Forecasting Spare Parts Demand Using Statistical Analysis
| Autor: | Shadi Masoud, Mazen Arafeh, Raghad Hemeimat, Lina Al-Qatawneh |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Consumption (economics)
021103 operations research Operations research Mean squared error Computer science Exponential smoothing 0211 other engineering and technologies General Engineering 02 engineering and technology Absolute deviation Moving average Spare part 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Statistical analysis Tracking signal |
| Zdroj: | American Journal of Operations Research. :113-120 |
| ISSN: | 2160-8849 2160-8830 |
| DOI: | 10.4236/ajor.2016.62014 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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