Integrating fuzzy logic and house risk method for operational risk management in automotive industry

Abstract

Risk management is very important for all organizations and nowadays companies need to be aware of the risks that endanger their operational activities. The object of this research is a motorcycle component manufacturing company that has not yet fully identified its operational risks and designed risk management even though these risks affect the company’s performance. Therefore, the purpose of this study is to identify operational risks and design preventive actions. This study combines fuzzy logic with the house of risk (HOR) model which consists of two phases. The first phase determines the dominant risk agents and the second phase determines the effective actions to deal with them. From the results, there are 22 risk events and 28 risk agents with fifteen dominant risk agents such as human error, data misinformation, and others. Twelve preventive actions were also obtained, such as improving communication both internally and externally and others.