
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.
 DMAIC
 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—2level 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 twolevel 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 nonlinear process responses, trouble shooting, and for comparing raw material, process technology, and product design alternatives.
 Review of 2level full and fractional factorials.
 Dealing with curvature by augmenting 2level 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

