A reliable and ensemble forecasting model for slow-moving and repairable spare parts: Data mining approach

Reliable prediction of spare parts is one of the most prevailing challenges for the manufacturing industry and equipment owners, which acts as a double-edged sword. If the objective function focuses on the lack of spare parts, it will cause capital sleep, and otherwise, it would make spare parts shortage. Therefore, the accuracy and reliability of this prediction have always been consequential. Howbeit, the non-uniformity of the spare parts in the real world is remarkable. Some of these parts have intermittent uses (fast-moving type), and some have less and irregular consumption (slow-moving type). Due to this uneven structure of slow-moving spare parts, the application of classical methods has low reliability and performance in forecasting. Therefore, this study concentrated on predicting slow-moving spare parts by developing a reliable and ensemble data mining approach with considering the managerial characteristics (e.g., repair ability and the irregularity of maintenance such as MTBF and MTTR...) along with their history of consumption (the amount of each order and the time interval between two charges). In the proposed approach, the amount of spare parts and the time of the following order is predicted based on an ensemble data mining model from the prediction of future consumption and the proba-bility of scraping in the repair process. This simultaneous attention to data types (consumption and repairs) for slow-moving parts improves data adequacy. Whereas the focus of the literature has been more on improving the data mining models' performance than on improving data adequacy; by emphasizing both issues at the same time, we can get more reliable results in predicting parts requests with more fluctuations in consumption. A clustering management model for classifying spare parts has been presented during the data preparation process to implement this model. And 51,335 slow-moving spare parts of a Steel Company have been organized and predicted. The results of this study reveal that the application of the proposed approach and ensemble model significantly increases the reliability of spare parts inventory (accuracy by up to 80% and newly defined reli-ability (RI) by up to 10%).

Automated summary

Reliable prediction of spare parts demand is a major challenge for the manufacturing industry and equipment owners. This study focuses on predicting slow-moving spare parts by developing a reliable and ensemble data mining approach. The results show that this approach significantly increases the reliability of spare parts inventory.