Human-ai Collaboration Models in Operations and Supply Chain Management
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Abstract
The integration of Artificial Intelligence (AI) into operational and supply chain processes is transforming traditional management models, creating new paradigms of Human-AI collaboration. This study investigates how different collaboration models enhance efficiency, decision-making, and resilience in operations and supply chain management (OSCM). Using a mixed-methods approach, including expert interviews (n=5) and a survey of 200 professionals, we develop the Adaptive Human–AI Collaboration Model (AHACM), a novel framework that explains how assistive, augmentative, and autonomous collaboration modes evolve with organizational data maturity and governance capacity. Through a literature review and analysis of case studies from manufacturing, logistics, and procurement, we identify three dominant models: AI-assisted decision- making, human-in-the-loop optimization, and autonomous AI-driven operations with human oversight. Key findings show that AI-assisted models improve forecasting and inventory control, human-in-the-loop systems enhance adaptability, transparency, and ethical compliance, and autonomous models strengthen real- time logistics and dynamic demand sensing. Challenges remain in data integration, workforce up skilling, and algorithmic transparency, influencing adoption across industries. The study concludes that the optimal Human-AI collaboration model depends on organizational maturity, data infrastructure, and strategic alignment. A hybrid approach, combining human intuition with AI-driven insights, emerges as the most effective strategy. By introducing AHACM, this paper offers both a conceptual and practical tool for assessing readiness and designing collaborative Human-AI systems in OSCM.
References
1. Benbya, H., & Leidner, D. (2018). How agile is your IT function? MIT Sloan Management Review, 59(4), 39–43.
2. Brintrup, A., Baryannis, G., Tiwari, A., Ratchev, S., Martinez-Arellano,
G., & Singh, J. (2023). Trustworthy, responsible, ethical AI in manufacturing and supply chains: Synthesis and emerging research
questions. arXiv preprint arXiv:2305.02314. https://arxiv.org/abs/ 2305.02314
3. Burger, P. (2025). Hybrid intelligent supply chains: Leveraging human and artificial intelligence for enhanced supply chain resilience. In M. Köhler & S. Stumpf (Eds.), Sustainability and digital transformation in supply chain networks (pp. 103–121). Springer. https://doi.org/10.1007/978-3-658-48750-8_6
4. Burger, T. (2025). Hybrid intelligent supply chains: Leveraging human– AI collaboration for operational resilience. Journal of Supply Chain Management Research, 61(2), 120–140. https://doi.org/10.1111/ jscm.12215
5. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. https://
doi.org/10.1177/001316446002000104
6. 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, Article/ 104132. https://doi.org/10.1016/j.compind.2024.104132
7. Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., & Wright, R. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice, and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
8. Financial Times. (2025). AI agents: From co-pilot to autopilot. Financial Times Tech Review.
9. Fragiadakis, G., Diou, C., Kousiouris, G., & Nikolaidou, M. (2024). Evaluating human-AI collaboration: A review and methodological framework. arXiv preprint arXiv:2402.01876. https://arxiv.org/abs/ 2402.01876
10. Guest, G., Bunce, A., & Johnson, L. (2020). How many interviews are enough? An experiment with data saturation and variability. Field Methods, 32(1), 59–82. https://doi.org/10.1177/1525822X19863616
11. Haindl, M., Reisch, L., & Kuhn, D. (2022). Human-in-the-loop decision- making: Integrating feedback for effective AI collaboration. Journal of Decision Systems, 31(3), 310–328. https://doi.org/10.1080/12460125.2022.2071942
12. Haindl, P., Buchgeher, G., Khan, M., & Moser, B. (2022). Towards a reference software architecture for Human-AI teaming in smart manufacturing. arXiv preprint arXiv:2209.09120. https://arxiv.org/abs/ 2209.09120
13 .Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2022). Multivariate Data Analysis (8th ed.). Cengage Learning.
14. Kushtia, H. J. (2023). The integration of artificial intelligence in supply chain management: A comprehensive review. ORGANIZE: Journal of Economics, Management and Finance, 2(3), 45–67.
15. Li, Z., Wang, Z., & Xu, X. (2022). Artificial intelligence for supply chain optimization: A review and future directions. Computers &
Industrial Engineering, 168, 108028. https://doi.org/10.1016/ j.cie.2022.108028
16. Makridakis, S. (2017). The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms. Futures, 90, 46–60. https:/
/doi.org/10.1016/j.futures.2017.03.006
17. Malakooti, B., & Raman, S. (2021). Artificial intelligence in operations and supply chain management: A review and bibliometric analysis. Expert Systems with Applications, 176, 114890. https://doi.org/10.1016/ j.eswa.2021.114890
18. MDPI. (2023). Intelligent supply chain management: A systematic literature review on artificial intelligence contributions. Information, 14(7), 356. https://doi.org/10.3390/info14070356
19. Meske, C., Bunde, E., Schneider, J., & Gersch, M. (2022). Explainable artificial intelligence: Objectives, stakeholders, and future research opportunities. Information Systems Management, 39(1), 53–63. https://doi.org/10.1080/10580530.2021.1894841
20. Michael, K., & Miller, K. W. (2020). The ethics of AI and robotics: Amapping of the issues. Computers in Society, 37(1), 1–21.
21. Nelson, J. P., Biddle, J. B., & Shapira, P. (2023). Applications and societal implications of artificial intelligence in manufacturing: Asystematic review. Journal of Manufacturing Systems, 66, 81–95.
22. NVivo (2024). NVivo qualitative data analysis software (Version 14). QSR International.
23. Queiroz, M. M., Ivanov, D., Dolgui, A., & Wamba, S. F. (2020). Impacts of epidemic outbreaks on supply chains: Mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Annals of Operations Research, 297(1–2), 635–682.
24. Revilla, E., Saenz, M. J., Seifert, R., & Ma, L. (2023). Human–artificial intelligence collaboration in prediction: A field experiment in the retail industry. Journal of Management Information Systems, 40(2), 295– 323. https://digitalsc.mit.edu/wp content/uploads/2024/02/Revilla- Saenz-Seifert-JMIS-2023_Human-Artificial-Intelligence-Collaboration- in-Prediction-A-Field-Experiment-in-the-Retail-Industry.pdf
25. Revilla, E., Saenz, M. J., Seifert, R., & Ma, Y. (2023). Human–AI collaboration in supply chains: From decision-support to supervisory
control. International Journal of Operations & Production Management, 43(5), 678–701. https://doi.org/10.1108/IJOPM-05- 2022-0312
26. Samuels, J., Smith, K., & Zhang, L. (2024). 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, 1165. https://doi.org/10.3389/frai.2024.1165
27. Samuels, J., & Ortega, P. (2024). Artificial intelligence and supply chain resilience: Lessons from the COVID-19 pandemic. International Journal of Production Research, 62(4), 1012–1034. https://doi.org/10.1080/00207543.2023.2245678
28. 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, Article/ 1477044. https://doi.org/10.3389/frai.2024.1477044
29. Schoenherr, T., & Speier-Pero, C. (2020). Data science, predictive analytics, and big data in supply chain management: Current state and future potential. Journal of Business Logistics, 41(2), 120–132. https://doi.org/10.1111/jbl.12235
30. 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(4), 611–632. https:// doi.org/10.1108/JMTM-06-2023-0201
31. Siagian, I., Widjaja, A., & Prasetyo, Y. (2021). Leveraging artificial intelligence to build supply chain resilience during COVID-19.
Operations Research Perspectives, 8, 100186. https://doi.org/10.1016/ j.orp.2021.100186
32. Simon, A., Kumar, S., & Verma, R. (2025). Human-artificial intelligence collaboration in supply chain outcomes: The mediating role of responsible artificial intelligence. Annals of Operations Research. https://doi.org/10.1007/s10479-025-06592-1
33. Stanford HCAI. (2025). Human-centered AI. Stanford University Human-Centered AI Institute. Retrieved from https://hai.stanford.edu/
34. Te’eni, D., Yahav, I., Zagalsky, A., Schwartz, D. G., & Silverman, G. (2023). Reciprocal human-machine learning: A theory and an instantiation for the case of message classification. Management Science, 69(7), 3625–3645. https://doi.org/10.1287/mnsc.2022.4432
35. Vossing, F., Mühlroth, C., & Bican, P. (2022). Trusting artificial intelligence in organizations: A framework for research and practice. Journal of Business Research, 141, 593–606. https://doi.org/10.1016/ j.jbusres.2022.05.019
36. Wamba, S. F., Gunasekaran, A., Dubey, R., & Ngai, E. W. T. (2018). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365. https://doi.org/10.1016/ j.jbusres.2016.08.009
37. Wamba, S. F., Queiroz, M. M., & Trinchera, L. (2021). Dynamics between artificial intelligence capabilities and supply chain resilience: A resource-based perspective. International Journal of Production Economics, 235, 108080. https://doi.org/10.1016/j.ijpe.2021.108080
38. Wang, G., Gunasekaran, A., Ngai, E. W. T., & Papadopoulos, T. (2021). Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 231, 107871. https://doi.org/10.1016/ j.ijpe.2020.107871 39. Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/ j.jmsy.2021.08.002
40. Yao, X., Lin, Y., & Ma, Y. (2022). A systematic review of AI in sustainable supply chain management. Resources, Conservation and Recycling, 185, 106457. https://doi.org/10.1016/j.resconrec. 2022. 106457
41. Zavolokina, L., Dolata, M., & Schwabe, G. (2020). FinTech transformation: How IT-enabled innovations shape the financial sector. Electronic Markets, 30(1), 1–9. https://doi.org/10.1007/s12525-019- 00373-8
42. Zhang, J., Li, S., & Chang, K. (2024). Factors affecting human–AI collaboration performance in financial services. Sustainability, 17(10), 4335. https://doi.org/10.3390/su17104335
43. Zhang, Y., Ren, S., & Liu, Y. (2021). A bibliometric analysis of artificial intelligence research in operations management. Technological Forecasting and Social Change, 165, 120529. https://doi.org/10.1016/j.techfore.2020.120529
44. Zhao, K., & Liu, Y. (2021). Artificial intelligence for healthcare operations: Opportunities and challenges. Production and Operations
Management, 30(4), 995–1010. https://doi.org/10.1111/poms.13323
45. Zhou, L., Chong, A. Y. L., & Ngai, E. W. T. (2022). Supply chain digitalization: A systematic literature review and future research directions. International Journal of Production Economics, 236,108149. https://doi.org/10.1016/j.ijpe.2021.108149
46. Zhuang, C., Liu, J., & Xiong, H. (2021). Human-cyber-physical systems in Industry 5.0: A survey. Journal of Industrial Information Integration, 26, 100256. https://doi.org/10.1016/j.jii.2021.100256
47. Zomaya, A. Y., & Sakr, S. (Eds.). (2021). Handbook of big data technologies. Springer. https://doi.org/10.1007/978-3-030-20922-6
48. Zorina, Y. (2025). Intelligent Supply Chain Governance: Integrating AI for Sustainable, Resilient, and ESG Compliant Procurement. Journal of Procurement and Supply Chain Management, 4(2), 1–12. https://doi.org/10.58425/jpscm.v4i2.431
49. Zwass, V. (2021). Ethical AI for business: Opportunities and challenges. Journal of Business Ethics, 173(3), 543–556. https://doi.org/10.1007/s10551-020-05430-w
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