Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned
Process Safety Management (PSM) is essential for mitigating risks in high-hazard industries, yet many organizations encounter challenges in implementation. DEKRA’s 7-workstream PSM model streamlines the CCPS 20-pillar framework, covering procedural, technical, and cultural areas. Case studies of the...
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| Format: | Article |
| Language: | English |
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AIDIC Servizi S.r.l.
2025-06-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/15208 |
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| author | Zahra Basiri Andrea Gritti Leonardo Michele Carluccio |
| author_facet | Zahra Basiri Andrea Gritti Leonardo Michele Carluccio |
| author_sort | Zahra Basiri |
| collection | DOAJ |
| description | Process Safety Management (PSM) is essential for mitigating risks in high-hazard industries, yet many organizations encounter challenges in implementation. DEKRA’s 7-workstream PSM model streamlines the CCPS 20-pillar framework, covering procedural, technical, and cultural areas. Case studies of the DEKRA PSM model highlight common pitfalls, such as relying on irrelevant KPIs, ineffective risk assessment frameworks, and weak leadership and cultural management. Pre-implementation KPIs often focus on lagging indicators like incident reports and regulations, which address past issues rather than preventing future risks. This approach overlooks leading indicators, such as near-miss reporting and hazard identification, which are crucial for anticipating potential risks. Improper risk assessment frameworks often miss hazard prioritization, leading to regulatory non-compliance and inconsistent safety performance This can lead to regulatory non-compliance and an inability to enhance safety over time, increasing the risk of operational failures and incidents Effective PSM requires strong leadership, and resource allocation, and open communication to build a proactive safety culture, with psychological safety enabling employees to report risks without fear, and ensuring continuous improvement and risk reduction. Organizations must take a holistic approach to PSM to overcome these challenges, aligning KPIs with continuous risk reassessment and avoiding reliance on completed PSM recommendations as the sole measure of success. The increasing complexity of the process industry calls for incorporating Artificial Intelligence (AI) and machine learning, for accurate risk prediction and system effectiveness of PSM systems. |
| format | Article |
| id | doaj-art-c3abf75d6a344818ac45bbdf1c143db7 |
| institution | Kabale University |
| issn | 2283-9216 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | AIDIC Servizi S.r.l. |
| record_format | Article |
| series | Chemical Engineering Transactions |
| spelling | doaj-art-c3abf75d6a344818ac45bbdf1c143db72025-08-20T03:32:54ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162025-06-01116Common Pitfalls in Psm Assessment - Case Studies and Lessons LearnedZahra BasiriAndrea GrittiLeonardo Michele CarluccioProcess Safety Management (PSM) is essential for mitigating risks in high-hazard industries, yet many organizations encounter challenges in implementation. DEKRA’s 7-workstream PSM model streamlines the CCPS 20-pillar framework, covering procedural, technical, and cultural areas. Case studies of the DEKRA PSM model highlight common pitfalls, such as relying on irrelevant KPIs, ineffective risk assessment frameworks, and weak leadership and cultural management. Pre-implementation KPIs often focus on lagging indicators like incident reports and regulations, which address past issues rather than preventing future risks. This approach overlooks leading indicators, such as near-miss reporting and hazard identification, which are crucial for anticipating potential risks. Improper risk assessment frameworks often miss hazard prioritization, leading to regulatory non-compliance and inconsistent safety performance This can lead to regulatory non-compliance and an inability to enhance safety over time, increasing the risk of operational failures and incidents Effective PSM requires strong leadership, and resource allocation, and open communication to build a proactive safety culture, with psychological safety enabling employees to report risks without fear, and ensuring continuous improvement and risk reduction. Organizations must take a holistic approach to PSM to overcome these challenges, aligning KPIs with continuous risk reassessment and avoiding reliance on completed PSM recommendations as the sole measure of success. The increasing complexity of the process industry calls for incorporating Artificial Intelligence (AI) and machine learning, for accurate risk prediction and system effectiveness of PSM systems.https://www.cetjournal.it/index.php/cet/article/view/15208 |
| spellingShingle | Zahra Basiri Andrea Gritti Leonardo Michele Carluccio Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned Chemical Engineering Transactions |
| title | Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned |
| title_full | Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned |
| title_fullStr | Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned |
| title_full_unstemmed | Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned |
| title_short | Common Pitfalls in Psm Assessment - Case Studies and Lessons Learned |
| title_sort | common pitfalls in psm assessment case studies and lessons learned |
| url | https://www.cetjournal.it/index.php/cet/article/view/15208 |
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