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論文名稱(中文) 非常態分配下製程能力指標之區間估計:以服務業應用為例
論文名稱(英文) Interval Estimation of Process Capability Indices under Non-Normal Distribution: with an Application to Service industries
校院名稱 成功大學
系所名稱(中) 統計學系碩博士班
系所名稱(英) Department of Statistics
學年度 98
學期 2
出版年 99
研究生(中文) 郭姿君
研究生(英文) Tzu-Chun Kuo
學號 r2695115
學位類別 碩士
語文別 英文
論文頁數 82頁
口試委員 指導教授-潘浙楠
口試委員-高正雄
口試委員-呂金河
中文關鍵字 製程能力指標  關鍵績效指標  服務品質  拔靴法  涵蓋率 
英文關鍵字 Process capability index  key performance index  service quality 
學科別分類
中文摘要 近年來,服務業創造出的產值已成為社為財富的主要來源,大部份先進國家
服務業之產值均遠超過製造業。在國內,服務業提供的就業機會及其所衍生之產
值亦持續不斷地成長。自Gronroos (1982)將品質概念導入服務領域後,與服務品
質相關的研究議題便受到廣泛的重視。由於服務品質主要係源自顧客主觀的感受,
因此如何將消費者在服務過程中所感知的服務品質加以量化並以非常態製程能
力指標衡量之係本研究探討的主題。
我們參考製造業製程能力指標訂定之原則,提出適用於服務業之關鍵績效指
標(Key Performance Index, KPI) ,並利用拔靴法(bootstrap)建構出KPI 之信賴區
間,再以涵蓋率(coverage probability)評估拔靴信賴區間之可靠度。研究結果顯示
當服務水準較低(90%或95%時),僅約需250 個樣本即可達到理想之信賴區間,
然而隨著服務水準的提高,則需要較大的樣本數才能獲得較可靠之信賴區間。
根據模擬分析結果,本研究進一步建構出在不同之服務水準下,欲達到特定
之涵蓋率所需樣本數的對照表,研究成果可作為業界在利用KPI 評估營運及服
務績效時的重要參考。
英文摘要 In recent years, the amount of monetary value produced by service industries has
become the main resource of social wealth, and it has exceeded the corresponding
amount obtained by manufacturing industries in most advanced countries. The job
opportunities and the created wealth from service in Taiwan are continuously growing.
The research topics related to service quality has been widely considered since quality
concepts were introduced by Gronroos in 1982. Due to the fact that service quality
mainly lies in customers’ perceptions, this thesis is focused on quantifying customers’
perceptions during the course of service and using Key Performance Index (KPI) to
measure the service performance.
Based on the definitions of process capability indices, we develop a Key
Performance Index, which is suitable to service industry. Then, a bootstrapping
method is used to construct KPI’s confidence interval and the coverage probability is
used to evaluate the reliability of the bootstrap confidence interval. The simulation
results indicate that for higher confidence and service levels are, larger sample size is
required.
Based on the simulation results, a table is further constructed in this paper to show
the required sample size under various combinations of service and confidence levels.
Hopefully, the KPI and its required sample size can be useful for performance evaluation in service industries.
論文目次 Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objectives service and confidence intervals 4
1.3 Research Procedure 5
Chapter 2 Literature Review 6
2.1 Normal Process Capability Indices 6
2.2 Non-Normal Process Capability Indices 8
2.3 Bootstrap 14
2.3.1 The standard bootstrap confidence interval (SB) 15
2.3.2 The Percentile Bootstrap Confidence Interval 16
2.3.3 The Biased-Corrected Percentile Bootstrap,BCPB 16
2.3.4 The biased-corrected acceleration percentile bootstrap confidence interval (BCa) 17
2.4 The Confidence Intervals of The Process Capability Indices 17
2.5 Service Quality 18
2.5.1 The definitions and the characteristics of service 18
2.5.2 Service Quality 21
Chapter 3 Key PerformanceIndex in service industry 30
3.1 Key Performance Index 30
3.2 Evaluating Bootstrapping KPI Confidence Intervals by Numerical Analysis 33
3.3 The demanding sample sizes under different service level. 40
Chapter 4 Practical Analysis 45
4.1 Practical example 45
Chapter 5 Conclusions and Suggestions 53
5.1 Conclusions 53
5.2 Suggestions 54
References 55
Appendix A: Tables 61
Appendix B: R Code 82
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