Decision Support Model for Fire Insurance Risk Analysis in a Petrochemical Case Study

Author(s):  
Hadis Z. Nejad ◽  
Reza Samizadeh

A decision support system was researched and applied to a case study in the petrochemical industry. The participants were an insurance company underwriting the policies of oil and gas refineries located in a major oil producing nation. The Chemical Process Quantitative Risk Analysis methodology was applied as a framework to implement uncertainty quantification and risk analysis using a specialized commercial DSS software product. A gas vapor explosion was simulated at an oil refinery, to predict the fire and radiation damage. Costs and risks were entered into the model based on historical data. Loss estimates were generated for equipment and buildings located various distances (pressures) from the explosion origin. Overall, the DSS model predicted an expected loss of over $14,000,000 USD for equipment located in the 50 meter explosion radius, which represented a loss ratio of almost 52%. The losses predicted from the DSS model were comparable to the literature and to experiences of the case study company. The margin of error from the DSS model was less than ±5% which made it very reliable according to benchmarks.

2013 ◽  
Vol 2 (1) ◽  
pp. 36-50
Author(s):  
Hadis Z. Nejad ◽  
Reza Samizadeh

A decision support system was researched and applied to a case study in the petrochemical industry. The participants were an insurance company underwriting the policies of oil and gas refineries located in a major oil producing nation. The Chemical Process Quantitative Risk Analysis methodology was applied as a framework to implement uncertainty quantification and risk analysis using a specialized commercial DSS software product. A gas vapor explosion was simulated at an oil refinery, to predict the fire and radiation damage. Costs and risks were entered into the model based on historical data. Loss estimates were generated for equipment and buildings located various distances (pressures) from the explosion origin. Overall, the DSS model predicted an expected loss of over $14,000,000 USD for equipment located in the 50 meter explosion radius, which represented a loss ratio of almost 52%. The losses predicted from the DSS model were comparable to the literature and to experiences of the case study company. The margin of error from the DSS model was less than ±5% which made it very reliable according to benchmarks.


Author(s):  
Leo Mršić

Chapter explains efficient ways of dealing with business problems of analyzing market environment and market trends under complex circumstances using heterogeneous data source. Under the assumption that used data can be expressed as time series, widely applicable multi variate model is explained together with case study in textile retail. This Chapter includes an overview of research conducted with a brief explanation of approaches and models available today. A widely applicable multi-variate decision support model is presented with advantages, limitations, and several variations for development. The explanation is based on textile retail case study with model wide range of possible applications in perspective. Complex business environment issues are simulated with explanation of several important global trends in textile retail in past seasons. Non-traditional approaches are revised as tools for a better understanding of modern market trends as well as references in relevant literature. A widely applicable multi-variate decision support model and its usage is presented through built stages and simulated. Model concept is based on specific time series transformation method in combination with Bayesian logic and Bayesian network as final business logic layer with front end interface built with open source Bayesian network tool. Explained case study provides one of the most challenging issue in textile retail: market trends seasonal/weather dependence. Separate outcomes for different scenario analysis approaches are presented on real life data from a textile retail chain located in Zagreb, Croatia. Chapter ends with a discussion about similar research’s, wide applicability of presented model with references for future research.


2021 ◽  
Vol 13 (5) ◽  
pp. 2832
Author(s):  
Yolandi Schoeman ◽  
Paul Oberholster ◽  
Vernon Somerset

The iron and steel industry is a major global industry that consumes vast quantities of energy and causes environmental degradation through greenhouse gas emissions and industrial waste generation, treatment, and disposal. There is a need to manage complex iron and steel industrial waste in Africa, which requires a system engineering approach to zero waste management as informed by multi-criteria decision-making. The purpose of the current study was to develop a hybrid four-step multi-criteria decision-support model, the i-ZEWATA (Industrial Zero Waste Tiered Analysis). I-ZEWATA acts as a road map to understand, design, assess, and evaluate the iron and steel industrial waste systems with the ultimate objective of moving towards and achieving a zero-waste footprint. The results demonstrate that iron and steel waste can be identified, visualized, prioritized, and managed to promote zero-waste by applying a system-engineered approach. Additionally, relationship patterns to environmental, social, operational, and economic aspects with system behavioral patterns and outcomes were identified. It was clear from the case study in South Africa that, although technology and solution investment is essential, waste management, valorization, and treatment components require a concerted effort to improve industrial waste operational management through effective zero-waste decision-support towards a circular economy.


2013 ◽  
Vol 8 ◽  
pp. 18-27 ◽  
Author(s):  
Kensuke Ishikawa ◽  
Naoko Hachiya ◽  
Tetsuya Aikoh ◽  
Yasushi Shoji ◽  
Katsuhiro Nishinari ◽  
...  

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