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◆ Big Data and Cognitive Computing2026-02-17· Analytics

Integration of Lean Analytics and Industry 6.0: A Novel Meta-Theoretical Framework for Antifragile, Generative AI-Orchestrated, Circular–Regenerative, and Hyper-Connected Manufacturing Ecosystems

Mohammad Shahin, Mazdak Maghanaki, F. Frank Chen

原始摘要(英文原文)· Original abstract
The convergence of Lean manufacturing principles with Industry 4.0 has yielded significant operational improvements, yet the emerging paradigm of Industry 6.0—characterized by antifragile, autonomous, and sustainable systems—demands a fundamental rethinking of existing analytical frameworks. This paper introduces the Industry 6.0 Lean Analytics (I6LA) Framework, a novel meta-theoretical approach that integrates Lean principles with the core concepts of Industry 6.0. By systematically analyzing the limitations of current Lean analytics in the context of Industry 6.0 requirements, we identify critical gaps in areas such as system resilience, AI-driven autonomy, and circular economy integration. The I6LA Framework addresses these gaps through four new theoretical pillars: Antifragile Lean Systems Theory, generative AI-Orchestrated Value Streams, Circular–Regenerative Analytics, and Hyper-Connected Ecosystem Integration. This research provides a new set of mathematical models for measuring antifragility, generative orchestration efficiency, and circularity, offering a comprehensive analytical toolkit for the next generation of manufacturing. The framework’s primary contribution is a paradigm shift from optimizing stable, human-in-the-loop systems to managing dynamic, autonomous ecosystems that thrive on volatility and are regenerative by design. This paper provides both a robust theoretical foundation and practical implementation guidance for organizations navigating the transition to Industry 6.0.
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Integration of Lean Analytics and Industry 6.0: A Novel Meta-Theoretical Framework for Antifragile, Generative AI-Orchestrated, Circular–Regenerative, and Hyper-Connected Manufacturing Ecosystems — 科研速览 Science Skim