Lean Six Sigma Black and Green Belt Training & Certification

From a business problem to a statistical model and back, the Dmaic Six Sigma framework identifies key drivers and root causes to sustain improvements.

Explore measures of central tendency and dispersion, including mean, median, mode, and standard deviation. Understand histograms, normal and skewed distributions, and when to use percentiles and quartiles for data insight.

Differentiate precision from accuracy through visuals and data, using standard deviation and range to judge consistency. Learn to balance effectiveness and efficiency to improve processes and satisfy customers.

Explore how process sigma relates to standard deviation, quantify variation against customer specifications, and compare Six Sigma performance with common quality levels.

Develop reliable and accurate performance measures by scrutinizing measurement systems and data, and align end-to-end metrics with customer expectations through clear definitions, exclusions, and sampling to manage variation.

Learn how to create a high-level Sipoc map from supplier input to customer outputs, capturing inputs, process steps, outputs, and suppliers for an end-to-end, customer-centric view.

Explore how a project charter legitimizes and guides delivery by detailing the business case, metrics, problem statement, scope, roles, sign-off, annualized financial benefits, and big Y alignment.

Compute the DPMO and sigma level, distinguish defects from defectives, and review data types—qualitative versus quantitative, discrete versus continuous.

Pinpoint the critical factors in the analyze phase by evaluating factors with data analysis techniques—box plots, cause-and-effect diagrams, control charts, Pareto analysis, and five why analysis—to finalize essential elements.

Use control charts to monitor processes, distinguish common and special causes, and flag data outside control or specification limits for root-cause analysis in the Six Sigma analyze phase.

Build p and np control charts for attribute data like daily customer complaints with varying lot sizes. Compute p-bar and np-bar, set upper and lower control limits, and interpret chart.

Use box plots to detect outliers and pinpoint root causes across loan processing stages—initial application review, underwriting, and final approval—by analyzing the interquartile range (Q1–Q3) and the median.

Learn to use a fishbone diagram and structured brainstorming to identify causes, organize them into categories, apply reframing techniques, and prioritize with Pareto analysis to improve processes.

Apply Pareto analysis to filter critical causes and focus on the vital few per the 80/20 principle, using an Excel Pareto chart to identify six factors with five why analysis.

Identify value added time and various non-value added types in lean processes, from setup and waiting to queue time, and examine training as value enabling.

Explore simple and multiple linear regression to identify critical factors driving customer satisfaction, using r-squared and correlation, with hands-on Excel steps: scatter plots, trend lines, and polynomial options.

Demonstrates multiple linear regression with dependent and multiple independent variables, confidence levels, anova significance, and r-squared to identify that resolution time and delivery time are significant, while tickets are not.

Learn logistic regression on a binary outcome, encoding y as numeric, and evaluate vibration, temperature, and tenure as predictors using the logit function in the Excel data analysis pack.

Explore how representative random samples infer population trends and apply hypothesis testing to evaluate null and alternative hypotheses, using p values and proportional representation.

Explore major hypothesis tests - z, one-sample t, two-sample t, paired t, F test, ANOVA, and chi-square - and learn to state null hypotheses, compute statistics, and interpret p values.

Explore how to run hypothesis tests in Excel, including two-sample t tests, paired t tests, ANOVA, and chi-square, using the data analysis pack and interpreting p-values and normality.

Learn to perform failure mode effects analysis (FMEA) to identify failure modes, assess severity, occurrence, and detectability, compute risk priority numbers, and prioritize preventive actions within a Six Sigma framework.

Explore ten practical fmea rating scenarios presented in a question-and-answer format, illustrating how to assign severity and likelihood ratings for defects from misaligned door seals to slow app performance.

In the improve phase, turn verified root causes into action by prioritizing high‑impact, in‑control items using the control impact matrix, piloting and validating in a controlled environment before scaling.

Sustain improvements by handing the process to operations, proving three consecutive months of performance, and securing an airtight, signed closure. Use mistake proofing and control charts to prevent degradation.

Explore mistake proofing or poka yoke, designing systems that make errors rare or impossible. Combine with SPC and control charts to detect remaining variation and sustain reliable performance.

Control charts monitor process stability in the control phase, using variable and attribute charts to sustain improvements and detect special causes with Western Electric rules.

Investigate issues through end-to-end process review using control charts and audits to identify root causes. Define corrective and preventive actions with clear ownership and timelines to sustain improvements.

emphasizes customer centricity and measurement, alignment on problem and goal, pilots for action plans, control charts, handover, and three-consecutive months of improvements with sign-off to complete six sigma's five-step process.

This case study examines a restaurant project using Lean Six Sigma approaches to cut service time from 20 to 15 minutes, linking improvements to revenue and customer satisfaction.