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Sales Forecasting of Truck Components Using Neural Networks

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Sales Forecasting of Truck Components Using Neural Networks Master of Science in Computer Science February 2020 Sales Forecasting of Truck Components using Neural Networks Yeshwanth Reddy Gaddam 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): Yeshwanth Reddy Gaddam E-mail: [email protected] 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: 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 fore- cast sales with the selected machine learning algorithms and to evaluate the perfor- mances 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 perfor- mances of the chosen models. Results: Based on the literature review Multilayer Perceptron (MLP), Recurrent Neural 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. Keywords: Sales forecasting, Artificial Neural Networks, Long Short Term Memory, Multilayer Perceptron, Recurrent Neural Network. Acknowledgments I would like to convey my gratitude to my supervisor Dr. Hüseyin Kusetogullari for his guidance and constructive suggestions. I could not have completed this research study without my supervisor’s constant support and encouragement. I would also like to extend my gratitude to my manager and supervisor at Volvo Group, Alain Boone and Nina Xiangni Chang for supporting me with my Thesis work. ii Contents Abstract i Acknowledgments ii 1 Introduction 1 1.1 Problem Description ........................... 2 1.2 Aim and Objectives ............................ 2 1.3 Research Questions ............................ 3 2 Background 4 2.1 Time-series Forecasting .......................... 4 2.1.1 Univariate Time Series ...................... 4 2.1.2 Multivariate Time Series ..................... 5 2.2 Model Selection .............................. 5 2.3 Forecasting Models ............................ 6 2.3.1 Artificial Neural Network ..................... 6 2.3.2 Multilayer Perceptron ...................... 7 2.3.3 Recurrent Neural Network .................... 7 2.3.4 Long Short Term Memory .................... 8 3 Related Work 10 4 Method 13 4.1 Literature Review ............................. 13 4.2 Experiment ................................ 13 4.2.1 Experimental Setup ........................ 14 4.2.2 Dataset .............................. 14 4.2.3 Data Preprocessing ........................ 14 4.2.4 Sliding Window .......................... 14 4.2.5 Kwiatkowski, Phillips, Schmidt and Shin (KPSS) Test .... 15 4.2.6 Normalization ........................... 16 4.2.7 Feature Importance ........................ 16 4.2.8 Walk Forward Validation ..................... 17 4.2.9 Performance Metrics ....................... 18 5 Results 20 5.1 Stationarity Test ............................. 20 5.2 Learning curve .............................. 20 iii 5.3 Forecasts .................................. 22 5.3.1 Multilayer Perceptron ...................... 22 5.3.2 Recurrent Neural Network .................... 23 5.3.3 Long Short Term Memory .................... 24 6 Analysis and Discussion 25 6.1 Analysis of Experiment results ...................... 25 6.1.1 Performance analysis using Mean Absolute Error ....... 25 6.1.2 Performance analysis using Root Mean Square Error ..... 25 6.1.3 Key Findings ........................... 26 6.1.4 Discussion ............................. 26 6.2 Limitations ................................ 27 6.3 Validity threats .............................. 27 6.3.1 Internal validity .......................... 27 6.3.2 Extenal validity .......................... 27 6.3.3 Conclusion validity ........................ 28 7 Conclusions and Future Work 29 7.1FutureWork................................ 29 References 30 A Supplemental Information 34 iv List of Figures 2.1 Univariate Time Series .......................... 5 2.2 Multivariate Time Series ......................... 5 2.3 Multilayer Perceptron ........................... 7 2.4 Recurrent Neural Network ........................ 8 2.5 Long Short Term Memory ........................ 9 4.1SlidingWindow.............................. 15 4.2 Normalization ............................... 16 4.3 Random forest Feature importance ................... 17 4.4 Walk Forward Validation ......................... 18 5.1 Kwiatkowski, Phillips, Schmidt and Shin Test ............. 20 5.2 Learning curve .............................. 21 5.3 MLP forecasts ............................... 22 5.4 RNN forecasts ............................... 23 5.5 LSTM forecasts .............................. 24 6.1 Mean Absolute Error of machine Learning models using walk forward validation ................................. 25 6.2 Root Mean Square Error of machine Learning models using walk for- ward validation .............................. 26 v List of Tables 3.1 Short Summary of the literature review results ............. 12 5.1 Multilayer Perceptron algorithm performance ............. 22 5.2 Recurrent Neural Network algorithm performance ........... 23 5.3 Long Short Term Memory algorithm performance ........... 24 6.1 Comparison of performance evaluation results ............. 26 vi Chapter 1 Introduction The advancement of technology has compelled major organizations to undertake a Data-driven decision making approach to make decisions by collecting and analyzing large amounts of information [1]. Retail industries have to consider many factors such as logistics, cost of material, cost of labor, customer demand which influence the manufacturing process. The above factors are complicated functions which when forecasted accurately helps organizations to strategically plan and increase their mar- ket share. However, inaccurate predictions can result in excessive storage or a short- age of goods [2]. This has lead the organizations to excessively invest in constructing data driven models which help organizations to take their decisions based on the information rather than intuition or observations. These data driven models provide reasonable insights towards future trends which in turn help to make proactive mea- sures. The present day situation showcases clients being ephemeral or short lived by switch- ing between competitors that satisfy their demands more effectively [3]. This puts pressure on the organizations to meet the customer requirements or lose the market share to other competitors that meet the customer demands more effectively. The ability to forecast accurately is one of the important factors in supply chain manage- ment for planning and decision making. Sales Forecasting plays an important role in any organization to identify the sales trend of the products for the future period [4]. These forecasts are also important for determining the profitable retail operations to meet customer demand, customer service, maintain storage levels and to identify probable losses. Sales forecasting is a complicated task because of various factors such as diversity of user’s demands, competitors, environmental factors and so on affect the process and has to be taken into consideration for good forecasts [5]. Research suggests that Artificial Neural Networks (ANN) can capture nonlinear re- lationships in the underlying dataset [6, 7, 8]. ANNs are also found suitable for building data-driven models to generate predictions using time series data. A lot of research has been going on over the last decade on the usage of Artificial Neural Network algorithms such as Multi-Layer perceptron and Recurrent Neural Networks, Long Short Term Memory for sales forecasting. [2]. 1 Chapter 1. Introduction 2 In this thesis, Artificial Neural Network algorithms are used to forecast the sales of Volvo Truck components which consist of the engine brake, front rims, instrument cluster display, Wheelbase, Tank cover, auxiliary radiator, and so on. The forecasts obtained from these models can be
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