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系統識別號 U0002-1106202023144900
中文論文名稱 台灣烘焙企業之經營效率與生產力分析–資料包絡法與Malmquist生產力指數之應用
英文論文名稱 Efficiency and Productivity Analyses of a Taiwan Bakery Enterprise–Application of DEA and Malmquist Productivity Index
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 108
學期 2
出版年 109
研究生中文姓名 張佶文
研究生英文姓名 Chieh-Wen Chang
學號 801620013
學位類別 博士
語文別 英文
口試日期 2020-06-01
論文頁數 61頁
口試委員 指導教授-吳坤山
共同指導教授-婁國仁
委員-翁振益
委員-陳宥杉
委員-曹銳勤
委員-陳水蓮
委員-吳坤山
中文關鍵字 烘焙業  資料包絡分析  麥氏指數  技術效率  總要素生產率  資產回報率 
英文關鍵字 bakery industry  data envelopment analysis  Malmquist index  technical efficiency  total factor productivity  return on assets 
學科別分類
中文摘要 基於人類的需求,食品工業不僅已成為民生行業,而且反映了國家或地區的發展水平,並顯示了人們的生活質量。烘焙業作為食品業之一,對它的快速增長和快速發展具有重要意義。因此,了解生產效率和深入研究烘焙業效率管理對於確保烘焙業公司的可持續性具有重要意義。本文旨在運用DEA-Malmquist模型評估85°C公司2011年至2016年的效率和生產率變化。通過採用資料包絡分析法(data envelopment analysis),發現規模效率(scale efficiency)高於純技術效率(pure technical efficiency),這表明技術效率(technical efficiency)低是導致純技術效率降低的主要原因,這表明85°C公司的在研究期間,尤其是在2015年第三季度的第三階段營運模式之後,效率水平逐漸提高。同時,第三階段營運模式在技術上比第一階段營運模式和第二階段營運模式更加有效,並且在純技術上更為有效,這表明及時改變了作戰方式戰略對提高運營效率有重大影響。此外,本文並發現資產回報率(return on assets)與技術效率成正比關係,這意味著保持在產品前線的生產能力決定了85°C公司的財務績效。 本文同時指出,從2011年到2016年,85°C的總生產率變化的總體平均值略有增加,並且生產率變化很容易受到技術進步的影響。此外,本文還發現,北部自有商店在2011-2016年期間的技術進步和總要素生產率(total factor productivity)變動最差。
英文摘要 Food industry has become not only a livelihood industry but also reflect the national or regional level of development and shows people’s quality of life. Bakery industry, as one of the food industry, is significant for its rapid growth and fast development. Thus, understanding the production efficiency and the in-depth research of the efficiency management of bakery industry are important to ensure the sustainability of the companies in the bakery industry. This thesis aims to evaluate the efficiency and productivity change of 85°C company from 2011 to 2016 by using the DEA-Malmquist model.
By adopting the Data Envelopment Analysis (DEA), it reveals that the low technical efficiency is the major reason for lower pure technical efficiency as the finding that the scale efficiency is higher than pure technical efficiency, which indicates that the 85°C company’s efficiency level has progressively improved during the period under study, particularly after the III-generation operations style period of 2015 Q3. Meanwhile, the III-generation operations style is more technically efficient and more pure-technically efficient than the I-generation and II-generation, shows that the timely change of operational strategy has a major impact on improving operational efficiency. Moreover, the findings of Return on Assets (ROA) was positively related to technical efficiency implies that a producer's ability to stay on the production frontier decides the financial performance of 85°C company.
This study also, dynamically, implies that the overall mean for total productivity change of 85°C increased slightly from 2011 to 2016, and the productivity change was easily affected by technical progress. Furthermore, the results also show that the north-district self-owned stores (which located in subtropical climate) have the worst technical progress and total factor productivity change during 2011-2016 period.
論文目次 TABLE OF CONTENTS
ABSTRACT(CHIENSE)...I
ABSTRACT...II
TABLE OF CONTENTS...IV
LIST OF FIGURES...VI
LIST OF TABLES...VII
Chapter 1 Introduction...1
1.1 Background...1
1.2 Statement of the Problem...4
1.3 Purpose and Objectives...5
1.4 Research Questions...6
1.5 Structure and outline of this study...6
Chapter 2 Literature Review...8
2.1 DEA Method on the Efficiency of Food Industry...8
2.2 DEA-Malmquist Index Model on Prodcutivity Change of Food Industry...10
2.3 The Application of Other Indicators in Food Industry...12
Chapter 3 Methodology...16
3.1 Overview of Data Envelopment Analysis...16
3.2 Overview of Malmquist Total Factor Productivity...18
3.3 Overview of Kruskal–Wallis Test...20
Chapter 4 Results of Static Efficiency Analysis...21
4.1 Case Company (85°C) Profile...21
4.2 Operational overview of 85°C...25
4.3 Empirical results...31
Chapter 5 Results of Dynamic Productivity Change Analysis...41
5.1 Data Collection...41
5.2 Dynamic Productivity Change Results and Discussion...44
5.3 Management Decision Matrix...48
Chapter 6 Conclusions...51
6.1 Findings and Discussions...51
6.2 Implications of the Study...54
6.3 Limitations of this Study and Future Research...55
References...56


LIST OF FIGURES
Figure 1-1. Taiwan consumer demand and potential for food...2
Figure 1-2. Average price spent on bakery products in USD...3
Figure 1-3. Taiwan contribution rates of baked and steamed food industry...4
Figure 1-4. Thesis structure and outline...7
Figure 4-1. Company Structure of 85°C...22
Figure 4-2. Store development overview of 85°C...26
Figure 4-3. Details of store development in the U.S....27
Figure 4-4. Trends in mean efficiency scores....37
Figure 4-5. Relationship between return on assets (ROA) and technical efficiency between 2011 and 2016....40
Figure 5-1. Trend of productivity change and its component in 85°C from 2011 to 2016....45
Figure 5-2. Management decision matrix of 85°C during 2011–2016 periods....50


LIST OF TABLES
Table 2-1 Categorization of the literature devoted to the efficiency of the food industry...14
Table 2-1 Categorization of the literature devoted to the efficiency of the food industry. (continued)...15
Table 4-1 Personnel information of 85°C...23
Table 4-2 Milestones of 85°C...24
Table 4-3 85°C business proportion of main products l...25
Table 4-4 Numbers of stores and employees of 85°C...26
Table 4-5 Details of store development in China...27
Table 4-6 Main product (service) areas of 85°C...28
Table 4-7 Summary statistics of all variables in the data envelopment analysis (DEA) model...33
Table 4-8 Correlation coefficients among inputs and outputs....34
Table 4-9 Efficiency scores from the Banker et al. (BCC) DEA model...36
Table 4-10 Average efficiency measures for different stores size (2011–2016) and results of the Kruskal–Wallis test....37
Table 4-11 Summary of overall efficiency and returns to scale....38
Table 4-12 Summary of CRS, IRS, and DRA of different panels....39
Table 4-13 Correlation coefficients among ROA and technical efficiency...40
Table 5-1 Correlation analysis among variables....42
Table 5-2 Means and standard deviations for the data of 85°C...43
Table 5-3 Average total factor productivity change and its decomposition of 85°C, 2011-2016...45
Table 5-4 The summary of Malmquist productivity index of 85°C, 2011–2016....47
Table 5-5 Variation of Efficiencies of 22 self-owned stores, 2011–2016....47
Table 5-6 Average change in TFP and its different components, and the Kruskal–Wallis test...48
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