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Multi-step sales forecasting in automotive industry based on structural relationship identification

  • Sa-ngasoongsong, Akkarapol
  • Bukkapatnam, Satish T.S.
  • Kim, Jaebeom
  • Iyer, Parameshwaran S.
  • Suresh, R.P.

Forecasting sales and demand over 6–24 month horizon is crucial for planning the production processes of automotive and other complex product industries (e.g., electronics and heavy equipment) where typical concept-to-release times are 12–60 month long. However, nonlinear and nonstationary evolution and dependencies with diverse macroeconomic variables hinder accurate long-term prediction of the future of automotive sales. In this paper, a structural relationship identification methodology that uses a battery of statistical unit root, weakly exogeneity, Granger-causality and cointegration tests, is presented to identify the dynamic couplings among automobile sales and economic indicators. Our empirical analysis indicates that automobile sales at segment levels have a long-run equilibrium relationship (cointegration) with identified economic indicators. A vector error correction model (VECM) of multi-segment automobile sales was estimated based on impulse response functions to quantify long-term impact of these economic indicators on sales. Comparisons of prediction accuracy demonstrate that VECM model outperforms other classical and advanced time-series techniques. The empirical results suggest that VECM can significantly improve prediction accuracy of automotive sales for 12-month ahead prediction in terms of RMSE (42.73%) and MAPE (42.25%), compared to the classical time series techniques.

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Article provided by Elsevier in its journal International Journal of Production Economics.

Volume (Year): 140 (2012)
Issue (Month): 2 ()
Pages: 875-887

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Handle: RePEc:eee:proeco:v:140:y:2012:i:2:p:875-887
Contact details of provider: Web page: http://www.elsevier.com/locate/ijpe

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