A comparative assessment of domino accident analysis methods in process industries using LMAW and DNMA techniques

Abstract

Investigating domino incidents in process industries is critical for enhancing safety and preventing cascading failures with potentially severe consequences. A review of existing accident investigative methods underscores the need for appropriate criteria and methodologies, ensuring comprehensive analysis, and thus effective prevention possibility. This study addresses this need by evaluating, comparing, and ranking the effectiveness of various investigative methods. Ranking techniques of alternatives are many and show a steady improvement trend. This research applies the latest: the Logarithmic Methodology of Additive Weights (LMAW) to assign importance weights to relevant criteria. It then utilizes the Double Normalization-Based Multiple Aggregation (DNMA) technique to evaluate and rank the methods. Based on comparisons and sensitivity analysis this dual approach ensures a robust and objective assessment of the methods used in accident analysis. The criteria of applicability, accuracy, and comprehensiveness were given the highest weights based on expert judgments. AcciMap emerged as the most effective among the methods assessed, demonstrating superior performance in various aspects of accident analysis, followed by CAST and FRAM. AcciMap achieved the top ranking, exhibiting the highest overall effectiveness. These findings offer guidance for selecting accident analysis methods, aiding managers and safety practitioners in process industries. By leveraging these insights, organizations can make informed decisions on the most suitable methods for investigating domino incidents, thereby improving safety measures and response strategies.