Governance and Explainability of Advanced Decision-Support Systems in Saudi Manufacturing: Integrating IFRS, Audit Controls and Management Accountability

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@article{1723405,
      author = {Muhammad Yasir Bashir},
      title = {Governance and Explainability of Advanced Decision-Support Systems in Saudi Manufacturing: Integrating IFRS, Audit Controls and Management Accountability},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2736-2748},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723405.pdf},
      abstract = {Advanced decision-support systems are increasingly embedded in forecasting, costing, inventory, procurement, maintenance, quality, and financial reporting across manufacturing. Their value, however, depends on whether recommendations can be understood, challenged, reconciled with accounting requirements, and assigned to accountable managers. This review develops an integrated governance framework for Saudi manufacturing that connects explainable artificial intelligence, International Financial Reporting Standards (IFRS), audit controls, and management accountability. Evidence published from 2020 to 2025 was synthesized across auditing, accounting information systems, responsible artificial intelligence, internal audit, financial reporting, and Saudi digital-transformation research. The literature indicates that predictive accuracy alone is insufficient in high-consequence decisions. Decision-support outputs require traceable data lineage, version-controlled models, explanations suited to user roles, control evidence, and explicit approval authority. Explainability is most useful when it clarifies influential drivers, uncertainty, alternative scenarios, and conditions under which a recommendation should not be followed. IFRS alignment requires an additional accounting-policy layer so that model outputs affecting inventory, impairment, provisions, useful lives, revenue, or estimates remain consistent with recognized accounting judgments and evidence. Auditability depends on reproducible inputs, logged overrides, access controls, validation, and retained documentation. The proposed framework therefore separates analytical recommendation from management authorization while linking both through a controlled evidence chain. For Saudi manufacturing, the framework supports Vision 2030 objectives by combining digital productivity with stronger reporting reliability, governance discipline, local analytical capability, and defensible executive decision-making.},
      keywords = {artificial intelligence; explainability; decision-support systems; IFRS; audit controls; management accountability; manufacturing; Saudi Arabia; Vision 2030; model governance},
      month = {September},
  }