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