IMPROVING THE PREDICTION AND ACCURACY of PARTS MARKETING PROMOTION PROGRAM FOR HEAVY EQUIPMENT SPARE PARTS BUSINESS
THROUGH DIGITALIZATION APPROACH
By
ANANG WAHYU WIBOWO 2-1952-024
MASTER’S DEGREE in
MASTER OF MECHANICAL ENGINEERING 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 Defence on 2nd February 2021
Anang Wahyu Wibowo 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.
Anang Wahyu Wibowo
_____________________________________________
Student Date
Approved by:
Dr. Ir. Gembong Baskoro, M.Sc
_____________________________________________
Thesis Advisor Date
Dr. Eng. Sumarsono Sudarto, ST,MT
_____________________________________ ________
Thesis Co-Advisor Date
Dr. Maulahimah Galinium, S.Kom, M.Sc
_____________________________________________
Dean Faculty of Engineering & Information Technology
Date
Anang Wahyu Wibowo ABSTRACT
IMPROVING THE PREDICTION AND ACCURACY of PARTS MARKETING PROMOTION PROGRAM FOR HEAVY EQUIPMENT SPARE PARTS BUSINESS
THROUGH DIGITALIZATION APPROACH By
Anang Wahyu Wibowo
Dr. Ir. Gembong Baskoro, M.Sc, Advisor Dr. Eng Sumarsono Sudarto, ST, MT Co-Advisor
SWISS GERMAN UNIVERSITY
In the face of changes in customer policy that are very fast due to changing market conditions, a promotional approach and a customer approach are also needed that are in accordance with existing conditions. This also occurs in the field of heavy equipment spare parts trading, where the spare parts offered and promoted to the customer must be in accordance with what the customer needs. It often happens now that what is trying to promote to customers is not in accordance with the needs due to the condition of the unit that is not operational or has even been standardized. In this research, the author tries to propose a prediction method for customer spare parts needs with a digital technology approach, where the purpose of this research is so that parts salesmen can easily find out customer needs quickly and continue with promotions to increase sales. In this research, the Author used a Focus Group Discussion (FGD) with the team and DMAIC analysis in finding the necessary improvements. The findings of this research are that by using a historical demand and sales approach and analyzed with a machine learning algorithm, it will obtain a prediction of the opportunity needed, besides that the Author also uses the End of Life ( EOL) Spare Parts approach to predict when there will be a change. With these two approaches, it can be used effectively in predicting the needs of heavy equipment spare parts.
Keywords: Machine Learning, Parts Prediction, Heavy Equipment Parts, Opportunity Prediction. Parts Sales Opportunity
Anang Wahyu Wibowo
© Copyright 2021 by Anang Wahyu Wibowo
All rights reserved
Anang Wahyu Wibowo DEDICATION
I dedicate this thesis to my beloved company, developing a method of selling heavy equipment spare parts in the future, more agile in seeing customer policies and better adapting to existing market changes, and I also dedicate to Swiss German University education to increase research, especially in the heavy equipment industry.
Anang Wahyu Wibowo ACKNOWLEDGEMENTS
I thank all the lecturers who have guided during the lecture, especially to Dr. Gembong Baskoro M.Sc as advisor and Dr. Eng Sumarsono Sudarto, ST,MT as co advisor so that this thesis can be completed well. Thank you also to the Transaction Sales Marketing (TSM) project team from both the Customer Support and Sales Department (CSSD) and the Differentiation and Digitalization (DAD) team who are closely related to this thesis. Thank you also to your beloved Family who always give encouragement in completing this Thesis.
Anang Wahyu Wibowo TABLE OF CONTENTS
Page
STATEMENT BY THE AUTHOR ... 2
ABSTRACT... 3
DEDICATION ... 5
ACKNOWLEDGEMEN TS ... 6
TABLE OF CONTENTS ... 7
LIST OF FIGURES ... 9
LIST OF TABLES ... 11
CHAPTER 1 - INTRODUCTION ... 12
1.1 Background ... 12
1.1.1 Macro Condition on Coal Mining and Impact to Heavy Equipment Industry ... 12
1.1.2 Heavy Equipment Parts Business ... 14
1.1.3 Winning Ratio and Close Lose Analysis ... 17
1.1.4 Interrelationship Diagram of Parts Sales Problem... 22
1.1.5 Clustering Customer ... 23
1.1.6 Competitor Analysis of Heavy Equipment Parts... 26
1.2 Research Problem ... 28
1.3 Research Question... 29
1.4 Research Objective ... 29
1.5 Expected Result... 29
1.6 Scope and Limitation ... 30
CHAPTER 2 – LITERATURE REVIEW ... 31
2.1 Big Data Analytic Can Created Business Value ... 31
2.2 Customer Behavior Base on Importance Analysis ... 32
2.3 Customer Transaction Prediction System ... 33
2.4 Predicting Customer Demand for Remanufacture Product ... 34
2.5 Forecast Demand Parts with Maintenance Policy Approach... 35
2.6 Predict Demand Parts by Install Base Information ... 36
2.7 Heavy Equipment Spare parts Prediction to Create Opportunity Sales... 38
Anang Wahyu Wibowo
CHAPTER 3 – RESEARCH METHODS ... 39
CHAPTER 4 – RESULT and DISCUSSION ... 41
4.1 Big Data Preparation ... 41
4.1.1Data Demand and Sales ... 41
4.1.2Interchange Parts Number ... 44
4.1.3Machine Operating Hour... 46
4.1.4Customer Clustering... 48
4.1.4.1 Clustering Method ... 50
4.2 Measure of Objective Research ... 51
4.3 Data Analysis ... 52
4.3.1Machine Learning ... 52
4.3.2XG Boost Machine Learning Concept ... 58
4.3.3Undercarriage Management System ... 59
4.3.4Result Summary ... 62
CHAPTER 5 – CONCLUSSION AND SUGGESTION ... 64
GLOSSARY ... 66
REFERENCES ... 68
CURRICULUM VITAE ... 70
Anang Wahyu Wibowo LIST OF FIGURES
Figures Page
1.1 Thermal coal price 2015-2019 ... 13
1.2 Total of sales unit in quantity... 13
1.3 Total of sale unit in ammount ... 14
1.4 Parts sales by product in IDR 2015-2019 ... 15
1.5 Parts sales by Commodity in IDR 2015-2019 ... 16
1.6 Komatsu parts commodity ... 16
1.7 Winning Ratio of Pars by Branch 2015-2019 ... 18
1.8 Winning Ratio of Parts by Site 2015-2019 ... 21
1.9. Close Loss analysis of spare parts ... 22
1.10 Interrelationship diagram of parts problem... 23
1.11 Clustering Customer ... 24
1.12 Sales Contribution by Clustering Customer ... 25
1.13 Competitor Genuine Komatsu Parts ... 26
1.14 Flow Process Promotion Approach to Customer ... 29
2.1 Illustrtion of PLC of Machine, Install base evolution... 36
2.2 Sketch of product sales, Install Base dan spare parts demand ... 38
3.1 Research of Frame Work ... 40
4.1 Data Demand analysis... 42
4.2 Interchange Parts Number... 45
4.3 Unit Operation Data 1 ... 47
4.4 Unit Operation data 2 ... 48
Anang Wahyu Wibowo
4.5 Clustering Customer Analysis ... 49
4.6 Clustering Customer Flow Diagram ... 50
4.7 Objective of Machine Learnig Project ... 51
4.8 Result of Algorithm Test for Demand Parts Heavy Equipment ... 57
4.9 XG Boost Machine Learning ... 58
4.10 Flow Chart XG Boost Machine Learning ... 59
4.11 Machine Learning Performance Control... 62
4.12 Close Won Sales Machine Learning ... 62
4.13 Undercarriage Management System ... 60
4.14 Undercarriage Prediction Concept ... 61
Anang Wahyu Wibowo LIST OF TABLES
Table Page
1.1 Main Competitor Activity Promotion ... 27
2.1 Comparison of Bank Customer Prediction with LR, ANN, XG Boost ... 34
4.1 Sample of Clustering Customer ... 49
4.7 Result of Algorithm Test ... 57
4.8 Data Feature for Machine Learning ... 60
