Forecasting demand is a critical issue for driving efficient operations in a manufacturing firm. Due to this reason firms are concerned to plan their operations and strive to improve their forecasting methods for having an edge over the competitors in market. The purpose of this paper is to evaluate various shrinkage methods for data containing large numbers of features. Here we focus on Class 8 Group 2 North America Heavy Duty (NAHD) market and macroeconomic indicators from ACT research economic database to forecast full 3 months out shipment of engines. Various pre-processing techniques are applied on all the variables and then they are further decomposed by applying Seasonal and Trend decomposition using Loess (STL) into its components (trend, seasonality and remainder). Then for each pre-processing technique the decomposition is analysed visually. After this the relative significance of the variance associated to each decomposed component is utilized to select the appropriate pre-processing technique for all the variables in order to ensure their stationarity for reliable forecasting accuracy. We applied several statistical as well as machine learning methods and obtained an ensemble of them to have minimal error in forecasting. It is also noticed that there is hardly any increase in accuracy when the number of features is increased beyond 15. Following are the few important R packages that were used in our analysis: forecast, forecastHybrid, tseries, readxl, xts, quantmod, e1071, lars.