Continue improvement plan and process improvement (持續改善計劃及制程改善),CIP 持續改善計劃 - CIP概念,持續改善之概念,(P01),What is CIP?,何謂CIP?Answer:有些人將 CIP 定義為“持續改善計劃” (Continue improvement plan)而另有些人將 CIP 定義為“持續改善進程” (Continue improvement process),(P02),What is CIP?,CIP與C/A有何不同? 如何區分?Answer:針對單一問題所提供之改善方法, 以解決單一問題為目標之改善對策稱之Corrective Action (C/A).而CIP強調的是對某些主觀或客觀存在而影響改善之因素, 提出逐步消弱/排除影響因素之方案, 進而達到預期改善目標稱之為 Continue Improvement Process.,(P03),Why we need CIP?,為何我們需要CIP? Answer:客戶需求瞬息萬變、技術創新不斷加速、產品生命周期不断缩短、市場競争日趨激烈，这些構成了影響现代企業生存與發展的三股力量：顧客（Customer）、競争（Competition）和變化（Change）---（簡稱3C）。
P04),Why we need CIP?,為何我們需要CIP? Answer: -Continued企業不能適應客户個性化需求，不能 迅速響應市場和變化，不能持續改善客户满意度，就無法生存和發展 持續的改善- 才能保持企業競爭力- 是企業永續經營的基础,(P06),持續改善計劃的建構程序,(P07),CIP 持續改善計劃 - 方法,持續改善過程及其階段性任務1. 發掘及選定課題--- 目的(例:降低成本, 改善品質,改善作業方式….)--- 實例: IBM 客戶要求 2003年度品質總檢討及其CIP計劃,(P08),How to create a CIP project?,持續改善過程及其階段性任務2. 制定課題之優次* 遵循時間局部性及空間局部性定理(Computer system Org. andArchitecture CPU design)--- 實例: 機種產量最大/機種問題點最多,(P09),How to create a CIP project?,持續改善過程及其階段性任務3. 確定改善之方向* 資料收集--- 實例: In-process yield data,OOBA record, Field returnF/A--- 技巧: 建立網路公共區域及時更新並分享資訊,(P10),How to create a CIP project?,持續改善過程及其階段性任務3. 確定改善之方向 - Continued* 資料/原因分析 (遵循時間局部性及空間局部性定理)--- 實例: SPC 分析 (Toolbox), --- 技巧: 設計實驗* 人員任務編組明確職責--- 實例: IBM 2003年度品質總檢討及其CIP計劃,(P11),How to create a CIP project?,持續改善過程及其階段性任務4. 設定目標--- 原則制定目標時, 就應考 慮“我們”是否有“能力”執行 --- 技巧: 依短中長期訂定 5. 擬定對策--- 原則 依資料分析得出之多個影響因素分別制定一至多個對策,(P12),How to create a CIP project?,持續改善過程及其階段性任務5. 擬定對策 - Continued--- 技巧: 依實施時程:短,中,長期訂定或依影響因素局部性定理 對策 6. 評比不同的對策--- 成員討論確認實施對策,(P13),How to create a CIP project?,持續改善過程及其階段性任務7. 對策之實施--- 策略:管理改變8. 確認效果--- 依訂定要項及目標定期檢討確認效果,(P14),How to create a CIP project?,持續改善過程及其階段性任務9. 鞏固效果/計劃未來--- 形成系統化文件化流程化管理10. 展現改善成果及導入標準化,(P15),How to create a CIP project?,一個常問的問題 ---,(P16),CIP 持續改善計劃 - 問題,在持續改善的過程中，如果所定之標的 無法達成時怎麼辦？,(P17),How to do if the we can not achieve target?,Answer:「 我們制定標的時，就已考慮我們是否有能力執行，並在檢討、分析、改進的過程中適時修正。
」無法達成目標或標的並不影響驗證的結果，但是必須針對不能達成的原因作改進」,(P18),How to do if the we can not achieve target?,Transition from Classical to Statistical Quality Control Management System 品質管控系統由傳統檢驗轉變為統計管制,Inspection 檢驗Pass/Fail Based on Specusing Attribute Data,Variation ReductionReduce Variation Based on Control Limitsusing Variable Data,,Identify Critical Parameters,,Traditional Economic Model of Quality of Conformance,,,,,Total cost,Cost due to nonconformance,,,Cost of quality assurance,“optimal level” of quality,100%,,Quality improvement based on Inspection,Modern Economic Model of Quality of Conformance,,,,,Total cost,Cost due to nonconformance,Cost of quality assurance,,100%,,Quality improvement based on Variation Reduction (SPC/Process Capability),,Non-Value Added operations result in:Higher procurement cost of products Higher probability of defects,Improve the Process To Reduce Non-Value-Added Operations,,Customer,,VLRR/CND,,,,Hidden Costs,- Confirmed No Defect - Verified Line Reject Rate - Verified Initial Field Incident Rate (VIFIR, 30 day) –Verified supplier field failures for the first 30 days of a product in the field (in DPPM). - Verified Field incident Rate, 90 days (90 day VFIR) –Verified supplier field failures for the first 90days of a product in the field (in DPPM).,What is the difference between quality control based on Inspection and Variation Reduction?Inspection refers to the manufacturing operations based on Attribute Data (Pass/Fail).Current manufacturing operations are focused mainly on Pass/Fail inspections. Much of variable data is measured, but the data is converted to attribute data for Pass/Fail inspection.,If you inspect 100%, will your customer experience no failure?If you inspect 100%, and inspect again 100%, and inspect again 100%, will your customer experience no failure?Can you and Customer achieve the reduction of FIR (and VFIR) based on inspection quality control?,We will work together to understand the following points:Even if you inspect 100%, your customer will still experience failures Inspection does not detect the process and product mean shift 100% Inspection does not reduce the variation in your process (and product),What is Distribution?,Traditional Inspection View,,,,,Lower Spec,Upper Spec,no loss,,nominal,tolerance,,,,Traditional view: There is no failure as long as a parameter is within specification,New View (Taguchi Loss Function),nominal,tolerance,,,,,,,,Failure rate, $,,,,Lower Spec,Upper Spec,no loss,,,Products still fail in time even if a parameter is within spec. The probability of failure increases as the parameter shifts away from the mean Reliability is “Quality over Time”,nominal,tolerance,,,,,,,,Failure rate, $,,,,Lower Spec,Upper Spec,no loss,,,,,,Where do we want to go – to reduce VLRR and VFIR?,Reduction of variation will improve Reliability.,Critical Process Parameters ( x ),,,,Product Attribute ( y(x) ),Metric (Y),VLRR,Sigma Level (Drive for 5),PA PB,1 2 3,Yield,Cpk,,,,,,Metric,What Measured,,,Metric (Y),Sigma Level (Drive for 5),。