A review of short and long term data and the impacts that variation has over time.
A review of short and long term data and the impacts that variation has over time.
Thanks John. I don't know the full history behind the situation, but from what I've heard, the 1.5 sigma issue was something that was derived from a situation or project many years ago (perhaps at Motorola or GE), but the debate is centered around there not being much evidence to support it. It's generally used as an estimate, so instead, I recommend just doing some process capability analysis that calculates the short/long term values for you (also expressed as Cpk vs. Ppk), then you won't have to worry about it. Frankly, in my many years of using LSS across hundreds of projects, I may have only encountered the potential use of that 1.5 sigma factor a couple of times.
Hi Matt. In the textbook there is a note about debate over the validity of 1.5sigma as an accepted variation for short-term processes over time. Would you expound on the debate? Thanks. John Maxwell
A general overview of Lean and Six Sigma concepts including some generic tools that can be used for finding, prioritizing, and managing Lean and Six Sigma projects and initiatives. |
1. StatStuff Orientation (What is StatStuff?) This video welcomes you to StatStuff by explaining some general concepts about how the videos are designed and how the site works. |
2. Introduction to Lean and Six Sigma An introduction to the fundamental concepts of the Lean and Six Sigma methodologies using the IPO model. |
3. Lean and Six Sigma Project Methodologies An introduction to five project methodologies (Lean, DMAIC, DMADV, DFSS & PMI) and when to use each. |
4. Corporate CTQ Drilldown A review of how to align a project opportunity to the overall business strategy and needs by understanding the business CTQs in a CTQ Drilldown. |
5. Project Financial Benefits A review of how to identify and categorize financial benefits from a project. |
6. Prioritization Using a QFD Tool A review of how a Quality Functional Deployment (QFD) tool can be used to prioritize items, such as project opportunities. |
7. Project Pre-Assessment Using a Min/Max Analysis A review of why it’s important to do a project pre-assessment and how to do it using a Min/Max Analysis. |
8. Key Roles in a Lean or Six Sigma Project A review of the project and functional roles in Lean Six Sigma projects like Green Belt, Black Belt, Sponsor, Champion, etc. |
9. Developing a Project Strategy Using IPO-FAT Tool A review of how to build a strategy for a project and how the IPO-FAT tool can be used for developing that strategy. |
10. Building a Project Storyboard A review with examples of how to effectively communicate the progress of a project using a project storyboard. |
11. Analysis of Behavior & Cognition (ABC) Model A review of the ABC model that explores how we think so we can understand the risks and evidence behind our decisions and how to influence others. |
12. Change Acceleration Process (CAP) Model A review of the CAP model that outlines a change mgmt method and set of tools for getting buy-in and ensuring successful implementation of the change. |
A mix of the most common Lean tools and concepts that are more specifically applied for improving process efficiencies. |
1. Introduction to Lean An introduction to Lean including a brief history, the philosophy of Lean, and a summary of some common Lean tools and concepts. |
2. System Flow Methods An introduction to the Lean concept of system flow methods such as one piece flow, push vs. pull systems, and just-in-time inventory. |
3. Kanban Systems An introduction to the Lean concept of improving and monitoring efficiency through visual cues called Kanban systems. |
4. Value Added An introduction to the Lean concept of identifying value-added and non-value-added steps within a process. |
5. 7 Deadly Wastes An introduction to the Lean concept on the 7 deadly forms of waste that can be found within a process. |
6. 5S Program An introduction to the Lean concept of the 5S program and how it can help keep improve and sustain efficiency in a process. |
7. Work in Process (WIP) An introduction to the Lean concept of work in process (WIP) as a form of significant waste within a process. |
8. Poka Yoke An introduction to the Lean concept of poka-yoke and how it can be used to help sustain process improvements. |
9. Spaghetti Diagram An introduction to the Spaghetti Diagram, a Lean tool that helps expose motion and transportation forms of waste. |
10. First Time Yield (FTY) and Rolled Throughput Yield (RTY) An introduction to first time yield (FTY) and rolled throughput yield (RTY) metrics and how they can be used to measure process performance. |
11. Takt Time An introduction to the takt time metric and how they can be used to measure process flow. |
12. Value Stream Maps (VSM) An introduction to Value Stream Maps (VSM), a Lean tool used for tracking various elements within the steps of a process. |
13. Adapting Lean to Six Sigma DMAIC Flow A description of how the Lean tools and concepts can be adapted to the Six Sigma DMAIC methodology. |
14. Leading a Lean Workout (Kaizen Event) A review of how to improve a process by leading a Lean workout (a.k.a. Kaizen Event). |
A general overview of tools and concepts that apply to Six Sigma projects, especially those using the DMAIC methodology. |
1. Problem Resolution Using DMAIC A review of how the DMAIC methodology follows the typical steps we follow when trying to resolve a problem. |
2. Risk Analysis: The Reason We Use Statistics A review of the importance of risk in our decision-making and how statistics can be used to measure that risk. |
3. Overview of Statistical Terms and Concepts A high-level review of the fundamental terms and concepts associated with statistics, such as population vs. sample data, distributions, etc. |
4. Transfer Function A review of the transfer function and the critical part it plays as a fundamental concept in the DMAIC methodology. |
5. The DMAIC Roadmap (Levels 1 & 2) A high-level roadmap through the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
The most common tools and concepts that pertain to the Define phase of the DMAIC methodology of Six Sigma which is intended to help us understand the problem we're trying to solve. |
1. Define Phase Roadmap (Level 3) A detailed roadmap through the Define phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
2. Building a Problem Statement A review of what a problem statement and background statement are and the characteristics of an ideal problem statement. |
3. Defining a Project Scope A review of what a project scope is, the value it adds to a project, and how to define it. |
4. Building a Project Team A review of how to build a project team and stakeholder analysis using the ARMI tool. |
5. Building a SIPOC A review of how to extend the IPO flow model by building a SIPOC. |
6. Building a Process Map A review of how to extend the IPO flow model and SIPOC by building a process map. |
7. Compiling Operational Definitions A review of what operational definitions are and how to compile them for a project. |
8. Setting Project Milestones A review of how to set milestones for a typical Six Sigma DMAIC project. |
9. Building a Project Charter A review of how to compile the various Define Phase tools for building a project charter for a typical Six Sigma DMAIC project. |
The most common tools and concepts that pertain to the Measure phase of the DMAIC methodology of Six Sigma which is intended to help us ensure we're gathering reliable data for the problem we're trying to solve. |
1. Measure Phase Roadmap (Level 3) A detailed roadmap through the Measure phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
2. The Necessity of the Measure Phase A review of why the Measure phase is so important to the DMAIC process and why it’s so often neglected. |
3. Different Sources of Data A review and comparison between the different sources of where we generally collect data. |
4. Data Configuration for Analysis A review of how to configure data into an ideal format for doing statistical analysis in Minitab. |
5. Advanced Excel Features A review of many features and functions in Excel that are often essential for configuring and analyzing data. |
6. Population vs. Sample Data A review of population data and sample data and how we use them in statistical analysis. |
7. Data Types A review of discrete and continuous types of data and the differences between each type. |
8. Distributions: Overview A review of distributions and how they can be formed using dotplots and histograms. |
9. Distributions: Normal A review of normal distributions and how to test their normality using a normality test. |
10. Distributions: Non-Normal A review of non-normal and bi-modal distributions and how to test them using a normality test. |
11. Central Tendency A review of the various measurements for central tendency, especially the mean and median. |
12. Spread A review of the various measurements for spread or variation that include variance, standard deviation, inter-quartile range, etc. |
13. Comparing Distributions and using the Graphical Summary A comparison of different types of simple distributions and how to run and interpret Minitab's graphical summary. |
14. Variation Causes (Common vs. Special) A review of the two main types of variation that can affect a process – common cause variation and special cause variation. |
15. Statistical Process Control (SPC) An introduction to some of the concepts of statistical process control (SPC) and how it’s used for measuring variation. |
16. Testing for Special Cause Variation A review of 8 different tests for special cause variation applied to an IM-R chart. |
17. Variation Over Time (Short/Long Term Data) A review of short and long term data and the impacts that variation has over time. |
18. Rational Sub-Grouping A review of how we sub-divide data for analysis using rational sub-grouping. |
19. Calculating a Sample Size A review of how to calculate a sample size using a Sample Size Calculator. |
20. Defining the Project Y A review of why we need to define a project Y and some methods for ensuring we’re defining the right project Y. |
21. Defining the VOC and Defects A review of what is the voice of the customer (VOC) and how it’s used for defining various types of defect measurements. |
22. Identify Root Causes: DCP Overview An introduction to the extended topic on identifying root causes using a variety of tools that will help build a data collection plan (DCP). |
23. Identify Root Causes: C&E Diagram An extension of the topic on identifying root causes using a cause & effect (C&E) diagram that will lead toward building a data collection plan (DCP). |
24. Identify Root Causes: 5 Whys An extension of the topic on identifying root causes using a 5 Whys approach that will lead toward building a data collection plan (DCP). |
25. Identify Root Causes: Combining the C&E Diagram & 5 Whys An extension of the topic on identifying root causes by showing how the C&E diagram and 5 Whys approach can be combined for building a DCP. |
26. Identify Root Causes: C&E Matrix An extension of the topic on identifying root causes by showing how the C&E Matrix is used after a C&E diagram and 5 Whys for building a DCP. |
27. Identify Root Causes: Building the DCP The last extension of the topic on identifying root causes by showing how to take the information gathered so far and build the DCP. |
28. MSA: Overview The first of an extended series on conducting a measurement system analysis (MSA) to help test the reliability of collected data. |
29. MSA: Planning & Conducting the MSA An extended review on the series on building a MSA that covers the first two steps on how to plan and conduct the MSA. |
30. MSA: Attribute ARR Test An extended review on the series on building a MSA, this covers the 3rd step of analyzing the results by using the Attribute ARR test. |
31. MSA: Gage R&R An extended review on the series on building a MSA, this covers the 3rd step of analyzing the results by using the Gage R&R test. |
32. MSA: Improving the Measurement System The last part of an extended review on the series on building a MSA, this covers the 4th step of improving the measurement system if the MSA fails. |
The most common tools and concepts that pertain to the Analyze phase of the DMAIC methodology of Six Sigma which is intended to help us apply analytical tests on the collected data for finding the root cause of the problem we're trying to solve. |
1. Analyze Phase Roadmap (Level 3) A detailed roadmap through the Analyze phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
2. Descriptive Statistics An introduction to some less common yet very useful statistics that help to describe the data we’re analyzing. |
3. Process Capability: Overview The first of a series of lessons about process capability; this lesson defines what process capability is and reviews a method for calculating it. |
4. Process Capability: Steps 1 - 3 As part of a series about process capability, this lesson reviews the first 3 steps for following a method for calculating the capability of a process. |
5. Process Capability: Step 4 (Normal Dist) As part of a series about process capability, this lesson shows how to assess the capability of a process that’s based on a normal distribution. |
6. Process Capability: Step 5 (Non-Normal Dist) As part of a series about process capability, this lesson shows how to assess the capability of a process that’s based on a non-normal distribution. |
7. Process Capability: Step 6 (Binomial) As part of a series about process capability, this lesson shows how to assess the capability of a process that’s based on discrete or binomial data. |
8. Defining Performance Objectives A review of how to define the performance objectives based on the results of a process capability analysis. |
9. Hypothesis Testing: Overview An introductory overview to an extended series about hypothesis testing. This lesson includes the general 4 step process used for hypothesis testing. |
10. Hypothesis Testing: Formal & Informal Sub-Processes An extension on a series about hypothesis testing, this lesson builds on the prior 4 steps for hypothesis testing by looking at the 6 basic sub-steps. |
11. Hypothesis Testing: Statistical Laws and Confidence Intervals An extension on a series about hypothesis testing, this lesson introduces some statistical concepts that are fundamental to most hypothesis testing. |
12. Hypothesis Testing: Finding the Right Statistical Test An extension on a series about hypothesis testing, this lesson reviews a chart that can help you find the right statistical test for your analysis. |
13. Hypothesis Testing: Proportions (Compare 1:Standard) An extension on a series about hypothesis testing, this lesson reviews the 1 Proportion Test as a measurement of proportions. |
14. Hypothesis Testing: Proportions (Compare 1:1) An extension on a series about hypothesis testing, this lesson reviews the 2 Proportions Test as a measurement of proportions. |
15. Hypothesis Testing: Proportions (Compare 2+ Factors) An extension on a series about hypothesis testing, this lesson reviews the Chi2 Test (Goodness-of-Fit & Association) as a measurement of proportions. |
16. Hypothesis Testing: Cent Tend-Normal (Compare 1:Standard) An extension on a series about hypothesis testing, this lesson reviews the 1 Sample T test as a central tendency measurement for normal distributions. |
17. Hypothesis Testing: Cent Tend-Normal (Compare 1:1) An extension on a series about hypothesis testing, this lesson reviews the 2 Sample T & Paired T tests as central tendency measurements for normal distributions. |
18. Hypothesis Testing: Cent Tend-Normal (Compare 2+ Factors) An extension on a series about hypothesis testing, this lesson reviews the ANOVA test as a central tendency measurement for normal distributions. It also explains what residuals and boxplots are and how to use them with the ANOVA test. |
19. Hypothesis Testing: Cent Tend-Non Normal (Nonparametric Tests Overview) An extension on hypothesis testing, this lesson explains what nonparametric tests are and how they’re used for non-normal distributions. |
20. Hypothesis Testing: Cent Tend-Non Normal (Compare 1:Standard) An extension on hypothesis testing, this lesson reviews the 1 Sample Sign & Wilcoxon tests as central tendency measurements for non-normal distributions. |
21. Hypothesis Testing: Cent Tend-Non Normal (Compare 1:1) An extension on hypothesis testing, this lesson reviews the Mann-Whitney test as a central tendency measurement for non-normal distributions. |
22. Hypothesis Testing: Cent Tend-Non Normal (Compare 2+ Factors) An extension on hypothesis testing, this lesson reviews the Mood’s Median & Kruskal-Wallis tests as central tendency measurements for non-normal distributions. |
23. Hypothesis Testing: Spread (Compare 1:Standard) An extension on hypothesis testing, this lesson reviews the 1 Variance test as a measurement of spread or variation. |
24. Hypothesis Testing: Spread (Compare 1:1) An extension on hypothesis testing, this lesson reviews the 2 Variance test as a measurement of spread or variation. |
25. Hypothesis Testing: Spread (Compare 2+ Factors) An extension on hypothesis testing, this lesson reviews the Test for Equal Variances as a measurement of spread or variation. |
26. Hypothesis Testing: Relationships (Overview) An extension on hypothesis testing, this lesson introduces the concepts of a correlation and regression as part of measuring statistical relationships. |
27. Hypothesis Testing: Relationships (Compare 1:1) An extension on hypothesis testing, this lesson reviews the Pearson Correlation and Fitted Line Plot as part of measuring statistical relationships. |
28. Hypothesis Testing: Relationships (Compare 2+ Factors) An extension on hypothesis testing, this lesson reviews the multiple regression and GLM as part of measuring statistical relationships. |
The most common tools and concepts that pertain to the Improve phase of the DMAIC methodology of Six Sigma which is intended to help us find and pilot what improvements will fix the root cause of the problem we're trying to solve. |
1. Improve Phase Roadmap (Level 3) A detailed roadmap through the Improve phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
2. Compiling Analysis Results A review of how the various results from many hypothesis tests in the Analyze phase can be compiled in a simplified format. |
3. Testing for Multicollinearity A review of how we can assess if the factors tested in the hypothesis tests in the Analyze phase have multicollinearity (i.e., interdependency). |
4. Brainstorm & Prioritize Solutions with a Workout A review of how we can run a workout to brainstorm and prioritize solutions that will fix the root cause. |
5. Brainstorm Solutions with an Affinity Diagram A review of how we can build an Affinity Diagram as part of a workout for brainstorming solutions that will fix a root cause. |
6. Prioritize Solutions with an Impact Matrix A review of how we can build an Impact Matrix and PICK chart as part of a workout for prioritizing solutions that will fix a root cause. |
7. Risk Assessment with a FMEA Tool A review of the importance of assessing risk and how to measure it using a FMEA tool. |
8. Piloting Solutions: The Process A review of the process for successfully building and executing a pilot, which is a method for testing potential improvements to implement. |
9. Piloting Solutions: Build the Pilot Plan A review of the process for successfully building a pilot plan, which is used for managing and communicating all potential improvements to pilot. |
The most common tools and concepts that pertain to the Control phase of the DMAIC methodology of Six Sigma which is intended to help us sustain the improvements that fixed the root cause of the problem we're trying to solve. |
1. Control Phase Roadmap (Level 3) A detailed roadmap through the Control phase of the DMAIC methodology that navigates the user through the various tools and concepts for leading a Six Sigma project. |
2. Building a Scorecard A review of when and how to build a scorecard for key metrics such as the output Y for improvements that were piloted or implemented. |
3. Control Charts: Finding the Right Control Chart A review of a method that can be used to easily find the right control chart for the situation at hand. |
4. Control Charts: I-MR Chart A review of when and how to use the I-MR control chart. |
5. Control Charts: Xbar-S Chart A review of when and how to use the Xbar-S control chart. |
6. Control Charts: P Chart A review of when and how to use the P control chart. |
7. Control Charts: U Chart A review of when and how to use the U control chart. |
8. Control Charts: Recalculating Control Limits A review of how control limits within control charts can be recalculated to account for process changes such as implementing improvements. |
9. Building a Control Plan A review of how to build a control plan to help sustain the implemented improvements. |
10. Documenting a New Process with SOPs A review of the necessity and how to build standard operating procedures (SOPs) when making changes to a process. |
11. Closing a Project A review of the actions that are essential for successfully closing a project. |
12. Getting Feedback with a Plus/Delta Tool A review of how to acquire feedback from the team by using various tools including a Plus/Delta tool. |