Certified Lean Six Sigma Green Belt Training (Accredited)

Define phase of DMAIC covers project identification, voice of the customer, and essential planning tools to set up Six Sigma project goals and outcomes, including DPMO and cost of quality.

Identify and select Six Sigma projects from internal and external inputs, using voice of the customer, market, and competitors, and apply input-process-output concepts with owners to benchmark processes.

Identify a process with input, output, and feedback, and apply a Six Sigma project to improve output by adjusting inputs, acknowledging output is a function of inputs.

Discover benchmarking basics for project selection by comparing processes, products, and performance to best in class. Distinguish internal versus external benchmarking and the three benchmarking types: process, performance, and strategic.

Learn how SIPOC maps a process by identifying suppliers, inputs, process steps, outputs, and customers to familiarize cross-functional teams with the workflow and plan improvements.

Identify owners and stakeholders during project identification and map their level of interest and influence using a simple matrix, with a globe layered view from core owners outward.

Identify customers and their data to determine requirements using the voice of the customer framework, distinguishing internal, external, and intermediate customers, and applying quality function deployment.

Gather customer data through surveys, focus groups, interviews, meetings, and observations. Craft clear, unbiased questions with rating scales, ensure historical relevance, and analyze feedback to define customer needs.

Translate the voice of the customer into product features using quality function deployment (QFD), capturing data from surveys and interviews and mapping what to how with targets and priority.

Explore practical quality function deployment (QFD) using a template to capture customer wants, assign weights, and analyze how options affect outcomes, aligning with ASQ body of knowledge.

Explore how voice of the customer translates into measurable CTQ and CTX requirements, with clinic examples on timeliness and doctor consultation time, and preview the Kano model.

Apply the Kano model to the voice of the customer, classifying features into must-be, one-dimensional, attractive, indifferent, and reverse qualities to map satisfaction against implementation.

Explore project management basics for Six Sigma projects, including the project charter, problem statement, scope, and resources, with examples and guidelines to avoid premature conclusions.

Define project scope within a Six Sigma charter; limit to two to three months, specify depth and width, and target vital few defects using Pareto analysis.

Learn to plot Pareto charts in Minitab 18 using consolidated defect data or raw lists, by navigating stats and quality tools and entering defects and frequencies.

Learn how to plan and track Lean Six Sigma projects using Gantt charts, CPM, and PERT, with DMAIC deliverables and overlapping activities over a 12-week timeline.

The lecture explains the critical path method (CPM) for planning projects, detailing dependencies, predecessors and successors, early and late start/finish times, float, and the CPM critical path.

Explore the PERT technique in project planning, using optimistic value, most likely value, and pessimistic value to compute the expected time and standard deviation, and compare with CPM and Gantt.

Explore how a six sigma project begins with a charter, scope, and planning tools. Understand how Gantt chart, PERT, and CPM support documentation and gate reviews through DMAIC stages.

Learn to identify, analyze, and respond to project risks using FMEA, risk registers, and probability and impact matrices, guiding plan, monitor, and control actions for Six Sigma projects.

Understand that risk can be positive or negative and apply strategies: avoid, mitigate, transfer, and accept for negative risk; exploit, enhance, share, and accept for positive risk, supported by FMEA.

Close projects by verifying achievement of charter objectives, archiving key documents, and capturing lessons learned—both positives and negatives—while revising processes and templates for future work.

Learn the 2022 body of knowledge additions for the certified lean six sigma green belt, including work breakdown structure with the 100% rule, and Toll Gate Review.

Learn how toll gate reviews serve as checkpoints in DMAIC, confirming completion of Define, Measure, Analyze, Improve, and Control stages and guiding progression or project cancellation.

Define risk as effect of uncertainty on objectives, acknowledging it may be positive or negative, and summarize ISO 31000–driven risk management: identify, assess, prioritize, and minimize risk or maximize opportunities.

Explore how business continuity planning helps organizations continue operations and recover from extreme events, and distinguish it from contingency planning for smaller issues.

This lecture explains 2022 updates to the Six Sigma Green Belt body of knowledge, comparing agile and traditional top-down project management, and detailing iterative, evolutionary, and waterfall approaches.

Learn to use affinity diagrams (K-J method) to group brainstorming ideas and survey results into meaningful clusters, guiding content, presentation, and practice quizzes for process improvement.

Explore inter relationship digraphs to map cause and effect relationships among factors like leadership, supervision, and maintenance. Identify root causes and drivers of poor quality using a fishbone diagram framework.

Explore how tree diagrams break goals and complex items into detailed categories and subcategories, illustrated by ASQ exam prep and product examples within DMAIC.

Use prioritisation matrices to compare choices and choose the best project in DMAIC by weighting criteria such as efficiency, pickup, look, and comfort, scoring options, and selecting the highest total.

Explore metrics diagrams, a management tool, visualizing relationships between groups with shapes like L, T, Y, X, and roof, linking sections to difficulty, conceptual knowledge, statistical knowledge, and business application.

Discover process decision program charts (pdpc), a tree-diagram tool that adds what could go wrong and counter measures to plan outcomes, illustrated by passing an exam.

Explore activity network diagrams for managing sequential tasks, detailing predecessors and successors, identifying bottlenecks on the critical path, and applying float and start/finish calculations across seven management and planning tools.

Learn how SWOT analysis, the 8th tool added in 2022 to the seven advanced quality tools, assesses internal strengths and weaknesses and external opportunities and threats to guide strategy.

Explore project performance using defects per unit (DPU) through weld example, and distinguish defect from defective while previewing DPMO, rolled throughput yield, cost of poor quality, and process capability indices.

See how rolled throughput yield (RTY) captures chain performance by multiplying single-process yields (p-d)/p across processes, yielding about 92 good units from 100 input units in the example.

Explore cost of poor quality by separating visible and invisible costs and classifying them as prevention, appraisal, and internal or external failures, and see how Six Sigma reduces cost types.

Compute dpmo by identifying defect opportunities, calculate defects per opportunity, and convert to defects per million opportunities, then relate it to six sigma levels, about 3.7 for this process.

Explore process capability indices Cp and Cpk and how they reveal a shaft production’s ability to meet specification and maintain quality.

Explore top-down, bottom-up, and horizontal communication in Six Sigma, with direct CEO messaging, worker feedback, and DPMO and rolled throughput yield as measures of business performance.

Explore Tuckman's model of team development, detailing the five stages—forming, storming, norming, performing, and adjourning—and how leadership roles evolve from directing to facilitating to delegating as teams complete projects.

Identify and address four negative team dynamics: overbearing, dominant, reluctant, and unquestioned acceptance of opinions as facts, and distinguish opinions from data and facts in team discussions.

Explore common negative team dynamics including group thinking, feuding, floundering, and rushing to accomplish, and learn leader strategies like the devil's advocate, independent experts, diversity, and realistic timelines.

Examine eleven negative team dynamics, from attributions to digressions and discounts, and learn how team leaders address these issues and keep meetings on scope.

Explore how Six Sigma team roles drive projects, from yellow belts to master black belts, with green and black belts serving as change agents and masters training and mentoring.

Identify executive sponsor, champions, and process owners and describe their Six Sigma roles. Explain how executive sponsors set vision, monitor program success; champions supply resources, process owners sustain improvements.

Explore team tools for decision making, including brainstorming, nominal group technique, and multi-voting. Learn to focus on quantity, withhold criticism, welcome unusual ideas, and combine ideas to improve solutions.

Overcome brainstorming imbalances with nominal group technique by silent idea generation, round-robin sharing, nonjudgmental discussion, and voting to rank and select ideas for problem identification, solution generation, and decision making.

Narrow down brainstorming ideas with multi-voting: assign letter codes, tally votes, and use one-third voting rounds to reach top ideas by group consensus.

Master team communication methods, including one-to-one channels and mass channels like face-to-face, video, email, newsletters, intranet, and team meetings. Align these with the define phase of Lean Six Sigma.

Explore the raci matrix for lean six sigma green belt work, clarifying who is responsible, accountable, consulted, and informed.

Explore the measure phase of DMAIC by understanding current processes through flowcharts and process maps and documentation, collecting data, performing measurement system analysis, and assessing process capability.

Learn to create and read process maps, or flowcharts, using five core symbols—start/end, process, arrow, diamond, and link—and extend to swim lane flow charts and sipoc.

Explore documentation layers, from quality manuals to procedures, work instructions, and forms, to map the current state of a process, and learn sampling, probability, and central limit theorem in measurement.

Explore basic probability concepts and the central limit theorem, including independent and mutually exclusive events, sample space, and probability calculations with dice and real-world examples.

Explore how Venn diagrams visually represent probability concepts, including the sample space and events A and B. Learn about mutually exclusive events, union, and intersection using dice examples.

Introduce mutually exclusive, independent, and dependent events, probability concepts with dice, coin tosses, and Venn diagrams, plus complementary events and the upcoming rules of multiplication and addition.

Explore the rule of multiplication in probability, distinguishing independent and dependent events with dice rolls and candy draws, and apply P(A∩B)=P(A)P(B|A) and P(A∪B)=P(A)+P(B)-P(A∩B).

Explore factorial, permutation, and combination concepts, including repetition cases and practical examples, and apply formulas such as 5!, NPR, and NCR with Excel references.

Explore the central limit theorem through sampling, descriptive and inferential statistics, and how sample statistics infer population parameters using means, standard deviations, and confidence intervals.

Explore the central limit theorem: the mean's sampling distribution tends toward normal with sample size, even for non-normal data; the standard error equals sigma/sqrt(n).

Explore binomial distribution with coin flips, calculating the probability of exactly x successes in n trials with probability p. Understand how discrete and continuous variables relate to different distributions.

Learn to compute the binomial distribution’s mean and variance (and standard deviation) using n p and n p (1-p), illustrated with coin flips and a 12% defect example.

Explore the Poisson distribution as a discrete data model, contrasts with binomial, and learn its properties, including average rate, independence, and rare events over time.

Apply the Poisson probability formula P(x, meu) with mean 3.6 to compute arrivals. Recognize that Poisson variance equals the mean and standard deviation is sqrt(mean).

Explore normal distribution, its mean and standard deviation, and how sigma levels shape bell curve. Learn area and probability rules, including 68–95–99.7% ranges and that exact values have zero probability.

Master the normal distribution formula with mu and sigma, standardize to the standard normal via z = (x - mu)/sigma, and use the z-table to find area under curve.

Explore normal and standard normal distributions, using z-values to compute bottle-volume probabilities around 150cc and areas to the right or left, plus sample means via the central limit theorem.

Explore how chi square distribution arises from squaring standard normal variables, its degrees of freedom dependent shape, and practical use of tables for hypothesis testing.

Explore the student’s t distribution, its link to normal and chi-square distributions, and how using sample standard deviation with degrees of freedom n-1 informs t tests and hypothesis testing.

Explore the F distribution, derived from two chi-square variables, with degrees of freedom nu1 and nu2, and its role in ANOVA and hypothesis testing.

Learn to collect and summarize data with Minitab 18, perform descriptive statistics, and plot distributions—normal and Poisson—while navigating the software interface for Six Sigma analysis.

Explore continuous versus discrete data and how measurement scales shape data collection. Apply sampling and descriptive statistics to the current-state DMAIC assessment.

Explore measurement scales by examining nominal, ordinal, interval, and ratio through the noir sequence. Identify how order, difference, and absolute zero define each scale with examples.

Explore sampling and data collection in six sigma, contrast probability and non probability sampling, and learn how samples infer population quality without measuring every item.

Learn about probability sampling and its subtypes: simple random sampling, systematic random sampling, stratified random sampling, and cluster sampling, and contrast with non probability sampling.

Discover non probability sampling types, including convenience, judgmental, and quota sampling, with practical examples illustrating how sample selection affects representativeness.

Record defect data with check sheets to capture frequency and location during sampling. Learn how to track defects like excess glue, weak joints, abrasion marks, and asymmetry in shoe manufacturing.

Explore data coding to simplify recording and calculating the mean and standard deviation from coded data, showing how adding or subtracting constants, multiplying, and truncation affect results with real examples.

Explore descriptive statistics, focusing on central tendency and variability; learn how mean, mode, and median summarize data and how range, interquartile range, and standard deviation reveal dispersion.

Explore how quantiles generalize data division in central tendency, then learn quartiles as four-part splits (q1, q2, q3) and percentiles, and visualize them with box and whisker plots.

Analyze dispersion through range, interquartile range, and standard deviation, and understand how variance and box-and-whisker plots illustrate data spread.

Analyze frequency distributions and histograms, fit a normal distribution with mean and standard deviation, and interpret probability density versus cumulative distributions for hypothesis testing using mini tab.

Learn to compute descriptive statistics in Minitab 18 for volume data, including mean, standard deviation, variance, min, max, range, quartiles, IQR, and mode, relevant for Six Sigma green belt training.

Explore scatter diagrams, a basic quality tool, to analyze how two variables relate—illustrated by car volume versus mpg and ice cream sales versus temperature.

In Minitab 18, create a scatter diagram of miles per gallon versus total volume, first as a simple plot, then with a linear regression line, and split by fuel type.

Use histogram, a seven basic quality tool, to bin data and reveal distribution. With 200 perfume bottles, observe near normal shape, mean 150, standard deviation 1.915, via Minitab.

Learn to draw a histogram of 200 perfume bottle volumes in Minitab 18, fit a normal distribution from sample data, and view box plots with descriptive statistics.

Explore the box and whisker plot, showing quartiles, median, and inter quartile range. Identify outliers using one point five times the inter quartile range, and compare histograms for skewness.

Open Minitab 18 and create a simple box and whisker plot using volume as the variable. Then proceed to the stem and leaf plot as the next graphical tool.

Learn stem and leaf plots as a graphical method to visualize data, using the first digit as the stem and leaves to form ascending, bin-like groups, with minitab demonstrations.

Create a stem and leaf plot in Minitab 18 by selecting graph, choosing stem and leaf plot, and adding volume; copy the result as picture or numbers for slides.

Explore normal probability plots to verify normality, compare histograms with a normal curve, and use p-values and 95% confidence intervals to inform hypothesis testing in Minitab.

Learn to create a normal probability plot in Minitab 18 from volume data, assess normality with p>0.05, and interpret the 95% confidence interval for the plot.

Explore the data collection plan in the measure phase of lean six sigma, including operational definitions, NOIR data types, data collection methods, and example with time to assemble.

Develop data quality checks to prevent garbage in, garbage out in six sigma analysis, and apply imputation to missing values while avoiding duplicates and coding errors.

Learn how measurement system analysis identifies variation in measurement by examining the operator, instrument, and procedure, comparing true versus reference values, and exploring gauge R&R and precision to tolerance.

Understand resolution as the smallest readable unit of a measuring instrument. Apply the 10 to 1 rule to divide tolerance, and prefer digital vernier gauge over tape for tight tolerances.

Explore measurement system analysis by defining accuracy, precision, and resolution, and explain how bias, linearity, stability, repeatability, reproducibility, and calibration affect closeness to the reference value.

Define precision as closeness of repeated readings and differentiate repeatability (one appraiser, one gauge) from reproducibility (multiple appraisers). The video explains EV, AV, and gauge R&R.

Explore the precision to tolerance ratio (PTR) and how measurement system variation relates to part tolerance. Apply PTR = 6 sigma over tolerance, with five point one five sigma.

Learn to differentiate process performance from process specifications and apply process capability concepts (cp, cpk, pp, ppk) to assess short- and long-term capability and sigma shift.

Learn to assess process capability by comparing natural variation to upper and lower specification limits, using LCL and UCL and three sigma bounds, with examples of capability versus non-capability.

Select processes that affect customer satisfaction, plan data collection, and perform measurement system analysis. Use normality checks and X-bar with R charts to compute Cp and Cpk for process improvement.

Learn how Cp and Cpk quantify process capability against specification. Explore sigma within and sigma overall, and how mean shifts affect these indices with practical film thickness examples.

Calculate Cp and Cpk under four conditions using random samples, normal distribution, a statistically controlled process, and sufficient sample size.

Compute Cp and Cpk from the process capability report using USL, LSL, and sigma within, and derive CPL and CPU to obtain Cpk while comparing Cp to Pp and Ppk.

Explore how sigma within and sigma overall drive Cp, Cpk, Pp, and Ppk calculations in control and not in control examples, using Minitab for film thickness and piston ring data.

Explore how CPM centers on the target in capability, contrasting it with Cp, Cpk, Pp, and Ppk, and connect target setting, Taguchi loss, and six sigma concepts to process performance.

Perform six pack and capability analyses in Minitab 18, selecting normal distribution, setting subgroup size and specification limits, and defining a target to obtain Cp, Cpk, Pp, Ppk, and Cpm.

Examine short-term and long-term process capability, sigma shift, and how six sigma achieves 3.4 DPMO by allowing a 1.5 sigma long-term shift.

Explore data with exploratory data analysis to understand variation and support hypothesis testing within DMAIC, and learn about multi-vari studies and variation types—positional, cyclical, temporal—with bearing and call center examples.

Explore multi-vari charts to distinguish positional, cyclical, and temporal variation in ball bearing diameters, and compare sources of variation across machines using mini tab.

Analyze temporal variation with a multi-vari chart in Minitab 18 using call center data to compare centers, types of requests, and days, and identify factors affecting call duration.

Explore the relationship between inputs and outputs using correlation and linear regression. Learn to calculate the correlation coefficient, distinguish correlation from causation, and derive a regression equation for prediction.

Compute the Pearson correlation coefficient from a scatter chart of hours studied vs test score using the standard formula, illustrating a positive, strong relationship.

Explain how the Pearson correlation coefficient r measures the strength and direction of a linear relationship, with examples of positive and negative trends and sample versus population distinctions.

Learn how the coefficient of determination (r squared) measures the variation in the dependent variable explained by the independent variable, illustrated by hours studied predicting 77% of marks.

Understand that correlation does not imply causation by examining the correlation coefficient and its square, and recognizing how a third factor can drive related variables.

Explains scatter plots, correlation coefficient r and r^2, and how hours studied relate to marks, deriving the regression equation y = a + b x via ordinary least squares.

Demonstrates the ordinary least squares method by finding the best fit line using residual sums of squares, and introduces intercept a, slope b, and y = a + b x.

Compute the regression equation y = a + b x using sums, derive the least-squares best-fit line, and assess significance with p-values to predict scores from hours studied.

Learn the basics of hypothesis testing, including statistical versus practical significance, steps, and type I/II errors, using sample statistics to infer population parameters with p-values.

Explore the difference between statistical significance and practical significance in hypothesis testing, with perfume-volume examples, and learn how sample size drives detectable differences and actionable decisions.

Explore hypothesis testing in the six sigma analysis phase, illustrating null and alternate hypotheses via a court analogy, and compare the test statistic to the critical value at alpha 0.05.

uses a 150 cc bottle volume with a 2 cc standard deviation to illustrate hypothesis testing, defining null and alternate hypotheses, one- and two-tailed tests, and the z test.

Explain type 1 and type 2 errors in hypothesis testing and how alpha controls risk, illustrated with perfume bottle examples and central limit theorem implications.

Understand type 1 and type 2 errors, alpha and beta, and how significance, confidence, and power shape hypothesis testing, including producer's risk and consumer's risk in acceptance sampling.

Explore type i and type ii errors in hypothesis testing, showing how sample size and alpha level affect power and error tradeoffs in lean six sigma decision making.

Learn hypothesis testing with null and alternate hypotheses, alpha levels, and z statistics. Interpret results using critical values and sample mean distribution from the perfume example.

Learn to find z critical values for the z test using the standard normal distribution and the z table, with alpha levels for two-tail and single-tail tests.

Compare the p value with alpha in hypothesis testing and decide to reject or fail to reject the null hypothesis using software outputs z, t, chi-square, F and ANOVA tests.

Explore hypothesis testing for means, variances, and proportions, covering one- and two-sample z and t tests, proportion and variance tests, paired t test, and ANOVA, with perfume and town examples.

Master the one sample z test by following the six hypothesis testing steps, setting alpha at 0.05, calculating the test statistic and critical value, and interpreting results.

Assess the one-sample z test using a perfume bottle example, computing z = 1.0 with n=100 and sigma=2, and conclude no change in mean volume at 95% confidence.

Apply a one-sample z test to assess if mean volume increased above 150 cc, with n=100, observed 150.2 cc, 95% confidence, and a one-tailed z comparison.

Perform a one-sample z test in Minitab 18 to see if mean volume differs from 150 cc at 95% confidence, using raw or summarized data with p-values guiding the decision.

this lecture covers the one sample t test, its conditions, and how to compute t calculated and t critical values to assess whether the mean changes, with a 150cc example.

Perform a one-sample t test in Minitab using summarized data to compare the sample mean to 150, reporting p-value 0.391 and accepting the null hypothesis.

Explore the one proportion test, its conditions, and how to use normal approximation for binomial data, including null and alternative hypotheses, z statistics, and interpretation of results.

Apply one proportion test using Minitab 18 with summarized data to test a hypothesized proportion, compare p-values under normal approximation versus exact methods, and interpret results about smoking prevalence.

Learn to test whether sample variance equals population variance using a one variance test with chi-square statistics. Understand conditions—random, independent, normally distributed data—and compare chi-square and f tests for variance.

Apply a one-variance test with chi-square distribution to a sample of 51 bottles, showing variance rise from 2 to 2.35 cc, leading to rejection of the null at 90% confidence.

Explore the second example of a one variance test, using 51 bottles with a sample standard deviation of 2.35 against the established 2cc at 90 percent confidence. Compute the chi-square value 69.03 with 50 degrees of freedom, apply two-tailed critical values 34.76 and 67.50, and conclude that the population variance has changed from 2cc, suggesting machine adjustment.

Use Minitab to perform a one variance test on a 51-sample set, testing whether the standard deviation is greater than or differs from hypothesised 2, with chi-square results and p-values.

Explore two-sample z tests by comparing means from two machines, define null and alternative hypotheses, and apply conditions for z tests with known population standard deviations.

Use a two-sample z test on 100 samples from each machine to compare mean volumes (151.2 vs 151.9 cc) at 95% confidence, rejecting the null of equal means.

Explore how to run a two-sample z test in Minitab 18 using a macro, and learn to substitute with a two-sample t test when the direct z test is unavailable.

explore two-sample t tests when population standard deviation is unknown and samples are small, including independent versus paired data, and equal versus unequal variances with pooled or separate variance formulas.

Explore a two-sample t test with equal variances, pool standard deviations, form null and alternative hypotheses, and decide significance at 95% confidence using t critical.

Learn the two-sample t test with unequal variances, using non-pooled degrees of freedom to compare means from machine A, B, and C and interpret the 95% two-tailed decision.

Explore two-sample t tests for equal and unequal variances using box-and-whisker plots to judge rejection of the null hypothesis, with machines A, B, and C.

Use Minitab 18 to perform two-sample t tests on raw data from machines A and B (equal variance) and A and C (unequal variance), interpreting p-values at 95% confidence.

Apply the paired t test to before and after data, compute differences, and use t = dbar/(s/√n) to assess mean change at 95 percent confidence.

Learn to perform a paired t test in Minitab 18 on before-and-after blood pressure data, test null hypothesis that means are equal, using 95% confidence and p-value interpretation.

Compare two population proportions using the two proportions test, learning the random sampling, independence, and normal approximation conditions and key terms like p1, p2, p1 hat, and p2 hat.

Compare two proportions for vendors A and B using the pooled method and z statistic at 95% confidence, concluding no significant difference in defect rates.

Demonstrates a two proportions test in Minitab using pooled estimates to compare vendor A and B defect rates, concluding no significant difference (p ≈ 0.23).

This lecture covers two variance tests using an F test, illustrating how to compute F, read F tables, and decide that variances are not equal for two machines.

Perform a two variances test in Minitab 18 to compare two machines' variances using sample variances or standard deviations, at 90 percent confidence, with p = 0.013.

Explore the transition from variance tests to ANOVA, comparing means across multiple machines while highlighting within and between variation, null and alternative hypotheses, and why ANOVA supersedes multiple t tests.

Understand why ANOVA is needed to compare means across more than two populations, such as machines A, B, and C, and why multiple two-sample t tests are inefficient.

Understand the concept of variation within and variation between in ANOVA through box and whisker plots and mean comparisons across machines, setting up the idea of the between-to-within variation ratio.

Explain analysis of variance, define variance with sum of squares and degrees of freedom, and form the F statistic from between and within mean squares to compare means across samples.

Calculate the F value and F critical to test whether the three machines' means differ, using ANOVA with 95 percent confidence.

Assess differences among three machines using one-way ANOVA in Minitab 18. Test whether mean volumes differ with 95 percent confidence; results show not all means are equal.

Master the chi square goodness of fit test for a specified distribution. Apply observed and expected counts, degrees of freedom, and critical values to decide the null hypothesis.

Apply a chi square goodness of fit test in minitab to compare observed versus expected shirt proportions, interpret the p value, and conclude whether the distribution follows the specified proportions.

Learn how contingency tables reveal relationships between two discrete variables, such as gender and smoking or operator and shift, using chi-square tests and null and alternative hypotheses with expected values.

Learn how to use Minitab to analyze contingency tables with cross tabulation and chi-square tests, determine expected counts, and interpret p-values to assess relationships between shift and operator.

Explore gap analysis within the updated body of knowledge, comparing current and desired future states using a predefined matrix to identify performance gaps in Six Sigma.

Consolidate the improve phase of the dmaic approach by selecting and implementing improvement ideas, using design of experiments, root cause analysis, and lean tools to reduce waste and cycle time.

Explore design of experiments to study how multiple input factors affect output, compare one-factor-at-a-time approaches, and reveal interactions.

Identify independent and dependent variables, factors and responses in design of experiments, and distinguish numeric and categorical factors with their levels using car mileage examples.

Explain how treatments are combinations of factor levels and how responses measure experiment outputs, using examples of number of passengers, air conditioner on/off, tire pressure, and speed.

Learn how error affects estimates in experiments, distinguish factors from nuisance inputs, and apply blocking, randomisation, replication, and repetition to control variability in design of experiments.

Explore design of experiments graphs and plots using a two-factor coffee test, with milk and sugar levels, full factorial 2^2 experiments, and randomized trials to visualize results.

Learn to use box plots and interaction charts for a two-factor, two-level study of sugar and milk. Detect factor effects and no interaction when lines run parallel.

Derive a two-variable equation to predict rating from milk and sugar, with B0, main effects B1 and B2, and no interaction in this example, tested against sample values.

design of experiments with two factors milk and sugar shows interaction; plots reveal non-parallel lines and curved trends, leading to an equation with an interaction term xs.xm.

Explore root cause analysis (RCA) in the DMAIC improve phase, applying 5 whys, fishbone diagrams, process mapping, and prioritisation matrices to identify root causes and implement corrective actions.

Explore implementation planning in lean Six Sigma, applying proof of concept, prototype, try storming, simulations, and pilot tests to validate recommendations before full-scale deployment in real environments.

Learn how lean tools drive waste elimination by understanding muda, mura, and muri, and apply techniques like pull systems, kanban, 5s, standard work, and poka-yoke.

Explore the eight types of waste—transportation, inventory, motion, waiting time, over processing, over production, defects, and underutilized skills—summarized as TIMWOODS for effective waste elimination.

Learn to eliminate waste with a demand-driven pull system, contrast with push, and apply kanban, 5s, standard work, and poka-yoke to minimize inventory and work-in-progress.

Learn how the kanban inventory control system uses kanban cards and a three-bin setup to trigger replenishment, support pull production, and reduce inventory waste.

Master 5S for workplace organization, translating Seiri, Seiton, Seison, Seiketsu, and Shitsuke into sort, set in order, shine, standardize, and sustain to eliminate waste and sustain a clean, organized culture.

Standardize work to ensure a single method and a baseline for waste elimination and improvement. Apply poka-yoke to prevent or detect errors: elimination at the source, detection, or prevention.

Reduce cycle time by applying continuous flow and setup reduction to transform batch operations into one-piece flow, cutting inventory and boosting productivity.

Explore cycle time reduction through continuous flow and setup reduction using smed (single minute exchange of die) to enable one piece flow, faster model switching, and improved line balancing.

Drive small, planned improvements with kaizen and kaizen blitz in lean environments to boost quality and productivity. Build a directed team, define a business case, and standardize the process.

Learn how SMED, or single minute exchange of die, enables quick changeovers to reduce inventory and boost machine utilization by distinguishing internal versus external setup.

Learn the basics of statistical process control, control charts, and common and special causes of variation to monitor and keep a revised process within specification limits.

Differentiate common causes from special causes to determine when to take action, using control charts and data types such as continuous and discrete to identify variation and apply corrective actions.

Learn rational subgrouping and optimal subgroup size for control charts, using x-bar and r charts to distinguish variation within and between subgroups as time snapshots.

Explore how to apply SPC control charts with rational subgrouping for continuous and discrete data, including IMR, Xbar-R, Xbar-S, and np, p, c, u charts.

Create an imr chart in minitab 18 from 25 individual pH measurements (individual values and moving range), using either the stat control charts path or the assistant.

Calculate IMR and XMR control limits in Excel from 25 pH samples using moving range and x-bar, then apply d2, d3, and d4 formulas.

Learn x bar r charts for variable data with five-item subgroups, using mean and range to set control limits on camshaft length data.

Demonstrates building an x bar r chart in Minitab 18 from a three-machine dataset, selecting five-item subgroups, and interpreting mean, range, and upper and lower control limits.

calculate x-bar and range control limits for machine one using excel, with x-bar bar 600.072, r bar 2.72, a2 0.577, and d3/d4 values to obtain the ucl and lcl.

Explore the x bar s chart for large subgroups, using the sample standard deviation instead of range, and compare it to the x bar r chart with formulas and software.

Demonstrates constructing an x bar s chart for a 10-item subgroup using Minitab and Excel, identifies out-of-control subgroups, performs root-cause analysis, and recalculates control limits for future production.

Explore np and p control charts for count data, using binomial distribution, constant subgroup size, and control limits to monitor defectives and percent defectives with a 500-item example.

Learn to create an np chart in Minitab 18 with a fixed 500-piece subgroup, interpret np bar, UCL, LCL, and update limits after removing sample 16 using Excel.

Learn how p charts adapt to variable subgroup sizes with changing control limits, contrast them with np charts, and apply their formulas to real-world defectives data.

Learn to build p charts in Minitab and Excel to monitor defectives in hospital records, compute p-bar and varying control limits due to changing subgroup sizes, and identify out-of-control signals.

Explore control charts for discrete data, focusing on C charts and U charts based on the Poisson distribution, and learn when to use C charts for total defects per unit.

Learn to construct a C chart in Minitab, identify out-of-control points, and recalibrate upper and lower control limits using the C bar formula, also showing calculations in Excel.

Explore the u chart for defects per unit with unequal subgroup sizes, compare it to the c and p charts, and apply the u-bar formula in Excel.

Build a U chart for transcription errors per page in Excel and compute U bar and control limits. Replace negative lower limits with zero and investigate outliers.

Explore all eight Nelson rules for control charts, beyond the basic rule of points beyond the control limits, including center line and three standard deviations, with minitab demonstrations.

Apply Minitab 18 to the x-bar r chart using all eight Nelson rules to identify assignable causes, with 1–3 sigma limits and points beyond limits across three machines.

Explore the control plan as a higher-level DMAIC control phase tool that involves process owners, revises with monitoring, and governs multiple characteristics using a wooden chair example.

Learn how to sustain Six Sigma improvements through document control, training plans, and audits, using the PDC cycle to monitor results and update procedures.

Explore how total productive maintenance pairs operators and maintenance to maximize overall equipment effectiveness (OEE) by improving availability, performance, and quality.

Master the visual factory in the control phase, using visual controls and andon to display shop-floor status, production plan versus actual, and problem signals with red, yellow, and green indicators.

Learn to distinguish total productive maintenance from predictive maintenance, apply vibration analysis, thermal imaging, oil and emission checks to predict failures, and implement visual controls for proactive maintenance.

Explore Jidoka, autonomation—the blend of automation with human touch. Understand the four steps: detect abnormalities, stop production, take immediate action, and root-cause prevention in the 2022 CSSGB upgrade.