Prevision and optimisation of repairable spare parts: A case study in the petroleum industry

Abstract

This study was motivated by the high costs incurred by an energy company for repairable spare parts for faulty mission-critical items, particularly with those that operate until failure. The objective of this paper is to develop and apply a method for repairable spare part inventory management of run-to-failure equipment. To achieve a robust method that incorporates the data collected from previous failures, such as environmental factors and operating conditions, we propose an optimisation approach based on an accelerated failure time model. Accelerated failure time is used as a reliability regression model with covariates to describe different operational conditions. An algorithm is also developed to consider the repairable nature of the equipment, predicting the number of spare parts based on the expected number of failures in the period and the equipment repair cycle. The proposed method is applied using data from three different power units of electrical submersible pumps, a mission-critical item in oil production. The results show an average reduction of 60.6 per cent in the required number of spare parts, considering an average fill rate of 95.33 per cent. This reduction implies an estimated annual savings of around US$664,720 in inventory costs, considering the analysed units.