臺灣博碩士論文加值系統

In recent years, Textile industry is considered "key" by the advantages: less investment, payback fast, cheap and abundant labor, solves many jobs and contribute largely on exports, nearness to growing markets. Beside that recent liberalization of the world's textile and apparel trade policies facilitate to the Asian region has become a potential global center for textile and clothing exports. Specifically, in 2014 Asia exported over $607.1 billion, imported $245.7 billion of textile and the industry now employs more than 40 million Asian workers.
This research develops an integrated method based on data Envelopment Analysis (DEA) to establish an assessment model management performance of the group countries market, which has been one of the Asia biggest and most attractive marketplace and by using the Malmquist productivity index (MPI), we can estimate the productivity change in the textile industry. Then through adoption of GM (1,1), we forecast the textile industry companies. The empirical results indicate that after analyzing, there are significant rises in the numbers of catch-up, frontier and Malmquist, which can illustrate that the profits of the company increase. As a sequences the problem of how to progress operation performance in accordance with the impacts of exterior conditions seems to be addressed.

ABSTRACT i
LIST OF TABLES v
LIST OF FIGURES vi
CHAPTER 1: INTRODUCTION 1
1.1 Research Background 1
1.2 Research motivation 4
1.3 Research organization 4
CHAPTER 2: LITERATURE REVIEW 5
2.1 Theoretical foundation 5
2.1.1 Definition Performance Measurement 5
2.1.2 Components of Performance Measurement 5
2.1.3 Evaluation of performance management 7
2.1.4 Measurement 7
2.2 The relationship performance measurement and performance management 9
2.3 Related research about GM (1,1) 9
2.4 Related researches about DEA 10
CHAPTER 3: RESEARCH METHODOLOGY 13
3.1 Grey systems theory 13
3.1.1 Grey Forecasting generating 13
3.1.2 The Procedure for Grey (1, 1) 14
3.2 Malmquist productivity index (MPI) 15
3.2.1 Analysis stage DEA 16
3.2.2 Malmquist productivity index (MPI) 16
3.2.3 Non-Radial Supper Efficiency Model (Super-SBM) 18
3.3 Measuring Performance Management Value 20
3.4 Research Procedure 21
CHAPTER 4: RESULT AND ANALYSIS 25
4.1 Collection the DMU 25
4.2 Person correlations 32
4.3 Textile company performance efficiency evaluation 34
4.3.1 Cacth-up efficiency change 34
4.3.2 Frontier-shift change 37
4.3.3 Productivity changes 40
4.4 Grey model GM (1,1) forecasting 44
4.5 Forecasting Accuracy of GM (1,1) 51
CHAPTER 5: CONCLUSION AND SUGGESTION 54
5.1 Conclusions and Contributions 54
5.2 Limitations 55
5.3 Directions for future research 55
REFERENCES 56

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