Because of long product development cycles, effective production planning of automobiles requires accurate demand forecasting in order to effectively managing resources and maximizing revenue. Errors in demand forecasts have often led to enormous costs and loss of revenue due to suboptimal utilization of resources. Since early 2000 India has been the largest manufacturer and consumer of farm tractors in the world. This paper develops multiplicative seasonal autoregressive integrated moving average (MSARIMA) and autoregressive moving average model with exogenous variable (ARMAX) to forecast monthly demand for farm tractor. The result indicates that ARMAX with real agriculture credit has found to be outperformed MSARIMA model in forecasting demand of farm tractors in the horizon of six months. The accurate monthly forecasting of farm tractor would help the manufacturers for better raw material, inventory and supply chain management. Keywords

Gross domestic product (GDP) is one of the key economic variables observed to assess the country’s overall economy. India is the third largest economy in the world but lagging in quality and timeliness of GDP reporting.1 The USA, UK, Euro zone and most of the developed countries have been providing better quality and timely information on GDP. Nowcasting is defined as estimation of very recent past, the immediate present and the very recent future (Giannone, Reichlin, & Small, 2008). Much of the work on GDP nowcasting uses pseudo real time data, whereas our research work has used a real time dataset for both the dependent and independent variables for nowcasting Indian GDP in real time. However, the real time datasets have issues of data revisions and biases, which have been handled in this article using a factor modelling approach with bridge model and vector auto regression model. We also explore the impact of within quarter new information flow and this will provide an opportunity to improve the nowcasting accuracy by using the most recent information.

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