Master of Science in computer science February 2018 Demand Forecasting Of Outbound Logistics Using Machine learning Ashik Talupula Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in computer science . The thesis is equivalent to 20 weeks of full time studies. The authors declare that they are the sole authors of this thesis and that they have not used any sources other than those listed in the bibliography and identified as references. They further declare that they have not submitted this thesis at any other institution to obtain a degree. Contact Information: Author(s): Ashik Talupula E-mail:[email protected] University advisor: Dr. Hüseyin Kusetogullari Department of Computer Science Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE–371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 Abstract Background. long term volume forecasting is important for logistics service provider 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.Sample data set is provided by the organiza- tion, which is the leading manufacture of Trucks, buses and construction equipment, organization has a customers from more than 190 markets and has a production fa- cilities 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 eval- uating the performance of the selected algorithms by conducting an experiment to articulate the possibility of using long-term forecasting in transportation. Methods. primarily, literature review was initiated to find a suitable machine learn- ing algorithm and then based on the results of 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 Net- works (CNN) for performing demand forecasting of outbound distributed products at country level. The methodology provided uses convolutions on historical loads. The output from the convolutional operation is supplied to fully connected layers to- gether 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. Keywords: Demand forecasting , time series , outbound logistics, machine learning. i Acknowledgments First of all, I would like to thank my university supervisor, Dr. Hüseyin Kuse- togullari. He was always open when I ran into a trouble spot or had a question about my research or writing query. He always permitted this paper to be my own work, but steered me in the right adirection whenever he thought I required it. I would also like to thank my supervisor at Volvo Teja Yerneni for supporting me not only with the thesis part but also in motivating and collaborating with the team at Volvo. Finally, I must express my deep appreciation to my parents and to my friends for offering me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis. Without them, this achievement would not have been feasible. Thank you. ii Contents Abstract i Acknowledgments ii 1 Introduction 1 1.1 Problem Statement . 2 1.1.1 Aim . 3 1.1.2 Objectives . 3 1.1.3 Research Questions . 3 2 Related Work 4 2.1 Time series forecasting . 6 3 Preliminaries 7 3.1 Forecasting . 7 3.2 Time series . 7 3.2.1 Univariate . 7 3.2.2 Multivariate . 7 3.2.3 Components of time series . 8 3.3 Time series forecasting as a supervised problem . 9 3.3.1 Supervised learning . 9 3.3.2 Sliding window approach for time series data . 9 3.4 Artificial Neural Networks . 9 3.4.1 Activation Functions . 10 3.4.2 Recurrent Neural Networks . 12 3.4.3 LSTM . 13 3.4.4 CNN . 14 3.5 ARIMA . 14 3.6 SVR . 15 3.7 Multiple parallel input and Multi step output. 16 4 Method 18 4.1 Data gathering . 19 4.2 Data pre-processing . 19 4.3 Data set . 20 4.4 Experiment setup . 20 4.5 performance metrics . 21 4.6 Walk forward Validation . 21 iii 5 Results 23 5.1 Learning curve . 23 5.2 FORECASTS . 24 5.3 Forecasting Performance . 24 5.4 Validity Threats . 25 6 Analysis and Discussion 26 6.1 Implementation . 26 6.2 Discussion . 27 7 Conclusions and Future Work 28 References 29 A Supplemental Information 32 iv List of Figures 1.1 Outbound process . 2 2.1 Time series . 6 3.1 Univariate time series . 7 3.2 multivariate time series . 8 3.3 time series decomposition . 8 3.4 Time series data . 9 3.5 supervised problem . 9 3.6 single layer perceptron . 10 3.7 Multi layer perceptron . 10 3.8 Sigmoid . 11 3.9 Tan-h . 11 3.10 Relu . 12 3.11 Recurrent and feed forward networks structure . 12 3.12 LSTM Architecture . 13 3.13 support vector regressor . 16 3.14 multivariate time series . 16 3.15 Transformation of input and output from the above series . 17 4.1 Data set . 20 4.2 Walk forward validation . 22 5.1 LSTM training graph . 23 5.2 CNN training graph . 23 5.3 Actual vs forecast using CNN . 24 5.4 Actual vs forecast using CNN . 24 5.5 Models performances . 25 A.1 Distribution of residuals . 32 A.2 Actual vs forecast using LSTM . 32 A.3 Decomposition of Time series . 33 A.4 forecsat using LSTM . 33 A.5 forecast using LSTM . 34 v Chapter 1 Introduction A supply chain consists of all activities bounded with moving goods from raw materi- als to the consumer [35]. Sales and Order Planning(SOP) is responsible for planning and agreeing volume from all business units for the upcoming months on the first hand. Then it plays the role of communication of those volumes to operation plants and production logistics to plan supply chain activities[33]. Logistics is the process of distribution of goods from point of origin to point of consumption to meet consumer requirements. Inbound logistics refers to transport, storage, delivery of goods coming inside a business and outbound logistics refers to the same for goods going outside of a business[34]. The process starts, when a customer places an order by connecting to the sales department, the order is then processed by sales department and assigns it to the production plant. Sales office provides the customer with customer delivery date (CDD). CDD is provided if, and only if goods are directly transported to the customer location and it is specified as Available at Terminal Date (ATD) and Indi- cated Customer Delivery Date(I-CDD) if the goods will pass through terminal and noted as transfer. An ATD shows when the order ought to be at the terminal and be prepared to stack onto the following transport unit and an I-CDD. The business volume of logistics has a sustainable growth with the advancement of the economy and improved offline and online technology thus, efficient logistics demand prediction is needed to manage their processes in an organized manner[18] . Forecasting is the process of predicting the future, based on past or current data. Forecasting plays an important role in sales and operations planning for taking strate- gic and planning decisions. Forecasted values are just the projections, we don’t get the exact value we only try to reduce the error with the help of forecasting tools and more sophisticated models. One can easily forecast sales by using different fore- casting techniques like ARIMA[22], SVM [20], ANN [23][37], LSTM[13],CNN[15][30], etc. by having the details of previous sales record and accurate demand details. 1 Chapter 1. Introduction 2 Figure 1.1: Outbound process Forecasting on outbound distributed products lowers the cost of warehousing and transportation by optimizing the logistic process through consolidation, capacity planning and collaboration using a third-party logistics provider. The purpose of the Thesis is to forecast outbound distributed products of a manufacturing company that uses third party logistics (3PL) services for distribution of their products through Air, water and road transportation. Third party services include handling logistics such as warehousing, packaging, fulfillment and distribution. 1.1 Problem Statement Most of the logistics service providers faces several challenges in managing warehouse and distribution of products such as capacity planning, freight volume. So, there is a need to study outbound processes of a manufacturing company for developing a proper plan to overcome the challenges. Transportation is the major part of Logistics, where securing the capacity in carriers would be the most concerned issue for the logistics services especially international logistics.