Digital Twin-Driven Lean Manufacturing: Optimizing Value Stream Flow

Charles Onyeka Nwamekwe, Ewuzie Nnamdi Vitalis, Igbokwe Nkemakonam Chidiebube, Emeka Celestine Nwabunwanne, Chukwuma Godfrey Ono

Abstract


This research investigates the integration of Digital Twin (DT) technology within Lean Manufacturing frameworks to optimize value stream flow, minimize waste, and enhance real-time decision-making capabilities. By synthesizing foundational concepts of Lean Manufacturing and DT, the paper examines the layered DT architecture, covering the physical, virtual, and communication interfaces, alongside Lean tools like Kaizen, Kanban, and Just-in-Time (JIT) that facilitate continuous process improvement. Case studies, particularly in the automotive sector, demonstrate DT's ability to increase production efficiency through predictive maintenance and simulation-based scenario planning, supporting Lean's waste reduction objectives. However, the paper identifies key implementation challenges, including legacy system integration, workforce adaptation, and data interoperability. Additionally, cybersecurity and data integrity concerns are analysed to highlight essential protocols for safe DT deployment. Future research directions propose advancements like AI-powered DTs, blockchain for enhanced traceability, and edge computing for low-latency applications. Key insights from industry case studies underscore the transformative impact of DTs on production efficiency, organizational resilience, and sustainable manufacturing outcomes, positioning Digital Twin technology as a cornerstone for next-generation lean manufacturing systems

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References


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DOI: http://dx.doi.org/10.17977/um010v8i12025p1-13

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