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研究生： Rifa Intania
Rifa Intania
論文名稱： Optimized System Dynamics Model of A Local Fashion Brand in Indonesia by Combining Leading and Lagging Indicators: An Initial Study
Optimized System Dynamics Model of A Local Fashion Brand in Indonesia by Combining Leading and Lagging Indicators: An Initial Study
指導教授： 歐陽超
Chao Ou-Yang
口試委員: 王孔政
Kung-Jeng Wang
郭人介
Ren-Jieh Kuo
學位類別： 碩士
Master
系所名稱： 管理學院 - 工業管理系
Department of Industrial Management
論文出版年： 2018
畢業學年度： 106
語文別： 英文
論文頁數： 122
中文關鍵詞： System Dynamics 、Genetic Algorithm 、Performance Measurement System 、Leading Indicators 、Lagging Indicators
外文關鍵詞： System Dynamics, Genetic Algorithm, Performance Measurement System, Leading Indicators, Lagging Indicators
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    • Nowadays fierce competition pushes any company to make decision faster and more accurate since any market changes can be more unpredictable. There then rises need to realize leading indicators of company performance to quickly evaluate the ongoing or planned strategies in a company; since it will give the managers to manage some decisions more accurately, fit, and reliable to the actual competition condition. This study proposes two new metrics as the leading indicators of company performance, namely advertising ‘reach’ and endorsement ‘likes’; and utilize best combination of marketing-mix elements to generate the highest profit for the company, with the profit being the lagging indicator of company performance. By using system dynamic approach and genetic algorithm to generate the highest profit of the company, this study will capture and utilize leading and lagging indicators that could be used to evaluate the performance of a company. This study uses the data from a small fashion company in Bandung, Indonesia. The result of the simulation that the higher the effectiveness of advertising and endorsement programs, the more they benefit the company in terms of Sales Volume and Number of Customers; while the higher the number of ‘reach’ and ‘likes’ will result in the bigger number of people aware of the existence of the brand.

    Nowadays fierce competition pushes any company to make decision faster and more accurate since any market changes can be more unpredictable. There then rises need to realize leading indicators of company performance to quickly evaluate the ongoing or planned strategies in a company; since it will give the managers to manage some decisions more accurately, fit, and reliable to the actual competition condition. This study proposes two new metrics as the leading indicators of company performance, namely advertising ‘reach’ and endorsement ‘likes’; and utilize best combination of marketing-mix elements to generate the highest profit for the company, with the profit being the lagging indicator of company performance. By using system dynamic approach and genetic algorithm to generate the highest profit of the company, this study will capture and utilize leading and lagging indicators that could be used to evaluate the performance of a company. This study uses the data from a small fashion company in Bandung, Indonesia. The result of the simulation that the higher the effectiveness of advertising and endorsement programs, the more they benefit the company in terms of Sales Volume and Number of Customers; while the higher the number of ‘reach’ and ‘likes’ will result in the bigger number of people aware of the existence of the brand.

    TABLE OF CONTENTS ABSTRACT ii ACKNOWLEDGEMENT iii TABLE OF CONTENTS iv LIST OF FIGURES viii LIST OF TABLES x CHAPTER 1 INTRODUCTION 1 1.1 Research Background 1 1.2 Research Questions 3 1.3 Research Objectives 3 1.4 Research Structure 3 CHAPTER 2 LITERATURE REVIEW 5 2.1 Performance Measurement System: Lagging vs. Leading Indicators 5 2.2 Constructs Review for Leading Indicators Development 7 2.2.1 Social Media Marketing 7 2.2.2 Brand Awareness 12 2.2.3 Brand Exposure 12 2.2.4 Advertisement in Social Media 13 2.2.5 Celebrity Endorsement in Social Media 14 2.2.6 Customer Engagement and Brand Post Popularity 15 2.2.7 Proposed Measure for Leading Indicators 15 2.3 Marketing Strategy: The Marketing Mix Elements 15 2.3.1. Product 16 2.3.2. Price 16 2.3.3. Place 16 2.3.4. Promotion 17 2.4 Proposed Measure for Lagging Indicators: Profit Optimization Approach 17 2.5 Theoretical Review of Modeling Approach 18 2.5.1 System Dynamics: A System Thinking Perspective 18 2.5.2 Genetic Algorithm 23 2.6 State of The Art and Research Position 25 CHAPTER 3 RESEARCH METHODOLOGY 33 3.1 Research Design 33 3.1.1. Problem Articulation (Boundary Selection) 34 3.1.2. Conceptual Model Development 34 3.1.3. Formulation of Dynamic Hypotheses 34 3.1.4. Formulation of Simulation Model 34 3.1.5. Model Testing 34 3.1.6. Scenarios Building 36 3.1.7. Scenarios Running 36 3.1.8. Comparison of scenarios 37 3.2 Object of The Study 37 3.3 Data Collection 37 3.4 Mathematical and Model Formulation for Profit Optimization Problem 37 3.5 Genetic Algorithm Structure 40 3.5.1. Initial Population 40 3.5.2. Fitness Evaluation 41 3.5.3. Crossover Mechanism 42 3.5.4. Mutation Mechanism 43 3.5.5. Stopping Criterion 44 3.6 System Dynamics Model Reference Used 44 3.6.1. Customer Model 45 3.6.2. Word-of-Mouth Model 46 3.6.3. Stock Model 47 CHAPTER 4 MODELS, FINDINGS, and ANALYSIS 48 4.1 Problem Articulation (Boundary Selection) 48 4.2 Conceptual Model Development 49 4.2.1 Summary of Interview Result 49 4.2.2 Additional Data Collection: Social Media Marketing Managers 52 4.2.3 Modification of Vensim PLE Models 53 4.3 Formulation of Dynamic Hypotheses 55 4.3.1. Model Boundary Chart 55 4.3.2. Causal-Loop Diagram 57 4.4 Formulation of Simulation Model 61 4.4.1. Gross Sales of Products Sub-model 62 4.4.2. Total Cost of Goods Sub-model 63 4.4.3. Revenue of Stores Sub-model 64 4.4.4. Total Overhead Cost Sub-model 65 4.4.5. Profit Sub-model 66 4.4.6. Seed Customers Sub-model 66 4.4.7. Customers Conversion Sub-model 67 4.4.8. People Aware Sub-model 69 4.5. Model Testing (Validation) 70 4.6. Formulation of Scenarios 72 4.7. Result of Simulation 76 4.7.1. Number of Customers 76 4.7.2. Sales Volume 77 4.7.3. Number of People Aware 78 4.7.4. Cumulative Profit 79 CHAPTER 5 CONCLUSION 81 5.1 Conclusion 81 5.2 Managerial Implication 82 5.3 Discussion, Limitation, and Suggestions for Future Research 82 REFERENCES 85 APPENDICES 93

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