| DC 欄位 |
值 |
語言 |
| DC.contributor | 工業管理研究所在職專班 | zh_TW |
| DC.creator | 張筱芬 | zh_TW |
| DC.creator | Hsiao-Fen Chang | en_US |
| dc.date.accessioned | 2009-5-23T07:39:07Z | |
| dc.date.available | 2009-5-23T07:39:07Z | |
| dc.date.issued | 2009 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=964306009 | |
| dc.contributor.department | 工業管理研究所在職專班 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 許多的品質工具以及手法被發展以及運用於各產業。這些工具以及手法協助組織確認出關鍵品質特性或是關鍵製程。這些方法也被廣泛運用於液晶顯示器產業。其中最為普遍運用的品質工具之一為失效模式分析，這項工具幫助組織確認出關鍵品質特性或是關鍵製程。此方法為藉由風險優先數的計算定義出關鍵品質特性或是關鍵製程，若此風險優先數為最大或是大於某特定數, 該項特性則視為關鍵品質特性或是關鍵製程。雖然對於RPN 之組成要素嚴重度(S)、發生度(O)、和難檢度(D) 有評分準則, 但此方法中評分結果仍被視為主觀的。
本研究主要為提議以資料探勘之方法論並自一本國面板廠商搜集相關資料。企圖以客戶聲音為目標, 將客戶聲音轉換成關鍵品質。我們運用了其中一項決策樹之方法, 分類及迴歸樹規納出影響每個批次產品品位之製程。進一步地，將導出之關鍵製程與每批次品位數以群集法分群並定一指標，藉由以此方法，可發現每一製程表現較好與較差的機台，關鍵製程中表現較好的機台便構成了關鍵路徑。
| zh_TW |
| dc.description.abstract | Many quality tools and techniques have been developed and deployed extensively in the industry-wide to help the organization to identify and determine the significant characteristic of product and critical process in the organization. Those quality tools are adopted in the TFT-LCD industry too. One of the most commonly used techniques is the failure mode and effect analysis (FMEA) that helps to identify and determine the significant characteristic of products and critical process in the organizations. The key characteristic and critical process is determined if its RPN is the biggest or bigger than a specific one. Although there are guidelines for the scoring, the outcome is relatively subjective. Although there are guidelines for the scoring, the outcome is relatively subjectively.
This research is proposing another data mining procedure and collecting the dataset from a local LCD manufacturer. We attempt to aim at the Voice Of Customer (VOC), i.e. what customer requirements to our product are and transfer the VOC to critical to quality. The one of the decision tree techniques, Classification and Regression tree (CART) will be adopted. . With CART modeling, the decision tree inducts the processes that affect the total quantity of product rank per lot. Further, product rank are grouped and analyzed by the clustering model. Also we define an index to decide the equipment performance. Based on the result, the superior and the inferior equipments of each process are identified. The critical path is composed of the superior equipments. Meanwhile, the result can help the management to analyze the variation between equipments.
| en_US |
| DC.subject | 資料探勘 | zh_TW |
| DC.subject | 決策樹 | zh_TW |
| DC.subject | 群集法 | zh_TW |
| DC.subject | 液晶顯示器 | zh_TW |
| DC.subject | data mining | en_US |
| DC.subject | decision tree | en_US |
| DC.subject | CART | en_US |
| DC.subject | Clustering | en_US |
| DC.subject | LCD | en_US |
| DC.title | 以決策樹法歸納關鍵製程暨以群集法識別關鍵路徑 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Critical Processes Induction with CART And Critical Path Identification with Clustering | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |