Your FREE Six Sigma Black Belt Flashcards 2026 – 300+ Cards

A data-driven methodology aimed at reducing variation and defects to achieve 3.4 defects per million opportunities (a 6σ process level).

An organization's structured plan defining the vision, infrastructure, training, and project pipeline for deploying Six Sigma enterprise-wide.

A senior leader who sponsors Six Sigma projects, removes barriers, secures resources, and links projects to business strategy.

An expert who trains and mentors Black Belts, leads deployment strategy, and provides technical and statistical guidance across projects.

A full-time Six Sigma leader who manages complex cross-functional improvement projects and coaches Green Belts.

A part-time practitioner who leads smaller projects and supports Black Belt projects while performing a regular job.

A team member with basic Six Sigma awareness who participates on project teams and supports data collection.

Top management sets the Six Sigma vision, allocates resources, and establishes accountability and alignment with strategic goals.

Strategic policy deployment that cascades a few breakthrough objectives from top leadership down through the organization with catchball alignment.

A strategic management tool tracking performance across four perspectives: financial, customer, internal process, and learning/growth.

A planning tool that evaluates internal Strengths and Weaknesses and external Opportunities and Threats.

Choosing projects aligned with organizational goals, customer impact, and financial return rather than convenience.

A tool that ranks candidate projects against weighted criteria such as benefit, effort, risk, and strategic fit.

Top-level customer needs that branch into critical-to-quality, cost, delivery, and safety requirements.

The few key areas where satisfactory results are essential for the organization to achieve its mission.

Quantifiable measures used to evaluate progress toward strategic and operational objectives.

Factors such as customers, employees, suppliers, and shareholders that shape an organization's goals and metrics.

Comparing processes and performance metrics to industry best practices or best-in-class organizations to identify improvement opportunities.

Comparing specific work processes against superior performers to adopt better methods.

The needs and requirements of the business, including profitability, growth, and shareholder value.

What the process is actually delivering, expressed through its statistical performance and capability.

A structured approach to transitioning individuals, teams, and organizations to a desired future state while minimizing resistance.

A change framework: create urgency, build a coalition, form a vision, communicate it, empower action, generate short-term wins, consolidate gains, and anchor change.

The financial return from improvement projects, typically measured as hard savings, cost avoidance, and revenue gains versus deployment cost.

The roles, training, governance, and tracking systems established to sustain a Six Sigma initiative.

A repository that monitors project status, financial validation, and benefits across the deployment portfolio.

Identifying parties affected by a project and assessing their interest, influence, and likely support or resistance.

The practice of defining, measuring, controlling, and improving processes to consistently meet customer requirements.

The person accountable for a process's performance, documentation, and ongoing improvement.

A high-level process map listing Suppliers, Inputs, Process, Outputs, and Customers to scope a project.

A visual diagram of the sequence of steps, decisions, and flows within a process.

A process whose steps span multiple departments or functions within the organization.

Measures such as cycle time, cost, quality, and customer satisfaction used to gauge process health.

The costs incurred from defects: internal failure, external failure, appraisal, and prevention costs.

Total cost of achieving quality, comprising prevention, appraisal, internal failure, and external failure costs.

Costs of activities that prevent defects, such as training, planning, and process design.

Costs of inspecting and testing to detect defects, such as audits and measurement.

Costs from defects found before delivery, such as scrap and rework.

Costs from defects found after delivery, such as warranty claims, returns, and lost goodwill.

The proportion of units completing a step correctly the first time without rework: FPY = good units / total units in.

The probability a unit passes all process steps defect-free: RTY = product of each step's first-pass yield.

The geometric average yield per process step: normalized yield = RTY raised to the power 1/(number of steps).

The rate at which a process produces completed units over a period of time.

The total time from the start to the completion of a process or task, including wait time.

The total elapsed time from a customer order or request to delivery of the product or service.

Work in process = throughput rate × lead time; relates inventory, flow rate, and time in a stable system.

A summary measure of how well a process meets requirements over the long term, such as Pp and Ppk.

The number of standard deviations between the process mean and the nearest specification limit; higher sigma means fewer defects.

A long-term mean drift assumption added to short-term capability, so a 6σ process yields 3.4 DPMO rather than ~2 ppb.

Any instance where a product or service fails to meet a specification or customer requirement.

A unit that contains one or more defects, regardless of how many.

Any characteristic of a unit that could be measured and could be defective; used in DPMO calculations.

The percentage of units that meet requirements out of the total produced.

Capability (Cp/Cpk) uses short-term within-subgroup variation; performance (Pp/Ppk) uses long-term overall variation.

Teams progress through Forming, Storming, Norming, Performing, and Adjourning.

The initial team stage marked by orientation, politeness, and dependence on the leader for direction.

The team stage characterized by conflict, competition, and challenges to authority and roles.

The team stage where members resolve differences, establish norms, and build cohesion.

The mature team stage where members work interdependently and productively toward goals.

The final team stage involving completion, disengagement, and recognition of accomplishments.

Defined responsibilities such as sponsor, leader, facilitator, timekeeper, scribe, and member.

A responsibility chart identifying who is Responsible, Accountable, Consulted, and Informed for each task.

Guiding group interaction so meetings stay focused, inclusive, and productive without dominating decisions.

A structured method where members silently generate ideas, share them round-robin, then rank or vote to prioritize.

A technique to narrow a large list of options through successive rounds of voting.

A group method for rapidly generating many ideas while deferring judgment.

A tool that organizes large numbers of ideas into natural groupings based on relationships.

Methods for managing disagreements, including collaborating, compromising, accommodating, competing, and avoiding.

Five conflict-handling styles based on assertiveness and cooperativeness: competing, collaborating, compromising, avoiding, and accommodating.

A decision that all team members can support and commit to, even if it is not everyone's first choice.

A dysfunction where the desire for harmony suppresses dissent and critical evaluation of alternatives.

Helping team members develop skills and performance through guidance, feedback, and questioning.

A developmental relationship where an experienced person guides a less-experienced one over time.

Using recognition, autonomy, purpose, and goal alignment to sustain member engagement and effort.

A motivation theory ordering needs from physiological, safety, social, and esteem to self-actualization.

A motivation theory distinguishing hygiene factors (which prevent dissatisfaction) from motivators (which create satisfaction).

Assessing team effectiveness using metrics, milestones, and member feedback against project goals.

A discussion aimed at reaching mutually acceptable agreement among parties with differing interests.

A document specifying what information will be shared with which stakeholders, how, and how often.

Fully concentrating on, understanding, and responding to a speaker to ensure accurate communication.

Practices such as agendas, ground rules, and minutes that make meetings efficient and effective.

The kickoff activity that aligns members on purpose, roles, charter, and ground rules.

Agreed behavioral norms that govern how team members interact during the project.

A tool listing driving and restraining forces affecting a change to plan how to strengthen or weaken them.

Securing support and commitment from those affected by or influential over the project.

The five-phase Six Sigma improvement cycle for existing processes: Define, Measure, Analyze, Improve, and Control.

The process of capturing customers' stated and latent needs, expectations, and preferences to drive design and improvement.

Critical to Quality: a measurable product or process characteristic whose performance standard must be met to satisfy the customer.

The first DMAIC phase that scopes the problem, goals, customer requirements, and project boundaries.

A document defining the problem statement, goal, scope, business case, team, and timeline for a project.

A concise, factual description of the gap between current and desired performance, without assigning cause or solution.

A SMART target for the project's outcome, specifying the metric, baseline, target, and timeframe.

The justification for a project, linking it to strategic goals and quantifying expected benefits.

The defined boundaries of what is and is not included in the project.

The uncontrolled expansion of project scope beyond its original boundaries.

Objectives that are Specific, Measurable, Achievable, Relevant, and Time-bound.

A diagram translating broad customer needs into specific, measurable quality requirements.

A model classifying customer requirements as must-be (basic), one-dimensional (performance), and delighters (excitement).

Basic Kano requirements that cause dissatisfaction if absent but do not delight when present.

Unexpected Kano features that greatly increase satisfaction when present but are not missed if absent.

Dividing customers into groups with similar needs to better target requirements and solutions.

Gathering customer needs through surveys, interviews, focus groups, complaints, and observation.

Used to group raw VOC statements into themes that reveal underlying customer needs.

A method that translates customer requirements into technical specifications using the House of Quality matrix.

The primary QFD matrix relating customer wants (whats) to technical requirements (hows) with a correlation roof.

The primary, secondary, and consequential measures used to judge project success.

The main measure directly tied to the project goal that defines success.

A measure that ensures improving the primary metric does not harm another important outcome.

A schedule of milestones and deliverables across the DMAIC phases.

A bar chart showing project tasks along a timeline with start dates, durations, and dependencies.

The longest sequence of dependent tasks that determines the minimum project duration.

A hierarchical decomposition of project deliverables into smaller, manageable work packages.

Identifying and assessing risks that could affect project schedule, cost, or outcome.

Comparing the expected costs and benefits of a project to justify investment.

A brief, compelling summary of a project's purpose and value for quick stakeholder buy-in.

Formal completion activities including documentation, validation of benefits, and handoff to the process owner.

Defects Per Million Opportunities = (defects / (units × opportunities)) × 1,000,000.

Process capability (potential): Cp = (USL − LSL) / (6σ); it ignores centering and assumes the process is on target.

Process capability index accounting for centering: Cpk = min[(USL − x̄)/(3σ), (x̄ − LSL)/(3σ)].

A measurement system study quantifying repeatability (equipment variation) and reproducibility (appraiser variation) as a percentage of total variation.

The DMAIC phase that quantifies the current process performance and validates the measurement system.

A document specifying what data to collect, how, by whom, when, and from where.

A precise, agreed description of what is measured and how, ensuring consistent data collection.

Measurable data on a continuous scale, such as time, length, or weight.

Count or category data, such as number of defects or pass/fail outcomes.

The complete set of items or events of interest in a study.

A subset of the population selected to represent it for analysis.

Selecting samples so every member of the population has an equal chance of selection.

Dividing the population into homogeneous strata and sampling from each to ensure representation.

The difference between a sample statistic and the true population parameter due to sampling.

The number of observations collected, balancing precision, confidence, and cost.

The arithmetic average of a data set: sum of values divided by the count.

The middle value of an ordered data set, robust to outliers.

The most frequently occurring value in a data set.

The difference between the maximum and minimum values: range = max − min.

The average squared deviation from the mean: σ² for a population, s² for a sample.

A measure of dispersion in the same units as the data, equal to √(variance): σ = √(variance). It is the most common measure of spread.

A relative measure of dispersion: CV = (σ / mean) × 100%.

A symmetric, bell-shaped distribution defined by its mean µ and standard deviation σ.

For a normal distribution, ~68%, ~95%, and ~99.7% of data fall within 1, 2, and 3σ of the mean.

The number of standard deviations a value lies from the mean: z = (x − µ) / σ.

A discrete distribution of the number of successes in n independent trials with constant probability p.

A discrete distribution modeling the count of events in a fixed interval given a constant mean rate.

The distribution of sample means approaches normal as sample size increases, regardless of the population's shape.

The study of measurement variation to ensure data are accurate, precise, and adequate for decisions.

How close measurements are to the true value, related to bias.

How close repeated measurements are to one another, related to repeatability and reproducibility.

The systematic difference between the average measured value and a reference (true) value.

How consistent the measurement bias is across the operating range of the gauge.

The consistency of a measurement system over time when measuring the same item.

Variation when one appraiser measures the same item multiple times with the same gauge (equipment variation).

Variation among different appraisers measuring the same item with the same gauge (appraiser variation).

Gauge R&R as a percent of total study variation; under 10% is acceptable and over 30% is unacceptable.

The number of distinct groups a measurement system can reliably distinguish; an ndc of 5 or more is desired.

An MSA for pass/fail or categorical data assessing within-appraiser, between-appraiser, and vs-standard agreement.

A measure of categorical measurement agreement beyond chance; values above 0.75 indicate good agreement.

A bar chart showing the frequency distribution of continuous data across intervals.

A graphical summary of data showing median, quartiles, and outliers.

A line plot of data over time used to spot trends, shifts, and patterns before adding control limits.

A plot of paired data points used to reveal the relationship between two variables.

The measured current performance level used as the reference for improvement.

Process performance using long-term overall variation: Pp = (USL − LSL) / (6σ_overall).

Process performance index for centering using overall variation: Ppk = min[(USL − x̄)/(3σ_overall), (x̄ − LSL)/(3σ_overall)].

Taguchi capability index penalizing deviation from a target T: Cpm = (USL − LSL) / (6√(σ² + (x̄ − T)²)).

A range, computed from sample data, expected to contain the population parameter with a stated confidence level.

The standard deviation of a sampling distribution: SE of the mean = σ / √n.

Failure Mode and Effects Analysis: a structured tool to identify potential failures and prioritize them by Risk Priority Number (RPN = Severity × Occurrence × Detection).

A bar chart ordering causes from most to least frequent, illustrating the 80/20 rule that ~80% of effects come from ~20% of causes.

Analysis of Variance: a hypothesis test comparing the means of three or more groups by partitioning total variation into between-group and within-group components.

The DMAIC phase that identifies and verifies the root causes of the problem using data.

The systematic process of identifying the fundamental causes of a problem rather than its symptoms.

An iterative questioning technique that asks 'why' repeatedly to drill down to a root cause.

A cause-and-effect diagram organizing potential causes into categories such as the 6 Ms.

Common cause categories: Man, Machine, Method, Material, Measurement, and Mother Nature (environment).

A tool relating process inputs to customer-weighted outputs to prioritize key inputs (X's).

A statistical procedure that uses sample data to decide between a null and an alternative hypothesis.

The default claim of no effect or no difference that the test seeks to disprove.

The claim of an effect or difference that the test seeks to support.

Rejecting a true null hypothesis (a false positive); its probability is alpha (α).

Failing to reject a false null hypothesis (a false negative); its probability is beta (β).

The maximum acceptable probability of a Type I error, commonly set at 0.05.

The probability of a Type II error; power equals 1 − β.

The probability of correctly rejecting a false null hypothesis: power = 1 − β.

The probability of observing data as extreme as the sample if the null hypothesis is true; reject H0 when p < α.

The threshold a test statistic must exceed to reject the null hypothesis at a given alpha.

A hypothesis test for means when the population standard deviation is known and the sample is large.

A hypothesis test for means when the population standard deviation is unknown, using the t-distribution.

A test comparing means of two related (paired) measurements, such as before-and-after on the same units.

A test comparing the means of two independent groups.

A test comparing two variances or assessing overall significance in ANOVA using the ratio of variances.

A test comparing the means of three or more groups based on a single factor.

An analysis of variance examining the effects of two factors and their interaction on a response.

The number of values free to vary in a calculation, used to select the correct reference distribution.

A test for categorical data assessing goodness-of-fit or independence using observed vs expected counts.

A χ² test comparing an observed frequency distribution to an expected distribution.

A χ² test assessing whether two categorical variables in a contingency table are associated.

A cross-tabulation of counts for two categorical variables used in chi-square analysis.

Distribution-free tests used when data are ordinal or not normally distributed.

A nonparametric test comparing two independent groups' medians or distributions.

A nonparametric alternative to one-way ANOVA comparing three or more independent groups.

A test for equality of variances across groups that is robust to non-normality.

A measure of the strength and direction of a linear relationship between two continuous variables.

Pearson's r ranges from −1 to +1, indicating the strength and direction of linear association.

The proportion of variation in the response explained by the model: R² = r² for simple regression.

A model fitting a straight line ŷ = b0 + b1x to predict a response from one predictor.

A model predicting a response from two or more predictor variables.

A regression for binary or categorical outcomes that models the probability of an event.

The difference between an observed value and the value predicted by the model: residual = y − ŷ.

Examining residuals for randomness, normality, and constant variance to validate a model.

Fitting a regression line by minimizing the sum of squared residuals.

A condition where predictors in a regression are highly correlated, inflating coefficient variance.

A graphical analysis separating variation into positional, cyclical, and temporal families to focus root-cause search.

Within-unit or within-piece variation across locations, one of the multi-vari families.

Piece-to-piece variation among consecutive units, one of the multi-vari families.

Time-to-time variation across hours, shifts, or days, one of the multi-vari families.

A situation where the effect of one variable is mixed with another, obscuring the true cause.

Design of Experiments: a structured method of varying input factors simultaneously to identify their effects and interactions on a response.

The DMAIC phase that develops, tests, and implements solutions to address verified root causes.

A systematic method to plan tests that efficiently estimate factor effects and interactions on a response.

An independent input variable deliberately varied in an experiment.

A specific setting or value of a factor in an experiment.

The measured output (dependent variable) studied in an experiment.

The average change in the response produced by changing one factor from low to high.

When the effect of one factor on the response depends on the level of another factor.

An experiment testing every combination of factor levels, e.g., 2ᵏ runs for k two-level factors. It estimates all main effects and interactions.

An experiment testing a carefully chosen subset of combinations to reduce runs while estimating key effects.

Aliasing where two or more effects cannot be separately estimated in a fractional design.

A measure of how badly effects are confounded; Resolution III, IV, and V indicate increasing clarity of effects.

Repeating experimental runs to estimate experimental error and improve precision.

Running experimental trials in random order to spread the effect of unknown variables.

Grouping experimental runs to remove the effect of a known nuisance variable from the analysis.

Runs at the midlevel of all factors used to detect curvature in the response.

A set of designs and models used to optimize a response and locate the best factor settings.

An RSM design adding axial and center points to a factorial to fit a second-order model.

A three-level RSM design for fitting quadratic models without extreme corner runs.

A small experiment, often fractional factorial, used to identify the vital few significant factors.

A graph such as a main-effects or interaction plot used to interpret DOE results.

A bar chart ranking the magnitude of factor effects to identify significant ones in a DOE.

A weighted tool ranking candidate solutions against criteria such as cost, impact, and feasibility.

A small-scale trial of a solution to validate effectiveness and uncover issues before full rollout.

A detailed plan specifying tasks, owners, timelines, and resources to deploy a solution.

A methodology focused on eliminating waste and maximizing customer value through flow and pull.

Defects, Overproduction, Waiting, Non-utilized talent, Transportation, Inventory, Motion, and Excess processing (DOWNTIME).

A step that transforms the product or service in a way the customer is willing to pay for.

A step that consumes resources but adds no value the customer would pay for.

A diagram of material and information flow used to identify waste and design a leaner future state.

The pace of production needed to meet demand: takt time = available production time / customer demand.

A visual signaling system that controls production and inventory using pull based on actual demand.

A workplace organization method: Sort, Set in order, Shine, Standardize, and Sustain.

A philosophy of continuous, incremental improvement involving everyone in the organization.

A focused, short-duration team workshop to rapidly improve a specific process.

Mistake-proofing devices or methods that prevent or immediately detect errors.

Single-Minute Exchange of Dies: techniques to reduce setup and changeover time to under ten minutes.

A program that maximizes equipment effectiveness through operator-led preventive and autonomous maintenance.

A productivity metric: OEE = Availability × Performance × Quality.

Production leveling that smooths volume and mix to reduce variation and waste.

Producing and moving one unit at a time through the process to reduce work in process.

A system that produces only in response to downstream demand rather than forecasts.

A method that improves throughput by identifying and managing the system's bottleneck constraint.

A drawing of the physical flow of people or material used to reveal excess motion and transport.

Documented best-known method specifying sequence, timing, and steps for a task.

Arranging workstations in close sequence to enable smooth flow and reduce transport and inventory.

Evaluating potential failures and unintended consequences of a proposed solution before implementation.

A time-ordered plot of a process statistic with a center line and control limits used to distinguish common-cause from special-cause variation.

The final DMAIC phase that sustains gains through monitoring, documentation, and control plans.

A document detailing how each key input and output will be monitored, measured, and responded to.

Using control charts and statistical methods to monitor and control process variation over time.

Inherent, random variation present in a stable process from many small sources.

Variation from an identifiable, non-random source that signals the process is out of control.

Statistically derived boundaries, typically at ±3σ from the center line, signaling special-cause variation.

Customer- or engineering-defined boundaries for acceptable product; distinct from control limits.

The average of a control chart statistic, plotted between the control limits.

A control chart for continuous measurement data, such as X̄-R or I-MR.

A control chart for count or classification data, such as p, np, c, or u charts.

A pair of charts monitoring the subgroup average (X̄) and range (R) for continuous data, typically subgroups of 2 to 9.

A pair of charts monitoring the subgroup average (X̄) and standard deviation (S), used for larger subgroups (n ≥ 10).

An individuals and moving-range chart used when data are collected one at a time (subgroup size of 1).

An attribute chart tracking the proportion defective with variable sample sizes.

An attribute chart tracking the number defective with a constant sample size.

An attribute chart tracking the count of defects per unit with a constant inspection area.

An attribute chart tracking defects per unit when the sample size varies.

Selecting subgroups so within-subgroup variation reflects only common cause and shifts appear between subgroups.

A set of run rules that flag out-of-control patterns, such as a point beyond 3σ or 2 of 3 beyond 2σ.

Patterns such as trends, runs, and zone violations used to detect special causes on a control chart.

A point or pattern indicating special-cause variation that requires investigation.

An exponentially weighted moving-average chart sensitive to small, sustained process shifts.

A cumulative-sum chart that accumulates deviations to detect small shifts quickly.

A simple chart-free method using zones relative to specification limits to control a centered, capable process.

Choosing the chart type based on data type (variable vs attribute) and subgroup size.

Embedding poka-yoke devices into the process to sustain error-free performance.

A documented, step-by-step instruction ensuring consistent execution of a process.

Using visual cues such as boards and color codes to make process status and abnormalities obvious.

Predefined corrective actions to take when a control chart or metric signals a problem.

The combination of charts, procedures, and responses that maintains a process in control.

A periodic review verifying that controls and procedures are being followed and remain effective.

Practices that lock in improvements through documentation, training, monitoring, and ownership transfer.

A process must be in statistical control before its capability indices (Cp, Cpk) are meaningful.

Transferring responsibility for the improved process to the process owner with documentation and training.

Documented insights from a project used to improve future projects and share knowledge.

An organization-wide philosophy of continuous improvement and customer focus that predates Six Sigma.

Plan-Do-Check-Act: an iterative four-step cycle for continuous improvement and control.

A methodology for designing new products or processes to meet Six Sigma quality from the start.

A DFSS roadmap: Define, Measure, Analyze, Design, and Verify.

DFSS creates new designs to prevent defects, while DMAIC improves existing processes by reducing defects.

Designing products and processes to perform consistently despite variation in noise factors.

Robust design techniques using orthogonal arrays and the signal-to-noise ratio to minimize variation.

A model expressing quality loss as proportional to the squared deviation from target: L = k(y − T)².

A Taguchi metric measuring robustness; higher values indicate performance less sensitive to noise.

Uncontrollable or costly-to-control variables that cause variation, addressed through robust design.

Design parameters that can be set to make a product robust against noise factors.

A balanced experimental matrix used in Taguchi methods to study many factors with few runs.

Setting component tolerances to balance quality and cost after parameter design.

A theory of inventive problem solving using principles and patterns abstracted from patents to resolve contradictions.

Quality Function Deployment cascades customer needs into design requirements across multiple houses of quality.

An FMEA focused on potential failure modes in a product or system design.

An FMEA focused on potential failure modes in a manufacturing or service process.

A DFSS tool that predicts and tracks the capability of a new design against its requirements.

The probability a product performs its intended function without failure over a specified time and conditions.

A concept-selection tool scoring design alternatives against a datum on weighted criteria.

Identifying and controlling the few design parameters that most affect customer-critical performance.