Sales Forecasting of Truck Components using Neural Networks

Abstract: Background: Sales Forecasting plays a substantial role in identifying the sales trends of products for the future era in any organization. These forecasts are also important for determining the profitable retail operations to meet customer demand, maintain storage levels and to identify probable losses. Objectives: This study is to investigate appropriate machine learning algorithms for forecasting the sales of truck components and then conduct experiments to forecast sales with the selected machine learning algorithms and to evaluate the performances of the models using performance metrics obtained from the literature review. Methods: Initially, a literature review is performed to identify machine learning methods suitable for forecasting the sales of truck components and then based on the results obtained, several experiments were conducted to evaluate the performances of the chosen models. Results: Based on the literature review Multilayer Perceptron (MLP), RecurrentNeural Network (RNN) and Long Short Term Memory (LSTM) have been selected for forecasting the sales of truck components and results from the experiments showed that LSTM performed well compared to MLP and RNN for predicting sales. Conclusions: From this research, It can be stated that LSTM can model com-plex nonlinear functions compared to MLP and RNN for the chosen dataset. Hence, LSTM is chosen as the ideal model for predicting sales of truck components.