MAINTENANCE OPTIMIZATION IN SMART MANUFACTURING FACILITIES: A SYSTEMATIC REVIEW OF LEAN, TPM, AND DIGITALLY-DRIVEN RELIABILITY MODELS IN INDUSTRIAL ENGINEERING

Abstract

This study presents a comprehensive systematic literature review aimed at analyzing and synthesizing current advancements, applications, and challenges in maintenance optimization within smart manufacturing facilities. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework, a total of 112 peer-reviewed articles published between 2010 and 2024 were rigorously selected and analyzed from major academic databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Google Scholar. The review focuses on three principal frameworks of maintenance optimization: Lean Maintenance, Total Productive Maintenance (TPM), and digitally-driven models incorporating Artificial Intelligence (AI), Machine Learning (ML), Cyber-Physical Systems (CPS), and Computerized Maintenance Management Systems (CMMS). Sectoral applications in the automotive, aerospace, and food processing industries were closely examined, revealing that predictive maintenance, digital twin technologies, and AI-enabled diagnostics significantly enhance reliability, reduce downtime, and improve overall equipment effectiveness. Furthermore, the study identifies persistent research gaps related to contextual adaptation in small and medium enterprises (SMEs), integration challenges between traditional and digital systems, and the lack of standardized benchmarking methods for evaluating maintenance performance. The findings contribute to a better understanding of the strategic evolution of maintenance from a reactive, cost-centered function to a proactive, data-driven enabler of industrial resilience and sustainability. This review provides actionable insights for practitioners, researchers, and policymakers seeking to align maintenance strategies with Industry 4.0 objectives, while also calling for more inclusive and empirically validated approaches in future research.