & Okeke, O. T. (2021). Framework for regulatory-compliant procurement in high-risk energy environments. International Journal of Multidisciplinary Research and Growth Evaluation, 2(6), 595–605. https://doi.org/10.54660/IJMRGE.2021.2.6.595-605
Okoruwa, P. O., Babatope, O. M., Akokodaripon, D. A., & Akinleye, O. K. (2024). Developing integrated digital platforms for enhancing transparency in procurement and supply chain management. International Journal of Multidisciplinary Research and Growth Evaluation, 5(6), 1719–1729.
Okoruwa, P. O., Babatope, O. M., Akokodaripon, D. A., & Akinleye, O. K. (2025). Digital procurement transformation approaches for strengthening efficiency in global supply chain management. International Journal of Multidisciplinary Research and Growth Evaluation, 6(5), 975–985.
Ominyi, M., & Anichukwueze, C. C. (2022a). A conceptual framework for harmonizing data protection compliance across GDPR, NDPR, and United States privacy regimes. International Journal of Social Sciences and Management Research, 8(5), 148–174. https://doi.org/10.56201/ijssmr.v8.no5.2022.pg148.174
Ominyi, M., & Anichukwueze, C. C. (2022b). Reviewing greenhouse gas disclosure and ESG compliance obligations for energy and infrastructure enterprises under evolving regulatory regimes. International Journal of Economics and Financial Management, 7(6), 94–124. https://doi.org/10.56201/ijefm.v7.no6.2022.pg94.124
Ominyi, M., & Anichukwueze, C. C. (2023). A review of algorithmic accountability and model risk management approaches in financial services. Journal of Accounting and Financial Management, 9(12), 218–238. https://doi.org/10.56201/jafm.v9.no12.2023.pg218.238
Ominyi, M., & Anichukwueze, C. C. (2024). Conceptualizing responsible AI governance structures for automated decision systems in regulated industries. International Journal of Social Sciences and Management Research, 10(11), 403–430. https://doi.org/10.56201/ijssmr.v10.no11.2024.pg.403.430
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024a). Developing a conceptual framework for AI-assisted compliance monitoring and anomaly detection in high-volume transaction environments. World Journal of Innovation and Modern Technology, 8(6), 204–228. https://doi.org/10.56201/wjimt.v8.no6.2024.pg204.228
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2024b). A systematic review of organizational readiness for the EU Artificial Intelligence Act in multinational enterprises. World Journal of Innovation and Modern Technology, 8(6), 185–203. https://doi.org/10.56201/wjimt.v8.no6.2024.pg185.203
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025a). A conceptual framework for multi-agent AI quality control in the review of regulated documents. International Journal of Social Sciences and Management Research, 11(8), 534–557. https://doi.org/10.56201/ijssmr.vol.11no8.2025.pg534.557
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025b). A review of intermediary liability and content governance obligations for digital platforms under the DSA, DMCA, and CDA regimes. International Journal of Social Sciences and Management Research, 11(8), 558–582. https://doi.org/10.56201/ijssmr.vol.11no8.2025.pg558.582
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2025c). Reviewing legal privilege and confidentiality challenges in enterprise deployment of large language models. Journal of Law and Global Policy, 10(3), 150–176. https://doi.org/10.56201/jlgp.vol.10.no3.2025.pg150.176
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2026a). A conceptual model for integrating legal, quantitative, and operational disciplines in next-generation regulatory risk leadership. International Journal of Social Sciences and Management Research, 12(7), 524–550. https://doi.org/10.56201/ijssmr.vol.12no7.2026.pg524.550
Ominyi, M., Anichukwueze, C. C., & Uzougbo, N. S. (2026b). Conceptualizing enterprise AI governance functions as institutional standards for auditable and defensible risk management. Journal of Accounting and Financial Management, 12(7), 326–356. https://doi.org/10.56201/jafm.vol.12.no7.2026.p326.356
Organisation for Economic Co-operation and Development. (2018). OECD due diligence guidance for responsible business conduct. OECD Publishing.
Organisation for Economic Co-operation and Development. (2023). OECD guidelines for multinational enterprises on responsible business conduct. OECD Publishing. https://doi.org/10.1787/81f92357-en
Oyesiji, S. O., Nwakamma, S., & Ojukwu, J. (2024). A conceptual framework for context-aware data curation in fine-tuning pipelines. International Journal of Engineering and Modern Technology, 10(11), 197–228. https://doi.org/10.56201/ijemt.v10.no11.2024.pg197.228
Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press. https://doi.org/10.4159/harvard.9780674736061
Perrow, C. (1984). Normal accidents: Living with high-risk technologies. Basic Books.
Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). ACM. https://doi.org/10.1145/3351095.3372873
Selbst, A. D. (2021). An institutional view of algorithmic impact assessments. Harvard Journal of Law & Technology, 35(1), 117–191.
Shahzadi, G., Jia, F., Chen, L., & John, A. (2024). AI adoption in supply chain management: A systematic literature review. Journal of Manufacturing Technology Management, 35(6), 1125–1150. https://doi.org/10.1108/JMTM-09-2023-0431
Sheffi, Y., & Rice, J. B. (2005). A supply chain view of the resilient enterprise. MIT Sloan Management Review, 47(1), 41–48.
Sodhi, M. S., & Tang, C. S. (2012). Managing supply chain risk. Springer. https://doi.org/10.1007/978-1-4614-3238-8
Tachizawa, E. M., & Wong, C. Y. (2014). Towards a theory of multi-tier sustainable supply chains: A systematic literature review. Supply Chain Management: An International Journal, 19(5/6), 643–663. https://doi.org/10.1108/SCM-02-2014-0070
United Nations. (2011). Guiding principles on business and human rights: Implementing the United Nations "protect, respect and remedy" framework. United Nations Human Rights Office of the High Commissioner.
Villena, V. H., & Gioia, D. A. (2018). On the riskiness of lower-tier suppliers: Managing sustainability in supply networks. Journal of Operations Management, 64, 65–87. https://doi.org/10.1016/j.jom.2018.09.004
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7(2), 76–99. https://doi.org/10.1093/idpl/ipx005
Wagner, B. (2019). Liable, but not in control? Ensuring meaningful human agency in automated decision-making systems. Policy & Internet, 11(1), 104–122. https://doi.org/10.1002/poi3.198
Wedraogo, L., Essandoh, S., Sakyi, J. K., Ibrahim, A. K., Okafor, C. M., Babalola, A. S., & Adenuga, M. A. (2025). Analysis of vendor management practices and their effects on supply chain performance. International Journal of Multidisciplinary Futuristic Development, 6(1), 42–54. https://doi.org/10.54660/IJMFD.2025.6.1.42-54
Wieland, A., & Durach, C. F. (2021). Two perspectives on supply chain resilience. Journal of Business Logistics, 42(3), 315–322. https://doi.org/10.1111/jbl.12271
Wu, L., Zhang, Z., Huang, J., Su, I.-H., & Tian, S. (2025). Digital technologies and supply chain resilience: A resource-action-performance perspective. Information Systems Frontiers, 27, 2137–2158. https://doi.org/10.1007/s10796-025-10595-1
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(2), 605–632. https://doi.org/10.1007/s10479-022-04983-y

Articles in Press, Accepted Manuscript
Available Online from 14 November 2026