English  |  正體中文  |  简体中文  |  Items with full text/Total items : 66984/66984 (100%)
Visitors : 23023263      Online Users : 132
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80300


    Title: 智慧面板廠應用精準抽樣規劃求解最佳人力配置
    Authors: 徐紹銘;SYU, SHAO-MING
    Contributors: 工業管理研究所在職專班
    Keywords: 驗收抽樣;整數線性規劃;品質成本;Sampling plan;Linear programming;Integer programming;Quality cost
    Date: 2019-07-24
    Issue Date: 2019-09-03 12:29:32 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著TFT-LCD技術進步飛速及廣泛應用,TFT-LCD面板已是電子產品中關鍵零組件之一,台灣是國際主要面板供應商前三名,市占率達32%;近年來隨著大陸地區產能開出,以及面板報價仍持續下跌,面板製造商在有限成本下透過持續改善產品良率及維持客端品質維護公司聲譽。面板產業雖已是高度自動化生產,仍期許透過智慧工廠、智慧製造、大數據、AI功能充分運用現有資源分配,有效將異常攔檢於工廠內,以降低異常品流至客端所產生額外的費用以及商譽的損失。由於OQC(Outgoing Quality Control)抽樣所批退的異常面板與客戶端所反應異常無明顯趨勢,且客戶端生產線批退率VLRR(Vendor Line Reject Ratio)一直無法有效降低;本研究藉由導入精準抽樣模型,提升OQC於出貨前抽樣檢驗攔檢能力,能有效極大化抽檢不良率,並透過分群抽樣概念,使異常品能有效地被抽中,藉由異常品批退須拉回做重工或是再次檢驗的動作,也有效降低客端VLRR、客端因不良停線重工成本費用,而在無形中也提升了公司的聲譽。
    本研究首先介紹TFT-LCD液晶顯示器製程以及面板常見的不良品缺陷種類,目的為說明建立精準抽樣模型主要依據是透過客退品解析主要異常問題所建立分群的模型,在人力限制情況下對分群後樣本執行抽樣檢驗,藉由最佳化分配各群組抽樣數量,預期使用此模型可使抽樣不良率極大化,在提升OQC在人力資源分配限制情況下可以抽樣到最大不良率的最大效益。
    ;With the technology improved and widely used of TFT-LCD and it have become key electronic components. Taiwan is the top three of panel makers and market share of 32% in the world. In recent years, production capacity increase of the China and panel prices continue to fall. Panel maker maintain the company reputation at the limited cost by continuously improving product yields and maintaining customer quality. Although panel maker is already highly automated production in panel industry, it is still pursue to make full use of existing resource allocation with intelligent factories, intelligent manufacturing, big data, and AI functions. It can effectively intercept abnormalities in the factory to reduce the extra sorting costs by abnormal product leakage to customer and lose of company reputation. Due to the trend of the abnormal panel percent ratio is not obviously with the OQC (Outgoing Quality Control) sampling reject and customer VLRR (Vendor Line Reject Ratio). Customer VLRR still has not been effectively reduced. In this research introduces the precision sampling model to improve OQC inspection capability before shipment, which can effectively maximize the sampling failure rate. The cluster sampling concept this action of reject the abnormal product to re-work or re-test can effectively reduce the cost of the customer′s VLRR and the customer′s failure to stop the product line, and invisibly increases the company′s reputation.
    Appears in Collections:[工業管理研究所碩士在職專班 ] 博碩士論文

    Files in This Item:

    File Description SizeFormat
    index.html0KbHTML32View/Open


    All items in NCUIR are protected by copyright, with all rights reserved.

    社群 sharing

    ::: Copyright National Central University. | 國立中央大學圖書館版權所有 | 收藏本站 | 設為首頁 | 最佳瀏覽畫面: 1024*768 | 建站日期:8-24-2009 :::
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback  - 隱私權政策聲明