EXPLAINABLE MACHINE LEARNING FOR PREDICTIVE RISK ASSESSMENT AND OPERATIONAL PERFORMANCE OPTIMIZATION IN INDUSTRIAL ENTERPRISES

This study proposes a conceptual framework for integrating explainable machine learning with predictive risk assessment and operational performance optimization in industrial enterprises. The objective is to connect operational-risk prediction, model interpretability, financial exposure estimation, and resource-constrained decision-making within a unified analytical process. The proposed methodology comprises six stages: conceptual enterprise data specification, data preparation and temporal alignment, predictive risk and performance modeling, Shapley Additive explanations (SHAP)-based interpretation, expected financial exposure estimation, and constrained multi-objective resource allocation. Conceptual data encompass production output, equipment utilization, maintenance records, quality indicators, workforce availability, inventory status, and operational costs. The framework illustrates how predicted event probabilities and conditional event costs can inform intervention prioritization while accounting for budget, labor, inventory, safety, and service constraints. Using illustrative conceptual data, a hypothetical operational unit with an 18% predicted event probability and a conditional event cost of 24,000 currency units yields an expected exposure of 4,320 currency units. This exposure estimate demonstrates the proposed calculation rather than an observed improvement. Unlike a conventional baseline-versus-after evaluation, the study does not report empirical performance gains or validated intervention effects. Its practical significance lies in providing a transparent, reproducible decision-support structure for future industrial validation.