Defining the Proper Model for Aviation Spare Parts Forecast
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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.
KeywordsDemand uncertainty Aftermarket Planning horizon Boosting ARIMA Exponential smoothing
- 1.Harris, F.W.: How many parts to make at once. Factory Mag. Manag. 10, 135–136, 152 (1913)Google Scholar
- 2.Wilson, R.H.: A scientific routine for stock control. Harvard Bus. Rev. 13(1), 116–128 (1934)Google Scholar
- 6.Bowersox, D.J., Closs, D.J., Cooper, M.B.: Supply Chain Logistics Management. Irwin and McGraw-Hill Higher Education, New York (2002)Google Scholar
- 7.Wdaa, A.S.: Hybrid artificial neural network and fuzzy logic for function approximation. J. Theor. Appl. Inf. Technol. 96(21), 7088–7097 (2018)Google Scholar
- 8.Murugan, A., Mylaraswamy, D., Xu, B., Dietrich, P.: Big data infrastructure for aviation data analytics. Published in IEEE International Conference on Cloud Computing in Emerging Markets (2014). https://doi.org/10.1109/ccem.2014.7015483
- 10.Goncalves, C., Kokkolaras, M.: Modeling the relationship between aviation original equipment manufacturers and maintenance, repair and overhaul enterprises from a product-service system perspective. In: Maier, A., Skec, Kim, H., Kokkolaras, M., Oehmen, J., Fadel, G., Salustri, F., Van der Loos, M. (eds.) DS 87-3 (ICED 17) Vol 3: Product, Services and Systems Design, Vancouver, Canada, 21–25 August 2017, pp. 389–398. McGill University, Montreal (2017)Google Scholar
- 14.Bala, P.K.: Purchase-driven classification for improved forecasting in spare parts inventory replenishment. Int. J. Comput. Appl. 10(9), 40–45 (2010)Google Scholar
- 15.Willmott, C., Matsuura, K.: Advantages of the Mean Absolute Error (MAE) Over the Root Mean Square Error (RMSE) in Assessing Average Model Performance. Center for Climatic Research, Department of Geography, University of Delaware, Newark (2005)Google Scholar
- 16.Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?—arguments against avoiding RMSE in the literature. NOAA Air Resources Laboratory (ARL), NOAA Center for Weather and Climate Prediction (2014)Google Scholar