Demand Forecasting Of Outbound Logistics Using Machine learning

Abstract: Background: long term volume forecasting is important for logistics service providers for planning their capacity and taking the strategic decisions. At present demand is estimated by using traditional methods of averaging techniques or with their own experiences which often contain some error. This study is focused on filling these gaps by using machine learning approaches. The sample data set is provided by the organization, which is the leading manufacturer of trucks, buses and construction equipment, the organization has customers from more than 190 markets and has production facilities in 18 countries. Objectives: This study is to investigate a suitable machine learning algorithm that can be used for forecasting demand of outbound distributed products and then evaluating the performance of the selected algorithms by experimenting to articulate the possibility of using long-term forecasting in transportation. Methods: primarily, a literature review was initiated to find a suitable machine learn- ing algorithm and then based on the results of the literature review an experiment is performed to evaluate the performance of the selected algorithms Results: Selected CNN, ANN and LSTM models are performing quite well But based on the type and amount of historical data that models were given to learn, models have a very slight difference in performance measures in terms of forecasting performance. Comparisons are made with different measures that are selected by the literature review Conclusions. This study examines the efficacy of using Convolutional Neural Networks (CNN) for performing demand forecasting of outbound distributed products at the country level. The methodology provided uses convolutions on historical loads. The output from the convolutional operation is supplied to fully connected layers together with other relevant data. The presented methodology was implemented on an organization data set of outbound distributed products per month. Results obtained from the CNN were compared to results obtained by Long Short Term Memories LSTM sequence-to-sequence (LSTM S2S) and Artificial Neural Networks (ANN) for the same dataset. Experimental results showed that the CNN outperformed LSTM while producing comparable results to the ANN. Further testing is needed to compare the performances of different deep learning architectures in outbound forecasting.