Advanced Machine Performance Management Using OEE and Deep-Loss Analysis: A Framework for Saudi Manufacturing Competitiveness under Vision 2030

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@article{1723406,
      author = {Mostafa Medhat Ismail Hafez},
      title = {Advanced Machine Performance Management Using OEE and Deep-Loss Analysis: A Framework for Saudi Manufacturing Competitiveness under Vision 2030},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2749-2761},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723406.pdf},
      abstract = {Saudi Arabia’s manufacturing ambitions under Vision 2030 require factories to convert installed capacity into reliable, high-quality and resource-efficient output rather than depend mainly on additional capital equipment. Overall Equipment Effectiveness (OEE) remains one of the most widely used measures for exposing availability, performance and quality losses, yet the literature shows that an aggregate OEE score can conceal the mechanisms that actually create lost capacity [1–4]. This review develops an advanced machine performance management framework that combines OEE with deep-loss analysis, defined here as a structured decomposition of OEE losses into event, duration, frequency, causal, economic and operational layers. Thirty sources published from 2020 to 2025 were critically synthesized, covering OEE theory, total productive maintenance, Lean 4.0, predictive maintenance, digital twins, machine learning, real-time shop-floor monitoring and Saudi industrial transformation [5–30]. The review finds that effective performance management depends on three transitions: from periodic OEE reporting to trusted event-level data; from six-loss categorization to causal and value-weighted prioritization; and from isolated corrective actions to closed-loop learning supported by analytics and governance. The proposed framework links machine signals, standardized OEE calculation, loss trees, causal analysis, intervention portfolios and verification of sustained gains. It is tailored to Saudi manufacturing by connecting factory-level outcomes to productivity, localization, quality, resilience and advanced-manufacturing objectives under Vision 2030. and workforce capability development at scale.},
      keywords = {overall equipment effectiveness; deep-loss analysis; machine performance management; total productive maintenance; predictive maintenance; Lean 4.0; Saudi manufacturing; Vision 2030},
      month = {September},
  }