Spare parts forecast of consumable and expendable materials in aviation industry is very challenging and is not very precise and accurate. As statistics shows, airline companies that have less than 100 aircraft in their fleet or maintenance organization that do less than 100 heavy maintenance checks during the year have more than 80% of spares they have on stock that refer to slow moving stock. And as material with little transaction history is involved then all the companies worldwide have difficulties to predict demand with high level of accuracy.
One of the reasons the forecast is not precise is that all the companies are trying to forecast spares based on the history of transactions and future insights are hardly used. This results either in elevated expenses or in underdelivery of spares parts that affects strongly the turnaround time of aircraft on maintenance.
This work reveals the correlation between aircraft quantity planned for maintenance and consumable spare consumption required for heavy types of repairs. As well by means of established correlation this paper defines the approach to make existing prediction models of spare parts more precise and define the algorithm to make the forecast more precise. Improvement achieved and the results on real data of existing aircraft maintenance organization are represented and shown in this paper.
Finding solution to this problem is very relevant because all the aircraft repair organizations never have stable demand for periodical maintenance works.
Precising the Accuracy of Aviation Spare Parts Forecast
Lect. Notes in Networks, Syst.
International Conference on Reliability and Statistics in Transportation and Communication ; 2020 ; Riga, Latvia October 14, 2020 - October 17, 2020
Reliability and Statistics in Transportation and Communication ; Chapter : 19 ; 205-212
2021-02-07
8 pages
Article/Chapter (Book)
Electronic Resource
English
Aircraft planning , Maintenace fast movers , Intermittent demand , Demand uncertainty , Aftermarket , Bootstrap , Slow moving parts , Sporadic Engineering , Computational Intelligence , Transportation Technology and Traffic Engineering , Operations Research/Decision Theory , Communications Engineering, Networks , Data Mining and Knowledge Discovery
Defining the Proper Model for Aviation Spare Parts Forecast
Springer Verlag | 2020
|Defining the Proper Model for Aviation Spare Parts Forecast
TIBKAT | 2020
|Engineering Index Backfile | 1944
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