- DMAIC
- Define, Measure, Analyze, Improve, Control — the core Six Sigma roadmap for improving an existing process.
- Six Sigma
- A data-driven methodology that reduces process variation and defects to improve quality, targeting 3.4 defects per million opportunities.
- Sigma (σ)
- The Greek letter for standard deviation; in Six Sigma it measures how much a process varies and how many sigmas fit between the mean and the spec limit.
- 3.4 DPMO
- The defect rate of a 'Six Sigma' process — 3.4 defects per million opportunities, allowing for the 1.5σ long-term shift.
- Defect
- Any output that fails to meet a customer requirement or specification.
- Defective
- A unit that contains one or more defects.
- DPMO
- Defects Per Million Opportunities — (defects ÷ (units × opportunities)) × 1,000,000; a standardized defect rate.
- DPU
- Defects Per Unit — total defects divided by total units inspected.
- Voice of the Customer (VOC)
- The expressed and implied needs, wants, and expectations of customers, gathered to define what quality means.
- Critical to Quality (CTQ)
- A measurable characteristic of a product or process whose performance standard must be met to satisfy the customer.
- DMADV / DFSS
- Define, Measure, Analyze, Design, Verify — Design for Six Sigma, used to create a new process or product rather than improve an existing one.
- Continuous improvement (Kaizen)
- An ongoing effort to make small, incremental improvements to processes, products, or services.
- Lean
- A methodology focused on maximizing customer value while eliminating waste (non-value-added activity).
- The 8 wastes (DOWNTIME)
- Defects, Overproduction, Waiting, Non-utilized talent, Transportation, Inventory, Motion, Excess processing.
- Value-added activity
- A step that transforms the product/service in a way the customer is willing to pay for, done right the first time.
- Non-value-added activity
- A step that consumes resources but adds no value the customer would pay for — a target for elimination.
- Value stream
- All the value-added and non-value-added steps required to bring a product or service to the customer.
- Value stream mapping
- A lean tool that diagrams the flow of material and information to expose waste and improvement opportunities.
- Project champion
- A senior leader who sponsors a Six Sigma project, removes barriers, and secures resources.
- Master Black Belt
- An expert who trains and mentors Black Belts and Green Belts and leads the Six Sigma deployment.
- Black Belt
- A full-time Six Sigma leader who runs complex projects and applies advanced statistical tools.
- Green Belt
- A part-time practitioner who leads smaller projects and supports Black Belt projects using core DMAIC tools.
- Yellow Belt
- A team member with basic Six Sigma awareness who supports projects and process data collection.
- Process owner
- The person accountable for a process's performance and for sustaining improvements after the project.
- Enterprise process
- A core, cross-functional process that delivers value across the whole organization.
- Cost of Poor Quality (COPQ)
- The total cost of defects — internal failure, external failure, appraisal, and prevention costs.
- Total Quality Management (TQM)
- An organization-wide approach to continuous quality improvement that predates and feeds into Six Sigma.
- Theory of Constraints (TOC)
- A method that improves throughput by identifying and managing the single biggest bottleneck (constraint).
- Business case
- The justification for a project: the problem, its cost, the goal, and the expected benefit to the organization.
- Roadmap (DMAIC vs DMADV)
- DMAIC improves an existing process; DMADV (DFSS) designs a new process or product to meet Six Sigma quality.
- Project charter
- The document that defines a project's problem, goal, scope, business case, team, and timeline; it authorizes the project.
- Problem statement
- A concise, fact-based description of what is wrong, where, when, and how big — with no causes or solutions.
- Goal statement
- A SMART target for the project metric (e.g., reduce cycle time from 10 to 6 days by Q3).
- SMART goal
- Specific, Measurable, Achievable, Relevant, Time-bound — the test of a well-written goal statement.
- Project scope
- The boundaries of the project — what is and is not included, defining where the process starts and stops.
- SIPOC
- Suppliers, Inputs, Process, Outputs, Customers — a high-level map that scopes the process in the Define phase.
- Kano model
- Classifies customer requirements as basic (must-be), performance (one-dimensional), and delighters (excitement) to prioritize features.
- Affinity diagram
- A tool that organizes a large set of ideas or VOC data into natural groupings.
- Critical-to-tree (CTQ tree)
- A diagram that translates broad customer needs into specific, measurable CTQ requirements.
- Stakeholder analysis
- Identifying who is affected by or can affect the project and planning how to engage each one.
- RACI matrix
- Maps each task to who is Responsible, Accountable, Consulted, and Informed; exactly one Accountable per task.
- Affinity vs tree diagram
- Affinity groups unstructured ideas; a tree diagram breaks a goal into progressively detailed sub-tasks.
- Interrelationship digraph
- A management tool that maps cause-and-effect links among many issues to find key drivers.
- Prioritization matrix
- A tool that weighs options against weighted criteria to choose the best one objectively.
- Tree diagram
- Breaks a broad goal or requirement into successive layers of detail (objectives → tasks).
- Matrix diagram
- Shows the strength of relationships between two or more groups of items in a grid.
- Activity network diagram (PERT)
- Shows the sequence and dependencies of project tasks and the critical path.
- Process Decision Program Chart (PDPC)
- Maps what could go wrong in a plan and prepares countermeasures.
- Gantt chart
- A bar chart of project tasks against a timeline, showing start, duration, and overlap.
- Work breakdown structure (WBS)
- A hierarchical decomposition of project work into manageable deliverables and tasks.
- Critical path
- The longest chain of dependent tasks in a project; it sets the shortest possible completion time.
- Voice of the Business (VOB)
- The needs and goals of the organization — cost, growth, profitability — balanced against the VOC.
- Voice of the Process (VOP)
- What the process is actually capable of delivering, shown by its data and control charts.
- Project metric (Y)
- The key output measure the project aims to improve (the 'big Y'), driven by process inputs (the x's).
- Y = f(x)
- The core Six Sigma idea: the output (Y) is a function of the process inputs/factors (x's); fix the x's to fix Y.
- Benchmarking
- Comparing your process performance against best-in-class to set targets and find improvement ideas.
- Cost-benefit analysis
- Comparing the expected costs of a project against its expected financial benefits.
- Project closure
- Formally ending the project: confirming goals met, documenting results, and handing off to the process owner.
- Team stages (Tuckman)
- Forming, storming, norming, performing, adjourning — predictable phases of team development.
- Nominal group technique
- A structured method where team members generate and silently rank ideas to reach consensus.
- Multivoting
- A group technique that narrows a large list of options to a few by rounds of voting.
- Brainstorming
- A group method for generating many ideas quickly without early criticism.
- Ground rules
- Agreed team norms for how members will work together, communicate, and make decisions.
- Negative brainstorming
- Listing ways to cause a problem, then reversing them into prevention ideas.
- Process map
- A flowchart showing every step, decision, input, and output of a process as it actually runs.
- Flowchart
- A diagram using standard symbols to show the sequence of steps and decisions in a process.
- Value stream map vs process map
- A value stream map adds material/information flow and timing data; a process map shows the step sequence.
- Data type: continuous
- Variable data measured on a continuous scale (time, weight, length) — more information per data point.
- Data type: discrete
- Attribute data counted in categories (pass/fail, number of defects) — less information per point.
- Nominal data
- Categories with no order (color, machine ID).
- Ordinal data
- Categories with a meaningful order but no fixed interval (small/medium/large, survey ratings).
- Population vs sample
- A population is the entire group of interest; a sample is a subset measured to infer about the population.
- Random sampling
- Each item has an equal chance of selection, reducing bias in the sample.
- Stratified sampling
- Dividing the population into subgroups (strata) and sampling each to ensure representation.
- Mean
- The arithmetic average — the sum of values divided by the count.
- Median
- The middle value when data is ordered; robust to outliers.
- Mode
- The most frequently occurring value in a data set.
- Range
- The difference between the largest and smallest values; a simple measure of spread.
- Variance (σ²)
- The average of the squared deviations from the mean; the square of the standard deviation.
- Standard deviation (σ)
- A measure of how spread out data is around the mean (written σ); it equals √variance.
- Normal distribution
- A symmetric, bell-shaped distribution defined by its mean and standard deviation.
- Empirical rule (68-95-99.7)
- In a normal distribution, ≈68% of data lies within ±1σ, ≈95% within ±2σ, ≈99.7% within ±3σ of the mean.
- Central limit theorem
- The distribution of sample means approaches normal as sample size grows, regardless of the population shape.
- Binomial distribution
- Models the count of successes in a fixed number of independent pass/fail trials.
- Poisson distribution
- Models the count of rare events in a fixed interval (e.g., defects per unit).
- Histogram
- A bar chart of the frequency of data across intervals; reveals the shape, center, and spread of a distribution.
- Box plot
- A graphical summary showing the median, quartiles, and outliers of a data set.
- Run chart
- A line plot of data over time used to spot trends, shifts, or cycles.
- Scatter diagram
- A plot of two variables to reveal whether and how they are related (correlation).
- Measurement System Analysis (MSA)
- A study that quantifies how much variation in the data comes from the measurement system itself.
- Gage R&R
- A study of measurement-system Repeatability (same operator) and Reproducibility (different operators).
- Repeatability
- Variation when the same operator measures the same item multiple times with the same gage.
- Reproducibility
- Variation when different operators measure the same item with the same gage.
- Accuracy (bias)
- How close a measurement is to the true value; bias is a consistent offset from truth.
- Precision
- How consistent repeated measurements are with each other (low spread), regardless of accuracy.
- Resolution (discrimination)
- The smallest increment a measurement system can detect.
- Stability (MSA)
- Whether a measurement system's bias stays consistent over time.
- Linearity (MSA)
- Whether measurement bias is consistent across the full range of the gage.
- Process capability
- How well a process meets its specification limits, compared to its natural spread.
- Cp
- Potential capability = (USL − LSL) ÷ 6σ; compares the spec width to the process spread, ignoring centering.
- Cpk
- Actual capability = min[(USL − mean), (mean − LSL)] ÷ 3σ; accounts for both spread and centering.
- Cp vs Cpk
- Cp assumes the process is centered; Cpk penalizes off-center processes. Cpk ≤ Cp always.
- Pp and Ppk
- Long-term performance indices, like Cp/Cpk but using the overall (long-term) standard deviation.
- Specification limits
- The customer/engineering limits (USL, LSL) that define an acceptable output; set by requirements, not the process.
- Control limits vs spec limits
- Control limits come from the process data (the voice of the process); spec limits come from the customer.
- Yield
- The proportion of units that pass without defects.
- First Time Yield (FTY)
- The fraction of units that complete a step correctly the first time, without rework.
- Rolled Throughput Yield (RTY)
- The product of the first-time yields of every step; the probability a unit passes the whole process defect-free.
- Sigma level (Z)
- The number of standard deviations between the process mean and the nearest spec limit.
- Baseline measurement
- The current process performance captured before improvements, used to prove the change worked.
- Operational definition
- A precise, agreed definition of what is measured and how, so data is consistent across people.
- Check sheet
- A simple structured form for collecting and tallying data in real time.
- Root cause analysis
- The process of finding the fundamental cause of a problem rather than treating its symptoms.
- 5 Whys
- Asking 'why?' repeatedly (about five times) to drill from a symptom down to the root cause.
- Fishbone (Ishikawa) diagram
- A cause-and-effect diagram that organizes potential causes by category (the 6 Ms) around a central spine.
- The 6 Ms
- Cause categories on a fishbone: Methods, Machines, Materials, Measurement, Manpower (people), Mother Nature (environment).
- Pareto chart
- A bar chart ordering causes by frequency to highlight the 'vital few' that drive most of the problem.
- Pareto principle (80/20)
- Roughly 80% of effects come from 20% of causes; focus effort on the vital few.
- Correlation
- A statistical relationship in which two variables move together; it does not prove causation.
- Correlation coefficient (r)
- A value from −1 to +1 measuring the strength and direction of a linear relationship between two variables.
- Regression analysis
- A method that models how a response variable changes as one or more input variables change.
- Simple linear regression
- Models the response Y as a straight-line function of one predictor X: Y = b₀ + b₁X.
- Scatter plot interpretation
- Points trending up = positive correlation; down = negative; no pattern = little/no correlation.
- Hypothesis testing
- A method to decide, with a stated risk, whether sample data supports a claim about a population.
- Null hypothesis (H₀)
- The default claim of no difference or no effect, which the test tries to disprove.
- Alternative hypothesis (Hₐ)
- The claim that there is a difference or effect — what you conclude if you reject H₀.
- p-value
- The probability of seeing data this extreme if H₀ were true; if p ≤ α, reject H₀.
- Alpha (α)
- The significance level — the accepted probability of a Type I error, often 0.05.
- Type I error
- Rejecting a true null hypothesis — a 'false positive'; its probability is α.
- Type II error
- Failing to reject a false null hypothesis — a 'false negative'; its probability is β.
- Confidence interval
- A range, computed from data, that likely contains the true population parameter at a stated confidence (e.g., 95%).
- t-test
- A hypothesis test comparing means when the population standard deviation is unknown / samples are small.
- ANOVA
- Analysis of Variance — tests whether the means of three or more groups differ significantly.
- Chi-square test
- Tests whether observed counts of categorical data differ from expected counts (e.g., independence).
- FMEA
- Failure Mode and Effects Analysis — a structured way to identify failure modes and prioritize them by risk.
- RPN (Risk Priority Number)
- Severity × Occurrence × Detection in an FMEA; higher RPN = higher priority to address.
- Severity, Occurrence, Detection
- The three FMEA ratings (each 1–10): how bad, how likely, and how hard to catch a failure is.
- Multi-vari study
- A graphical study that classifies variation as within-piece, piece-to-piece, or time-to-time to localize its source.
- Confounding
- When the effects of two factors cannot be separated, obscuring which one drives the response.
- Causation vs correlation
- Correlation means two variables move together; causation means one drives the other — correlation alone never proves it.
- Graphical analysis
- Using charts (histograms, box plots, scatter, Pareto) to explore data before formal statistics.
- Sources of variation
- Distinguishing common-cause (inherent) from special-cause (assignable) variation in the data.
- Practical vs statistical significance
- A result can be statistically significant yet too small to matter practically — judge both.
- Design of Experiments (DOE)
- A structured method of changing input factors deliberately to learn their effect on the output.
- Factor
- An input variable deliberately changed in an experiment (e.g., temperature).
- Level
- A specific setting of a factor in an experiment (e.g., 100°C and 150°C).
- Response
- The output measured in an experiment to judge the effect of the factors.
- Main effect
- The average change in the response caused by changing one factor from low to high.
- Interaction
- When the effect of one factor on the response depends on the level of another factor.
- Full factorial design
- An experiment testing every combination of factor levels to estimate all effects and interactions.
- Fractional factorial design
- A DOE using a carefully chosen subset of runs to study many factors economically.
- One-factor-at-a-time (OFAT)
- Changing one input at a time; inefficient and misses interactions — DOE is preferred.
- Kaizen event
- A focused, short (often week-long) team effort to rapidly improve a specific process.
- 5S
- Sort, Set in order, Shine, Standardize, Sustain — a lean method for an organized, efficient workplace.
- Poka-yoke (mistake-proofing)
- A device or design that prevents or immediately detects errors so defects cannot pass downstream.
- Kanban
- A visual signal that pulls work or replenishes inventory only when needed, limiting overproduction.
- Pull system
- Production triggered by actual downstream demand rather than a forecast (push).
- Just-in-Time (JIT)
- Producing or delivering only what is needed, when it is needed, in the amount needed.
- Single-Minute Exchange of Die (SMED)
- A lean method to drastically reduce equipment changeover/setup time.
- Standard work
- The documented, current best way to perform a task, ensuring consistency and a baseline to improve.
- Takt time
- The pace of customer demand = available production time ÷ customer demand; sets the rhythm of production.
- Cycle time
- The time to complete one unit or one cycle of a process step.
- Lead time
- The total elapsed time from a customer request to delivery.
- Theory of constraints (in Improve)
- Improve throughput by exploiting and elevating the bottleneck before optimizing elsewhere.
- Cellular manufacturing
- Arranging equipment and workstations by product flow (a cell) to cut transport and waiting.
- Pilot study
- A small-scale trial of a proposed solution to confirm it works before full rollout.
- Cost-benefit of solutions
- Weighing each candidate improvement's expected benefit against its cost and risk to choose the best.
- Solution selection matrix
- A weighted matrix that scores candidate solutions against criteria like impact, cost, and effort.
- Total Productive Maintenance (TPM)
- A program that involves operators in maintaining equipment to reduce breakdowns and defects.
- Spaghetti diagram
- A drawing of the physical path of a product or person to expose wasted motion and transport.
- Visual management
- Using visual signals (boards, color, labels) so status and abnormalities are obvious at a glance.
- Heijunka (level loading)
- Smoothing production volume and mix to reduce batching, inventory, and overburden.
- Control plan
- A document specifying how each key process input/output will be monitored and what to do if it goes out of control.
- Statistical Process Control (SPC)
- Using control charts to monitor a process over time and distinguish common- from special-cause variation.
- Control chart
- A time-ordered plot with a center line and upper/lower control limits used to detect special-cause variation.
- Upper/Lower Control Limit (UCL/LCL)
- Lines at ±3σ from the center line; points beyond them signal a special cause.
- Center line
- The process average (or target) on a control chart, between the control limits.
- Common-cause variation
- Inherent, random variation present in a stable process; do not react to individual points.
- Special-cause variation
- Variation from an identifiable, assignable source that makes a process unstable; investigate and remove it.
- In statistical control
- A process showing only common-cause variation — stable and predictable.
- Out of control
- A process showing special-cause variation — a point beyond the limits or a non-random pattern.
- Tampering (over-adjustment)
- Reacting to common-cause variation as if it were special; it increases variation.
- X-bar and R chart
- A control chart pair for continuous data in subgroups: X-bar tracks the average, R tracks the range (spread).
- X-bar and S chart
- Like X-bar & R but uses the standard deviation (S) for spread; preferred for larger subgroups.
- Individuals & Moving Range (I-MR)
- A control chart for continuous data collected one point at a time (subgroup size of 1).
- p-chart
- An attribute control chart for the proportion defective with varying sample sizes.
- np-chart
- An attribute control chart for the number defective with a constant sample size.
- c-chart
- An attribute control chart for the count of defects per unit with a constant sample size.
- u-chart
- An attribute control chart for defects per unit with varying sample size.
- Variables vs attributes charts
- Variables charts (X-bar/R, I-MR) plot continuous data; attribute charts (p, np, c, u) plot counts.
- Control chart selection
- Choose the chart by data type (continuous vs attribute) and subgroup size.
- Western Electric / Nelson rules
- Pattern rules (e.g., a run of 7, points beyond 3σ) that flag a likely special cause.
- Rational subgroup
- A small sample chosen so variation within it is only common-cause, isolating special-cause between subgroups.
- Process drift
- A slow, gradual shift of the process mean over time, often caught by control charts.
- Sustaining improvements
- Embedding gains via control plans, standard work, training, and monitoring so the process does not regress.
- Standard Operating Procedure (SOP)
- The documented, approved method for performing a task to keep the improved process consistent.
- Response plan (OCAP)
- An Out-of-Control Action Plan: predefined steps to take when a control chart signals a problem.
- Audit
- A periodic check that the improved process and its controls are being followed and still work.
- Mistake-proofing (Control)
- Poka-yoke controls that hold the gains by preventing the defect from recurring.
- Process handoff
- Transferring the controlled process and its documentation to the process owner at project close.
- Pre-control
- A simple stoplight technique using zones around the target to decide whether to keep running or adjust.
- Lessons learned
- Documented insights from the project used to improve future projects and share knowledge.
- Dashboard / scorecard
- A visual display of key process metrics over time used to monitor sustained performance.
- Control phase deliverables
- A control plan, updated SOPs, training, monitoring charts, and a documented handoff to the owner.
- History of Six Sigma
- Pioneered at Motorola in the 1980s and popularized by GE in the 1990s as a quality-improvement methodology.
- Quality (definition)
- Conformance to requirements and fitness for use — meeting or exceeding customer expectations.
- PDCA cycle
- Plan-Do-Check-Act — Deming's iterative cycle for continuous improvement.
- Deming
- W. Edwards Deming, a quality pioneer known for PDCA, the 14 points, and emphasizing reducing variation.
- Juran
- Joseph Juran, who developed the quality trilogy (planning, control, improvement) and applied the Pareto principle.
- Customer loyalty
- The likelihood a customer continues to buy and recommend; a downstream goal of quality improvement.
- Internal vs external customer
- Internal customers are the next step in your own process; external customers are the end users who buy the output.
- Process vs product
- A process is the set of activities that produces an output; the product/service is that output.
- Throughput
- The rate at which a process produces completed output over time.
- Bottleneck
- The step that limits the throughput of the whole process — the constraint.
- Charter scope creep
- Uncontrolled expansion of project boundaries; the charter and sponsor guard against it.
- Elevator speech
- A 30-second summary of the project's problem, goal, and value for stakeholders.
- Project selection
- Choosing projects tied to strategy with measurable, achievable goals and clear customer impact.
- Voice of the Customer tools
- Surveys, interviews, focus groups, complaints, and observation used to gather VOC.
- CTQ vs CTC vs CTD
- Critical-to-Quality, Critical-to-Cost, and Critical-to-Delivery characteristics derived from customer needs.
- Team roles
- Sponsor/champion, team leader (Belt), members, process owner, and facilitator — each with defined duties.
- Consensus
- A decision all team members can support, even if it is not everyone's first choice.
- Force-field analysis
- Lists driving and restraining forces for a change to plan how to strengthen or weaken each.
- Milestone
- A significant checkpoint or deliverable date in the project schedule.
- Deliverable
- A tangible, verifiable output the project must produce.
- Subgroup
- A small sample of items collected together under similar conditions for a control chart.
- Sampling bias
- Systematic error from a sample that does not represent the population.
- Coefficient of variation
- The standard deviation divided by the mean; a unitless measure of relative variation.
- Quartiles
- Values dividing ordered data into four equal parts (Q1, Q2=median, Q3).
- Interquartile range (IQR)
- Q3 − Q1; the spread of the middle 50% of the data, robust to outliers.
- Skewness
- A measure of how asymmetric a distribution is around its mean.
- Kurtosis
- A measure of how heavy-tailed or peaked a distribution is compared to normal.
- Z-score
- The number of standard deviations a value is from the mean: (x − mean) ÷ σ.
- Exponential distribution
- Models the time between independent events occurring at a constant rate.
- Attribute agreement analysis
- An MSA for discrete data that checks whether appraisers rate items consistently and correctly.
- %R&R
- The percentage of total variation consumed by the measurement system; under 10% is generally acceptable.
- Number of distinct categories (ndc)
- An MSA metric of how many groups the gage can reliably tell apart; ≥5 is desired.
- Process sigma calculation
- Convert DPMO to a Z (sigma) value using a normal table or DPMO-to-sigma conversion.
- Opportunity (defect)
- Any chance for a defect to occur on a unit; used to compute DPMO.
- Data collection plan
- A plan defining what data to collect, how, by whom, and how often, with operational definitions.
- Multicollinearity
- When predictor variables in a regression are themselves correlated, distorting their estimated effects.
- Residual
- The difference between an observed value and the value predicted by a regression model.
- R-squared
- The proportion of variation in the response explained by the regression model (0 to 1).
- Power of a test
- The probability of correctly rejecting a false null hypothesis (1 − β).
- Beta (β) risk
- The probability of a Type II error — failing to detect a real effect.
- Sample size (test)
- The number of observations needed to detect an effect at a chosen risk and power.
- Paired t-test
- Compares the means of two related measurements (e.g., before vs after on the same units).
- Two-sample t-test
- Compares the means of two independent groups.
- F-test
- Compares two variances or tests overall significance in ANOVA/regression.
- Contingency table
- A table of categorical counts used in a chi-square test of independence.
- Hypotheses about variance
- Tests (chi-square, F) that compare process spread rather than averages.
- Cause validation
- Confirming a suspected root cause with data before fixing it, not just by opinion.
- Box-and-whisker comparison
- Side-by-side box plots used to compare distributions of groups quickly.
- Randomization (DOE)
- Running experimental trials in random order to spread out the effect of unknown influences.
- Replication (DOE)
- Repeating experimental runs to estimate experimental error and improve precision.
- Blocking (DOE)
- Grouping experimental runs to remove the effect of a known nuisance variable.
- Center points (DOE)
- Extra runs at the mid-level of factors used to detect curvature in the response.
- Response surface methodology
- An advanced DOE that models curvature to find optimal factor settings.
- Screening experiment
- A fractional design used to identify the few significant factors from many.
- Gemba
- The 'real place' where work happens; lean practitioners go to gemba to observe directly.
- Work cell
- A self-contained group of resources arranged for one-piece flow of a product family.
- One-piece flow
- Moving a single unit through steps without batching, cutting WIP and lead time.
- Setup reduction
- Cutting changeover time so smaller batches and more flexibility become economical.
- Mistake-proofing types
- Prevention (stops the error) and detection (flags the error) poka-yoke devices.
- Implementation plan
- A detailed plan of tasks, owners, and dates to roll out the chosen solution.
- Subgroup size effect
- Larger subgroups make X-bar charts more sensitive to small shifts in the mean.
- Run rule (run of 7)
- Seven consecutive points on one side of the center line signals a non-random pattern.
- Trend rule
- Several consecutive increasing or decreasing points signals a possible special cause.
- Zone rules (sigma zones)
- Patterns within the A/B/C sigma zones of a control chart that flag instability.
- Chart recalculation
- Control limits are recomputed when a verified, permanent process change occurs.
- False alarm (control chart)
- An out-of-control signal when no special cause exists — a Type I error on the chart.
- Capability after control
- Recompute Cp/Cpk only once the process is in statistical control, never before.
- Training plan (control)
- Ensuring operators are trained on the new standard work so improvements hold.
- Metric ownership
- Assigning each control metric an owner responsible for monitoring and acting on it.
- Project replication
- Spreading a proven solution to similar processes or sites to multiply the benefit.
