Certified Lean Six Sigma Black Belt Training (Accredited)

Examine how Six Sigma impacts stakeholders, learn benchmarking to set industry targets, and review performance and financial measures, including KPI, KBI, OKR, revenue growth, and ROI.

Learn to measure customer loyalty with net promoter score, retention rate, customer lifetime value, and csat, including promoters, detractors, and passives, to boost retention and advocacy.

Explore line of sight as the alignment of individual, department, and organizational performance measures with the overall strategy, using customer satisfaction goals and waste reduction as examples.

Explore financial measures such as revenue growth, market share, and margins to assess a company's financial health. Distinguish hard costs from soft costs and learn about gross and net margins.

Explore lean six sigma black belt team management, covering team formation, facilitation, dynamics, and training, with roles, selection criteria, stages of development, communication, decision making methods, and meeting management.

Identify and apply key Six Sigma team roles—leader, facilitator, coach, scribe, timekeeper, and members—to drive vision, decisions, and continuous improvement through structured meetings and open communication.

Identify the right team members for a Six Sigma project by assessing influence, openness to change, required skills, and availability, with access to subject matter experts.

Explore intrinsic and extrinsic motivational techniques, including recognition, empowerment, goal clarity, and professional development, while examining Maslow, Herzberg, and McGregor theories to lead Six Sigma teams.

Explore Bruce Tuckman’s five stages of team development—forming, storming, norming, performing, and adjourning—and how a leader guides teams through these phases in a Six Sigma project.

Develop a clear team communication plan for Six Sigma projects by applying the sender-receiver model, channels, feedback, and audience-based messaging to ensure alignment.

Identify and apply team interventions across roles including the team leader, management, coaches, and facilitators, to align group behavior, resolve conflicts, and optimize Six Sigma project performance.

Learn to run effective meetings with a structured agenda, time management, and clear pre-work, ensuring the right people, actionable outcomes, and focused discussions.

Explore team decision-making methods used in Six Sigma projects, including consensus, nominal group technique, and multi voting, to foster group buy-in while solving root causes identified by fishbone diagrams.

Explore modes of learning for team training, from in-person classroom to online and blended formats. Include microlearning, mobile learning, coaching and mentoring, webinars, workshops, simulation, and Kirkpatrick model evaluation.

Explore four levels of training evaluation—reaction, learning, behavior, and results—using reaction feedback, pre/post tests, behavior observation, and KPI impact to measure training effectiveness.

Explore the define phase of DMAIC, covering voice of the customer, business case and project charter, and tools like CTQ trees, SIPOC, Kano model.

Identify internal and external customers in the voice of the customer and segment them by demographic, geographic, behavioral, and psychographic factors to align project outcomes.

Translate the voice of the customer into specific CTQs by mapping drivers to critical to quality, build a CTQ tree, and guide data-driven Six Sigma projects.

Explore the Kano model for prioritizing customer needs, examining the five categories: basic needs, performance needs, excitement needs, reverse needs, and indifferent needs, and how implementation level shapes satisfaction.

Define the project charter to justify and align Six Sigma projects with organizational goals, detailing eight elements including business case, problem statement, scope, and smart goals.

Explore the project charter elements, focusing on project performance measures, monetary and non-monetary benefits, and how to plan, review, and approve charters using tools like Gantt charts and WBS.

Learn to use tree diagrams to break down an activity into finer components, compare with work breakdown structure, and examine restaurant service quality, including staff training and menu excellence.

Analyze activity network diagrams to map dependencies and predecessors, determine the critical path and float, and apply PERT estimates for duration and variability.

Discover the interrelationship diagraph, a visual tool to map how factors influence outputs, identify drivers, and locate root causes of poor quality.

Explore how hidden factories due to defect repairs and rework inflate cycle time, raise work in progress, reduce throughput, and disrupt takt time in process flow metrics.

Map the value stream to reveal material and information flow, identify non-value added activities, and drive future-state improvements through current-to-future state mapping, gamba walks, and work instructions.

Explore the four measurement scales nominal, ordinal, interval, and ratio (noir) and how they determine data type, order, meaningful differences, and suitable central tendency and dispersion measures.

Learn how sampling selects pieces from a population to infer its characteristics, including probability versus non-probability sampling, bias, accuracy and precision, sampling error, and sample statistic versus population parameter.

Explore sources of variation in measurements, focusing on measurement system variation and the accuracy–precision distinction, including bias, linearity, stability, and the upcoming repeatability and reproducibility.

Explore Gage R&R ANOVA result interpretation with measurement by part, comparing run charts and nine readings per part across three operators, revealing part-to-part variation and size differences.

Analyze the components of variation in a Gage R&R study, including part-to-part variation and repeatability versus reproducibility, and interpret ANOVA p-values to identify significant factors.

Explore gage r&r with anova method calculations, deriving sums of squares, degrees of freedom, mean square, and f values to assess part and operator variation in measurement system analysis.

Explain how the number of distinct categories (NDC) equals the 97% Gage R&R intervals fitting into part variation, using NDC = 1.41 PV / GRR, with truncation.

Analyze attribute agreement with one another by examining eight assessments per sample, showing 74% agreement among four operators and a 95% confidence interval to evaluate consistency.

Analyze all operators versus standard in attribute agreement analysis, presenting a 74% agreement with a 60-85% confidence interval and discuss the kappa value.

Learn how kappa value measures inter-appraiser agreement in attribute agreement analysis, using observed and chance agreement to assess nominal or ordinal data and interpret tables from Minitab.

Analyze correctness as the agreement with the standard, using kappa statistics and Kendall's correlation coefficients to show strong alignment overall, with Eric's difficulty identifying the rating three warranting investigation.

Apply measurement system analysis beyond qa to ensure reliable data across the organization, using accuracy, precision, gage r&r, and aaa in marketing, sales, engineering, supply chain, and operations.

Explore descriptive statistics by examining central tendency through mean, median, and mode, including their sensitivity to outliers and behavior under skewed distributions.

Explore interquartile range, quartiles, and box and whisker plots to assess data dispersion, identify outliers, and compare manual calculations with software like Excel and Minitab.

Learn to compute descriptive statistics in Minitab, including mean, standard deviation, variance, min, max, quartiles, interquartile range, skewness, and kurtosis, and to build box-and-whisker plots with outliers.

Explore graphical methods with scatter plots to visualize relationships between two variables, identify patterns and correlation strength using the correlation coefficient, illustrated by temperature and ice cream sales.

Histograms show the distribution of continuous data by counting frequencies in bins on the x-axis and displaying counts on the y-axis, illustrating normal and skewed patterns.

Explore frequency distribution with classes on the x-axis and frequencies on the y-axis, noting discrete data. Recognize the cumulative frequency concept and the ogive curve, contrasting with a histogram.

Explore fundamental probability concepts, including theoretical (classical) and empirical probability derived from experiments. Learn about experiments, trials, events, sample spaces, and simple and complex events using coin and die examples.

Represent the sample space with a Venn diagram to illustrate events like A, B, and C, including mutually exclusive, union and intersection, complementary events, and independent versus dependent events.

Apply the multiplication rule to independent and dependent events, calculating intersections; then use the addition rule for unions, including mutually exclusive and non mutually exclusive cases, with the complementary rule.

Explore conditional probability by narrowing the sample space, illustrated with drawing an ace given a spade from a 52-card deck, yielding 1/13. Note Bayes theorem and factorials, permutations, combinations.

Explore how the Poisson distribution differs from the binomial, uses lambda for the mean and variance, and apply the Poisson PMF to events per time or space.

Explore the normal distribution, a continuous Gaussian curve symmetric about the mean with mean, median, and mode equal, and learn to use standard normal tables to find areas.

Explore the chi square distribution, its use in goodness of fit and test of independence, and how it becomes more normal with larger degrees of freedom.

Explore the exponential distribution as a continuous, memoryless model of waiting times, using the PDF and CDF with lambda, and connect to Poisson, reliability, and queueing contexts.

Explore the Weibull distribution, a flexible reliability model for time-to-failure with shape k and scale lambda that determine the pdf and cdf, where exponential is a special case.

Explore process capability, measuring a process's ability to meet customer demand using indices such as Cp, Cpk, Pp, and Ppk, with control chart concepts like I-charts and specification limits.

Explore the process capability indices Cp and Cpk, comparing specification limits to process spread; Cp assumes centering, while Cpk accounts for centering, using random samples from a normal distribution.

The CPM index adjusts Cp for the target value, showing how close mu is to T; for example, with U=10, L=5, T=7.5, mu=7, sigma=0.5, Cpm=1.18.

Explore the four process capability indices Cp, Cpk, Pp, and Ppk, and distinguish short-term within versus long-term overall sigma, with coating thickness data and Minitab examples.

demonstrates using minitab to compute Cp, Cpk, Pp, and Ppk from 25 rolls with three measurements each, verifying normality and applying capability analysis with 47–53 limits.

Learn to assess process capability for non-normal data by selecting appropriate distributions or applying Box-Cox transformation in Minitab, and interpreting Cp, Cpk, Pp, and Ppk.

Compare process performance with specifications, using Cp, Cpk, Pp, and Ppk, discuss control and specification limits, and cover metrics like percent defective, DPMO, FPY, and RTY.

Explore short-term Cp/Cpk and long-term Pp/Ppk process capability, using sigma within and sigma overall, R-bar/d2, and the 1.5 sigma shift to explain 3.4 DPMO in Six Sigma during measure phase.

Explore the analyze phase of DMAIC, covering correlation and regression, hypothesis testing, risk analysis, and methods such as factor analysis, MANOVA, FMEA, and root-cause analysis.

Learn to construct a 95% confidence interval for the population correlation coefficient using Fisher's z transformation, then interpret whether zero lies within that interval.

Explore how the coefficient of determination (R squared) measures the variance in the dependent variable explained by the independent variable, with simple linear regression where R squared equals r squared.

Learn simple linear regression, modeling the relationship between dependent and independent variables with the line y = beta0 + beta1 x, using hours studied and test scores as examples.

Explore how hypothesis testing uses sample data to infer population parameters, form null and alternate hypotheses, and interpret alpha and p-values for one- and two-tailed tests.

Distinguish statistical significance from practical significance in hypothesis testing, interpret p-values against alpha, and assess real-world impact and sample size needs for reliable conclusions.

Compute the required sample size for hypothesis tests on means and proportions, using margin of error and 95% confidence level, illustrated with study time and laptop ownership examples.

Learn about tolerance intervals that cover a specified proportion of the population with a given confidence, and compute them from the sample mean, standard deviation, and the tolerance factor k.

Define null and alternate hypotheses, set alpha, and compare test statistics to critical values or p values to decide on mean, variance, or proportion tests.

Apply the one sample z test to compare a sample mean to a hypothesized population mean, using known population standard deviation or large samples, with null and alternative hypotheses.

Learn to compute z critical values for a one-sample z test with a calculator, covering left/right tails and two-tailed 0.05 (≈1.96) and single-tail values (-1.64, 1.28).

demonstrates performing a one sample z test in Minitab using summarized or raw data, with known standard deviation and a 95% confidence level, interpreting the p-value against the hypothesized mean.

Perform a one sample t test in Minitab. Using summarized data, n=25, x̄=151, s=1.5, p=0.001, reject the null that the mean is equal to or less than 150.

Use the one proportion test to determine if a sample proportion differs from the population proportion. Learn hypotheses, assumptions for normal approximation, and the z statistic with a practical example.

Explore one variance test with chi-square to compare a sample variance to a hypothesized variance, covering assumptions, null/alternative hypotheses, degrees of freedom, and critical values.

Perform a two sample t test for independent samples to compare means with unknown population stdev and small samples, using equal variance pooling or Welch t test.

Learn how the paired t test analyzes dependent data by comparing before and after measurements, calculating differences, and testing mu_d using t statistics and critical values.

Use the two-variance test with the F distribution to compare two independent sample variances. Learn hypotheses, assumptions, and how to compute the F statistic and critical values.

Apply one-way anova in Minitab to compare three machines' perfume volumes, interpret p-values, and use Tukey's test to identify which machines differ.

Explore nonparametric tests as distribution-free alternatives for hypothesis testing when normality is uncertain or samples are small, using methods like Mann-Whitney U, Wilcoxon, Kruskal-Wallis, and Fisher exact test.

Explore gap analysis, comparing current performance with the desired state to identify improvement opportunities, using tools like benchmarking, SWOT, fishbone diagram, and Pareto analysis to plan action and monitor progress.

Explore the eight types of waste (Tim Woods) in lean thinking, from transportation and inventory to defects and underutilized skills, and link them to the DMAIC analyze phase.

Develop and implement solutions to root causes in the improve phase to optimize processes, and learn design of experiments, lean methods such as 5S, Kanban, Kaizen, and implementation planning.

Explore design of experiments (DoE) as a systematic method to study how multiple factors affect a process, including independent and dependent variables. Compare one-factor-at-a-time with multi-factor designs to reveal interactions.

Define independent variables or factors, dependent variable or response, and levels; describe treatments, error, replication, and interaction, and preview the full factorial design in the next video.

Explore how noise factors influence design of experiments and apply blocking, replication, and randomization to handle known controllable and unknown uncontrollable factors, plus ANCOVA with covariates.

Explore balanced design in design of experiments, where each factor level appears equally to improve precision and robustness, using a two-factor, two-level example with replications.

Learn design of experiments by comparing full factorial and half factorial designs, using three two-level factors (tire pressure, speed, AC) to reduce runs while avoiding confounding.

Learn about confounding and resolution in design of experiments (DoE), compare full and half factorial designs, and identify when main effects and interactions are confounded or clear.

Plan a design of experiments by clarifying objectives, identifying control and noise factors, selecting a response variable, and choosing an appropriate design (full or fractional factorial, screening or partial).

Demonstrates performing a full factorial DoE in Minitab with three factors and two replicates, randomizing runs, and analyzing mileage with Pareto, normal, and half-normal plots.

Learn one-factor experiments in design of experiments, holding other conditions constant and using completely randomized, randomized block, or latin square designs to study yield and factor interactions.

In the improve phase, learn lean methods to reduce waste and improve processes. Discover pull systems, Kanban, 5S, standard work, and poka-yoke to minimize inventory and align production to demand.

Explore the 5S workplace organization framework—Sort, Set in Order, Shine, Standardize, Sustain—and learn how to create a safe, efficient, and well-organized work environment that reduces waste.

Standard work defines efficient, documented steps for repetitive work, baselines, and alignment with takt time, work sequence, and standard work-in-progress (swip) to improve consistency, quality, safety, and waste reduction.

Improve phase teaches cycle time reduction through lean methods, removing non-value added activities and using continuous flow, SMED, and Heijunka to speed production and cut costs.

Discover how SMED reduces downtime in changeovers by converting internal setups to external ones, using quick-release fasteners, and standardizing processes to boost productivity and cut inventory.

Explore Kaizen, or continuous improvement, through small, daily changes that reduce waste, improve quality and efficiency, and boost workplace morale, with Kaizen Blitz as a focused, time-boxed improvement event.

Identify the system constraint and act on it to optimize throughput, applying the five steps—identify, exploit, subordinate, elevate, and repeat—across physical, policy, and market constraints.

Explore the drum buffer rope system in theory of constraints, using buffers and rope signals to pace production around the welding station and reduce inventory.

Explore statistical process control (SPC) to monitor a process in real time, use control charts to distinguish common and special variation, and reduce variation on critical, easy-to-measure quality variables.

Explore rational subgrouping in statistical process control, distinguishing within-subgroup variation (common causes) from between-subgroup variation (special causes), and apply 3–5 item subgroups to IMR or XMR charts.

Learn to select the right control chart for continuous and attribute data, assess subgroup size, and explore seven common charts: Xbar-R, Xbar-S, IMR, NP, P, C, and U.

Learn to use the i-mr (xmr) chart for continuous data, pairing the x chart and moving-range chart to detect shifts and apply rule that the lower control limit is zero.

Explain when to use the Xbar-S chart over Xbar-R based on subgroup size, and calculate control limits with Xbar bar, S bar, and A3, B3, B4 in a can-filling example.

Demonstrates setting up an Xbar-S chart in Minitab for subgroups, assessing mean stability and out-of-control signals, and omitting problematic subgroups.

Master the p chart for the proportion defective in discrete data. Use variable subgroup sizes, binomial distribution, and p-bar to compute upper and lower control limits with hospital records.

Use np charts for constant-size defectives; multiply p-bar by n to form the center line, upper and lower control limits, illustrated by 100 items daily over ten days yielding four.

Learn to interpret control charts using Nelson's eight rules to detect special causes, trigger investigation, and take action for process improvement.

Boost equipment reliability and overall equipment effectiveness through total productive maintenance, including autonomous and planned maintenance across eight pillars, reducing downtime, defects, and costs.

Maintain control by reanalyzing the measurement system after process improvements, using measurement system analysis to ensure the new tighter tolerances are detectable, especially when equipment, conditions, or process complexity change.