Conceptualizing a Lean?Standardized Predictive Maintenance Framework for High?Vacuum Deposition Equipment

rather than empirical evidence, and the balance of our support is drawn from adjacent IJEMT domains (rotating machinery, semiconductor FDC, aerospace prognostics) rather than from deposition-equipment-specific PdM studies, of which relatively few exist [48]. 8.3 Future work These limitations define a clear research agenda. The most important next step is empirical validation: case studies and, ideally, controlled or quasi-experimental deployments of LSPdM on real deposition tools, measuring the proposed KPI families before and after adoption. Such studies should test the paper’s central proposition—that integrating standardized work with prognostics yields better availability and quality than either alone—and quantify the cost of the governance overhead. Second, methodological work is needed on prognostics tailored to vacuum-specific signals (pump-down dynamics, RGA spectra, reactive-mode electrical signatures), for which the generic machinery of vibration-based bearing prognostics is only partially transferable. Third, the digital-twin layer invites development of hybrid physics-plus-data models of vacuum systems that could improve interpretability and mitigate data scarcity through simulation-based augmentation [37], [39]. Fourth, transfer learning across tool fleets and recipes deserves study as a route to overcoming per-tool data scarcity. Fifth, the socio-technical mechanisms of the feedback loop— how to design operator labeling and trust-calibration so that humans and models genuinely co- improve—warrant investigation drawing on operations-management and human-factors methods. Finally, formalizing the governance/change-control process that keeps standardized work and versioned analytics synchronized is a concrete, high-value target for design-science research. 9. Conclusion High-vacuum deposition equipment sits at the intersection of extreme economic consequence and physically intricate, condition-dependent failure. The maintenance community has developed two powerful but largely separate answers to keeping such equipment running: the lean/TPM tradition, which excels at standardized work, human ownership, visual management, and continuous improvement but times intervention by interval rather than condition; and the predictive/PHM tradition, which forecasts degradation with increasing sophistication but frequently under-specifies the standardized, governed human action needed to turn a forecast into avoided downtime. This paper has argued that these traditions are complementary and has proposed the Lean-Standardized Predictive Maintenance framework to integrate them, resolving their underlying standardization-versus-adaptation tension through a single architectural principle: standardize the decision architecture and the human procedures, while explicitly versioning and governing the adaptive analytics. Grounded in a failure-mode-and-effects synthesis of vacuum pumps, seals, leaks, gas delivery, sources, and contamination, the framework couples a five-layer architecture— sensing, versioned analytics, a standardized decision/trigger layer, digital work instructions with visual management, and a kaizen-driven governance layer—with explicit roles, data flows, a phased roadmap, a maturity model, and a balanced KPI set spanning OEE, MTBF, and RUL accuracy. 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