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课程主页: https://www.udemy.com/course/lean_six_sigma_green_belt-a_solid_foundation/
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Coursera 上的“Lean Six Sigma Green Belt - A comprehensive course”课程概述，由 Parag Dadeech 教授，为学员提供全面的精益六西格玛知识和实用技能。本课程共分六个部分，包含 34 个讲座、测验和实例，旨在帮助学员理解并应用精益六西格玛理念，以应对激烈的市场竞争和不断变化的客户需求，从而提升组织价值和运营效率。 **第一部分：历史视角与六西格玛入门** 介绍六西格玛的起源、特点，以及精益与六西格玛（Lean + Sigma）的结合优势。重点讲解六西格玛方法论和 DMAIC（定义、测量、分析、改进、控制）方法。 **第二部分：定义（Define）阶段** 涵盖该阶段的核心活动，包括设定 SMART 目标、项目识别、客户之声（VOC）的收集与语音（VOB）、业务之声（VOP）、过程之声（VOP）的概念，以及不良质量成本（COPQ）。此外，还讲解 CTQ（关键质量特性）的细化、SIPOC（供应商、输入、过程、输出、客户）过程图以及项目章程的关键要素、团队选择、团队成长阶段和阶段性评审（Toll gate reviews）。 **第三部分：测量（Measure）阶段** 重点讲解测量阶段的活动，包括操作定义、数据收集方法、各类过程图（流程图、跨职能流程图、价值流图 VSM）及其益处，以及前置时间（Lead Time）与处理时间（Process Time）的概念。课程还将区分增值活动（VA）与非增值活动（NVA），讲解数据类型、数据收集计划；总体与样本，以及不同的抽样计划。此外，还涉及测量系统分析（MSA）、精确度与准确性，以及可接受的误差范围。基本统计学内容包括集中趋势（如均值、中位数）和变异性度量（如标准差）。课程还介绍正态分布、标准正态分布和 Z-值概念，以及过程基线（稳定性）、运行图（Run charts）、过程稳定性、过程能力（Cp、Cpk）和 Sigma 水平的计算，并讲解了产出量（Yield）和百万机会缺陷数（DPMO）。 **第四部分：分析（Analyze）阶段** 讲解分析阶段的活动和工具，包括鱼骨图（Ishikawa Diagram）和 5 Whys 分析法。还介绍头脑风暴、数据分层和检查表（Check sheets）。深入讲解失效模式与影响及危害性分析（FMEA）的历史、过程和示例。此外，课程还涉及变量之间的关系建模，包括相关性（Correlation）及其系数和应用，以及回归分析（Regression），包括简单线性回归和多元线性回归。 **第五部分：改进（Improve）阶段** 介绍改进阶段的活动，以及生成解决方案的创意工具，如 SCAMPER、思维导图（Mind Mapping）和六顶思考帽（Six Thinking Hats）。重点讲解精益改进工具，如 5S 技术、推拉系统（Push vs Pull）。详细介绍快速换模（SMED）的概念、步骤和实践案例。最后，介绍持续改进（Kaizen）的理念、过程，以及解决方案的试点和全面实施。 **第六部分：控制（Control）阶段** 讲解控制阶段的活动、不同类型的控制方法和统计过程控制（SPC）的介绍。区分普通原因（Common Causes）与特殊原因（Special Causes），并解释使用 SPC 的原因。阐述控制图（Control Charts）的绘制步骤、合理子组（Rational Sub-group）的含义，以及常见的“失控”信号。指导学员如何选择正确的控制图，包括用于变量数据的 Xbar-R 图及其示例，以及用于离散数据的 P 控制图及其示例，并定义了“缺陷”和“不合格品”。最后，介绍防错法（Poka Yoke）的理念、方法和示例，并提供结论性总结。
Dear Learners,I welcome you to this comprehensive course on "Lean Six Sigma Green Belt". I am Parag Dadeech with leadership experience in India & globally across a multiple sectors of industry. This course has been prepared quite meticulously to ensure that you are able to grasp all the major concepts with ease & be able to apply these concepts practically. I am quite confident that this course shall impart you with a thorough knowledge on the concepts & tools of the Lean Six Sigma. It will also help you to progress on the Black-Belt training, with lots of confidence & ease, whenever you do decide to pursue it. In the present times of cut-throat competition & ever demanding needs of the customer, the knowledge of Lean Six Sigma will be essential for organizations to deliver increased value & achieve higher operational efficiencies. This course is covered in "6 Sections with 34 lectures" & various quizzes and illustrations, during the different phases of the course. I do sincerely hope that you have an enjoyable & valuable learning experience. I would also request you to reach out to me on how you found this course & if you seek any clarification on any of the concepts taught in this course. Thank you, for enrolling.Section-1 (Historical perspective & introduction to Six Sigma)Lecture-1• Historical perspective• Features of Six-Sigma• Lean + Sigma (a very good tool)Lecture-2• Six Sigma methodology• Introduction to DMAIC methodologySection-2 (Define Phase)Lecture-3• Define phase activities• SMART Goals• Project identification• VOC (Voice of customer)Lecture-4• Translate VOC to CTQ's• VOB (Voice of Business)• VOP (Voice of Process)• COPQ (Cost of Poor Quality)Lecture-5• CTQ Drill down• Process Mapping (SIPOC Analysis)• Key elements of Project CharterLecture-6• Team selection• Team growth stages• Toll gate reviewsSection-3 (Measure Phase)Lecture-7• Measure phase activities• Operational definition• Methods to collect data• Types of Process MapsLecture-8• Flow chart & Cross functional flow chart• Value stream mapping & Benefits of VSM• Lead Time & Process TimeLecture-9• What is Value• Value adding activities (VA)• Non-Value adding activities (NVA)Lecture-10• Types of Data• Data collection planLecture-11• Population & sample• Different types of sampling planLecture-12• Measurement system analysis• What is precision & accuracy • How much Error is acceptableLecture-13• Basic statistics• Measures of central tendency• Measures of variationLecture-14• Concept of Normal distribution• Standard Normal distribution• Concept of Z-valueLecture-15• Process Base Line (stability of process)• Run chartsLecture-16• What is process stability • Cp (potential process capability)• Sigma level & PPMLecture-17• What is Cpk • What should be desired Cpk • Sigma Level across different business sectorsLecture-18• Yield (Multi-step process)• Concept of DPMOSection-4 (Analyze Phase)Lecture-19• Analyze phase activities• Some analysis Tools• Ishikawa Diagram• 5 Why AnalysisLecture-20• Brainstorming• Data stratification• Check sheetsLecture-21• What is FMEA & history of FMEA• FMEA ProcessLecture-22• Illustrations of FMEALecture-23• Modelling relationship between variables (Correlation)• Correlation coefficient• Uses of correlation studyLecture-24• Modelling relationship between variables (Regression)• Simple Linear regression & multiple Linear regressionSection-5 (Improve Phase)Lecture-25• Improve phase activities• Generate solution ideas• SCAMPER & Mind Mapping• Six Thinking HatsLecture-26• Lean Tools for improvement• 5S Technique• Push versus Pull systemsLecture-27• What is SMED• Steps in SMED• Practical illustration of SMEDLecture-28• What is Kaizen• Kaizen Process• Piloting the solution & Full-scale implementationSection-6 (Control Phase)Lecture-29• Control Phase • Types of control methods• Introduction to Statistical Process control (SPC)Lecture-30• Common Causes & special causes• Illustration of Common Causes & special causes• Why use SPCLecture-31• Procedure for control charts• What is Rational sub-group• What are common "out of control" signalsLecture-32• How do you select correct "control chart"• Control chart for "variable data"• The Xbar-R chartLecture-33• Illustration of Xbar-R chart• Control chart for discrete data• What is meant by "defect" and "defectives"• The P-control chart & illustration of P-chart.Lecture-34• What is Poka Yoka• Poka Yoka Methodology• Illustrations of Poka Yoka• Conclusive RemarksWarm Regards,Parag Dadeech
