One Meus Ginting Student Date Approved by: Dr

By

ONE MEUS GINTING 21951009

MASTER’S DEGREE in

MASTER OF INFORMATION TECHNOLOGY

ENGINEERING AND INFORMATION TECHNOLOGY FACULTY

SWISS GERMAN UNIVERSITY The Prominence Tower

Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia Revision after Thesis Defense on 27 January 2021

January 2021

One Meus Ginting STATEMENT BY THE AUTHOR

I hereby declare that this submission is my work and to the best of my knowledge, it contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgment is made in the thesis.

One Meus Ginting

Student Date

Approved by:

Dr. Eka Budiarto, S.T.,M.Sc.

Thesis Advisor Date

Dr. Bagus Mahawan, B. Eng., M. Eng.

Thesis Co-Advisor Date

Dr. Maulahikmah Galinium S.Kom., M.Sc.

Dean of Faculty of Engineering and Date

Information Technology

One Meus Ginting ABSTRACT

DEMAND FORECASTING IN FMCG COMPANY USING MACHINE LEARNING AND STATISTICAL ANALYSIS FOR INVENTORY CONTROL OPTIMIZATION

By

One Meus Ginting

Dr. Eka Budiarto, S.T.,M.Sc., Advisor Dr. Bagus Mahawan, B. Eng., M. Eng., Co Advisor

SWISS GERMAN UNIVERSITY

Challenges exacerbated by companies today are increasingly heavy, in particular, FMCG or Fast Moving Consumer Goods because of market shifting and consumer behavior always changing especially during a Covid 19 pandemic. Knowing how customer demand and behavior is key to success for FMCG Company, make inventory cost always low, and produce customer satisfaction and loyalty to the products. Consumer demand will drive a factory to have good production planning and make a company more competitive than the others because have a good supply chain, decreases loss of sales opportunity, and increase productivity. Since ERP implementation many reports were found that the sales and marketing department complained about the often unfulfilled consumer demand due to the absence of stock (out of stock), causing loss of potential sales (lost sales). This phenomenon appears to be suspected due to the ERP system's lack of customer demand forecasting as a production planning module. This research has compared the statistical analysis with machine learning based on time series consumer demand for 10 products to provide the best demand forecasting. The research using the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. Feature engineering involves technical variables like quantity demand and seasonality, fundamental economic like IHSG and USD to IDR exchange rates, and new features in demand forecasting at is pandemic awareness level. During the Covid 19 pandemic, the pandemic level of awareness of society affected to demand rate cause this variable is crucial to demand forecast. ANN is the best model of machine learning for demand forecasting tasks in an FMCG company, confirmed by consistently giving the smallest of MAPE. Demand forecasting using machine learning has been successful to increase supply chain performance by providing better production planning than before implementation. Enhancement of supply chain performance confirmed by decreased loses sales and increased inventory turnover ratio.

Keywords: FMCG, demand forecasting, production planning, statistical analytic, machine learning

One Meus Ginting

@Copy Right 2021 By One Meus Ginting

All Rights Reserved

One Meus Ginting DEDICATION

I would like to dedicate this research project to my lovely wife Rebecca Claudia Kiswara, my daughters Alika Gloria Dira Ginting and Aleeza Vania Dira Ginting, my Parents, and my

beloved country Indonesia.

One Meus Ginting ACKNOWLEDGEMENT

I would like to express my deepest gratitude to Dr. Eka Budiarto, S.T., M.Sc. and Dr. Bagus Mahawan, B. Eng., M. Eng. as my Thesis advisor for their support and guidance during my research project. I would like to thank my family, especially my wife, Rebecca Claudia for their moral supports throughout my pursuit of a master's degree in SGU. Finally, I would like to thank

all of my direct supervisors, my team at work, and my friends especially Mr. M. Ibnu Purwoko Batch 24 MIT SGU for their companionship, and to the countless number of people

who have helped me throughout this research project, either directly or indirectly

One Meus Ginting TABLE OF CONTENTS

Page

STATEMENT BY THE AUTHOR……… 2

ABSTRACT……….. 3

DEDICATION……….. 5

ACKNOWLEDGEMENT………... 6

TABLE OF CONTENTS……… 7

LIST OF FIGURES……… 10

LIST OF TABLES………... 11

1. INTRODUCTION……… 13

1.1 Background……….. 13

1.2 Research Problems………... 17

1.3 Research Objectives………. 18

1.4 Significance of the Study………. 18

1.5 Research Questions……….. 19

1.6 Hypothesis……… 19

1.7 Research Scope……… 19

1.8 Research Structure……… 19

2. LITERATURE REVIEW………... 21

2.1 Fast Moving Consumer Goods (FMCG)………. 21

2.1.1 Production Forecasting System……… 22

2.1.2 Production Planning………. 22

2.1.3 Lost Sales………. 23

2.1.4 Over Stock………... 23

One Meus Ginting

2.2.1 Time Series……….. 23

2.2.2 Linear Regression……… 24

2.3 Machine Learning……… 25

2.3.1 Random Forest………. 27

2.3.2 Artificial Neural Networks……….. 28

2.3.3 Support Vector machine……….. 30

2.4 Feature Engineering………. 30

2.4.1 Technical Feature………... 31

2.4.2 Fundamental Economic Feature……… 32

2.4.3 Pandemic Awareness Level ………. 32

2.5 Future Ranking………. 33

2.6 Cross-Industry Standard Process for Data Mining……….. 34

3. RESEARCH FRAMEWORK………. 34

3.1 Research Framework………... 34

3.1.1 Business Understanding………... 35

3.1.2 Data Understanding………. 36

3.1.3 Data Preparation……….. 37

3.1.4 Modelling……… 38

3.1.5 Evaluation……… 40

3.1.6 Deployment and Validation……… 40

4. RESULTS AND DISCUSSIONS……… 42

4.1 Experiment Setup………. 42

4.1.1 Hardware Setup……… 42

4.1.2 Software Setup………. 42

4.2 Research Result……… 43

4.2.1 Parameter Fine-Tuning……… 43

4.2.2 Feature Rank………. 44

4.2.3 Training Sets and Test Sets Result……… 45

4.2.4 Deployment Result……… 58

One Meus Ginting

5. CONCLUSIONS AND RECOMMENDATIONS………. 62

5.1 Conclusions………... 62

5.2 Recommendations………. 63

Appendix A. Dataset Information……….. 64

Appendix B. Orange 3.27 Model Canvas……….. 67

Appendix C. Training and Test GLWMIN in CS006………... 68

REFERENCES……….. 70

One Meus Ginting LIST OF FIGURES

Page

Figure 1.1 FMCG Products of PT Jaya Swarasa Agung………. 14

Figure 1.2 Lost Sales August 2019 – July 2020……….. 15

Figure 1.3 Inventory Moving Stock August 2019 – July 2020………... 15

Figure 1.4 Research Problems Fishbone Diagram……….. 17

Figure 2.1 Algorithm of Random Forrest for Regression ……….. 25

Figure 2.2 A Neural Network with Hidden Layers ……… 27

Figure 3.1 Phase of the CRISP-DM model………. 31

Figure 3.2 Data Preparation pipeline………... 35

Figure 3.3 Deployment Stage……….. 38

Figure 4.1 Tricks Training Sets………... 42

Figure 4.2 Tiles Training Sets………. 43

Figure 4.3 Domoo Long Training Sets……… 44

Figure 4.4 Domoo Corn Training Sets……… 45

Figure 4.5 Nitchi Rice Training Sets………... 47

Figure 4.6 Nitchi Pasta Training Sets……….. 48

Figure 4.7 Nitchi Mini Training Sets……….. 49

Figure 4.8 Nitchi Toples Training Sets………... 51

Figure 4.9 Wasuka Mini Training Sets……… 52

Figure 4.10 Wasuka Roll Training Sets……….. 53

One Meus Ginting LIST OF TABLES

Page

Table 2.1. Advantage of Statistic and Machine Learning Model……… 23

Table 4.1 Hardware Setup………... 39

Table 4.2 Software Setup……… 40

Table 4.3 Parameter fine-tuning each model……… 39

Table 4.4 Feature rank each a feature……… 40

Table 4.5 MAPE before and after fine-tuning………. 41

Table 4.6 MAPE Tricks Training Sets……… 42

Table 4.7 MAPE Tricks Test Sets………... 42

Table 4.8 MAPE Tiles Training Sets……….. 43

Table 4.9 MAPE Tiles Test Sets………. 43

Table 4.10 MAPE Domoo Long Training Sets……… 44

Table 4.11 MAPE Domoo Long Test Sets……… 45

Table 4.12 MAPE Domoo Corn Training Sets………... 46

Table 4.13 MAPE Domoo Corn Sets……….. 46

Table 4.14 MAPE Nitchi Rice Training Sets……….. 47

Table 4.15 MAPE Nitchi Rice Test Sets……… 47

Table 4.16 MAPE Nitchi Pasta Training Sets……… 48

Table 4.17 MAPE Nitchi Pasta Test Sets……… 48

Table 4.18 MAPE Nitchi MiniTraining Sets……….. 50

Table 4.19 MAPE Nitchi Mini Test Sets………. 50

Table 4.20 MAPE Nitchi Toples Training Sets………... 51

Table 4.21 MAPE Nitchi Toples Test Sets………. 51

Table 4.22 MAPE Wasuka Mini Training Sets………... 52

One Meus Ginting

Table 4.24 MAPE Wasuka Roll Training Sets……… 54

Table 4.25 MAPE Wasuka Roll Test Sets………... 54

Table 4.26 Loss Sales Q4 2020………... 54

Table 4.27 Turnover Ratio of 2020………. 55

Table 4.28 MAPE Compilations………. 55