ArticleFrontiers in public health2026
Decision support framework for prioritizing labor protection measures to enhance workplace safety and compliance in Industry 4.0 environments.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Ensuring adequate labor protection in Industry 4.0 environments has become increasingly complex due to the integration of automation, cyber-physical systems, smart sensors, and digital safety technologies. The selection of appropriate labor protection measures requires a systematic and transparent decision-support framework capable of handling multiple, often conflicting, evaluation criteria. Methods: This study proposes a comprehensive multi-criteria decision-making (MCDM) framework for prioritizing labor protection measures. Four objective weighting techniques, Entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), Method based on the Removal Effects of Criteria (MEREC), and Criteria Impact Loss on System (CILOS), were integrated and fused using the Bonferroni aggregation operator. The Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was employed as the primary ranking approach. Comparative validation was performed using TOPSIS, VIKOR, EDAS, WASPAS, and PROMETHEE-II. Robustness assessment included rank correlation analysis (Kendall's tau and Spearman's rho), one-at-a-time (OAT) sensitivity analysis, consensus ranking through Borda and Copeland rules, and a Stability Index evaluation. Results: The results consistently identify Personal Protective Equipment (PPE) as the most preferred labor protection measure, achieving the highest MARCOS utility value (0.7124) and demonstrating strong robustness underweight perturbation scenarios. Administrative Controls ranked second, exhibiting exceptional stability across sensitivity analyses. Safety Training Programs demonstrated competitive performance but moderate sensitivity to variations in weight. High inter-method agreement was observed, with Kendall's tau (t) and Spearman's rho (r) values exceeding 0.80, confirming ranking consistency across MCDM techniques. Discussion: The proposed integrated MCDM framework provides a robust, objective, and reproducible approach for prioritizing labor protection measures in Industry 4.0 workplaces. By combining multiple objective weighting schemes with compromise-based ranking and comprehensive stability assessment, the model enhances decision transparency and reliability. The framework supports evidence-based formulation and strategic implementation of safety policies in digitally transformed industrial environments.
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