Human-ai Collaboration Models in Operations and Supply Chain Management

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 upskilling, 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.