Optimizing Environmental Inspection Operations on Industrial Facilities Through Data-Driven Risk Classification: A Case Study

This study introduces a practical example of developing a customized Risk-Based Inspection (RBI) framework and classification system for a group of concrete batching facilities. Using risk scores from the RiCHES-DCT screening tool, the framework categorizes facilities into four risk levels (extremely high, high, medium, and low). It proposes corresponding inspection frequencies (quarterly, semi-annual, annual, and bi-annual). The study emphasizes the need to tailor environmental inspection schedules to individual facility characteristics through regular risk assessments and continuous compliance monitoring. This dynamic approach optimizes inspections, resource allocation, and risk management strategies while providing general guidelines. The RBI classification system in this study, applied to six years of historical data from 21 concrete batching facilities, categorizes most facilities as Medium Risk with a wide score range (27.48 to 98.78) and a standard deviation of 18. The resulting visual representation provides a clear view of risk distribution and holds promise for enhancing compliance across various industrial sectors. The findings pave the way for future research on applying RBI systems in different domains, applying machine learning and AI, assessing system effectiveness, and developing customized inspection schedules. Ultimately, the proposed framework and classification system place the groundwork for an effective risk-based inspection and management strategy in the cement and cement products sector, promoting resource efficiency and overall compliance.