Integrating Lean, ISO 50001, and Industry 4.0 Analytics for Industrial Energy Management: A Systematic Review of KPIs, Governance, and Implementation Factors

Abstract

Aims: Effective industrial energy management (IEM) has become a strategic requirement for industrial companies because of rising and fluctuating energy costs, stricter environmental regulations and increasing competitive pressure. Although interest is growing in the integration of Lean/continuous improvement (CI), ISO 50001 energy management systems (EnMS) and Industry 4.0/AI analytics, the evidence base remains fragmented, with inconsistent key performance indicators (KPIs), limited governance models and weak evidence of sustained improvement. Study Design: Systematic review. Methodology: This systematic literature review synthesised academic and professional evidence across these three domains. It applied predefined inclusion and exclusion criteria focused on industrial environments and measurable outcomes, following PRISMA 2020 guidance (Page et al., 2021). Searches were conducted in Web of Science, Scopus, IEEE Xplore and Google Scholar for publications from 2000 to 2025, supplemented by practitioner sources from ISO, IEA, DOE and UNIDO. In total, 23 studies met all inclusion criteria after title/abstract and full-text screening. Results: A framework-based data extraction approach was used to capture Lean/LSS practices, ISO 50001 PDCA elements, analytics capability types and KPI measurement structures. The synthesis produced three main outputs: (i) an integration taxonomy linking AI/Industry 4.0 analytics capabilities with PDCA governance processes and Lean execution mechanisms; (ii) a KPI framework and dictionary designed to address measurement inconsistency; and (iii) a checklist and operating model outlining minimum organisational requirements and boundary conditions for energy-performance implementation. The review also identified continuing inconsistencies in integration specificity, data interoperability, sustainment evidence and KPI comparability. Conclusion: Collectively, these outputs provide a unified reference model for designing, assessing and maintaining analytics-supported energy management systems in industrial and manufacturing contexts. They should be interpreted as synthesis-based outputs that require empirical validation across diverse manufacturing sectors.