1.0 Introduction: Overview of Six Sigma
1.1 Value of Six Sigma
Recognize why organizations use six, Origins of six sigma
1.2 Organizational drivers and metrics
2.0 Six Sigma—Define
Recognize key drivers for business (profit, market share, customer satisfaction, efficiency, product differentiation) and how key metrics and scorecards are developed and impact the entire organization.
2.1 Process elements
Define and describe SIPOC
2.2 Owners and stakeholders
Identify process owners, internal and external customers, and other stakeholders in a project.
2.3 Identify Customers & Customer Segmentation
Identify and classify internal and external customers as applicable to a particular project, and show how projects impact customers.
2.4 Collect & Classify Customer data
VOC, Survey Methods, Kano Analysis.
2.5 Translate Customer requirements
Translate customer feedback into project goals and objectives, including critical to quality (CTQ) attributes and requirements statements. Use of Quality Function Deployment (QFD) to translate customer requirements into performance measures
2.6 Project Identification & Planning tools
Define, select, and use 1) Affinity Diagrams, 2) Interrelationship Digraphs, 3) Tree Diagrams, 4) Prioritization Matrices, 5)Matrix Diagrams, 6) Process Decision Program (PDPC) Charts, and 7) Activity Network Diagrams – Gnatt Charts, PERT & CPM.
2.7 Organizational goals and six sigma projects
Describe the project selection process, including knowing when to use six sigma DMAIC methodology.
2.8 Project Charter & Project Metrics
Define and describe elements of a Project Charter. Development of metrics – COQ, DPU, DPMO, RTY
2.9 Team stages and DMAIC
Define and describe the stages of team evolution, including forming, storming, norming, performing, adjourning, and connectivity with DMAIC.
2.10 Six sigma - team roles and responsibilities
3.0 Six Sigma—Measure
Describe and define the roles and responsibilities of participants on six sigma teams, including black belt, master black belt, green belt, champion, executive, coach, facilitator, team member, sponsor, process owner, etc.
3.1 Process Mapping
Develop and review process maps, flowcharts, etc.
3.2 Process inputs and outputs
Identify process input variables and process output variables (SIPOC), classify as CTQs & CTPs including Control & Noise CTPs.
3.3 Probability and statistics
Distinguish between enumerative (descriptive) and analytical (inferential) studies, and distinguish between a population parameter and a sample statistic.
3.4 Basic probability concepts
Describe and apply concepts such as independence, mutually exclusive, multiplication rules, etc.
3.5 Types of data and measurement scales
Identify and classify continuous (variables) and discrete (attributes) data. Describe and define nominal, ordinal, interval, and ratio measurement scales.
3.6 Data collection methods
Define and apply methods for collecting data such as check sheets, Stratification, coded data, etc.
3.7 Techniques for assuring data accuracy and integrity
Define and apply techniques such as random sampling, stratified sampling, sample homogeneity, etc.
3.8 Descriptive statistics
Define, compute, and interpret measures of dispersion and central tendency, and construct and interpret frequency distributions and cumulative frequency distributions.
3.9 Graphical methods
Depict relationships by constructing, applying and interpreting diagrams and charts such as stem-and-leaf plots, box-and-whisker plots, run charts, scatter diagrams, Pareto charts, etc. Depict distributions by constructing diagrams such as histograms, normal probability plots, etc.
3.10 Probability distributions
Describe and interpret binomial, and Poisson, normal, chi square, Student’s t, and F distributions
3.11 Central limit theorem and sampling distribution of the mean
Define the central limit theorem and describe its significance in the application of inferential statistics for confidence intervals, control charts, etc.
3.12 Measurement system analysis
Calculate, analyze, and interpret measurement system capability using repeatability and reproducibility (GR&R), measurement correlation, bias, linearity, percent agreement, and precision/tolerance (P/T)
4.1 Cause Analysis
Root Cause Analysis, Cause & Effects Analysis.
4.2 Failure Mode and Effects Analysis (FMEA)
Define and describe failure mode and effects analysis (FMEA). Describe the purpose and use of the risk priority number (RPN).
4.3 Run Charts
Plotting Sequential data & analyze for Normality, Trends, Patterns.
4.4 Multi-Vari studies
Create and interpret multi-vari studies to interpret the difference between positional, cyclical, and temporal variation; apply sampling plans to investigate the largest sources of variation. (Create)
4.5 Simple linear correlation and regression
5.0 Six Sigma—Improve and Control
Interpret the coefficients of co-relation & determination – r & R2 and determine; recognize the difference between correlation and causation. Interpret the linear regression equation and determine its statistical significance. Use regression models
5.1 Process capability and performance
Identify, describe, and apply the elements of designing and conducting process capability studies, including identifying characteristics, identifying specifications and tolerances, developing sampling plans, and verifying stability and normality.
5.2 Process performance vs. specification
Distinguish between natural process limits and specification limits, and calculate process performance metrics such as percent defective. (Evaluate)
5.3 Process capability indices
Define, and calculate Cp and Cpk, and assess process capability.
5.4 Process performance indices
Define, and calculate Pp, Ppk, and assess process performance.
5.5 Short-term vs. long-term capability
Describe the assumptions and conventions that are appropriate when only short-term data are collected and when only attributes data are available. Describe the changes in relationships that occur when long-term data are used, and interpret the relationship between long- and short-term capabilities as it relates to a 1.5 sigma shift.
5.6 Process capability for attributes data
Compute the sigma level for a process and describe its relationship to Ppk.
5.7 Statistical Process Control (SPC)
Define and describe how rational sub-grouping is used. Describe the objectives and benefits of SPC, including controlling process performance, identifying special and common causes, etc.
5.8 Selection and application of Control Charts
Identify, select, construct, and apply the following types of control charts: X-bar −R, Xbar− s, individuals and moving range (I-mR / X-mR), Pre-Control chart, median & moving range, p, np, c, and u.
5.9 Analysis of control charts
Interpret control charts and distinguish between common and special causes using rules for determining statistical control.
5.10 Control plans, SOPs, Work Instructions.
Developing these documents and holds the gains, and assist in implementing controls and monitoring systems.