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所在平台: Udemy |
课程主页: https://www.udemy.com/course/tabtrainer-minitab-process-capability-analysis-continuous/
课程评论：没有评论
**课程名称：** Minitab® 数据分析——制程能力分析 (Tabtrainer® 认证系列) **课程概述：** 本课程由 TÜV 认证的六西格玛专家、Tabtrainer® 创始人 Murat Mola 教授主讲，旨在提供基于科学的质量培训和统计思维实践。您将深入学习如何使用 Minitab® 进行制程能力分析，并以实际的滑板轴压铸工业数据为例进行实践。课程将帮助您掌握以下关键技能： * **数据准备与探索性分析：** 学习导入和清洗质量数据，利用描述性统计评估数据分布，识别异常值和初步趋势，并通过 Anderson-Darling 检验验证正态分布假设。 * **制程稳定性分析：** 构建和解读 X-bar 和 R 控制图，应用 AIAG 控制图八项检验识别过程不稳定因素，理解子组结构与组内、组间变异的区别，并判断过程是否稳定到可以进行能力分析。 * **能力指标解读：** 熟悉 Cp, Cpk, Pp, Ppk, Cpm (Taguchi 指数) 等关键制程能力指标，理解整体能力与潜在能力的区别，解释过程中心偏移（Katayori），并通过直方图、密度函数和 Z 值（Sigma 水平）进行可视化解读。同时，学习使用 PPM 值（百万分之一）评估报废率。 * **根本原因分析与优化：** 利用供应商代码 ID 数据进行根本原因调查，创建点图比较不同供应商的原材料质量，实施技术与供应商相关的改进措施，并通过“制程能力六项工具”（Capability Sixpack）评估改进前后的场景。 * **最终评估与最佳实践：** 比较改进前后在能力指标和控制图上的结果，解读可视化和统计输出以确定长期能力，学习如何保存、记录和沟通能力项目，并了解何时判定制程统计上是合格的，以及如何维持性能。 **学习成果：** 完成本课程后，您将能够： * 完整地进行从头到尾的制程能力分析。 * 运用关键绩效指标评估和比较制程能力。 * 识别制程不稳定的原因和高变异性。 * 实施针对性的改进以满足客户要求。 * 高效使用 Minitab® 工具，包括控制图、能力图和“制程能力六项工具”。 本培训将赋能学员在实际工业环境中做出数据驱动的决策，清晰地沟通制程能力，并支持可持续的质量改进。
Welcome to the Tabtrainer® Certified Series - your expert platform for science-based quality training and applied statistical thinking.In this course, you'll gain deep, hands-on expertise in Process Capability Analysis using Minitab®, guided by real industrial data from the die casting of skateboard axles. You'll learn to evaluate and optimize your processes using industry-standard metrics such as Cp, Cpk, Pp, Ppk, and Cpm, while applying AIAG and ISO-compliant methods to ensure long-term process capability.Led by Prof. Dr. Murat Mola, TÜV-certified Six Sigma expert and founder of Tabtrainer®, this course bridges theory and practice to help you identify process instability, reduce scrap, and communicate capability clearly to technical and non-technical stakeholders alike:1. Data Preparation & Exploratory AnalysisHow to import and clean quality dataUse of descriptive statistics to assess central tendency and dispersionIdentifying outliers and initial trendsPerforming the Anderson-Darling test to verify normal distribution assumptions2. Process Stability AnalysisConstruction and interpretation of X-bar and R control chartsApplication of all 8 AIAG control tests to identify process instabilitiesUnderstanding subgroup structures and the difference between within-group and between-group variationVerifying whether the process is stable enough to begin capability analysis3. Capability Metrics and Their InterpretationIntroduction to key process capability indicators: Cp, Cpk, Pp, Ppk, and the Cpm (Taguchi Index)Understanding the difference between overall and potential capabilityExplanation of process centering and process shift (Katayori)Visual interpretation through histograms, density functions, and z-benchmarks (Sigma Level)Assessing scrap rates using PPM values (Parts Per Million)4. Root Cause Analysis and OptimizationRoot cause investigation using supplier-coded ID dataCreation of dot plots to compare raw material quality across suppliersImplementing technical and supplier-related improvement actionsEvaluating before-and-after scenarios using the Capability Sixpack tool5. Final Evaluation and Best PracticesComparing pre- and post-optimization results based on capability indices and control chartsInterpretation of visual and statistical outputs to determine long-term capabilityGuidance for saving, documenting, and communicating capability projectsUnderstanding when a process can be considered statistically capable, and how to sustain performanceLearning Outcome:By the end of this course section, participants will be able to:Apply the complete capability analysis cycle from start to finishUse key performance indicators to assess and compare process capabilityIdentify causes of process instability and high variationImplement targeted improvements to meet customer requirementsUse Minitab® tools efficiently, including control charts, capability plots, and the Capability SixpackThis training empowers learners to make data-driven decisions, communicate process capability clearly, and support sustainable quality improvements in real industrial settings.
