IASSC Lean Six Sigma Black Belt Exam Prep 2026 - ACCREDITED

Understand muda, mura, muri, waste, unevenness, and overburden, and learn to balance production with hejunka and standardized work, illustrated by the truck story.

Apply the Toyota Production System to eliminate waste, using Just-In-Time, Jidoka, Heijunka, standardised work, and Kaizen to create a stable, high-quality, low-cost, fast-flow manufacturing.

Learn to map the current state with value stream mapping, perform a Gemba walk, measure process, cycle, and lead times, and optimize information flow and bottlenecks with kaizen.

Design a lean future state by mapping value stream and eliminating waste; implement Haijunka, Kanban, continuous flow, and pacemaker to meet customer demand.

Discover how lean time metrics—cycle time, tag time, and lead time—reveal bottlenecks, balance lines, and drastically reduce lead time in a custom furniture production scenario.

Explore the theory of constraints and the five focusing steps to identify bottlenecks, then apply drum buffer rope, throughput accounting, and Lean and Six Sigma integration to maximize throughput.

Measure phase establishes a baseline by collecting continuous fill volume data across shifts with a clear data collection plan and reliable measurement system, then assesses process capability and sigma level.

Explore detailed process mapping with swimlane diagrams to reveal cross-department handoffs and the hidden factory, enabling measurement of handoff efficiency and cycle time for Six Sigma improvements.

Apply advanced failure mode and effects analysis (fmea) to proactively deconstruct processes, identify failure modes, and compute severity, occurrence, and detection to drive actions that reduce the rpn.

Identify the hidden factory and measure true process health with FPY and RFPY, plus Little's law, to reveal value-added versus non-value-added time and reduce lead time.

Rajan demonstrates probability fundamentals—random experiments and sample spaces, independent versus dependent events, Bayes' theorem, discrete versus continuous distributions, and normal distribution concepts for process reliability.

Master data sampling to diagnose latency in a high-volume Mumbai payment hub using systematic and cluster sampling, while building a bias-free framework and managing periodicity and cluster homogeneity.

Master bias, linearity, and stability studies in measurement system analysis to validate tools, calibrate gauges, and ensure data integrity for Lean Six Sigma Black Belt projects.

Investigate crossed gauge R&R versus nested gauge R&R to distinguish measurement system variation, using non-destructive and destructive testing concepts, with real-world case studies and a practical decision framework.

Master destructive testing and MSA by applying batch homogeneity and nested gauge R&R to separate measurement error from process variation, with long-term control charts.

Tackle measurement system analysis failures by isolating repeatability and reproducibility, standardizing procedures with operational definitions, and validating corrective actions through post-correction gauge R&R, including attribute agreement analysis.

Transform skewed process data using Box-Cox and Johnson transformations to achieve normality, then interpret transformed CPK and defect rates for non-normal data in Lean Six Sigma Black Belt contexts.

Explore the essential distinction between short-term zst and long-term zlt process capability, reveal the 1.5-sigma drift, and connect cp/cpk and pp/ppk to strategic interpretation and capability gap.

Master the analyze phase by turning data into proof of root causes, mastering patterns of variation—common and special—through positional, cyclical, and temporal variation analysis using multivary charts.

Explore multivary studies as passive observation to map positional, cyclical, and temporal variation, construct a multivariate chart, and validate root causes through targeted testing.

Learn how one-way ANOVA compares three or more group means by partitioning between-group and within-group variance, using the F-statistic, with Tukey post hoc tests and key assumptions.

Understand how p-values distinguish luck from real effects, balance alpha and beta risks, and boost statistical power through a 5-step hypothesis decision process.

Compare two process variances using the f-test to decide if one is more variable, with null and alternative hypotheses, f-statistic, p-value, and normality checks.

Master paired t-tests for pre/post analysis by analyzing mean differences within the same subjects, verifying assumptions, and interpreting p-values versus practical significance.

Learn to analyze proportions with one and two proportion tests, interpret p-values and alpha, and apply hypothesis testing and power concepts for Six Sigma quality improvements.

Compare four courier partners on delivery speeds with a non-parametric Kruskal-Wallis test on skewed data, using ranks to assess medians and report H-statistic and p-value, Dunn's post hoc with Bonferroni.

Explore Mood's median test, a robust non-parametric method comparing medians across two or more independent groups using a grand median and chi-square based contingency table, addressing outliers with p-value interpretation.

Explore how multiple linear regression uses several predictors to predict a response, addressing linearity, additivity, multicollinearity, and interpreting adjusted r-squared, p-values, and coefficients.

Identify multicollinearity in regression using the variance inflation factor, then apply variable selection or combining variables and validate the improved model with adjusted R squared and residual plots.

Explore how logistic regression handles binary and ordinal outcomes, predicting probabilities with odds ratios. Assess goodness-of-fit with concordance index and classification tables to deploy prescriptive improvements in Six Sigma projects.