A hierarchical cause-effect analysis of risk factors in the petrochemical industry, and their assessment using the cloud model approach

Document Type : Original Article

Authors

1 Ph.D. student in Operation Management and Decision Science Department, College of Management, University of Tehran, Tehran, Iran.

2 Professor of Technology and Innovation Management Department, College of Management, University of Tehran, Tehran, Iran.

3 Ph.D. graduate in of Technology and Innovation Management Department, College of Management, University of Tehran, Tehran, Iran.

Abstract

Introduction and Objectives: The petrochemical industry faces numerous risks due to its technical, economic, and environmental complexities, including financial challenges, sanctions, and social issues, which can lead to significant losses in project cost, time, quality, and scope. Effective risk management in this sector not only mitigates these losses but also plays a critical role in enhancing project efficiency and success. However, inadequate or incorrect risk management can result in project delays or reduced productivity. Thus, accurate risk identification and assessment are essential as the foundational steps in the risk management process. This study aims to provide a comprehensive framework for analyzing and evaluating risks in petrochemical projects. By focusing on mapping the hierarchical network of risk interactions and prioritizing them, the research seeks to develop a practical tool for improving risk management and preventing potential damages. The objective is to offer actionable insights for project managers to make informed decisions and manage risks effectively across various stages of the project lifecycle.
Research Methodology: This study adopts a positivist paradigm, designed as an applied and descriptive-survey research. Quantitative data were collected using structured questionnaires and credible documents from a target population comprising risk management experts and senior petrochemical managers with practical and theoretical experience in the field. A two-stage hybrid approach was employed for data analysis. In the first stage, the DEMATEL-ISM model was used to identify causal relationships between risks and construct their hierarchical structure, calculating metrics such as centrality (e.g., 4.39 for C1) and causality (e.g., 0.607 for C1) to determine the importance and causal role of each risk. In the second stage, a combination of FMEA (Failure Mode and Effects Analysis) and CM-TOPSIS was applied to quantitatively assess and rank risks based on factors like severity, occurrence, and detectability. The process was validated through expert judgment in two phases to ensure accuracy and reliability. This hybrid approach provided a deeper and more multidimensional perspective compared to traditional methods like FMEA alone.
Findings: The study yielded two key findings. First, DEMATEL-ISM analysis produced a hierarchical network illustrating the causal relationships among risks. This network revealed that risks such as “lack of attention to regional welfare and social issues” (C5) and “failure to document lessons learned” (C10) act as root causes, influencing higher-level risks like “financial challenges” (C1) and “equipment supply issues” (C9). Second, the FMEA-CM-TOPSIS assessment prioritized risks, identifying C1 (with a closeness coefficient of 0.758511) and C9 (CC of 0.562901) as the most critical risks requiring active management. Path analysis further showed that C5 and C10 exacerbate overall project risk by impacting intermediate factors (e.g., C1 and C9). Compared to traditional FMEA, this hybrid method offered greater precision and detail in analyzing interdependencies, enabling the identification of key vulnerabilities in the project structure.
Conclusion: By integrating DEMATEL-ISM and FMEA-CM-TOPSIS, this research presents an innovative framework for risk identification, analysis, and evaluation in petrochemical projects. The approach enhances the prioritization of critical risks and, through path analysis, enables data-driven decision-making for managers, ultimately reducing losses and boosting efficiency. Future research could explore the probability of risk occurrence using historical data and impact networks. Additionally, leveraging data mining to automate risk identification and prediction based on past industry patterns could further improve risk management processes. This study marks a significant step toward advancing risk management in the petrochemical sector, paving the way for more sophisticated future investigations.

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References

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