Contact us at 731-425-8826 or workforce@jscc.edu to develop a training plan for your organization.
MFG-SS-101 | Introduction to Six Sigma and DMAIC
Duration: 8 Hours Format: In-Person or Online Audience: All Manufacturing Employees
Six Sigma is a data-driven methodology for eliminating defects and reducing variation in manufacturing and business processes. This introductory course provides an accessible overview of Six Sigma philosophy and the DMAIC problem-solving framework (Define, Measure, Analyze, Improve, Control). Participants will learn how Six Sigma relates to Lean and other continuous improvement efforts already underway in their organization.
Learning Objectives
- Define Six Sigma and explain its goals in terms of defects per million opportunities (DPMO)
- Describe the five phases of the DMAIC methodology and the key activities in each phase
- Explain how Six Sigma complements Lean manufacturing and continuous improvement efforts
- Identify the different Six Sigma belt levels and their roles in a Six Sigma organization
- Recognize opportunities in their work area where Six Sigma methods could reduce defects
MFG-SS-102 | Six Sigma White Belt
Duration: 4 Hours Format: In-Person or Online Audience: All Manufacturing Employees
The Six Sigma White Belt course provides foundational awareness for employees who participate in or support Six Sigma improvement projects. No prior knowledge of statistics or quality methods is required. This half-day course gives participants the vocabulary, context, and understanding they need to contribute meaningfully to their organization's Six Sigma initiatives.
Learning Objectives
- Explain the fundamental concepts and goals of Six Sigma in plain language
- Describe how variation in a process affects product quality and customer satisfaction
- Define key Six Sigma terms including DPMO, CTQ, VOC, and process capability
- Describe the roles of team members, champions, and belts in a Six Sigma project
- Explain how the DMAIC framework applies to problem-solving in their work area
MFG-SS-103 | Six Sigma Yellow Belt
Duration: 16 Hours (2 Days) Format: In-Person Audience: Front-Line Employees, Technicians, Team Leads
The Six Sigma Yellow Belt course builds on White Belt awareness with hands-on use of basic quality and problem-solving tools. Participants will practice data collection, basic statistics, process mapping, cause-and-effect analysis, and control chart reading through guided exercises. Yellow Belts support Green and Black Belt projects and can lead small-scale improvement projects in their own work areas.
Learning Objectives
- Apply basic data collection methods and construct simple data summary tables and charts
- Create process maps and flowcharts to document current-state processes
- Use cause-and-effect (fishbone) diagrams and affinity diagrams to organize improvement ideas
- Interpret basic descriptive statistics including mean, median, range, and standard deviation
- Read and interpret simple control charts to distinguish common and special cause variation
- Participate effectively as a team member on a Six Sigma Green or Black Belt project
MFG-SS-104 | Six Sigma Green Belt Preparation
Duration: 40 Hours Format: In-Person or Hybrid Audience: Quality Professionals, Engineers, Supervisors
This comprehensive course prepares participants for the responsibilities of a Six Sigma Green Belt, including leading small-to-medium improvement projects and supporting Black Belt projects. The curriculum covers all five DMAIC phases in depth, with instruction in measurement system analysis, hypothesis testing, regression analysis, FMEA, and project leadership. Participants are guided through a project application as part of the course.
Learning Objectives
- Lead a Six Sigma DMAIC improvement project from Define through Control
- Develop a project charter and SIPOC diagram to scope and define an improvement project
- Conduct Measurement System Analysis (MSA) including gage R&R studies
- Apply hypothesis testing (t-tests, ANOVA, chi-square) to analyze process data
- Use regression analysis to identify and quantify relationships between process inputs and outputs
- Conduct a Failure Mode and Effects Analysis (FMEA) to prioritize risk reduction efforts
- Develop a control plan and implement monitoring methods to sustain improvements
MFG-SS-105 | Minitab for Six Sigma Practitioners
Duration: 16 Hours (2 Days) Format: In-Person or Hybrid Audience: Green Belt Candidates, Quality Analysts
Minitab is the industry-standard statistical software used by Six Sigma practitioners worldwide. This two-day hands-on course teaches participants to use Minitab for the most common Six Sigma analyses, including data visualization, control charts, process capability, hypothesis testing, and regression. Participants will complete exercises using realistic manufacturing datasets throughout the course.
Learning Objectives
- Navigate the Minitab interface and manage data worksheets effectively
- Create and interpret graphical summaries including histograms, boxplots, and scatter plots
- Construct and analyze control charts (X-bar R, I-MR, p, c) using Minitab
- Calculate process capability indices (Cp, Cpk, Pp, Ppk) and interpret the results
- Conduct and interpret hypothesis tests including t-tests, ANOVA, and chi-square tests
- Perform simple and multiple regression analysis and interpret the output
- Use Minitab to support DMAIC project analysis from Measure through Control
MFG-SS-106 | Design of Experiments (DOE) Introduction
Duration: 8 Hours Format: In-Person Audience: Engineers, Quality Professionals, Six Sigma Practitioners
Design of Experiments (DOE) is a powerful statistical tool that allows manufacturers to efficiently identify the process inputs that most significantly affect output quality — and to optimize those inputs for maximum performance. This introductory course covers factorial experiment design, factor and response selection, and how to analyze and interpret DOE results to drive process improvement.
Learning Objectives
- Explain the purpose of Design of Experiments and when it is appropriate to use DOE
- Identify factors, responses, and levels for a manufacturing DOE application
- Design a full or fractional factorial experiment for a manufacturing process
- Analyze DOE results to identify significant main effects and interactions
- Interpret a main effects plot and interaction plot to understand factor relationships
Apply DOE findings to optimize process settings and improve product quality
