SUPPLY AND DEMAND ANALYSIS BY COMPARISON OF FORECASTING METHOD IN MANUFACTURING MOTORCYCLES TIRES
By
Ananda Tri Rizki 21952018
MASTER’S DEGREE In
MASTER OF MECHANICAL ENGINEERING - ENGINEERING MANAGEMENT FACULTY OF ENGINEERING AND INFORMATION TECHNOLOGY
SWISS GERMAN UNIVERSITY The Prominence Tower
Jalan Jalur Sutera Barat No. 15, Alam Sutera Tangerang, Banten 15143 - Indonesia
February 2021
Revision after Thesis Defense on January 25th, 2021
Ananda Tri Rizki STATEMENT BY THE AUTHOR
I hereby declare that this submission is my own 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 acknowledgement is made in the thesis.
Already Signed by Mr. Ananda Tri Rizki
Ananda Tri Rizki
_____________________________________________
Student Date
Revision after Thesis Defense on January 25th, 2021 Approved by:
APPROVED
Dr. Ir. Gembong Baskoro, M.Sc.
_____________________________________________
Thesis Advisor
Date
APPROVED
Dr. Ita Mariza, M.M.
_____________________________________________
Thesis Co-Advisor Date
Dr. Maulahikmah Galinium, S.Kom., M.Sc.
_____________________________________________
Dean Date
Ananda Tri Rizki ABSTRACT
SUPPLY AND DEMAND ANALYSIS BY COMPARISON OF FORECASTING METHOD IN MANUFACTURING MOTORCYCLES TIRES
By
Ananda Tri Rizki
Dr. Ir. Gembong Baskoro, M.Sc., Advisor Dr. Ita Mariza, M.M., Co-Advisor
SWISS GERMAN UNIVERSITY
As one of the motorcycle tire manufacturing industries in Indonesia, it is faced with uncertainty of capacity and resources, due to the mismatch of forecasts causing an increase in inventory to 2,207,537 tires, the highest number in the last five years. The purpose of this paper is to research to analyze differences in sales forecast, demand, supply, and production from January 2015 to February 2020, then measure the error rate of demand data using POM for Windows with the Naïve Method (NM), Moving Average (MA), Weighted Moving Average (WMA), Exponential Smoothing (ES), Exponential Smoothing with Trends (ESWT), Regression/Trend Analysis (R/TA), and Multiplication Decomposition (MD Seasonal). The lowest error measurement results using the Multiplicative Decomposition (MD Seasonal) method have a Mean Absolute Deviation (MAD) of 303,577 and a Mean Absolute Percentage Error (MAPE) of 14.15%. Using the Multiplicative Decomposition (MD Seasonal) method, demand forecast had been obtained as a reference for capacity planning such as machine resources and manpower planning, so that there were reduced production from 80,000 pcs/day to 60,000 pcs/day and makes stock inventory decrease to 757,997 pcs.
Keywords: Forecast, Demand, Supply, Inventory, POM.
Ananda Tri Rizki
© Copyright 2021 by Ananda Tri Rizki
All rights reserved
Ananda Tri Rizki DEDICATION
I dedicate this thesis to: my family, my colleagues,
the company where I make a living, the Swiss German University where I studied, and the Unitary State of the Republic of Indonesia.
Ananda Tri Rizki ACKNOWLEDGEMENTS
A big thank you. The author would like to convey and say profusely to:
1. Allah Subhanallahu Wa Ta'ala, because it is with help that this thesis can be completed on time.
2. Bunda Ana, Hafiz, Zahra and Azri as the writer's wife, children and all of my big family who understand the existing conditions and provide continuous support to the author so that this thesis can be completed on time.
3. Management and all leaders who have given me the opportunity to continue my education from Bachelor to Master Degree.
4. Mr. Petrus Heri Pranoto as Plant Head and Mr. Reza Widhiarto as Department Head who have provided attention and support.
5. Mr. Dr. Ir. Gembong Baskoro, M.Sc., as the Director of Strategic Development and Cooperation - Swiss German University as well as the Main Advisor.
6. Mrs. Dr. Ita Mariza, M.M., as the Director of the Gajah Tunggal Polytechnic as well as the Companion Advisor.
7. Mr. Dr. Maulahikmah Galinium, S.Kom., M.Sc., as Dean of the Faculty of Engineering and Information Technology at Swiss German University.
8. Mrs. Anis Choirunnisa, S.T., M.Kom. and Mrs. Siti Ayu Diana Lestari, as a facilitator between Lecturers and Students, who always provides support, direction and all forms of assistance to us as students in various matters.
9. All fellow students who are always enthusiastic and support each other at all times.
10. All parties who have helped the preparation of this research.
I also apologize if there are deficiencies or mistakes that have been done either intentionally or unintentionally.
Tangerang, February 2021
Author Ananda Tri Rizki
21952018
Ananda Tri Rizki TABLE OF CONTENTS
Page
STATEMENT BY THE AUTHOR ... 2
ABSTRACT ... 3
DEDICATION ... 5
ACKNOWLEDGEMENTS ... 6
LIST OF FIGURES ... 9
LIST OF TABLES ... 11
CHAPTER 1 - INTRODUCTION ... 12
1.1 Background ... 12
1.2 Research Problem ... 14
1.3 Research Objective ... 14
1.4 Significance of Study ... 14
1.5 Research Questions ... 14
1.6 Scope and Limitation ... 15
CHAPTER 2 - LITERATURE REVIEW ... 16
2.1 Literature Review ... 16
2.2 Sales Forecast (SF) ... 17
Forecasting Techniques ... 18
Qualitative Forecasting Techniques ... 21
Quantitative Forecasting Techniques ... 22
Forecasting Verification and Control ... 25
Checking the Reliability of the Forecasting Model ... 26
2.3 POM for Windows ... 27
2.4 Capacity Planning ... 28
CHAPTER 3 - RESEARCH METHODS ... 29
3.1 Research Framework ... 29
Problem Identification ... 30
Problem Researches ... 30
Literature and Literature Studies ... 30
Data Collection ... 30
Make Demand Prediction Calculations ... 31
Ananda Tri Rizki
Calculating Capacity Planning ... 31
Results and Analysis ... 32
Recommendation ... 32
3.2 Time Line ... 32
3.3 Types and Sources of Data ... 33
3.4 Data Collecting ... 33
Sales Forecast ... 33
Demand ... 35
Supply ... 36
Production ... 37
Inventory Stock ... 38
Machine and Manpower Capacity ... 39
CHAPTER 4 - RESULTS AND DISCUSSIONS... 40
4.1 Movements Demand ... 40
4.2 Demand Forecasting ... 41
Demand with Naive Method ... 41
Demand with Moving Average Method... 42
Demand with Weighted Moving Averages Method ... 46
Demand with Exponential Smoothing Method ... 46
Demand with Exponential Smoothing with Trend Method ... 49
Demand with Regression/Trend Analysis ... 51
Demand with Multiplicative Decomposition (Seasonal) ... 52
Recommended Demand Forecast ... 53
4.3 Demand Forecast Result ... 53
4.4 Resource Planning ... 55
CHAPTER 5 - CONCLUSIONS AND RECOMMENDATIONS ... 57
5.1 Conclusions ... 57
5.2 Recommendations ... 57
REFERENCES ... 59
CURRICULUM VITAE ... 61
Ananda Tri Rizki LIST OF FIGURES
Figures Page
Figure 1.1 Trend Sales Forecast, Demand, Supply, and Stock 2015 to Feb, 2020 ... 13
Figure 1.2 Stock Finish Good Tire in Warehouse form Jan, 2015 to Feb, 2020 ... 13
Figure 2.1 Horizontal Data Patterns ... 19
Figure 2.2 Seasonal Data Pattern ... 19
Figure 2.3 Cycle Data Pattern ... 20
Figure 2.4 Trend Data Patterns ... 20
Figure 3.1 Research Framework. ... 29
Figure 3.2 Sales Forecast from January 2015 to February 2020. ... 34
Figure 3.3 Demand from January 2015 to February 2020. ... 35
Figure 3.4 Supply from January 2015 to February 2020. ... 36
Figure 3.5 Supply from January 2015 to February 2020. ... 38
Figure 3.6 Inventory Stock from January 2015 to February 2020. ... 39
Figure 3.7 Machine and Manpower Capacity ... 39
Figure 4.1 Trend of Sales Forecast, Demand, Supply, Production and Stock ... 40
Figure 4.2 Result Naive Method Demand ... 41
Figure 4.3 Graph Naive Method Demand ... 41
Figure 4.4 Demand with Moving Average ... 42
Figure 4.5 Result Moving Averages Demand 3 months ... 44
Figure 4.6 Graph Moving Averages Demand 3 months ... 44
Figure 4.7 Track Signal Moving Averages Demand 3 Months ... 45
Figure 4.8 Input Weighted Moving Averages Demand ... 46
Figure 4.9 Result Weighted Moving Averages Demand ... 46
Figure 4.10 Errors as a Function of α ... 47
Figure 4.11 Demand with Exponential Smoothing α: 0,21 ... 48
Ananda Tri Rizki
Figure 4.12 Result Demand with Exponential Smoothing α: 0,21 ... 49
Figure 4.13 Graph Demand with Exponential Smoothing α: 0,21 ... 49
Figure 4.14 Demand with Exponential Smoothing with Trend ... 50
Figure 4.15 Result Demand with Exponential Smoothing with Trend ... 51
Figure 4.16 Graph Demand with Exponential Smoothing with Trend ... 51
Figure 4.17 Demand with Regression/Trend Analysis ... 52
Figure 4.18 Demand with Multiplicative Decomposition (Seasonal)... 52
Figure 4.19 Demand Forecast with Multiplicative Decomposition (Seasonal) ... 54
Figure 4.20 Graph Demand Forecast ... 54
Figure 4.21 Trend of Sales Forecast, Demand, Supply, Production and Stock From January 2015 to November 2020 ... 55
Figure 4.22 Resource Planning. ... 56
Ananda Tri Rizki LIST OF TABLES
Table Page
Table 2.1 Example of a Tracking Signal from a Forecasting Model ... 26
Table 3.2 Time Line Activity ... 32
Table 3.3 Sales Forecast ... 34
Table 3.4 Demand ... 35
Table 3.5 Supply ... 36
Table 3.6 Production ... 37
Table 3.7 Inventory Stock ... 38
Table 4.1 Error Demand... 40
Table 4.2 Recommended Demand Forecast ... 53
Table 4.3 Result Demand Forecast ... 54
Table 4.4 Demand Forecast ... 55
