應用數據分析改進製程良率之研究

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姓名

簡亦楚(Yi-Chu Chien) 查詢紙本館藏

畢業系所

企業管理學系在職專班

論文名稱

應用數據分析改進製程良率之研究
(The Application of Data Analytics on Yield Rate)

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摘要(中)

資訊技術絕對是企業取得競爭優勢功不可沒的角色。不僅是將人工轉為機器自動化、將紙本文件轉為電子數據等數位化(Digitalization)動作，而是運用資訊技術進行整合管理、數據分析、決策輔助等提升商業價值的行為。更因如此，近期多數企業在思考公司未來成長發展時，都開始將數位轉型(Digital Transformation) 考慮在內。
在眾多的數位技術中，近幾年機器學習(Machine Learning)發展蓬勃，在各領域皆有很好的成效，如客製化行銷、醫療診斷輔助、語音及圖形識別。引領了許多企業紛紛投入資源，無論是與資料科學專家合作抑或自行訓練資訊專業人才。但是該如何成功導入機器學習至企業當中，企業所面臨的困難與挑戰是什麼?特別是以中小企業在資源與資金使用的限制條件下，他們該如何有效的準備及運用?
本研究以如何運用機器學習並實作數據分析，進而改進製程良率為目標。採用台灣某製造高爾夫球桿頭公司的生產數據資料，搭配決策樹演算法(Decision Tree)提出對應之訓練模型與評估方式，並根據生產數據之訓練結果建立規則，進而輔助企業調整生產過程的製程參數及控制生產過程的變因。

摘要(英)

Information technology plays the critical role in business success. Companies gain competitive advantage by adopt IT strategies. Not only automation for manpower reduction, paper to digital transformation. Companies use information technology to integrated business management, data analytics and decision supporting for increase business value. Therefore, digital transformation strategy becomes the most important plan for many companies.
These days, machine learning is the most popular and important technology. It is evolving at such a rapid pace, enabling great results in many areas. Such as marketing personalization, clinical decision support, voice and image recognition. Machine learning adoption is increasing in many industries. But, what are the difficulties and challenges firms face? Especially, the small and medium-sized enterprises which with the resource limitation.
The research focus on build the application of data analytics on yield rate. Use the golf club head manufacturing process data which provided by an anonymous company. The research builds the training model and evaluation method with decision tree algorithm. According to the training result, provides the suggestion which help the firm to adjust manufacturing parameters and avoid producing defective products.

關鍵字(中)

★ 數據分析
★ 良率改善
★ 機器學習
★ 決策樹

關鍵字(英)

★ Data Analytics
★ Yield Improvement in Manufacturing
★ Machine Learning
★ Decision Tree

論文目次

摘要 .............................................................................................................................................I
ABSTRACT .............................................................................................................................. II
目錄 .......................................................................................................................................... III
圖目錄 ....................................................................................................................................... V
表目錄 .................................................................................................................................... VII
一、緒論 .................................................................................................................................... 1
1-1 研究動機與目的............................................................................................................ 1
1-2 論文架構........................................................................................................................ 3
二、文獻探討 ............................................................................................................................ 4
2-1 決策樹演算法................................................................................................................ 4
2-2 評估指標........................................................................................................................ 7
2-3 資料前處理.................................................................................................................. 10
三、研究設計與方法 .............................................................................................................. 11
3-1 高爾夫球桿頭生產流程簡介...................................................................................... 12
3-2 資料分析...................................................................................................................... 15
3-2-1 資料前處理步驟說明 ...........................................................................................15
3-2-2 Y 值的假設..........................................................................................................18
3-2-3 分析資料應用範圍 ...............................................................................................19
3-3 訓練模型設計.............................................................................................................. 23 3-3-1 訓練手法 ...............................................................................................................23
3-3-2 評估指標 ...............................................................................................................25
3-3-3 參數設定 ...............................................................................................................26
3-4 規則建立...................................................................................................................... 27
四、實證分析 .......................................................................................................................... 30
4-1 資料描述...................................................................................................................... 30
4-2 實驗結果與討論.......................................................................................................... 32
4-2-1 應用範圍訓練結果之比較 ...................................................................................32
4-2-2 瑕疵定義方法訓練結果之比較 ...........................................................................49
4-2-3 過抽樣法(Oversampling)訓練結果之比較..........................................................52
五、結論及未來展望 .............................................................................................................. 54
參考文獻 .................................................................................................................................. 56

參考文獻

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指導教授

陳炫碩

審核日期

2021-7-21

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