Courses for Six Sigma Belt Certifications

ZDM offers the following courses to students interested in earning their Six Sigma designations.

Click a Six Sigma course title to view the course details:

Six Sigma Course 1:
Intro to Six Sigma and Measurement Reliability—4 days

Objectives: Develop the context for Six Sigma methodology, lay the groundwork for understanding six sigma tools, and prepare participants for implementing measurement process validation systems.

  • Intro to Six Sigma—What it really means, benefits and cautions.
  • Variation—the real consequences, a general and specific strategy against variation.
  • Basic Statistics—Histograms, Distributions, Parameters and Statistics, Detecting Process Change, the Central Limit Theorem.
  • Basic Control Chart Theory—Defining the distribution of common cause variation, rational subgroup sampling, converting the histogram to a control chart.
  • X and MR charts—sampling and data treatment for calculating control limits, calculation of control limits.
  • Measurement Process Validation—Precision vs. Accuracy and their relevance to process control, Differentiating measurement objectives, Repeatability and Reproducibility, Qualifying test methods and their application and technicians, continuous validation
  • SOPs for measurement process R&R studies and validation.
  • Project assignments.

Six Sigma Course 2:
Control Charting—4 days

Objectives: Prepare participants for the selection, application, and analysis of statistical control charts.

  • Review of basic statistics, basic control chart theory, rational subgroups, X and MR charts.
  • Xbar and R charts—their construction, their use, when to and when not to apply.
  • Group charts and other solutions to multiple stream processes (injection, blow molding, filling processes).
  • The Exponentially Weighted Moving Average (EWMA) chart, advantages and their application for PET preform and container manufacture.
  • Attributes charts—P, nP, C, U charts and alternatives for PET preform and container manufacture.
  • Control chart analysis, the required organizational discipline, Pareto charts for summarizing special causes and developing systemic solutions.
  • SOPs for control chart selection, sampling, interpretation and reaction to control chart signals.
  • Daily cross functional process performance review
  • Ishikawa fishbone diagrams for team problem solving
  • Project assignments.

Six Sigma Course 3:
Targeting and Assessing the Effectiveness of Six Sigma Processes and Quality Systems—4 days

Objectives: Prepare participants to assess the effectiveness of Six Sigma efforts and the adequacy of quality systems, how to measure process capability and performance and the effects on productivity.

  • Process mapping and a functional analysis of the quality system.
  • Preparing for the process and product design through Failure Modes and Effects Analysis (FMEA).
  • Assuring the adequacy of processes through an analysis of their capability of meeting specifications (Cp, Cpk).
  • Daily assessment of process performance relative to specifications (Pp, Ppk).
  • Process performance with regard to stability and variation.
  • Measuring the effects of the Six Sigma methodologies on the bottom line—Equipment and Raw Material Effectiveness Ratings (EER and RME).
  • Analysis, interpretation, communication of capability and performance measurement, and the discipline required to make it work for us.
  • SOPs for the creation of and use of capability studies and performance measures.
  • Project assignments

Six Sigma Course 4:
Design of Experiments—2-level factorials—4 days

Objectives: Prepare participants for the designing and performing basic statistically designed experiments for process design modeling and trouble shooting

  • Intro to designed experiments (DOE), comparison with traditional methods of studying processes.
  • Review of basic statistics and the concepts of statistical significance.
  • SOP for designing and documenting a DOE and design strategy.
  • Design and manual analysis of two-level factorial studies including calculation of effects, normal probability plots of effects, determining statistical significance with T and F tests, interaction, plots, and model creation.
  • Design and analysis with software including analysis of effects, models, effects and interaction plots, and process optimization and model extrapolation.
  • Fractional factorial designs—aliased effects, risks associated with them and how to manage them, fold over designs.
  • Running confirmation studies.
  • DOE strategies for PET preform and blow manufacture, labeling, palletizing processes and measurement processes.
  • SOPs for designing, documenting and reporting on DOEs.
  • Project assignments

Six Sigma Course 5:
Design of Experiments—Response Surface Methodology (RSM), Robust Design—4 days

Objectives: Prepare participants for the designing and performing advanced statistically designed experiments for process design and optimization, modeling non-linear process responses, trouble shooting, and for comparing raw material, process technology, and product design alternatives.

  • Review of 2-level full and fractional factorials.
  • Dealing with curvature by augmenting 2-level factorial design to create RSM designs—design decisions, interpretation of results, reading interaction and contour plots.
  • Designing studies when initiating the study with RSM designs.
  • Reaching true optimization with following RSM designs following the path of steepest ascent—maximizing predicted response levels.
  • Developing robust processes with RSM designs, comparing solutions for robustness.
  • Comparing performance of process technologies, raw materials, product design for PET preforms and containers, process windows, confidence intervals, box and whisker plots.
  • SOPs for comparison studies for process technology, raw materials, product design.
  • Project assignments.

Six Sigma Course 6:
Hypothesis Tests, Regression analysis—4 days

Objectives: Teach participants to compare processes with hypothesis tests and relate variables through regression analysis.

  • Review of hypothesis testing concepts and relation to control charting, and DOE.
  • Summary of typical hypothesis tests: T, paired T, F, ANOVA—which are applicable when.
  • Selecting and performing hypothesis tests.
  • Scatter diagrams for relating two variables.
  • Intro to regression analysis.
  • Least squares method of regression analysis.
  • Assessing the reliability of models developed through regression analysis—R2, Confidence and Prediction Intervals
  • SOPs for running and diagnosing hypothesis and regression analysis.
  • Project assignments