CMQ/OE Certification Prep. - Part IV

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课程名称:CMQ/OE认证准备 - 第四部分 概述:CMQ/OE认证准备课程的第四部分专注于质量管理工具。该课程与更新后的CMQ/OE知识框架(第五版2021)完全对齐。 课程内容涵盖: A. 问题解决工具 1. 七大经典质量工具:选择、解释和评估这些工具的输出,包括帕累托图、因果图、流程图、控制图、检查表、散点图和直方图。 2. 基本管理和规划工具:选择、解释和评估亲和图、树形图、过程决策程序图、矩阵图、优先级矩阵、相互关系图和活动网络图的输出。 3. 过程改进工具:选择、解释和评估根本原因分析、Kepner-Tregoe、PDCA、六西格玛DMAIC和故障模式与效果分析(FMEA)。 4. 创新与创造力工具:使用各种技术和练习进行创造性决策和问题解决,包括头脑风暴、思维导图、旁向思维、批判性思维和五个为什么。 5. 质量成本(COQ):定义并区分预防、评估、内部和外部故障成本类别,并评估一个类别的变化对其他类别的影响。 B. 过程管理 1. 过程目标:描述如何建立、监控和测量过程目标,并评估其对产品或服务质量的影响。 2. 过程分析:使用各种工具分析过程,并评估其在程序、工作指令及其他文件基础上的有效性,识别瓶颈、提高产能、改善吞吐量、减少周期时间和消除浪费。 3. 精益工具:识别和使用精益工具,如5S、准时生产(JIT)、看板、价值流图、快速转换(单分钟换模)、 poka-yoke、改善和标准工作。 4. 约束理论(TOC):定义约束理论的关键概念,如系统链、局部与系统的优化及物理约束与政策约束。 C. 测量:评估和指标 1. 基本统计使用:使用统计技术识别何时、什么以及如何测量项目和过程。 2. 抽样:定义和描述基本抽样技术,如随机抽样和分层抽样。 3. 统计分析:计算基本统计值,如集中趋势的度量和离散度的度量,并对控制图进行评估以做出数据驱动决策。 4. 测量系统分析:理解测量术语及基本概念,掌握重复性和再现性的差异。 5. 趋势与模式分析:通过图表识别数据趋势。 6. 过程变异分析:区分常见原因和特殊原因变异。 7. 过程能力认知:识别过程能力及性能指标。 8. 可靠性术语:定义和描述基本可靠性度量。 本课程注重园艺智力层次,涵盖记忆、理解、应用、分析、评估和创造等认知水平,帮助学员全面提升质量管理能力。

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CMQ/OE Certification Prep. Course: Part IV: Quality Management ToolsWhat is covered in this course?This course is fully aligned with the updated CMQ/OE Body of Knowledge ( FIFTH EDITION 2021 ) CMQ/OE Certification Prep. Course-Part IV: Quality Management ToolsPart IV: Quality Management ToolsA. Problem-Solving Tools1. The seven classic quality toolsSelect, interpret, and evaluate the output from these tools: Pareto charts, cause-and-effect diagrams, flowcharts, control charts, check sheets, scatter diagrams, and histograms. (Evaluate)2. Basic management and planning toolsSelect, interpret, and evaluate the output from these tools: affinity diagrams, tree diagrams, process decision program charts (PDPCs), matrix diagrams, prioritization matrices, interrelationship digraphs, and activity network diagrams. (Evaluate)3. Process improvement toolsSelect, interpret, and evaluate tools such as root cause analysis, Kepner-Tregoe, PDCA, Six Sigma DMAIC (define, measure, analyze, improve, control), and failure mode and effects analysis (FMEA). (Evaluate)4. Innovation and creativity toolsUse various techniques and exercises for creative decision-making and problem-solving, including brainstorming, mind mapping, lateral thinking, critical thinking, the five whys, and Design for Six Sigma (DFSS). (Apply)5. Cost of quality (COQ)Define and distinguish between prevention, appraisal, internal, and external failure cost categories and evaluate the impact that changes in one category will have on the others. (Evaluate)B. Process Management1. Process goalsDescribe how process goals are established, monitored, and measured and evaluate their impact on product or service quality.(Evaluate)2. Process analysisUse various tools to analyze a process and evaluate its effectiveness on the basis of procedures, work instructions, and other documents.Evaluate the process to identify and relieve bottlenecks, increase capacity, improve throughput, reduce cycle time, and eliminate waste. (Evaluate)3. Lean toolsIdentify and use lean tools such as 5S, just-in-time (JIT), kanban, value stream mapping (VSM), quick changeover (single-minute exchange of die), poka-yoke, kaizen, standard work (training within industry), and productivity (OEE). (Apply)4. Theory of constraints (TOC)Define key concepts of the TOC: systems as chains, local versus system optimization, physical versus policy constraints, undesirable effects versus core problems, and solution deterioration.Classify constraints in terms of resources and expectations as defined by measures of inventory and operating expense. (Understand)C. Measurement: Assessment and Metrics1. Basic statistical useUse statistical techniques to identify when, what, and how to measure projects and processes.Describe how metrics and data-gathering methods affect resources and vice versa. (Apply)2. SamplingDefine and describe basic sampling techniques such as random and stratified. Identify when and why sampling is an appropriatetechnique to use. (Understand)3. Statistical analysisCalculate basic statistics: measures of central tendency (mean, median, mode) and measures of dispersion (range, standard deviation, and variance). Identify basic distribution types (normal, bimodal, skewed) and evaluate run charts, statistical process control (SPC) reports, and other control charts to make data-based decisions. (Evaluate)4. Measurement systems analysisUnderstand basic measurement terms such as accuracy, precision, bias, and linearity. Understand the difference between repeatability and reproducibility in gage R & R studies. (Understand)5. Trend and pattern analysisInterpret graphs and charts to identify cyclical, seasonal, and environmental data trends. Evaluate control chart patternsto determine shifts and other trend indicators in a process. (Evaluate)6. Process variationAnalyze data to distinguish between common and special cause variation. (Analyze)7. Process capabilityRecognize process capability (Cp and Cpk) and performance indices (Pp and Ppk). (Understand)8. Reliability terminologyDefine and describe basic reliability measures such as infant mortality, end of life (e.g., bathtub curve), mean time betweenfailures (MTBF), and mean time to repair (MTTR). Understand the value of estimating reliability to meet requirements or specifications. NOTE: Reliability calculations will not be tested. (Understand)LEVELS OF COGNITIONBASED ON BLOOM'S TAXONOMY Remember: Recall or recognize terms, definitions, facts, ideas, materials, patterns, sequences, methods, principles.Understand: Read and understand descriptions, communications, reports, tables, diagrams, directions, regulations.Apply: Know when and how to use ideas, procedures, methods, formulas, principles, theories.Analyze: Break down information into its constituent parts and recognize their relationship to one another and how they are organized; identify sub-level factors or salient data from a complex scenario.Evaluate: Make judgments about the value of proposed ideas, solutions, et cetera, by comparing the proposal to specific criteria or standards.Create: Put parts or elements together in such a way as to reveal a pattern or structure not clearly there before; identify which data or information from a complex set is appropriate to examine further or from which supported conclusions can be drawn.

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