Six Sigma Quality Management Training Course

Participants can define process boundaries, operational measures and a credible data collection plan.

• SIPOC maps define suppliers, inputs, outputs, customers and boundaries
• Swimlane flowcharts expose handoffs, queues, rework loops and approvals
• Value-stream maps quantify processing time, waiting time and inventory
• Operational definitions remove ambiguity from defect classifications and measures
• Data collection plans specify sources, sampling, owners and frequencies
• Gemba observations record actual work sequences without assumed process steps

Participants can assess whether measurement systems and source data are suitable for analysis.

• Measurement system analysis distinguishes repeatability, reproducibility, bias and stability
• Gage R&R studies assess measurement variation before capability conclusions
• Attribute agreement analysis tests inspector consistency for categorical decisions
• Calibration records link instruments, tolerances, traceability and maintenance intervals
• Sampling plans balance confidence, inspection effort and operational disruption
• Data integrity checks identify missing values, duplicates and coding errors

Participants can establish a baseline using descriptive statistics and graphical analysis.

• Descriptive statistics summarise centre, spread, shape and unusual observations
• Histograms and box plots reveal distribution patterns and outliers
• Run charts distinguish routine variation from emerging performance shifts
• Pareto analysis ranks defect categories by frequency, cost and impact
• Stratification separates variation by product, shift, location or supplier
• Scatter plots test plausible relationships before formal statistical modelling

Participants can prepare an approved Define-phase pack for a focused improvement project.

• Project charters state problem, baseline, goal, scope and expected benefits
• Stakeholder analysis identifies decision-makers, affected teams and communication needs
• CTQ trees connect customer needs with measurable process requirements
• High-level process maps establish start points, endpoints and key interfaces
• Project milestones sequence Define, Measure, Analyse, Improve and Control work
• Risk registers identify data, resource, approval and implementation constraints

Participants can quantify process performance against specification limits and customer requirements.

• Normality assessment guides capability method selection and transformation decisions
• Calculate Cp, Cpk, Pp and Ppk against specification limits
• Capability baselines quantify defect risk, yield and sigma performance
• Non-normal capability methods address skewed, bounded and discrete data
• DPMO and rolled throughput yield compare multistep process performance
• Translate capability findings into financial and customer-impact statements

Participants can select, interpret and respond to control charts for process monitoring.

• Control-chart selection matches data type, subgrouping and production conditions
• Xbar-R and Xbar-S charts monitor continuous process variation
• p, np, c and u charts track attribute performance
• Western Electric rules identify signals requiring investigation and response
• Rational subgrouping separates within-subgroup noise from process movement
• Control limits differ from customer specifications and tolerance limits

Participants can identify, prioritise and verify root causes of process failure.

• Fishbone diagrams structure potential causes across people, methods, machines and materials
• 5 Whys tests causal chains beyond the first visible failure
• Cause-and-effect matrices prioritise input variables against CTQ requirements
• Failure mode and effects analysis ranks severity, occurrence and detection
• Process FMEA actions reduce high-priority failure risks before implementation
• Verify root causes with data rather than consensus or anecdotes

Participants can use hypothesis tests to evaluate whether observed differences are meaningful.

• Null and alternative hypotheses frame measurable improvement questions
• Confidence intervals express estimate precision alongside point estimates
• Two-sample t tests compare means before and after changes
• Proportion tests evaluate defect-rate differences between process conditions
• Chi-square tests assess associations within categorical defect data
• Practical significance complements p-values when selecting improvement actions

Participants can model relationships between process inputs and performance outcomes.

• Correlation analysis distinguishes association from evidence of causal influence
• Simple regression estimates output response to a single input
• Multiple regression models critical inputs while checking residual assumptions
• Interaction effects reveal conditions where factor influence changes
• Regression diagnostics identify outliers, multicollinearity and nonconstant variance
• Translate model results into controllable operating parameter ranges

Participants can plan controlled experiments to identify and optimise important process factors.

• Factorial designs test several input factors with efficient experimental runs
• Randomisation, replication and blocking protect experiment validity
• Screening designs identify vital factors among many candidate inputs
• ANOVA separates factor effects from experimental error variation
• Response surface methods optimise settings after factor screening
• Pilot protocols define safety, approvals, measures and stopping criteria

Participants can select, test and implement countermeasures supported by evidence.

• Generate countermeasures using kaizen, poka-yoke and standard work
• Solution-selection matrices compare benefit, risk, cost and implementation effort
• Future-state maps document redesigned flow, controls and expected gains
• Pilot plans specify hypotheses, baseline, measures, ownership and review dates
• Implementation plans sequence training, communications, resources and change control
• Benefit calculations validate savings, capacity release and service improvements

Participants can apply Lean methods to remove waste and improve process flow.

• Eight wastes expose non-value-adding activity within administrative and production processes
• 5S standards organise workplace conditions for reliable visual management
• Kanban signals control replenishment and limit work in progress
• SMED principles reduce changeover time through internal-external task separation
• Standard work documents sequence, timing, quality checks and escalation points
• Takt time aligns process capacity with customer demand rates

Participants can establish ownership, monitoring and response mechanisms that maintain process gains.

• Control plans specify CTQs, limits, sampling methods, responses and owners
• Reaction plans define containment, escalation and corrective-action responsibilities
• Visual management makes status, abnormalities and standards immediately visible
• Statistical process control sustains gains through scheduled chart review
• Standard operating procedures embed revised methods, controls and training
• Audit checklists test adherence, effectiveness and recurring risk signals

Participants can apply quality planning methods to new processes, products and services.

• DMADV distinguishes new-design work from DMAIC improvement projects
• Quality function deployment translates customer needs into design characteristics
• Design FMEA prevents risks before release or service launch
• ISO 9001 process approach links objectives, controls, evidence and review
• Supplier quality requirements define acceptance criteria, audits and corrective actions
• Management review metrics connect project results to quality objectives

Participants can build, review and present a complete DMAIC improvement project with evidence-based recommendations.

• Complete DMAIC project pack integrates charter, baseline, analysis, solution and controls
• Executive storyboard presents problem, evidence, savings, risks and ownership
• Peer review challenges assumptions, measurement validity and causal claims
• Control-plan review confirms monitoring frequency, response thresholds and accountabilities
• Implementation roadmap sequences pilots, approvals, communications and handover actions
• Present project recommendations using data, visuals and decision-focused language