A hybrid ANP and machine learning framework for contractor health safety environment and quality performance evaluation

Abstract Contractor-related deficiencies remain a critical challenge in workplace health and safety management within high-risk industries, particularly in the oil and gas sector, where heterogeneous safety practices and fragmented oversight mechanisms undermine effective risk control. Existing contractor evaluation approaches often rely on checklist-based or lagging indicators, offering limited ability to capture interdependencies among safety dimensions or to differentiate contractor performance in a meaningful and decision-relevant manner. This study develops and validates the Contractor Health, Safety, Environment, and Quality Performance Index (CHPI) as an integrated, evidence-based framework for systematic contractor performance evaluation. The study adopted a multi-method approach integrating expert judgment and archival compliance data. Health and safety indicators were identified through literature review and expert consultation, refined using Content Validity Index assessment, and weighted using a Fuzzy Analytic Network Process to capture interdependencies. The resulting weights were combined with standardized contractor records to compute CHPI scores. Robustness was confirmed through sensitivity analysis demonstrating stable contractor rankings, while a Random Forest-based analysis was used as a complementary validation to assess alignment between expert-based weights and data-driven importance. The results show that the CHPI enables nuanced differentiation of contractor performance, supports pre-contract screening and targeted intervention strategies, and enhances transparency in performance-based regulation. By integrating interdependency-aware weighting with empirical validation, the CHPI provides a scalable and adaptive decision-support tool that can strengthen contractor governance, improve safety performance monitoring, and support societal progress through more accountable risk management in high-risk industrial environments.