A FIS Risk Analysis Approach as a Tool to Prioritize Risks

In this work, a Fuzzy Inference System (FIS) as a risk analysis tool was developed to prioritize risks. The developed FIS risk analysis tool was applied to assess the risks in a drug dispensing process in a pharmacy, and also in a chemical process industry. The tool uses Bow-tie analysis and fuzzy concepts to analyze and prioritize a risk by computing its score. The risks, risk factors, and impacts were identified based on reported events and expert's knowledge. Bow-tie analysis was used to determine the factors that cause the occurrence of a risk and the impacts of a risk. Fuzzy estimates for the risk factors and impacts were obtained from the expert. The developed Mamdani FIS was used to compute the risk score. Based on the risk score, the risks were prioritised. Proper mitigation plans are suggested to control the risk events based on their risk score. For comparison, Failure Mode Effects Analysis (FMEA) was applied on the drug dispensing process and on chemical process industry. The results from the application of FMEA method agrees with the results obtained from the application of the proposed FIS risk analysis tool in both cases. The advantages of the developed FIS risk analysis tool over FMEA are: FIS risk analysis tool considers risk factors and impacts of a risk event to calculate its risk score whereas FMEA does not consider them. In some cases FMEA assigns same Risk Priority Number (RPN) to different risks irrespective of the severity, occurrence and detect ability scores. But FIS risk analysis tool gives a unique score to the risk event based on the expert's knowledge. Recent studies show a method, lean fuzzy bow-tie analysis method on a chemical industry. In order to verify the developed FIS risk analysis tool, its results were compared against lean fuzzy bow-tie analysis method results on chemical industry and the results were similar. The advantage of FIS risk analysis tool over lean fuzzy bow-tie analysis is that the developed tool reduces the computational effort and assigns a unique score to the risk event rather than assigning a range (example: high, medium, low) to the risk.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Industrial Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2016

Author dc:creator

• Mettu, Salini

Advisors dc:contributor.advisor

• Mayorga, Rene
• Peng, Wei

Committee members dc:contributor.committeemember

• Stilling, Denise
• Hussein, Esam

Language dc:language.iso

en

DOI

https://doi.org/10.82465/3864

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/7693