A Hybrid Fine-Kinney and Entropy-Weighted TOPSIS Approach for Risk Prioritization in Chemistry Laboratories

Abstract

The Fine-Kinney method, commonly used in Occupational Health and Safety (OHS), is prone to subjective and inconsistent prioritization since it weights the three parameters of probability (P), exposure (E), and consequence (C) equally and based on expert judgment. In this study, by integrating the Entropy Weighting Method (EWM) and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) into the Fine-Kinney method, a systematic analysis framework is presented in which the relative importance of the parameters is determined statistically and produces more balanced results. The study consisted of nine stages: laboratory selection and scope definition, hazard classification and identification with a checklist, selection of relevant hazards for chemistry laboratories, risk scoring with Fine-Kinney, criteria weighting with EWM, risk prioritization with TOPSIS, statistical validation and correlation analysis, sensitivity analysis with seven weighting scenarios, and interpretation of results. The findings of EWM showed that the “C” parameter was the most effective criterion (C, w_j = 0.4214). Sensitivity analysis revealed that rankings remained largely consistent and the method’s susceptibility to subjectivity decreased. The top priority hazards were grouped under the “Fire-Explosion-Emergencies (FIREX)” category; hazards coded H30 and H31 were among the top-10 hazards in all scenarios (100%), while H29 was among the top-10 in 86% of scenarios. This study demonstrates that the Fine-Kinney approach, built on subjective data, can be made more reliable and repeatable with EWM-based TOPSIS, and adds methodological depth to the decision support process in laboratory safety.