Integrating Lean Six Sigma and Digital Twins for Predictive Optimization in Supply Chain and Operational Excellence
Elizabeth Asorose, William Adams
Abstract
The integration of Lean Six Sigma (LSS) methodologies and digital twin technology represents a transformative approach to enhancing supply chain efficiency, reducing waste, and enabling real-time predictive optimization.Lean Six Sigma, a structured framework for process improvement, focuses on minimizing defects, improving quality, and increasing operational efficiency.However, its reliance on historical data and static analysis presents limitations in highly dynamic supply chain environments.By incorporating digital twins-virtual replicas of physical assets and processes-organizations can achieve a real-time, data-driven decision-making ecosystem that enhances LSS capabilities.Digital twins utilize sensor-based data, AI-driven analytics, and real-time simulations to create a continuously updated model of supply chain operations.This integration allows organizations to identify bottlenecks, predict failures, optimize inventory levels, and enhance workflow efficiency beyond traditional LSS methodologies.Furthermore, predictive analytics embedded within digital twins facilitates proactive decision-making, reducing variability and ensuring continuous operational excellence.This paper explores the synergistic application of Lean Six Sigma and digital twins in supply chain management, analyzing their combined impact on waste minimization, process optimization, and resilience building.It proposes a hybrid framework that leverages LSS-driven problem-solving with digital twin-enabled simulations to enhance visibility, accelerate root cause analysis, and drive sustainable process improvements.By aligning operational excellence strategies with Industry 4.0 advancements, this research provides insights into how organizations can achieve greater agility, cost reduction, and predictive supply chain optimization.