臺灣博碩士論文加值系統

在越南，建築業在經濟中扮演著重要的角色。 2016年，該國建築業的產值估計為1089.3萬億越南盾，佔國內生產總值的6.19％。在合適的時間和地點，對工業區，城市地區，住宅區和遊樂園進行規劃和投資，有助於社會穩定和經濟發展，並且是每個政府需要重視的問題。特別是在越南仍然是發展中國家快速地工業化和現代化。現在，企業在全球化環境中做生意和競爭，發生了很多變化，這就需要一個適當的發展戰略來有效地利用機會並充分應對所出現的挑戰，以確保實踐高效率和可持續發展。企業應掌握機會，界定投資領域，發展並找到解決方案，以有效利用公司現有資源。企業可以在業務領域發展可持續競爭力，並在整個行業和企業的發展中形成協同效應。在嚴重的貿易合作中單打獨鬥是不可能的，他們必須相互合作，共同發展。但由於經費不足和籌資困難，對企業生產經營和投資的負面影響不斷加大。在原材料價格上漲的情況下，產品銷售價格沒有上漲，這會影響企業的生產經營情況。在這種情況下，為了發展穩定和可持續發展，管理者必須做出根本性的改變，以提高企業的經營效率。
為了幫助管理者在管理中獲得更多可持續和突破性的解決方案，本研究採用了混合型資料包絡分析和灰色模型，來選擇供應鏈中最合適的策略合作夥伴，每個企業的實力和實踐經營戰略的目標。這項研究的解決方案可以提高業務效率，節省材料，生產和運輸成本，同時減少負面環境影響，幫助企業實踐可持續發展。

In Vietnam, the construction industry plays an important role in the economy. In 2016, the production value of the country’s construction industry was estimated at VND 1,089.3 trillion, accounting for 6.19% of gross domestic product [1]. The planning and investment in industry zones, urban areas, residential districts, and amusement parks at the right time and right place contribute to social stability and economic development, and are important issues for every government—especially in Vietnam which remains a developing country with regard to rapid industrialization and modernization speed. Now, enterprises are doing business and competing in an globalization environment with a lot of changes, which requires an appropriate development strategy to exploit opportunities effectively and handle posed challenges adequately in order to ensure to achieve high efficiency and sustainable development. Enterprises should seize opportunities and define areas for investing, developing as well as find solutions to take effectively advantage of the existing resources of the company. They can develop the sustainable competitiveness in the areas of their operations and create synergy in the development of the entire sectors and businesses. It is impossible to act single-handed in the severe trade cooperation, they have to cooperate and ally with each other to develop together. However, due to lack of funds and difficulties in raising funds, the negative impact on business production and investment of enterprises has been increasing. In the case of the increasing price of raw materials, the product selling price did not increase, which would have affected the production and business activities of enterprises. In that situation, in order to develop stability and sustainably, managers must make fundamental changes to improve an enterprise’s business efficiency.
In order to help managers have more sustainable and breakthrough solutions in management, the author used hybrid DEA (data envelopment analysis) and GM (1,1) (Grey model) to select the most appropriate strategic partner in supply chain for construction companies to promote the strength of each enterprise and to achieve the goals in business strategy. The solution of this research could improve business efficiency and save materials, production, and transport costs yet reduce the negative environmental impact to help the business develop sustainability.

摘要 iii
ABSTRACT iv
ACKNOWLEDGEMENTS vi
CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xii

CHAPTER 1: INTRODUCTION 1
1.1 Introduction 1
1.2 Research motivation 3
1.3 Research objectives 5
1.4 Research structure 5
CHAPTER 1 SUMMARY 7

CHAPTER 2: LITERATURE REVIEW 8
2.1 Value chain of construction industry 8
2.1.1 Inputs in the value chain of construction 8
2.1.2 Construction process 10
2.1.3 Outputs in the value chain of construction 11
2.2 Data envelopment analysis (DEA) 12
2.2.1 Overview of DEA 12
2.2.2 Selection method of analytical elements in the DEA 13
2.2.3 Some basic models in DEA 13
2.2.4 Literatures regarding data envelopment analysis 15
2.3 Introduce about Grey system theory 16
2.3.1 Overview of Grey system theory 16
2.3.2 Predictive content and processes in Grey system theory 17
2.3.3 The process of GM (1,1) prediction 19
2.3.4 Literatures regarding Grey system theory 19
CHAPTER 2 SUMMARY21

CHAPTER 3: RESEARCH METHODS 22
3.1 Research development 22
3.2 Data collection 24
3.2.1 DMUs collection 24
3.2.2 Inputs/outputs collection 25
3.3 Application of mathematical modeling developments 29
3.3.1 Non-radial super efficiency model (Super–SBM) 29
3.3.2 Grey forecasting model (1,1) 31
3.3.3 Malmquist productivity index (MPI) 32
CHAPTER 3 SUMMARY 35

CHAPTER 4: RESEARCH RESULTS 36
4.1 Pearson correlation 36
4.2 Analysis alliance 37
4.2.1 Analysis before alliance 37
4.2.2 Analysis after alliance 38
4.2.3 Partner alliance selection 41
4.3 Results and analysis of the Grey forecasting 43
4.3.1 Results Grey forecasting 43
4.3.2 Mean absolute percentage error 48
4.3.3 Results forecasting of the performance rankings in the future 49
4.3.4 Results forecasting of the malmquist productivity index 51
CHAPTER 4 SUMMARY 53

CHAPTER 5: CONCLUSIONS 54
5.1 Conclusions 54
5.2 Limitations and future research 54
REFERENCES 56

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