【正文】 9,243 135,666 159 999,941 911,492 125,072 131 999,928 903,199 115,070 108 In Excel: ppm = (1normsdist(Z))*1,000,000 Z=normsinv(1ppm/1,000,000) 67 Six Sigma Successes at GE 050010001500202325001996 1997 1998 1999ActualIn millionsCost BenefitFrom GE Stockholder Report 1998 Estimate 68 SIX SIGMA BREAKTHROUGH IMPROVEMENT TRADITIONAL CONTINUOUS IMPROVEMENT TIME IMPROVEMENT RATE SIX SIGMA VERSUS CONTINUOUS IPROVEMENTS 69 Key Drivers Basic Issues 1. Basic Organizational Capabilities 2. Manufacturing Process Variations 3. Business Process Variations 4. Customer requirements 5. Quality of Specifications 6. Supplier Capabilities ? Skillsets and tools required to implement process improvements in businesses are lacking. ? Lack of product manufacturing process capabilities result in high COPQ (.. Rework, scrap, field failure). ? Transition of market demands to engineering is poor. ? Product cost estimation is often wrong resulting in poor financial performance and incorrect manufacturing decisions. ? Frontend customer definitions/requirements inadequate. ? Engineering systems and processes for design and documentation are often inadequate and wrong. ? Specifications sent to suppliers/subcontractors very considerably in their quality, resulting in poor quality parts. ? Poor capture of design intent. ? Lack of mature supply base management (.. qualification of suppliers certification of parts) results in poor quality parts/services, late deliveries, higher part/service costs. 70 Process Input: Business Goals and Targets Select the Right Projects Select and Train the Right People Plan and Implement 6σ Improvement Program Manage for Excellence Sustain the Gains 71 營(yíng)收目標(biāo)為 32億 產(chǎn)品成本降低 10% Time Delivery Reduction Delivery Inventory Quality Productivity Cost 交貨達(dá)成率 95% 採(cǎi)購(gòu)成本 降低 5% 報(bào)廢率 降低 5% 製造費(fèi) 降低 10% 實(shí)質(zhì)用人費(fèi) 降低 20% Project 焦距對(duì)準(zhǔn) Y’ s 72 Project Leader 管理階層 Break Belt Process Owner 6σOffice 主計(jì)室 審核估算 Saving Project Approval 登錄到 6σ system 自動(dòng)給 ID Saving 計(jì)算及進(jìn)度控管 提 供 資 料 提供資料 確認(rèn) saving金額及彙整報(bào)告 審核 確認(rèn)project完成 確認(rèn)無(wú)其他成本 績(jī)效維持否 矯正 追蹤 NO 差異分析報(bào)表 Initiate Project Closure 追蹤一年 (每季一次 ) Project Process Map 8blocker 估計(jì) Saving Project 彙整 Project Approval 73 Team Charter ?Problem Statement ?Specific Numeric Goals ?Time to Complete ?Potential Barriers ?Team Members 74 Rule 1: No two objects are alike, therefore, variation exists everywhere! ? Product performance ? Service quality ? Process outputs (y’s) Concept of Variation The philosophy of Six Sigma is to reduce variation. Rule 2: Variation exists in two states of control: Controlled: referred to as mon cause, is a stable or consistent pattern of variation over time (predictable) Uncontrolled: referred to as special cause, is a pattern that changes overtime (unpredictable) Rule 3: To control and reduce variation you must first understand, quantify, AND interpret variation in a data set. 75 Statistics to Understand Variation How do we understand and quantify variation? We use the science of statistics. What is statistics? Statistics is the science of collecting, analyzing, presenting, and interpreting data. How statistics help in understanding and quantifying variation? Statistical methods and tools are used to effectively determine Y=f(x): 1) Transform data from a state of random miscellaneous nature to orderly and cumulative knowledge. ? Quantify Y’s 2) Quantify cause effect relationships. ? Quantify x’s on Y’s 3) Inferential measuring tool. ? Confidence in the influence of the x’s over time 76 Concepts of Data ? Data are observations made upon our environment. Examples: individuals, customers, processes, products, services, times, events. (Note: Data described in terms of numbers eliminates ambiguity.) ? Data are raw material used to interpret reality and must be in context. ? Contains at least 2 values (also known as variables) ? Defined and interpreted equally by all observers ? Able to take action on data ? Data is classified into two categories: ? qualitative ? quantitative What are data? Data is a key source of information. 77 Qualitative Data Qualitative data characteristics: ? A minimum of 2 variable values or representation of 2 variables exist. Examples: ? Can assume a finite number of variable values. ? Usually exists in integer form. ? Representation of variables can be ranked or nonranked. Examples: ? No meaningful information exists between variables. Qualitative data is monly referred to as attribute data. ? 1,0 ? Pass/Fail on product tests ? Win/Loss on marketing process ? Agree/Disagree on customer survey ? Number of product defects ? Number of customer requirements ? Numerical (1to 5, 1 to 1, 0 to 100) Ranked: ? On product performance: excellent, very good, good, fair, poor ? Salsa taste test: Mild, Hot, Very Hot, MMS (makes me suffer) ? Customer survey: strong agree, agree, disagree, strongly disagree NonRanked: ? In a pany: Dept A, Dept B, Dept C ? In a shop: Machine 1, Machine 2, Machine 3 ? Types of transportation: boat, train, plane 78 Data: Quantitative Quantitative data characteristics: ? Data can potentially take on any value. Examples: ? Numeric values have equal units of measure. Examples: ? Meaningful information exists between variables. ? Dimension of time ? Length of feature ? Interest rates ? , 1/4. 20, , 1,000,000 ? Fahrenheit and Celsius for te