Certified Lean Six Sigma Green Belt Exam Prep

Explore lean six sigma concepts and tools, including six sigma history, statistics, DMAIC and DMADV, lean principles, change management, and team and stakeholder alignment for project selection.

Identify critical factors in the analyze phase using cause-and-effect analysis, hypothesis testing, and correlation. Move to improve and control through brainstorming, benchmarking, PWG matrix, and design of experiments.

Form, storm, norm, and perform guide Six Sigma team formation, aligning duties from top management to process owners with a project charter, clear roles, and productive stage progression.

Explore the define phase of Dmacc, defining the problem, scope, and deliverables, develop the team charter and guidelines, map processes, translate voice of customer into requirements, and identify quick wins.

Learn to translate voice of customer insights (voc) into critical to customer requirements (csrs) by collecting voc data via focus groups, face-to-face interviews, mail, internet, listening posts, and observations.

Explore the quality function deployment, known as the house of quality, to translate customer wants into design features, map relationships, perform competitive assessment, and derive target values.

Define tools like psypoke to map suppliers, inputs, process, outputs, and customers, reveal gaps, and trigger cause-and-effect thinking through feedback loops, illustrated by closing the general ledger at month end.

learn how to create a complete project charter within the define phase, detailing the business case, opportunity and problem statements, scope, goals, timeline, and project team.

Learn how lean eliminates waste to deliver customer value, and apply the theory of constraints through a five-step process to identify, exploit, subordinate, elevate, and repeat bottlenecks.

Identify the bottleneck in a six-step process, match other steps to the bottleneck pace, elevate the constraint and subordinate non-bottlenecks, then analyze throughput, inventory, and flow time via Little's law.

Value stream mapping identifies value-added and non-value-added activities, creates current and future state maps, and highlights opportunities to reduce waste via a prioritisation matrix.

Learn to identify muda, or waste, across seven forms—overproduction, inventory, waiting, motion, over-processing, transportation, and rework—and apply lean principles to minimize costs and improve value for the customer.

Five S stands for sort, setting order, shine, standardize, and sustain, a lean method to create an organized, clean, and high-performance workplace with traceability and standardized flow.

apply poka yoke techniques to prevent or reveal errors using warning, autocorrect, or shutdown controls, with examples like diesel nozzle and petrol vehicle distinctions and correct plug socket fits.

Learn to apply SMED, a lean manufacturing quick changeover methodology that minimizes downtime by pre-staging tools and materials, reducing setup time, wastes, and costs while boosting throughput and customer satisfaction.

Kanban is a visual signboard card that communicates what work to do and when, triggers the next process, and supports just-in-time scheduling—not an inventory control system—illustrated by parts retrieval flow.

Apply the DMCC measure phase to define and measure the current process with a validated measurement system, perform measurement system analysis, and plan data collection for capability assessment.

Explore measurement system analysis to determine whether the measurement system adds variability, and learn how accuracy, precision, stability, and linearity, along with calibration and traceability, impact reliability.

Identify baseline process performance and establish measurement systems, including the balanced scorecard, in the measure phase. Analyze data with run charts, control charts, scatterplot, and correlation to reveal trends.

Assess precision and data types in measurement systems, exploring repeatability, reproducibility, linearity, stability, calibration, traceability, and continuous versus discrete data.

Evaluate measurement system analysis for continuous data, then attribute data, applying bias, linearity, accuracy thresholds, and gage R&R thresholds, with ANOVA or average and range methods to segregate variation sources.

Demonstrates a gauge R&R study in Minitab, evaluating measurement variation across three parts and three operators, and interpreting x bar charts against tolerance and control limits.

Navigate the session window to review the R and R study inferences, test null and alternate hypotheses, and interpret p-values against alpha for measure part 5.

Assess whether parts and operators contribute to the study using null and alternate hypotheses; a p value above 0.05 supports no impact, while gauge R and R is not acceptable.

Explore attribute agreement analysis for discrete data by evaluating repeatability and reproducibility among three students rating 25 samples twice, using a trainer as reference standard and analyzing results in MindTap.

Explore attribute agreement analysis in measure part 8, assessing repeatability of appraisers, accuracy versus standard, and reproducibility using kappa values, par value, and CAPA value.

Explore the distinction between descriptive and inferential statistics, and learn how population, samples, and sampling methods shape valid conclusions, with baselines like DPU and DPMO calculations.

Compute the true roll throughput yield by accounting for scrap and rework across multiple processes, then multiply individual yields to reveal the defect-free probability.

Compute rolled throughput yield across multiple processes and apply DPO-based sigma level concepts, including long-term versus short-term shifts, with practical Lean Six Sigma examples.

Analyze process capability with CPK, CP, P, C, and PQ to reflect short-term and long-term deviations and gauge six sigma performance for a centered process.

Identify the vital few input factors that most affect the output using the fishbone diagram and the cause-and-effect matrix.

Explore the Pareto approach to identify the vital few defects driving most problems, using mini tab to build a Pareto diagram and group remaining defects as miscellaneous.

Analyze relationships between two variables with scatterplots, and validate insights with data analysis; build scatterplots in MindTap and interpret box plot for dispersion and outliers.

Formulate null and alternate hypotheses, set alpha and beta errors, interpret p-values to validate sampling results, assess confidence level and power, and infer population mean and standard deviation.

Learn how to select and apply hypothesis tests for various data types, including one-sample z tests, t tests, Mann–Whitney tests, and ANOVA, with examples.

Analyze a one-sample t-test to assess whether the bolt mean diameter equals ten mm, using 20 normal samples at 95% confidence, with p = 0.966.

Analyze a one-sample sign test for median on a 20-student dataset against the standard 82, perform a normality check, and not reject the null with p-value 0.8238 and alpha 0.05.

Compare Ram and Sham performance on turnaround times by testing normality, checking equal variances, and conducting a two-sample t-test for means, finding no significant difference (p=0.736).

Examine part 9 to determine whether fuel mileage improves with additive by formulating null and alternate hypotheses and applying normality checks and the Mann-Whitney test when needed.

Understand when to apply the right test for means under varying external conditions. Use a two-sample t test for different materials or machines, and for windmill comparisons under same conditions.

Use a one-way ANOVA to determine whether the mean ticket costs differ across five circuses, after validating normality, testing variances, and examining outliers such as Gem and Starlight.

Explain how to perform one-proportion, two-proportion, and chi-square tests for discrete data, using practical examples to test hypotheses against external standards and assess differences across populations.

Explore how scatterplots and correlation analysis reveal relationships between sales and advertisement expenses in continuous data, quantify strength with the correlation coefficient, and validate via p-value indicating no correlation.

Analyze the relationship between promote and sales using regression, evaluating model strength with R square adjustment, and interpreting the equation sales equals 25.1 plus 0.762 times promote.

Explore design of experiments to identify key factors and optimize treatments, and learn how replication, the main effect, and the interaction effect influence responses in a two-factor example.

Explore the improve and control process through brainstorming guidelines, benchmarking, and multi-voting, plus evaluating concepts with weighted criteria and validating implementation.

establish a control mechanism to sustain improvements by applying data-type specific charts, including imr, x-bar, c, and np charts, with a new chart for variable subgroup sizes.

Examine control charts for quality, including individual moving range, X-bar charts, and p, np, and c charts, with fixed and variable subgroup sizes and defect data.