Lean Six Sigma Process Improvement Training Course

Participants can create a project charter with a clear problem statement, scope and measurable target.

• Problem statements separate observed facts from assumed causes
• SMART targets specify baseline, direction, magnitude and deadline
• In-scope and out-of-scope boundaries prevent uncontrolled project expansion
• Stakeholder analysis identifies sponsors, process owners and affected teams
• Cost of poor quality quantifies visible and hidden losses
• Benefits registers distinguish financial, service, risk and capacity outcomes

Participants can convert customer evidence into measurable requirements and service specifications.

• Voice-of-customer sources include complaints, surveys, interviews and observation
• Kano analysis separates basic, performance and attractive customer needs
• CTQ trees link customer expectations to measurable process requirements
• Operational specifications define acceptable limits for customer-facing outputs
• Internal customers and suppliers reveal hand-off requirements and constraints
• Requirement statements distinguish needs, wants, assumptions and solutions

Participants can define process boundaries, customers, suppliers and high-level process requirements.

• SIPOC identifies suppliers, inputs, process steps, outputs and customers
• Start and end points establish measurable process boundaries
• Supplier requirements identify input quality, timing and completeness conditions
• Output definitions prevent inconsistent counting of cases and transactions
• Process owners clarify decision rights, accountability and escalation routes
• High-level process steps expose missing hand-offs and unclear responsibilities

Participants can document a current-state process map that reveals hand-offs, delays and decision points.

• Swimlane maps show roles, hand-offs, decisions and rework loops
• Standard work documents the approved sequence, timing and expected output
• Decision diamonds identify approval rules and exception pathways
• Touch time, wait time and elapsed time reveal delay sources
• Process walk-throughs validate maps against actual work rather than procedures
• Rework loops capture correction, return and duplicate-entry activities

Participants can create a data collection plan with unambiguous measures and sampling rules.

• Operational definitions specify exactly what each measure includes and excludes
• Data collection plans define source, owner, frequency and sampling method
• Discrete and continuous data require different analytical treatments
• Numerator and denominator rules ensure consistent rate calculations
• Stratification fields capture location, product, channel, team and time
• Baseline periods avoid seasonal distortions and temporary process changes

Participants can assess whether measurement methods are sufficiently consistent for process analysis.

• Accuracy, precision, bias and stability describe measurement system performance
• Attribute agreement analysis tests consistency between defect inspectors
• Repeatability measures variation when one appraiser repeats observations
• Reproducibility measures variation between different appraisers using identical items
• Calibration records confirm instruments meet required measurement tolerances
• Data validation checks missing values, duplicates, outliers and coding errors

Participants can establish a credible baseline using descriptive statistics and visual analysis.

• Mean, median and mode describe central process performance
• Range, variance and standard deviation quantify process spread
• Histograms reveal distribution shape, clusters, gaps and extreme values
• Box plots compare process performance across teams or categories
• Run charts show performance movement over time before control limits
• Percentiles describe customer experience beyond average process performance

Participants can assess whether a stable process can meet defined specification limits.

• Specification limits reflect customer requirements rather than historical performance
• Cp and Cpk compare process spread and centring against specifications
• Defects, defectives and opportunities require distinct counting rules
• First-pass yield measures outputs completed correctly without rework
• Rolled throughput yield exposes accumulated losses across multiple process steps
• DPMO supports comparison of defect rates across different opportunities

Participants can prioritise improvement effort using frequency, impact and stratified process data.

• Pareto charts rank defect categories by cumulative contribution
• Stratified Pareto analysis separates causes by product, team or channel
• Heat maps identify concentrated failure patterns across process segments
• Cost-frequency matrices distinguish frequent nuisances from expensive failures
• Trend analysis identifies deterioration, seasonality and intervention effects
• Problem prioritisation combines customer impact, risk, cost and controllability

Participants can generate, organise and validate plausible root causes using process evidence.

• Fishbone diagrams organise causes under people, methods, machines and materials
• Five Whys traces causal chains beyond the first visible symptom
• Cause-and-effect matrices rank input variables against customer requirements
• Process observation distinguishes actual workarounds from documented procedures
• Fault tree analysis traces combinations of conditions leading to failure
• Root-cause statements require evidence, mechanism and measurable effect

Participants can identify non-value-adding activity and target constraints that restrict process flow.

• TIMWOODS identifies transport, inventory, motion, waiting and overproduction wastes
• Value-added analysis distinguishes customer value, business value and waste
• Spaghetti diagrams expose unnecessary movement of people, items and information
• Bottleneck analysis identifies the resource limiting overall process throughput
• Little's Law links work-in-progress, throughput and cycle time
• Queue analysis identifies arrival variability, service time and capacity imbalance

Participants can design a future-state process that reduces delay, hand-offs and unnecessary work.

• Future-state maps specify revised flow, controls, roles and hand-offs
• Takt time aligns required output pace with available working time
• Kanban signals control replenishment and work release between process steps
• Workload balancing redistributes tasks to reduce unevenness and overload
• Standard work combinations define sequence, timing and work-in-progress limits
• Poka-yoke designs prevent errors before they reach downstream customers

Participants can select and interpret basic hypothesis tests for process improvement questions.

• Null and alternative hypotheses frame measurable process improvement questions
• P-values indicate evidence strength against a stated null hypothesis
• Confidence intervals show plausible ranges for estimated process effects
• Two-sample t-tests compare average performance between independent groups
• Paired t-tests assess before-and-after changes within matched cases
• Chi-square tests compare categorical defect rates across process groups

Participants can use correlation and regression to investigate relationships without overstating causation.

• Scatter plots reveal direction, strength, clusters and nonlinear relationships
• Correlation coefficients quantify linear association between two variables
• Simple regression estimates output changes associated with input variation
• Residual plots test whether regression assumptions appear reasonable
• R-squared indicates variation explained by the fitted regression model
• Confounding variables require process knowledge before causal conclusions

Participants can develop improvement options and select actions against agreed decision criteria.

• Brainwriting generates improvement ideas without early group dominance
• Affinity diagrams group proposed solutions into related intervention themes
• Impact-effort matrices compare expected benefit against implementation burden
• Weighted decision matrices score alternatives against agreed selection criteria
• Constraint analysis tests staffing, technology, policy and compliance limitations
• Solution statements link proposed changes directly to validated root causes

Participants can use FMEA to identify implementation risks and prioritise preventive controls.

• FMEA identifies failure modes, effects, causes and existing controls
• Severity ratings assess consequence for customers, compliance and operations
• Occurrence ratings estimate the likelihood of each potential failure
• Detection ratings assess whether controls identify failures before release
• Risk priority scoring prioritises preventive actions and accountable owners
• Post-action FMEA reviews confirm whether residual risks remain acceptable

Participants can plan a controlled pilot, evaluate results and prepare wider implementation.

• Pilot charters define test scope, duration, participants and success measures
• Before-and-after comparisons require consistent measures and comparable conditions
• Pilot sampling plans capture performance, defects, feedback and unintended effects
• Implementation plans sequence actions, dependencies, owners and decision gates
• RACI matrices clarify responsibility, accountability, consultation and information flows
• Change impact assessments identify affected roles, procedures, systems and controls

Participants can select control charts and define responses to process signals.

• Common-cause variation requires system-level improvement rather than local correction
• Special-cause variation signals unusual conditions requiring prompt investigation
• Individuals charts monitor continuous measures recorded one case at a time
• p-charts monitor proportions of defective items across changing sample sizes
• Control limits differ from customer specification limits and targets
• Reaction plans assign actions for out-of-control signals and trends

Participants can create a control plan that assigns ownership, monitoring and escalation after project closure.

• Control plans specify measures, methods, frequency, limits and response actions
• Process ownership assigns accountability for maintaining revised standard work
• Visual management makes status, exceptions and priorities visible to teams
• Benefits realisation tracks savings, capacity, service and quality outcomes
• Standard operating procedures incorporate approved changes and control points
• Project closure records lessons, residual risks and follow-up review dates

Participants can build, review and present a complete DMAIC improvement project pack for a defined operational problem.

• Project charter presents problem, scope, baseline, target and expected benefits
• SIPOC and current-state map establish process boundaries and hand-offs
• Data plan and baseline analysis support a defensible problem diagnosis
• Root-cause evidence links analytical findings to process observations
• Future-state design, FMEA and pilot plan justify proposed improvements
• Control plan defines measures, ownership, escalation and benefits tracking