Spare parts failure prediction of an automobile under criticality condition

Abstract

Many studies on spare parts planning classified items based on the levels of importance using conventional approaches. Classification of spare parts based on the stated approach without considering failure value and/or its consequence may not withstand the test of time due to continuing technological advancement or environmental degradation. This study solved the stated problem by developing a system that is capable of dynamically determining critical equipment/spare parts based on failure rates using ABC analysis. In this analysis, all operable items were considered to be non-critical and they became critical when they approached failure time. These transitions were prompted by items’ failure conditional probability within the limits of 1, 2/3, 1/3 for highly critical, critical and less critical items, respectively. The most critical item(s) (A class) with highest failure value/consequence were sorted out based on specificity (one manufacturer’s item) and generality (many manufacturers’ item). Failure remedy was achieved by applying modified classical inventory model which considered heterogeneity in item failure. The stated conditions were integrated into a time series, linear regression model. The performance evaluation results showed that the new scheme was efficient in spare part failure criticality classification, consequence analysis and remedy. The practical implication of the findings indicated that the developed system could serve as a suitable alternative to the static classification style of the conventional approach in term of cost savings.