保險業的經營績效一直受到投資者和主管機關的關注，保險公司的效率評估與流程改善亦是學術研究的重點。本論文擬研究2017-2020泰國非人壽保險業者的經營效率，利用動態網路資料包絡分析與多目標規劃計算其效率分數。本研究發展三種模式：動態黑盒子資料包絡分析 (DBDEA)、靜態網路資料包絡分析 (SNDEA)、與動態網路資料包絡分析 (DNDEA) ，用以比較各決策單位在不同期別的整體效率及服務、財務部門效率，並利用實證研究對泰國非人壽保險業者進行排序與分析。

The operational performance of insurance companies has been receiving attention from investors and policyholders. The performance evaluation and process improvement of insurance firms have been focused on by several studies. This study aims to evaluate the efficiency of 49 non-life insurance companies in Thailand during 2017 to 2020 by approaching dynamic network data envelopment analysis (DNDEA) and multi-objective programming (MOP) to retrieve efficiency scores. In this thesis, three models: dynamic black-box data envelopment analysis (DBDEA), static network data envelopment analysis (SNDEA), and dynamic network data envelopment analysis (DNDEA) are compared where each decision-making units (DMUs) is according to two processes in service and financial divisions. The results of the efficiency scores are analyzed, and the insurers are ranked based on average efficiency scores.

ACKNOWLEDGEMENTS i
ABSTRACT ii
ABSTRACT (摘要) iii
TABLE OF CONTENTS iv
LIST OF TABLES vi
LIST OF FIGURES vii
1 INTRODUCTION 9
1.1 Research Background 9
1.2 Research Objectives 11
1.3 Research Design 12
2 LITERATURE REVIEW 15
2.1 Data Envelopment Analysis (DEA) 15
2.1.1 CCR Ratio Model 18
2.1.2 CCR LP Model 18
2.1.3 BCC Ratio Model 19
2.1.4 BCC LP Model 20
2.2 Network Data Envelopment Analysis 20
2.3 Dynamic Network Data Envelopment Analysis 22
2.4 Evaluation of the Insurance Industry 23
2.5 Moving Averages 30
3 METHODS 31
3.1 Variable Selection 31
3.1.1 Data Sample and Pre-processing 32
3.2 Multi-Objective Programming (MOP) 35
3.2.1 Dynamic Black-Box Data Envelopment Analysis (DBDEA) 38
3.2.2 Static Network Data Envelopment Analysis (SNDEA) 39
3.2.3 Dynamic Network Data Envelopment Analysis (DNDEA) 41
4 RESULTS AND DISCUSSION 43
4.1 DEA Efficiency Score 43
4.1.1 DBDEA 43
4.1.2 SNDEA 46
4.1.2 DNDEA 51
4.2 Research Results 57
4.2.1 Efficiency Analysis 57
4.2.2 Correlation analysis 63
4.2.3 Comparing the results using different models 65
4.2.4 Moving Average Forecasting 69
5 CONCLUSION 71
REFERENCES 73
APPENDIX 79
Appendix A: Moving averages forecast 79
Appendix B: Lingo Coding 99

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