Risk Analysis Including Organizational Aspects In Process Industries
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The lack of management of organizational change has been found to be a contributing factor in a number of accidents. Proper management of organizational change considering the possible process safety incidents is essential to solve this problem. The key to successfully managing organizational change is to effectively assess the associated risks. In this research an Organizational Change Risk (OCR) model is developed for the purpose of a quantitative risk assessment that includes organizational change aspects. This model quantitatively assesses the effect of various organizational changes on the risk of process safety incidents and shows how and how much organizational change influences the risk. The OCR model starts from identifying the major incident scenario and developed Bow-Tie (BT) diagram which illustrate a scenario from causes to effects. Then Organizational Change Factors (OCFs), which show the status of change in an organization in comparison to its previous condition and determined by survey and indicator metric, are incorporated into the Bow-tie. Finally, the Bayesian Network (BN) model is applied for the sake of dynamic analysis to calculate the frequency of the incidents and consequences. In order to demonstrate the applicability, the model is tested using a hypothetical case of a company that made organizational change by divesting one of their plants to another company. The results of the case study show that the model is useful in identifying the organizational factors that predominantly affect the risk, quantifying the change of frequency of process safety incidents due to organizational change, evaluating the impact on the safety barriers that mitigate and prevent incidents, and determining how much organizational factors have to be improved to decrease process safety incidents.
Park, Younggil (2015). Risk Analysis Including Organizational Aspects In Process Industries. Master's thesis, Texas A & M University. Available electronically from