METHODS FOR FORECASTING DEMAND IN REPAIRING AN AIRLINE’S REPAIRABLE LINE REPLACEABLE UNIT PARTS
South African Journal of Industrial Engineering, ISSN: 2224-7890, Vol: 33, Issue: 4, Page: 60-80
2022
- 16Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Captures16
- Readers16
- 16
Article Description
Scattered failure frequency, variable and complex influencing factors, and a low accuracy in predicting inventory demand are characteristics of line replaceable unit (LRU) parts. Some high-priced repairable LRU (HR-LRU) parts have a considerable impact on the cost of aircraft spare parts.This study presents procedures to identify the optimal model for forecasting the demand for HR-LRU parts. First, a traditional prediction model, seven single measurement models, and four combined models were selected and used to predict failure data. Subsequently, evaluating indexes were selected for assessment to obtain the optimal model. Finally, we compared the actual and predicted values to verify the conclusions drawn during the previous evaluation step. The results indicated that, among the single models, the negative binomial regression model and the Holt-Winters model were most suitable for HR-LRU parts. The SSE (sum of squares error) and MAE (mean absolute error) of the negative binomial regression were the lowest at 118.4114 and 1.97352 respectively, and the Holt-Winters model’s MAE was the lowest at 1. 13270. The IOWA operator prediction model and the error reciprocal variable weight combination method produced predictions closest to the actual values among the combined models. In addition to constructing a set of processes to prediction, we also discuss the fit of different methods, the reasons for the change in the guaranteed rate, and the reasons for the occurrence of special years. We also compare the similarities and differences between this article and other papers.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85143841716&origin=inward; http://dx.doi.org/10.7166/33-4-2734; http://sajie.journals.ac.za/pub/article/view/2734; http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2224-78902022000400006&lng=en&tlng=en; http://www.scielo.org.za/scielo.php?script=sci_abstract&pid=S2224-78902022000400006&lng=en&tlng=en; http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2224-78902022000400006; http://www.scielo.org.za/scielo.php?script=sci_abstract&pid=S2224-78902022000400006; https://dx.doi.org/10.7166/33-4-2734
Stellenbosch University
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