AI-Assisted Project Monitoring in Manufacturing Systems: Enhancing Operational Performance and Risk Management

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@article{1720066,
      author = {Mushtaq Quader Sharoz},
      title = {AI-Assisted Project Monitoring in Manufacturing Systems: Enhancing Operational Performance and Risk Management},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {2599-2608},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720066.pdf},
      abstract = {AI-assisted project monitoring is a governance capability, not a dashboarding technology. This distinction drives the Closed-Loop Operational Intelligence and Manufacturing Governance Architecture (CLOIMGA): a five-stage framework positioning AI monitoring as a closed governance cycle connecting sensing, prediction, prioritization, action, and learning. The paper synthesises peer-reviewed literature published between 2020 and 2025 and integrates implementation evidence from a regional FMCG organization deploying Power BI operational dashboards across a multi-site production network in the IMEA region. Before deployment, monthly reporting consumed seven hours of manual effort, audit preparation required two weeks, and approval cycles averaged 23 days; within twelve months, reporting effort fell to under one hour, audit preparation to two days, approval cycle time by roughly 40 per cent, and forecast accuracy improved by approximately 25 per cent. The implementation produced two findings not previously documented with this specificity: AI analytics can detect governance quality failures, including approval-threshold gaming invisible to conventional review; and the data architecture built at deployment becomes the foundation for progressively more capable AI governance in future cycles. A five-level operational intelligence maturity model, a four-phase adoption roadmap, and a manufacturing use-case illustration are provided. The measure of success for AI-assisted monitoring is not model sophistication but governance outcomes: better decisions, fewer disruptions, safer work, and more predictable delivery.},
      keywords = {AI-Assisted Monitoring, CLOIMGA, Manufacturing Governance, Operational Intelligence, Predictive Analytics, Industry 4.0, Power BI, Governance Maturity, Vision 2030},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1720066}
  }