Total Quality Management (TQM) has traditionally been based on core principles such as customer focus, continuous improvement, process control, employee involvement, and data-driven decision-making. The rise of Industry 4.0 technologies—including Artificial Intelligence (AI), the Internet of Things (IoT), Big Data Analytics, Digital Twins, Blockchain, and Cyber-Physical Systems—has reshaped these principles, leading to the emergence of TQM 4.0. This evolution transitions quality management from reactive, manual processes to predictive, autonomous, and real-time systems, enabling greater precision, agility, and sustained excellence.This paper presents a comprehensive critical review of contemporary TQM literature, examining its evolution, methodological advancements, and cross-industry applications, while systematically exploring the challenges of integrating Industry 4.0 technologies into quality management. Based on this analysis, it proposes a conceptual digital framework for TQM 4.0 that combines traditional quality principles with advanced digital tools and incorporates the Lean Six Sigma (LSS) DMAIC cycle to drive continuous improvement, enhance customer value, and achieve operational excellence in smart environments. The study also outlines strategic objectives and key performance indicators (KPIs) to facilitate effective monitoring, evaluation, and ongoing refinement of TQM 4.0 initiatives, addressing key challenges such as system integration, workforce upskilling, data governance, and cybersecurity through practical solutions. Ultimately, this research demonstrates how the integration of TQM and Industry 4.0 technologies enables organizations to boost quality performance, strengthen resilience, and maintain competitive advantage in an increasingly digital and dynamic landscape.
Attia Hussien Gomaa (Sun,) studied this question.
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