Advanced Lean Six Sigma

Advanced Lean Six Sigma Tools and Techniques

What you'll learn
Advanced Tools of Lean Six Sigma
Control Charts
Continuous Data Analysis
Descrete Data Analysis
Hypothesis testing
Linear Regression
Requirements
Six Sigma Fundamentals, General Understanding of statistics
Description
In this course we will:Review six sigma tools such as PDCA, DMAIC, and SIPOCReview statistics, sampling, and how to use excel to do statistical analysisWe will learn about:Measurement system analysis and attribute agreement analysisThe baseline of process performance for discrete data including Yield, DPU, DPMOStatistical process control and control charts, ImR, XbarR, and XbarS charts.Statistical process control charts analysis and how to identify if a process has an issue.The baseline for continuous data and continuous data analyses, sampling distribution, standard error and coefficient of variation, and Z-score, and T-scoresHypothesis testing, confidence interval and central limit theorem, one-sample z-test, t-test, and ANOVALinear regression, covariance, and correlation, use excel for linear regression analysisAt the end of this course, we will complete the Lean Six Sigma project togetherThis course requires you to be familiar with key concepts of quality and lean six sigma as well as some basic knowledge of statistics.This course includes multiple examples. Every time a new concept is introduced, I provide real-life examples and how to apply the new concept.I will walk you through difficult statistics concepts and will teach you how to use statistical tools and interpret the results of the analysis.We will use excel, do to all the heavy lifting for us, and our job will be to learn how to apply the tools and understand the results.I am including all templates and supporting materials and a lot of useful tables that are needed for the analysis, Templates, and tables will be very helpful for you to get started with your lean six sigma project.

Overview

Section 1: Introduction

Lecture 1 Course outline

Section 2: Introduction

Lecture 2 Sig Sigma tools review

Lecture 3 Intorduction to statistics

Lecture 4 2.3 Introduction to sampling

Lecture 5 2.4 Excel Statistics Introduction

Section 3: 3 Measurement System Analysis

Lecture 6 3.1 Measurement System Analysis Introduction

Lecture 7 3.2 Components of Measuring system

Lecture 8 3.3 MSA Example

Lecture 9 3.4 MSA Attribute agreement

Section 4: 4 Baseline and Process Performance

Lecture 10 4.1 Baseline

Lecture 11 4.2 Yield

Lecture 12 4.3 DPU AND DPMO

Section 5: 5 Statistical Process Control

Lecture 13 5.1 Statistical Process Control

Lecture 14 5.2 Individual and Moving Range Control Charts

Lecture 15 5.3 XbarR Charts

Lecture 16 5.4 XbarS Charts

Lecture 17 5.5 Chart Analysis

Section 6: 6 Baseline - Continuous Data

Lecture 18 6.1 Continuous data analysis

Lecture 19 6.2 Normal distribution and Z score

Lecture 20 6.3 Z Score

Section 7: 7 Hypothesis testing

Lecture 21 7.1 Confidence interval

Lecture 22 7.2 Central Limit Theorem

Lecture 23 7.3 Hypothesis Testing

Lecture 24 7.4 Hypothesis Testing in Excel

Lecture 25 7.5 Thought experiment

Lecture 26 7.6 ANOVA

Section 8: 8 Linear Regression

Lecture 27 8.1 Covariance & Correlation coefficient

Lecture 28 8.2 Regression

Lecture 29 8.3 Regression in Excel

Section 9: 9 Project

Lecture 30 9.1 Review of the key concepts

Lecture 31 9.2 Project review

Section 10: Final Word

Lecture 32 Final Word

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