Nowadays airline margin and the margin of the maintenance providers for aircraft depend strongly on the possibility to reduce the time AC is on the ground due to maintenance reason. Industry statistics show that the ground time because of aircraft repair is influenced by the lack of a spare part needed to be installed. Research in this area shows that in Russia 70–75% of time aircraft spend on the ground due to this reason.
A focus-group survey of companies based in USA showed that according to almost 30% of Maintenance Repair Organisation representatives consider the key area to tackle is improving the accuracy of spare parts consumption forecast and optimizing supply chain management. This means that in order to increase the availability of aircraft the factor of spare parts delivery is of vital importance to reduce the airplane ground time and improve dispatch reliability.
Today in civil aviation there is no standard model or instrument to plan material requirements. Many companies use “so called” min/max planning. In reality, the accuracy of this model turns out to be insufficient and this leads either to spare parts deficit or to overstock at the warehouse.
This work aims to define where the problem in the accurate forecast is, what models exists and what are the best models that suits spare parts consumption prediction.
The result of this work is to define the model that will be more accurate compared to classical approach of forecast in condition of demand uncertainty. This action is supposed to increase significantly planning accuracy of Maintenance Repair Organisation or Airline company and help business to reduce costs.
Defining the Proper Model for Aviation Spare Parts Forecast
Lect. Notes in Networks, Syst.
International Conference on Reliability and Statistics in Transportation and Communication ; 2019 ; Riga, Latvia October 16, 2019 - October 19, 2019
2020-03-29
9 pages
Article/Chapter (Book)
Electronic Resource
English
Demand uncertainty , Aftermarket , Planning horizon , Boosting , ARIMA , Exponential smoothing Engineering , Computational Intelligence , Transportation Technology and Traffic Engineering , Engineering Economics, Organization, Logistics, Marketing , Data Mining and Knowledge Discovery , Applied Statistics
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