Human–AI Collaboration Models in Supply Chain Decision-Making

The growing adoption of artificial intelligence (AI) in the supply chain management has been supported by peer-reviewed journal articles and academic materials published at least since 2018.shifted the emphasis of the focus on the fully automated decision systems to the human-AI collaborative decision making models. Although previous literature has looked at the use of AI and optimization of algorithms in supply chains, it has not yet synthesized the interactions between humans and AI and the way they affect decisions and share power and influence across strategic, tactical, and operational levels of supply chains. The current research is a systematic literature review of human-AI collaboration models in supply chain decision-making and synthesizes. The paper classifies collaboration models as advisory, delegative, interactive, hybrid, and human-in-the-loop models, and aligns them with major decision domains, in the context of planning, forecasting, risk management, resilience, and sustainability. The review also analyzes facilitating mechanisms, such as trust, explainability, responsibility, and transparency, which determine the success and acceptability of collaborative AI systems. As empirical evidence shows, when humans and AI work together effectively, the quality, responsiveness, and resilience of decisions in complex and uncertain contexts increase; nevertheless, due to authority misalignment, the lack of transparency in AI models, and inadequate governance systems, human-AI collaboration may deteriorate the performance and confidences.