AI-BASED OPERATOR BEHAVIOR MONITORING AND COST OPTIMIZATION USING DIGITAL TRACEABILITY IN MANUFACTURING SYSTEMS

Abstract

The integration of Artificial Intelligence (AI) with digital traceability frameworks in manufacturing systems has emerged as a transformative paradigm for operator behavior monitoring and cost optimization. This paper investigates how AI-driven monitoring platforms, when combined with Industrial Internet of Things (IIoT) sensor networks, machine vision systems, and digital thread architectures, enable real-time tracking of operator performance, identification of inefficiency patterns, and systematic reduction of operational costs. Drawing on evidence from Industry 4.0 implementations, the paper presents a comprehensive analysis of system architecture, behavioral analytics methodologies, cost-benefit frameworks, and implementation challenges. Findings suggest that AI-powered traceability systems reduce production defect rates by 15–30%, lower operational costs by 20–35%, and improve overall equipment effectiveness (OEE) by 10–12%, while providing granular, auditable records of human operator activities across the manufacturing lifecycle.