航空公司之可靠性管制計畫（Reliability Control Program）目的在於透過監控航機或附件之性能表現，探究造成航機性能可靠性不佳之主因，並且執行與維護計畫、航機系統、零附件與訓練需求有關之必要改進方案，確保航機系統與附件的故障率落在既定的可接受範圍之內。
在航機系統的可靠性表現方面，主要透過個別航機系統之每月故障延誤與取消率（Technical Delay and Cancellation Rate）來監控航機系統表現狀態，針對三月移動平均比率（3-month rolling average）超出上限控制值（Upper Control Limit, UCL）的航機系統進行分析與檢討其成因，並且提出改善方案，加以改進。
本研究之目的在於依據實際之航機故障延誤與取消資料，應用變異數分析、羅吉斯模式等不同面向的方法，針對包括延誤次數、延誤時間資料進行分析，探討現行航空公司可靠性管制計畫上限控制值設定的可能缺點。並且提出包括現行做法共四種上限控制值設定方式，藉由系統模擬（Simulation）實驗來測試這些上限控制值的設定，透過包括成功警告能力、錯誤警告以及延誤成本等幾方面進行討論。
結果發現，考慮成功警告能力與錯誤警告情況所造成之影響，本研究建議採用連續警告才進行調查的方式來進行管控較為合適。而在連續警告才進行調查的可靠性管控方式下，期望調查成本在所有的模擬情境中皆低於期望延誤成本，航空公司應該以減少延誤成本為首要目標，故使用第一年之三月平均延誤率平均值90％信賴區間上限值做為次年之上限控制值，再以第二年每月之三月平均延誤率進行比對管控的方式來設定上限控制值較為合適。
若航空公司進行成因調查的成本較延誤成本高40％以上時，則其應該以減少調查的發生為主要目標，故建議可以採用第一年三月平均延誤率95％信賴區間上限值做為上限控制值，並以第二年每月之三月平均延誤率進行比對的方式較佳。

Airlines’ reliability control program is aiming at discovering root causes of poor aircraft system or component reliability through monitoring their performances and execute appropriate corrective actions involving changes of aircraft maintenance program(AMP), modification of aircraft system or component, and relevant training requirements to ensure the reliability performance of aircraft system or component are within the predetermined and acceptable level.
As for aircraft system reliability, it is the monthly technical delay and cancellation rate that is used to monitor each aircraft systems’ performance respectively. For those aircraft systems whose three-month rolling average delay rate exceed the predetermined upper control limit(UCL), which will generate “Alert”, engineers should conduct investigations to find out root causes and propose corrective actions to restore the system performance to an acceptable level.
Therefore, the goal of this research is to analyze real technical delay and cancellation data through different approaches including analysis of variance(ANOVA) and logistic regression modeling to discuss the possible deficiencies of setting up UCL. At the same time, this research also proposes three different methods of setting up the UCL, and further applies the methodology of system simulation to conduct the experimental test of those proposed settings.
The result of this research recommends that in considering the successful alert ability, false alert conditions, and flight delay and investigation cost, it is better to conduct investigations when continued alert signal appeared, and use the upper limit of 90% confidence interval of previous year’s average 3-month rate to set up next year’s UCL and compare it with the 3-month rate of the next year. On the other hand, if the cost of each investigation is 40% higher than each flight delay cost, it is suggested that setting up the UCL with the upper limit of 95% confidence interval of the previous year’s average monthly rate, and compare it to next year’s 3-month rolling average rate will be more suitable.
The main contribution of this research is to provide a guideline of adjusting UCL. However, exactly which method to apply is deeply depending on each airlines policy and the difference of its flight delay and investigation cost.

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