六西格玛设计和可靠性设计

六西格玛管理6西格玛管理是获得和保持企业在经营上的成功并将其经营业绩最大化的综合管理体系和发展战略。

是使企业获得快速增长的经营方式。

经营业绩的改善包括：·市场占有率的增加·顾客回头率的提高·成本降低·周期降低·缺陷率降低·产品/服务开发加快·企业文化改变是自上而下地由企业最高管理者领导并驱动的过程革新方法。

由最高管理层提出改进/革新的目标（这个目标与企业发展战略与远景密切相关）、资源和时间框架。

这种革新方法由定义、度量、分析、改进、控制（DMAIC）的结构化的改进过程为核心。

DMAIC用于三种基本改进流程：6西格玛产品/服务实现过程改进6西格玛业务流程改进6西格玛设计SSDP·在实施上由“勇士Champion”、“大黑带MBB”、“黑带BB”“绿带GB”四级经过培训职责明确的人员作为组织保障。

这种革新方法强调定量方法/工具的运用，强调对顾客需求/满意的详尽定义于量化表述每一阶段都有明确的目标并由相应的工具或方法辅助。

SIGMA系统的效益任何企业在建立和实施6 Sigma系统之后，除了能够大幅度的节省物料和劳动力，还能够：提高客户满意度缩短作业周期改善生产力改善企业生产能力和增加产量全面降低不良率增强产品可靠性降低半成品(WIP)数量改善流程"你的公司可能要用超过25%的营业额来处理问题。

但对于成功实施6 Sigma 的公司来说，用来进行处理问题的费用还不到营业额的5%。

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质量管理工程中的创新方法与工具应用质量管理工程是现代企业不可或缺的一项重要工作。

随着市场竞争的加剧和消费者对产品质量的要求不断提高，企业必须不断创新，采用新的方法和工具来提高产品质量。

本文将介绍一些质量管理工程中的创新方法与工具，并探讨它们的应用。

一、六西格玛方法六西格玛方法是一种以数据为基础的质量管理工具，它通过收集和分析大量的数据，帮助企业找出生产过程中的问题，并提出改进措施。

六西格玛方法注重数据的准确性和可靠性，通过统计分析和数学模型，帮助企业找到问题的根源，从而实现质量的提升。

在质量管理工程中，六西格玛方法被广泛应用于生产过程的改进。

企业可以通过收集和分析生产过程中的数据，找出导致产品质量问题的原因，并采取相应的措施来解决这些问题。

通过六西格玛方法，企业可以实现质量的持续改进，提高产品的一致性和稳定性。

二、故障模式和影响分析（FMEA）故障模式和影响分析（FMEA）是一种通过分析潜在故障模式和其对系统性能的影响，来预防和减少故障发生的方法。

FMEA方法通过对产品和生产过程进行全面的分析，找出潜在的故障点，并采取相应的措施来预防故障的发生。

在质量管理工程中，FMEA方法可以帮助企业预防和减少产品故障。

通过对产品和生产过程进行详细的分析，找出可能导致故障的原因，并采取相应的措施来消除这些潜在的故障点。

通过FMEA方法，企业可以提前预防故障的发生，提高产品的可靠性和稳定性。

三、质量功能展开（QFD）质量功能展开（QFD）是一种将消费者需求转化为产品设计要求的方法。

QFD方法通过收集消费者的需求和期望，将其转化为产品设计要求，并将这些要求传递给生产部门，以确保产品的质量满足消费者的期望。

在质量管理工程中，QFD方法可以帮助企业将消费者的需求转化为产品设计要求，并确保产品的质量满足消费者的期望。

通过QFD方法，企业可以更好地理解消费者的需求，并将其转化为产品设计要求，从而提高产品的市场竞争力。

四、敏捷质量管理敏捷质量管理是一种将敏捷开发方法与质量管理相结合的方法。

六西格玛设计的可靠性和维修性设计可靠性作为质量的时间延续特性，已越来越多地受到人们的重视。

六西格玛设计的核心是稳健设计(包括QFD、系统设计、实验设计、参数设计、容差设计等方法)，其宗旨是提高产品抵御环境变化、制造误差和磨损老化等各种干扰的能力，减少产品质量波动。

而实质上，稳健设计在减少产品质最波动的同时，也肯定提高了产品的可靠性。

下面天行健管理顾问介绍面向可靠性的经典设计方法。

可靠性设计的目标是在顾客所要求的寿命期内不出或尽可能少出故障，即满足顾客关于寿命和平均故障间隔时间的要求并降低全寿命周期费用(LCC)。

这个目标只有从产品研制开始就紧密结合产品研制深入开展可靠性设计和分析工作才有可能达到。

可采用的可靠性设汁方法包括可靠性指标论证与确定，可靠性分配与预计，制定和贯彻可靠性设计准则，开展简化设计、热设计、降额设计、余度设计、耐环境设计等；可采用的可靠性分析方法包括FMEA分析以及故障树分析(FTA)、事件树分析(ETA)、热分析、容差分析等。

1、可靠性指标论证与确定对于电子产品等偶然失效占统治地位的产品，应论证与确定平均故障间隔时间MTBF的指标，对于耗损失效占统治地位的产品，应论证与寿命指标；对于兼有偶然失效和耗损失效的产品应论证与确定平均故障间隔时间MTBF和寿命两种指标。

产品的寿命不是越长越好，应当根据顾客的需求来确定。

在产品的寿命期间，平均故障间隔时间MTBF应尽可能长。

2、可靠性分配与预计为了保证产品能满足顾客对可靠性的指标要求，应当在设计早期自顶向下地将基本可靠性和任务可靠性指标分配到各部件和零件，并自下而上地对零部件和整机的基本可靠性和任务可靠性指标进行预计，以便评估在实现可靠性指标方面设计方案的可行性。

通过可靠性预计，可以发现可靠性的薄弱环节，对这些薄弱环节应采取设计和工艺的改进措施，以提高产品的可靠性水平。

3、可靠性设计准则可靠性设计准则是有助于提高产品可靠性的定性设计要求的归纳总结，应制定并要求设计员贯彻可靠性设计准则，在设计评审时进行可靠性设计准则的符合性检查。

六西格玛数据分析技术引言六西格玛（Six Sigma）是一种数据分析和质量管理方法，旨在通过识别和减少过程中的变异性，提高组织的业绩和质量。

本文将介绍六西格玛数据分析技术的基本原则、方法和工具，以及应用六西格玛进行数据分析的步骤和注意事项。

1. 六西格玛的基本原理六西格玛方法是基于统计学原理的质量管理方法，它将过程的能力和稳定性与业绩目标进行比较，通过数据分析来改进和优化过程。

六西格玛的核心理念是尽量减少过程中的变异性，从而提高产品或服务的质量和一致性。

六西格玛方法的三个基本原理如下： - 过程的总体性能可以通过统计学指标（如标准差）来度量和评估。

- 通过减少特定因素的变异性，可以提高过程的性能和一致性。

- 通过采取数据驱动的决策和改进方法，可以优化过程并实现质量目标。

2. 六西格玛数据分析方法六西格玛数据分析方法主要包括以下步骤： 1. 定义阶段：明确业务目标、定义过程和关键业务指标（KPIs），并建立项目计划和团队。

2. 测量阶段：收集和测量数据，分析数据的稳定性和能力，确定过程中的变异性源。

3. 分析阶段：通过统计分析和数据挖掘技术，识别和验证导致问题或变异性的根本原因。

4. 改进阶段：制定和实施改进方案，测试和验证改进效果，并进行过程重组和优化。

5. 控制阶段：确保改进方案持续有效，建立过程控制机制和绩效管理体系。

3. 六西格玛数据分析工具六西格玛数据分析方法使用了多种统计工具和技术，其中一些常用的工具包括： - 散点图：用于显示两个变量之间的关系和趋势。

- 直方图：用于显示数据的分布情况和频率。

- 控制图：用于监控过程的稳定性和能力。

- 核心六西格玛分析图表：包括关系矩阵图、因果图、故障模式和效应分析（FMEA）等。

- 回归分析：用于识别和验证不同变量之间的相关性和影响。

- 设计实验：通过对多个因素进行测试和分析，确定对结果影响最大的因素。

4. 六西格玛数据分析的注意事项在应用六西格玛数据分析技术时，需要注意以下事项： - 持续学习和提升技能：六西格玛数据分析方法需要一定的统计学和质量管理知识，持续学习和提升技能对于有效应用该方法至关重要。

（六西格玛管理）精益六西格玛简介精益六西格玛简介质量是促进国防科技工业持续健康发展的重要推动力，也是确保武器装备研制生产和发展的关键。

为了于降低成本、提高速度的同时提供高质量、高可靠性的产品，越来越多的管理者开始关注“精益的速度”和“六西格玛的质量”的融合问题—精益六西格码，这种新的管理方法能够使企业兼顾速度、成本和质量，这壹点是以往任何壹种管理方法均不能做到的。

精益六西格玛于国内外的研究和应用使得于军工领域进行推广应用具有重要的价值。

壹、精益六西格玛管理1.精益生产精益（LeanProduction，LP）的思想起源于本世纪40年代后期第二次世界大战以后，日本丰田汽车公司。

丰田汽车公司经理大野耐壹于福特汽车公司先进管理方法的基础上，进壹步发展了其理念，于组织、管理和用户的关系、供应链、产品开发和生产运作等方面，使工作效率和利润率均得到大幅度的提高-即以越来越少的投入获得越来越多的产出。

自从1996年沃麦克和琼斯的《精益思维》壹书出版以来，许多组织采用精益方法取得了不同程度的成功。

精益生产的基本思想是消除浪费，降低成本。

精益思想的关键出发点是价值，它将浪费定义为：“如果不增加价值就是浪费”，且且将浪费归结为七种，即：过剩生产浪费、过度库存浪费、不必要的材料运输浪费、不必要的动作浪费（寻找零件等）、下壹道工序前的等待浪费、由于工装或产品设计问题使零件多次加工处理的浪费、产品缺陷浪费。

2.六西格玛管理六西格玛管理（SixSigma）最初的起源是Motorola公司。

而真正把六西格玛这壹高度有效的质量战略变成管理哲学和实践，从而形成壹种企业文化的是于杰克•韦尔奇领导下的通用电气公司。

六西格玛是壹套系统的业务改进方法体系，旨于对组织业务过程进行突破性的持续改进，实现顾客和其他关联方满意。

它通过系统地、集成地采用业务改进过程，实现无缺陷的六西格玛过程设计（DesignforSixSigma，DFSS），且对现有过程进行定义（Define）、度量（Measure）、分析（Analyze）、改进（Improve）、控制（Control），简称DMAIC流程，消除过程缺陷和无价值作业，从而提高质量和服务、降低成本、缩短周期时间，达到顾客完全满意，增强企业竞争力。

工业4.0术语：DFSS六西格玛设计_工四术语（编号364）英文全称：DFSS，Design for Six Sigma中文名称：六西格玛设计（注：有时为了区别“面向运营的六西格玛”，也称为“面向设计的六西格玛”）中国制造2025提出之后，制造业的转型升级成为一只在弦之箭。

成功实现转型，赶超德国、日本等制造强国，绝不只是制造与信息化结合这样简单，首先要解决困扰中国设计制造行业多年的质量问题。

然而，质量问题，可不是简单呼唤一下工程师的精益求精，或者倡导“工匠精神”，就能手到病除地解决问题。

中国制造业的质量，必须在源头上进行有效的系统化设计。

而“面向设计的六西格玛”DFSS（Design for Six Sigma），正是这样的一件利器。

DFSS是正向设计思路从传统的测绘仿制或逆向工程的产品研发模式转为以顾客需求为驱动的正向设计将成为关键，DFSS在制造业转型的过程之中必定能发挥重要的作用。

DFSS倡导精细化的正向设计方法，这给设计人员会增加很多工作量，也改变了设计员的设计思维和工作习惯，因此这不仅是一种方法论的应用，而是在设计领域推动的一场管理变革。

工四100术语解读DFSS（Designfor Six Sigma）六西格玛设计，是一套应用于新产品开发的方法论，可使产品在低成本下实现六西格玛质量水平（百万机会缺陷率3.4）。

DFSS融合先进的设计理念和方法，为设计师提供面向产品质量和可靠性的正向设计方法。

DFSS遵循系统工程的科学逻辑，如果未来应能够自然地融入到产品研发体系之中，成为工程师研发活动的日常工具，那将全面提升企业自主创新能力。

DFSS以顾客需求为驱动，通过应用场景分析、卡诺分析、质量功能展开（QFD）等工具，准确把握顾客的需求，并将顾客需求转化成为技术要求，确保在设计过程中“以客户为中心”。

在设计过程中，基于系统工程、实验设计（DOE）、可靠性工程、面向制造性和装配性的设计（DFMA）等技术与方法，确定顾客需求与系统、子系统、部件、零件之间的传递函数，实现定量化描述顾客需求转化的过程，并逐层优化设计参数和公差，权衡分析后得到最优的设计结果。

Design for SixSigma(DFSS)& Design for Reliability(DFR) 六西格玛设计和可靠性设计The Journey1998 – Seagate adopts Six Sigma defect reduction,cost savings1999 – Lean in Manufacturing &Supply ChainIntro BE July 20102001 – DFSS in Product & ProcessDevelopmentPage 2DFSS in the BeginningIterativeUse of historical requests Test and re-testShort term estimates Isolated CTQ optimizationPredictiveRequirements hierarchy Model buildingLong term estimates System optimizationInitial Approach:Top down Educate the masses in design centers -> “DFSS Certified”• DFSS Foundation – 2 weeks of Statistics • DFSS Project – Systems Engineering – 3 days Train the suppliers and factory BrB/BB/MBBs in DFSSIntro BE July 2010Page 3What Is Design for Six Sigma?Design for Six Sigma (DFSS):• Allows us to set “need-based” requirements for CTQs and to evaluate our capability to meet those requirements.• Is a process that focuses on predictive product design. • Emphasizes the use of statistical methods to predictproduct quality early in the design process.• Is a complement to good engineering/decision making practices.Intro BE July 2010Page 4Six Sigma Improvement Methodology1 ADefineYES2NO1.MeasureIdentify2.YES3NOAnalyzeDesign3.OptimizeYES4NOImprove5YESA4.NOValidate5.ControlA high level Business need is identified(CTQ gap)Does a Current Business Process/Product exist to address the gapAre the Processes/Products that support your key outputs optimized but still not capable of meeting customer requirements?Is the solution or part of the solution a new process, product, or service.Does the capability of one or more KPIV need to be improved to optimize KPOV?Intro BE July 2010Page 5Statistical DesignIdentify DesignOptimize ValidateIntro BE July 2010Identify Customer RequirementsTranslate Into Critical To Quality (CTQ) Measures and Key Process/Product Output Variable (KPOV) LimitsFormulate Designs/Concepts//SolutionsValidate The Measurement Systems Evaluate DesignsFor Each Top Level CTQ, Identify Key Product/Process Input Variables (KPIV’s) Develop Transfer Functions Between KeyInput and Output VariablesOptimize DesignPerform Tradeoffs to Ensure that All CTQ’s Are MetNot OKNot OK OKException ReviewDetermine TolerancesAssess Process Capability to Achieve Critical Design Parameters and Meet CTQ Limits DFSS ScoringTest & ValidationPerform Tradeoffs to Ensure that All CTQ’s Are MetNot OK OKNot OK Exception ReviewAssess Performance, Failure Modes, Reliability and RisksOKFeasibility Point TollgateNot OKPage 6BreakthroughSix Sigma and Design for Six SigmaDesign for Six SigmaDesign robust products so thatspecs can be loosenedDefectsDMAIC Six SigmaFocus on reducing variation around the meanLower Spec LimitUpper Spec Limit• Design for Six Sigma and “Standard” Six Sigma work together!Intro BE July 2010Page 7Design EvolutionFROMEvolving Design requirements Design rework Build and test performance assessment Performance and manufacturability after product is designed Quality is “tested in”REACTIVEIntro BE July 2010TODisciplined CTQ flowdown Controlled design parameters Performance modeled and simulated Design for robust performance and manufacturabilityPREDICTIVEPage 8Key Elements• Systems relationships Transfer Functions, KPIV & KPOV• Statistical Design: Meeting not only target but address variations in design• Identify, Design, Optimize, & Verify (IDOV)Intro BE July 2010Page 9Systems Engineering - FlowdownQFD/FMEASystem CTQsSubsystem CTQsSub-assembly CTQsComponents CTQsProcess CTQsIntro BE July 2010Page 10Systems View Of a Hard Disc Drive38 CTQsCustomer CTQsServo-Mech RSS-H/MMech ServoProcess CTQs7 CTQsElec/InterfaceASIC111 Subsystem CTQs FirmwareAssembly/TestCert/Test>120 Factory CTQsHSA HGA Motor/Base HDA Encl. Head Media Channel/PreampComponent CTQs...Intro BE July 2010Page 11Transfer FunctionWhat is a Transfer Function?X1X2X3f(X1,X2,…, Xn)Y…Xn• It is a relationship of the CTQ (Y) to the key input variables (X’s). • It is not necessarily as rigorous as a process model. • It is key to predicting product performance before buildingprototypes.Intro BE July 2010Page 12Getting to the y = f(x1, x2…)Physical Models - dedicated experts ü Explore design space – run simulations with DOE ü Model management processStatistical Models ü DOE, Regression, Response Surface, etc ü Parametric data analysis – especially for reliability ü MSA“All models are wrong, some are useful.” - George BoxIntro BE July 2010Page 13Flowdown/Flowup ProcessSystemIdentify Customer CTQs. Translate into System CTQs.Identify Measurement for each system CTQ.Adjust tradeoffs to reduce cost (as new σ improvementsare made).PNCTrade off mean/variance requirements to x1,x2,…,xn to best meet system CTQ need.Determine Specifications for each system CTQ (Y).Identify Transfer FunctionY=f(x1,x2,…,xn)YesCapabilitiesof allNox1,x2,…,xnknown?Obtain process capabilities for those x’s that are not yetknown.Use transfer function and experience/judgement to allocate requirements for x1,x2,…,xn to meet systemCTQ need.SubsystemsIntro BE July 2010Page 14After y = f(x1,x2..), then…Internally developed tool – handles up to 20 transfer functions Ø Runs Sensitivity Analysis, Monte Carlo simulation and determines PNC Ø Optimizes for a Figure of Merit (cost, PNC, Z-score, user specified) Ø Helps set tolerances for all inputsOptimize to a Figure of MeritWhat the customerwantsInput w VariationsIntro BE July 2010Page 15Transfer FunctionsMeeting expectation?Screened Parts?Allocate OptimizedSpecsDesign & Engineering Benefits• KPOVs & KPIVs defined by transfer function • Clear ownership of CTQs • Visibility for trade-off managementIntro BE July 2010Page 16DFSS Process IntegrationCTQ FlowdownCustomer• Marketing Inputs • Product RoadmapsPNCCTQ’sSystem• System Models/Specs • System Eng. RoadmapPNCCTQ’sSubsystems• Subsystem Simulations • Subsystem RoadmapsPNCCTQ’sComponents• Eng. Design Tools • Process CharacterizationPNCCTQ’sParts• Parts CharacterizationParts/Process/Performance Capability FlowupOwnersMarketing /Systems EngineeringSystems EngineeringSubsystem EngineeringDesign Process Centers Mfg/Suppliers/Service Mfg/Suppliers/Sourcing Design TeamsIntro BE July 2010Page 17Prospects• Understanding customer needs • Complete understanding of systems relationships • Considers not only the target but the variation indesign • Integrating models & simulators to estimate Probabilityof Non-Conformance (PNC) • Not about the number 6 but a cultural changeIntro BE July 2010Page 18Design OpportunityMost current Six Sigma effort is here.$Must move quality effort here!Cost to Correct Quality and ReliabilityResearchDesignPrototypeDefects are:Difficult to see/predict Easy to fixProductionCustomerEasy to see Costly to fixIntro BE July 2010Page 19Cost to Design and Manufacture Product6 Sigma vs. Optimal SigmaDESIGN COST MATERIALS COST MANUFACTURING COSTOptimal SettingIntro BE July 2010ZST LEVELPage 20What workedProduct & Process Development culture transformed by DFSS ü More rigorous VOC process ü Doing Systems Engineering vs components (organization change) ü Speaking the “same language” in CTQ flow down (requirements) ü Emphasis on transfer function development - Models, DOE, regression, etc. ü Using statistical thinking vs target only - Monte Carlo simulation, tolerance analysis, etc ü Applying DFR early in product & technology development, FMEAs up front ü More data driven decisionsAvg Development TimeIntro BE July 2010Page 21But Something Still Needs Beefing Up1998 – Seagate adopts Six Sigma1999 – Lean in Manufacturing &Supply ChainIntro BE July 20102001 – DFSS in Product & ProcessDevelopment2006 – Revised Design forReliability (DFR)Page 22Design for ReliabilityDFSSANOVA RegressionHypothesis TestingVOC FlowdownQFD FMEADFREnvironmental & Usage ConditionsLife Data AnalysisPhysics of FailureGeneral Linear Model Control Plans Accelerated Life TestingMSAReliability GrowthSensitivity AnalysisModelingDOEWarranty PredictionsTolerancingFA recognition– Many common tools – DFSS enables achieving high quality at launch with nominal stress conditions – DFR focuses on achieving high quality over time and across stress levelsIntro BE July 2010Page 23Enhanced DFR ProcessUpfront use of DFR Assessment Matrix in the development cycle to identify and address reliability issuesModeling Physics ofFailureDFR Summary page: Key Reliability Risks / Failure ModesIssues from prior productsParetos , Post Mortem, …Competitive AnalysisNew technologiesFMEA’s , brainstorming, …Prioritized list of key reliability risksSys FMEANew market environmental & usage conditionsPotential Failure mode *CFM team?Maturity of physics of failure modelsUnderstand fieldenvironment stressorsEffective Stress testEffective FA recognitionParametric data analysisManufacturing/ supplier controlstrategy/ metrologyDFR TeamDesign OptionsArea Specific RepresentativeFailure Mode 1YesFailure Mode 2YesFailure Mode 3YesFailure Mode 4 NoFailure Mode 5YesFailure Mode 6NoFailure Mode 7YesFailure Mode 8 Yes• The status of the DFR activities will be updated at each progra m phase gate with a DFR review of the activities associated with the stoplight matrix above.• New Key Reliability Risks / Failure Modes should be added or pa rked when engineering data justifies that action.© Seagate ConfidentialPage 2Intro BE July 2010Page 24Integration into Product DevelopmentProduct Planning, Design and Development ProcessVOCLessons LearnedRequirements Management Phase-Gates & DeliverablesData Storage DeviceDesign for Design for SixReliabilitySigmaEngineering Models and Six Sigma Tool SetsIntro BE July 2010Page 25The Journey Forward1998 – adopts DMAIC Six SigmaToday – Business Excellence1999 – Lean in Manufacturing &Supply Chain2000 – DFSS in Product & ProcessDevelopment2006 – Integrated DFRwith DFSS2007 – Research ExcellenceIntro BE July 2010Page 26Integration into Product DevelopmentLean Design & DevelopmentProduct Planning, Design and Development ProcessVOCLessons LearnedRequirements Management Phase-Gates & DeliverablesData Storage DeviceDesign for Design for SixReliabilitySigmaEngineering Models and Six Sigma Tool SetsIntro BE July 2010Page 27Tools We UseSIX SIGMA• Traditional DMAIC toolset• Traditional DFSS toolset• DFR tools• Value StreamMapping • Value-add Analysis • Error-proofing • 5S • Cycle time analysis • Benchmarking • 5 why’s • Potential problemanalysis • Work measurement•Setup reduction•Pull systems•Total productive maintenance•Shop floor management• OEE•Lean assessment•Lean diagnostic•48 hour study •Layout optimizationLEAN•Batch size reduction•Time studies•Work sampling•Red flag analysisChange Mgmt•Current reality tree •Future reality tree •Conflict resolutionThroughput focus•Critical chain project mgmt •Prerequisite tree •Transition TreeTOCIntro BE July 2010Page 28Business Excellence“Today” and “Tomorrow” elementsLeanDFSS/DFRDMAIC 6σIntro BE July 2010Research & Technology DevelopmentFutureCommitment to technology developmentAdvanced Drive Integration & PlatformTomorrowStaging, aligning and integrating technologyProduct/ ComponentDesign & Manuf.TodayExecuting to product plansFactory & DeliveryPage 29SLAM II Context DiagramProduct and Technology Portfolio ManagementProduct Planning Process Platform Integration/Technology AlignmentBi-Annual ProcessesFramework Mini MR MRMiniPOREMGen 1 Gen 2Start EM RR Gen 1 RR Gen 2SAD CTU orDRArch.MR Declare Declare Declare Declare ECQPTADrive Development à(Click here forAdvanced Drive Development (ADD) Feasibility Phase 0 DesignIntegration Qualification PilotRampMilestoneDefinitions)FrameMRDrive Development Primary Market Segment-work MRMini MRMini DRADD ExitFeas ExitEMD/ Ph0 ExitProduct Phase-Based Gen1DeclareGen2 DeclareCTU DeclareSADProcPeTAssesEC MarketT-36 T-32T-25T-22T-19T-15T-10T-6T-2T=0T+4PS MarketT-32 T-28T-25T-22T-15T-12T-9T-6T-2T=0T+X# Months prior to SADSeagate ConfidentialIntro BE July 2010Page 30Learning ObjectivesAfter completing this training, the student will be able to:•Tie together the tools and methodology covered in thisclass.•Understand how DFSS, DFR and DMAIC are interrelated.•Apply the knowledge gained to current projects.IDOV ProcessFeasibility Point TollgateException ReviewPerform Tradeoffs to Ensure thatAll CTQ ’s Are MetOKNot OKNot OKNot OKValidateOptimizeDesignIdentifyOKTranslate Into Critical To Quality (CTQ) Measures and Key Process/Product Output Variable (KPOV) LimitsFormulate Designs/Concepts//Solutions Evaluate DesignsFor Each Top Level CTQ, Identify Key Product/Process Input Variables (KPIV ’s)Identify Customer RequirementsDevelop Transfer Functions Between KeyInput and Output VariablesAssess Process Capability to Achieve Critical Design Parameters and Meet CTQ LimitsOptimize Design DFSS ScoringDetermine TolerancesTest & ValidationAssess Performance, Failure Modes,Reliability and Risks Validate The Measurement SystemsNot OKException ReviewOKPerform Tradeoffs to Ensure thatAll CTQ ’s Are MetNot OKStatistical DesignWhat ’s NeededRM Software & Business ProcessIntegration intoProductDevelopment Flow & Phase-Gate ProcessTools Development& Model ManagementIdentify VOC, CTC, Environmental,System Level CTQsRequirement Management common repository, data structure, CTQ dictionary, flowdownDesign & Optimize Transfer FunctionsAllocationsTools Application simulators, models, DOEs, Monte Carlo, optimization, etc.VerifyStress Test, MSAMeasurement Systems & Builds sample sizes, cost, qualification test, etc.Appendix: DFSS Phase ReviewIdentify Phase1. What are you designing?2. Who is the customer?3. What business need will your design fill?4. When is your design needed?5. What does the cost/benefit (effort-to-impact) analysis show?6. What priority does this development effort have in the list of active and future projects?7. Who is going to champion this design effort?8. What are the CTQ requirements for this project?9. How are you sure these are the correct and complete list of requirements? (TTM, technical, environmental, etc)10. How did you determine which requirements are critical and which are non-critical?11. What are the targets and limits for each CTQ requirement?12. How did you determine the limits for each requirement?13. What requirements or limits do you expect to change either before or after project completion? How do you plan to handle this?14. How will you measure the CTQ’s? Who owns the equipment?15. What are the potential technological barriers? Describe your plan to overcome those barriers (alternative technology, costs, etc)?16. What elements of your design will be leveraged from existing designs, and/or will be used in future designs?17. What data do you have on existing similar designs?18. How does your design compare to our competitors?19. What resources are available (both personnel and budget)?20. Who are the critical players who can significantly impact this project? Are they “on board” with the development?21. What is your timeline and milestones?22. What obstacles do you foresee? Describe how you plan to overcome them?23. What does the feasibility / risk assessment indicate? What is your risk mitigation plan?I8-1Design Phase24. What design(s) are you considering?25. Where did the design(s) come from?26. Which design best satisfies the CTQ requirements?27. What existing knowledge are you leveraging into this design?28. What are the most complex elements of your design?29. What are the critical manufacturing/process steps for your design?30. Have you demonstrated technological/manufacturing feasibility?31. What is the risk associated with each design? (risk elements include: time to market, cost, capability, meeting volume,necessary resources, technological barriers, customer receptiveness, environmental regulations and vendor/supplier support)32. What data have you collected on the design(s)?33. How was the data collected?34. What additional output will you need to measure?35. What are the gauge R & R’s for all key measurable inputs and outputs? Who takes the measurements? Who owns the gauging?36. If a better gauge is needed, what would be the cost?37. What are the critical outputs (Vital Few) affecting each CTQ?38. What are the critical inputs (Vital Few) affecting each critical output?39. Who participated in developing the list of ALL (Trivial Many) the inputs/outputs initially analyzed and what were they?40. How were the critical inputs/outputs determined?41. What are the functional relationships between the critical outputs and the CTQ’s?42. What are the functional relationships between the critical inputs and critical outputs43. What are the tentative optimums for the inputs/outputs?44. What data do you have to support your decisions?45. How did you collect your data?46. How many parts and why?47. How do you know that you took enough samples to see a real effect and not just noise? What is your confidence that the effects is real?48. For suppliers, do they agree with your analysis of what the Vital Few are?49. What will be the process flow for your design?50. Who are the potential suppliers?51. What is the supplier’s capacity? Is it sufficient to meet short and long term capacity?I8-1Optimize Phase52. What are the product tolerances for each critical input/output?53. How were the tolerances determined?54. What data do you have to support these tolerances?55. How did you collect your data?56. How many parts and why?57. How do you know that you took enough samples?58. What is the capability for each tolerance?59. Is the capability score based on short or long-term estimates of variability?60. How sensitive is the performance to the critical inputs varying at the same time (i.e. interactions) over their tolerance ranges?61. Which environmental factors impact your design the most?62. How will you compensate for environmental influences?63. What are the key reliability issues?64. How did you test for reliability?65. What is your confidence in the predicted level of capability and reliability?66. Who are the suppliers? Have they been qualified? What is their capability?67. How will the parts be inspected?68. Do you have standards to ensure inspection test reproducibility?69. What does the product design / process flow diagram look like?70. Which steps in the process are value added and which are non-value added (rework, testing, inspecting, etc)?71. What is your plan for eliminating non-value added work?72. Are all the CTQ/S limits met or exceeded by using these product/process tolerances? If not, how do you plan to resolve that fact?73. What data do you have to support that all the CTQ/S’s are being met by this design?74. What is the predicted capacity?75. What are the biggest capacity constraints?76. What is the predicted cost?77. What are the areas of greatest risk?78. What is your plan for mitigating the risk? Is the risk acceptable?I8-2Validate Phase79. What is your validation test plan and criteria?80. What data do you have to support that the CTQ’s have been met?81. What is your confidence that the CTQ’s have been met?82. Which variables are the most important to control?83. What type of process control is being implemented?84. What are the action limits and action plans?85. What is the timing of the implementation?86. Who is involved with the implementation?87. Who will take the long-term responsibility for maintaining the controls?88. What plans do you have in place to revisit the process in the future to ensure the capability is being maintained?89. When will you transfer your design?90. How will you verify successful transfer of your design?All Phases91. What success(es) have you had in this phase (beyond what you expected)?92. What roadblocks did you encounter that you needed or still need help with?93. What do you see as your next steps?94. What would you have done differently?I8-2Appendix: MiscAcronyms and SymbolsRSM Response Surface Methodology RSS Root Sum of Squaress standard deviation of a sample s 2Variance of a sample S pSystem Capability IndexSDM Statistical Design Methods SESystems EngineeringSea.DOT Seagate Design Optimization Tool SEI Software Engineering Institute SPC Statistical Process Control SS Sum of SquaresSS p Subsystem Capability Index S/W Software T Target Level TF Transfer FunctionTol ToleranceTTM Time to MarketUCL Upper Confidence Limit (Upper Control Limit in SPC)USL Upper Spec limit VOC Voice of the Customer WC Worst Casex Mean of a sampleZNumber of σ‘s that can fit between Mean and Spec limitI & T Integration & Test Phase of a Program IDOV Identify, Design, Optimize, Validate IV Independent VariableKPIV Key Product/Process Input Variable KPOV Key Product/Process Output Variable KT Kepner-TregoeLCL Lower Confidence Limit (Lower Control Limit in SPC)LSL Lower Spec LimitMAIC Measure, Analyze, Improve, Control MBB Master Black BeltME Mechanical EngineeringMGPD Multi-Generation Product Development MS Mean Sum of SquaresMSA Measurement Systems Analysis MTBF Mean Time Between Failures MTTF Mean Time To Failure p probability of an occurrence PCB Printed Circuit BoardPCD Process Capability Database PCM Process Capability ModelsPNC Probability of Non-Conformance to specificationsPp, Ppk Long term capability measures PPM Parts per MillionQFD Quality Function Deployment R&R Repeatability & Reproducibility RPNRisk Priority NumberµMean of a populationσStandard Deviation of a Population σ2Variance of a population 1-D One dimensional linear stack-up ANOVA Analysis of VarianceBBBlack BeltBOM Bill of MaterialsCp, Cpk Process Capability Index, Short Term CI Confidence Interval COQ Cost of Quality CTQ Critical to Quality df Degrees of Freedom DFA Design for AssemblyDFM Design for Manufacturability DFSS Design for Six Sigma DoEDesign of ExperimentsDPLOC Defects per line of code DPPM Defective Parts per Million DPU Defects Per Unit DV Dependent Variable EE Electrical Engineering ETTR Elapsed Time To RepairFEA Finite Element AnalysisFMEA Failure Modes and Effects Analysis GLM General Linear ModelGR&R Gage Repeatability & Reproducibility H/WHardware。

随着改革开放和经济社会全球化地不断推进，我国在社会不断进步，经济不断发展的同时，汽车行业也有了很大进步，尤其体现在汽车设计中。

为了更好满足人们对汽车性能等方面的要求，积极在汽车设计中对6Sigma设计进行系统的应用是非常重要的。

因此，本文主要从汽车设计的相关特点入手，对DFSS在汽车设计中的应用进行系统分析与研究，从而更好地促进我国汽车行业的发展与进步。

为了更好地推进汽车设计的进步与发展，重视6Sigma设计是非常重要的。

不仅可以在最大程度上满足当前广泛的性能需求，还能对汽车行业的整体进步有着重要的影响。

而从6Sigma自身的性质来讲，能在汽车设计中进行系统的应用，对于提升汽车产品质量具有重要的意义。

因此，汽车行业的相关企业要积极根据自身实际发展情况，进行有针对性的汽车设计，更好促进汽车设计科学化发展。

1 DFSS、 6Sigma以及汽车设计1.1 6Sigma与DFSS之间的关联6Sigma中文为六西格玛，DFSS则为六西格玛设计。

6Sigma在很大程度上是DFSS的重要表现形式，对于产品与流程的设计具有重要指导意义。

1.2 6Sigma的概念六西格玛，其主要指的是一种管理策略。

在实际对其进行应用的过程中，要制定极高的目标，并在此过程中，对数据以及分析结果进行有效的收集，从而更好地减少产品和服务缺陷。

而在实际发展过程中，由于汽车设计需求的特殊性，积极对六西格玛的管理策略进行系统的应用是非常重要的。

1.3 DFSS的特点从特点的角度来看，DFSS主要包括以下3个方面：一是基于顾客关注焦点的特点介绍，为了更加充分地对客户需求进行了解，通常会对市场信息进行广泛收集，土曾强设计工作开展的有效性；二是基于关键过程的优化设计介绍，对于提升汽车设计水平具有重要的促进意义，可以大大提升产品自身的性能；三是基于成本与可靠性的特点分析，在实际汽车产品设计的过程中，积极对其成本问题进行系统的控制与管理是非常重要的，这样可以在保证企业自身经济利益的同时大大提升产品设计的可靠性。

六西格玛设计（DFSS）-- IDDOV模式课程介绍六西格玛作为一种过程改进方法，一般遵循的是DMAIC思路。

其工作重点是有选择地改进过程，克服造成过程失败的根本原因，减少缺陷。

但由于内在特性，DMAIC可能在大约5%的过程改进项目中失败。

一个比较常见的原因是:过程的状况太糟，已经失去了改进的意义，在这种情况下需要的是一个新过程。

还有一种情况，现行过程能力的局限性太大，渐进式变更和改进已不足以再次提升它，所需要的是一个新的过程。

这时如果把六西格玛改进方法运用于新产品的引入活动，各种问题就将搅和在一起。

想在新产品引入中继续尝试和验证“六西格玛改进”将遭遇两个障碍：第一，没有什么可以度量，因为是“新产品”；第二，在处理问题时往往围绕产品或服务，而不是过程。

在这种情况下“六西格玛改进”就难以满足新的要求，于是“六西格玛设计（Design For Six Sigma ，简称DFSS）”就应运而生。

今天六西格玛设计与六西格玛改进并列成为六西格玛管理中的两大方法体系。

DFSS系统方法的核心是，在产品的早期开发阶段应用完善的统计工具，从而以大量数据证明预测设计的可实现性和优越性。

在产品的早期开发阶段就预测产品或服务在客户处的绩效表现是实现更高客户满意度、更高利润和更大市场占有率的关键。

DFSS可以使企业从以下方面获得利润：当产品满足顾客需求，提高了本公司产品在市场的占有率，销售量增加带来利润的增加。

产品质量超出了顾客的预期，生产具有魅力质量的产品企业就有提高价格的理由。

提价为企业带来利润。

六西格玛从客户声音的收集、质量机能展开、健壮设计（参数设计、容差设计）、失效模式与影响分析、可制造性技术等使产品实现了低成本下的高质量，经过参数设计的产品其理论成本必然下降，即原材料等成本下降为企业带来了直接的经济效益。

此外高质量意味着产品质量稳定，产品良好的稳定性，也减少了内外质量故障等劣质成本，同时还为企业降低了管理成本。

六西格玛设计方法论字母六西格玛（Six Sigma）是一种以数据为基础的管理方法，旨在通过减少变异、提高过程质量和效率，从而实现持续的质量改进。

六西格玛方法论起源于20世纪80年代的美国，最初由摩托罗拉公司引入，并在后来被通用电气公司广泛采用。

六西格玛的核心思想是通过统计分析和数据驱动的方法，将业务过程的缺陷率控制在每百万次操作中不超过3.4次，从而实现业务质量的显著提升。

六西格玛的名称来源于统计学中的标准差符号σ，它代表了一个过程的变异性。

而“六西格玛”一词则表示了一个极高的质量水平，相当于每个过程在正常分布曲线中位于6个标准差之内。

根据统计学的原理，当一个过程能够达到六西格玛的水平时，其缺陷率已经非常低，几乎可以忽略不计。

六西格玛方法论的实施需要按照一定的步骤进行。

首先，确定关键的业务过程，并建立适当的指标来衡量其性能。

其次，收集和分析大量的数据，以了解过程的当前状态和存在的问题。

然后，通过应用各种统计工具和技术，找出造成问题的根本原因，并制定改进策略。

最后，跟踪和监控改进效果，确保所采取的措施能够持续产生积极的影响。

在六西格玛方法论中，有一些常用的工具和技术被广泛采用。

例如，流程图可以帮助识别和分析业务流程中的关键环节和瓶颈，帮助确定改进的重点。

而直方图和散点图则可以用来分析数据的分布和相关性，找出异常值和异常规律。

此外，假设检验和方差分析等统计方法可以用来验证改进效果的显著性和可靠性。

六西格玛方法论的应用范围非常广泛，几乎适用于任何类型的组织和业务过程。

许多公司和组织都将六西格玛作为其质量管理体系的核心方法，以实现业务的持续改进和优化。

六西格玛不仅可以用于制造业的质量管理，还可以应用于服务行业和软件开发等领域。

六西格玛的实施可以带来许多益处。

首先，通过减少缺陷和变异性，可以提高产品和服务的一致性和可靠性，从而增强顾客满意度。

其次，六西格玛还可以帮助组织降低成本和提高效率，通过优化业务流程和资源利用率来实现。