Background The manufacturing decision-making sector has been revolutionized by the application of Artificial Intelligence (AI). Traditional decision-making models are static and inflexible in dynamic and networked production processes. Methods An AI-based collaborative decision-making (CDM) framework can effectively resolve the abovementioned issues. For Real-Time CDM, Social Network Analysis (SNA) was utilized by this AI-based CDM. Decision-making accuracy, communication, and flexibility are enhanced through the deployment of SNA and AI algorithms in the proposed Clinical Decision-Making using Artificial Intelligence (CDM-AI) framework. Operational risks are reduced, which may support the framework in quality control, logistics management, production planning, and resource allocation. The real-time solution is practical in dynamic environments because it ensures adaptation and scalability, in contrast to conventional methods. Results The proposed CDM-AI architecture significantly enhanced operational outcomes and manufacturing efficiency. The results clearly show that the proposed method improved manufacturing efficiency by 97.68% and reduced operating costs by 33.42%. The proposed work also resulted in improved Resource Utilization (RU) by 98.47%, with improved decision-making speed and scalability by 97.41%.
Liu et al. (Thu,) studied this question.