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.

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.

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.

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.

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

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 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 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 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.

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

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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.