Forecasting commercial vehicle production using quantitative techniques - Badri Toppur; T. C. Thomas; Cristina Blanco González-Tejero; Sorin Gavrila Gavrila (Contemporary Economics, 2023)
Firms selling commercial vehicles often face difficulties due to recessions in the globalized economy. Manufacturers are keen to anticipate demand in future quarters to optimize their production schedules. In this study, commercial vehicle production data from a leading Indian automotive manufacturer were analyzed us- ing moving averages, exponential smoothing, seasonal decomposition and autoregressive integrated moving average (ARIMA) models with the goal of forecasting. The results reveal that the ARIMA (0,1,1) model effectively predicts the sectoral downturn coinciding with the global financial crisis of 2008. As life returns to normal after the financial crisis caused by COVID-19, such models may be used to strategically move past the disruption.
Keywords: C22; B23; G01; 330; demand forecasting; Python programming language; seasonal decomposition; Box-Jenkins methodology