Abstract
Spare parts are very essential in most industrial companies. They are characterized by their large number and their high impact on the companies’ operations whenever needed. Therefore companies tend to analyze their spare parts demand and try to estimate their future consumption. Nevertheless, they face difficulties in figuring out an optimal forecasting method that deals with the lumpy and intermittent demand of spare parts. In this paper, we performed a comparison between five forecasting methods based on three statistical tools; Mean squared error (MSE), mean absolute deviation (MAD) and mean error (ME), where the results showed close performance for all the methods associated with their optimal parameters and the frequency of the spare part demand. Therefore, we proposed to compare all the methods based on the tracking signal with the objective of minimizing the average number of out of controls. This approach was tested in a comparative study at a local paper mill company. Our findings showed that the application of the tracking signal approach helps companies to better select the optimal forecasting method and reduce forecast errors.
Highlights
In industries that have a production system, the product goes through multiple processes and undergoes several machines in order to get the final product intended
The results show that the reliance on root mean squared error (RMSE) instead of mean absolute deviation (MAD) has small impact on the comparison between methods
Maintaining sufficient stocks of spare parts is essential for any organization in order to quickly carryout repair operations and prevent the stoppage of production operations
Summary
In industries that have a production system, the product goes through multiple processes and undergoes several machines in order to get the final product intended. Different quantitative and qualitative forecasting methods are applied to predict the future demand of spare parts in order to consistently ensure their availability. This research is motivated by the problem of forecasting spare parts demand at a local paper mill company where spare parts of different types are stocked to ensure their availability and to avoid any shortcuts in the production line when damages occur; the company tends to analyze its demand and try to estimate its future consumption roughly depending on experience. One of the approaches used in selecting a forecasting method from several ones is to choose the one with the best performance based on certain calculated performance measures Some of these performance measures that have been suggested in literature are: mean error (ME), mean squared error (MSE) and mean absolute deviation (MAD).
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