Digital Twins, AI, Blockchain, and Lean Six Sigma in Manufacturing: Strategies for Operational Excellence and Bottleneck Reduction

Abstract

Digital twins (DTs), artificial intelligence (AI), blockchain, and Lean Six Sigma (LSS) provide a holistic solution for enhancing operational excellence and minimizing bottlenecks in the context of Industry 4.0 manufacturing systems. In this study, the technologies are analyzed and discussed with regard to their integration to improve production monitoring, predictive maintenance, production process optimization, quality management and supply chain traceability. Digital twins create real-time virtual models of physical assets and manufacturing systems, and AI-based algorithms make it possible to detect anomalies, predict outcomes and make intelligent decisions. The secure transaction records and automated compliance mechanisms introduced by blockchain enhance data integrity, transparency, and accountability. Lean Six Sigma complements these technologies by systematically eliminating waste, reducing process variation and continuously improving processes. Applications of engineer to order, make to order, assemble to order and make to stock manufacturing strategies are covered with an emphasis on capacity planning, decoupling points, buffer management and constraint identification. In addition to these, manufacturing execution systems and discrete event simulation aid in the identification and measurement of bottlenecks, including throughput, cycle time and Equipment Effectiveness. The practical aspects of implementation that need to be addressed include interoperability, data quality, cyber security, model validation, scalability, and human supervision, especially in legacy-based facilities. A phased adoption strategy beginning with high value pilot applications, followed by ongoing validation and expansion is recognized as a viable adoption approach. In general, the integrated framework has the promise of increasing manufacturing productivity, product quality, operational resiliency and transparency of the supply chain.