Developing a Model for Safety Risk Assessment under Uncertainty for the Manufacturing Industry: A Case Study of Pole Factory Hazards in Riyadh, Saudi Arabia. (19th March 2021) - Ahmed, Aref; Alkahtani, Mohammed; El-Tamimi, Abdulaziz M.; Kaid, Husam; Abidi, Mustufa Haider
Abstract : Many occupational injuries occur in the manufacturing industry due to hazardous events. The available studies and statistics on occupational safety in the Kingdom of Saudi Arabia demonstrate the need for improving the work environment by introducing effective techniques for analyzing and assessing safety risks to control the most hazardous events. This study aims to develop a general model for assessing safety risks by integrating Monte Carlo simulation (MCS) and fuzzy set theory (FST) to overcome the uncertainty and unavailability of data on the severity and likelihood of hazards. MCS uses the ModelRisk software for modeling hazards that exhibit randomness and uncertainty and have historical data. In contrast, FST uses a Matlab code to assess expert judgment about hazards featuring epistemic uncertainty or unavailable historical data. The Al-Babtain Pole Factory in Riyadh was selected as a case study in the manufacturing environment to prove the applicability and effectiveness of the developed model. From the 371 hazards identified using the Occupational Health and Safety Assessment Series 18001, only five were analyzed using the two model techniques. The likelihood and severity of these five hazards were collected and analyzed to obtain the risk levels. A list of hazards and their processing priorities were then produced. According to the risk values calculated using both techniques, Hazard5 was found to be the most hazardous event, followed by Hazard1 . The results of the proposed model demonstrated the distributions, statistics, percentiles, and risk limits for the selected hazards. These outputs support decision-making and increase the effectiveness and flexibility of safety risk assessments, which means that the proposed model is reliable and applicable for SRA under uncertainty and data unavailability in the manufacturing industry.