Risk-cost model for FMEA approach through Genetic algorithms – A case study in automotive industry

Abstract

Failure mode and effects analysis (FMEA) is a proactive method for eliminating the potential failures emerged from various systems such as products, processes, designs, or systems. FMEA utilizes the risk priority number (RPN) to determine the risk priority order of failure modes. RPN is calculated by multiplying the values of three risk factors: severity (S), detection (D) and occurrence (O) Although FMEA techniques are used in different industries in many situations when multiple experts give their opinions about one failure mode, the risk evaluations can be vague and imprecise, which could arise conflicting evidence that is hard to manage. This paper presents a risk-cost analysis model that uses Genetic algorithms to generate an FMEA for the raw materials and finished parts reception process, in automotive industry. Unlike the classic FMEA analysis, in our model the risk factors D and O are determined by resources cost analysis involved in their improvement. Here comes the Genetic algorithm that will determine the optimal cost under an acceptable risk. The proposed solution uses modern information technology for data acquisition (complex event processing), automation of analysis process (artificial intelligence) and long-term support for quality staff in FMEA analysis (real time and batch data analytics).