Agile Six Sigma Training Course

Participants can convert customer evidence into measurable critical-to-quality requirements and project measures.

• Voice-of-customer sources: complaints, interviews, surveys, observations and service data
• CTQ trees linking customer needs, drivers, requirements and specifications
• Kano analysis distinguishing basic, performance and attractive customer requirements
• Operational definitions that make defects observable and consistently counted
• Service-level requirements expressed through time, accuracy, completeness and accessibility
• Translating stakeholder expectations into measurable project success criteria

Participants can define process boundaries, suppliers, inputs, outputs and customers before detailed mapping.

• SIPOC diagrams establishing suppliers, inputs, process, outputs and customers
• Start and end points selected from the customer perspective
• Process boundary decisions preventing scope creep and local optimisation
• Input classification: controllable factors, noise factors and critical inputs
• Output measures aligned with CTQs, defects, time and cost
• High-level process validation with process owners and frontline staff

Participants can organise improvement work through visible workflow, short feedback cycles and explicit delivery policies.

• Kanban boards visualising improvement work, blockers, ownership and completion
• Work-in-progress limits exposing bottlenecks, queues and overloaded specialists
• Backlog prioritisation using customer impact, effort, risk and evidence
• Daily coordination focused on flow, impediments and next actions
• Review cycles gathering sponsor, customer and process-owner feedback
• Retrospectives converting delivery lessons into revised working agreements

Participants can map current-state work and identify delay, handoffs, rework and non-value-adding activity.

• Swimlane maps showing roles, handoffs, decisions, systems and controls
• Value stream maps separating processing time, waiting time and inventory
• Takt time compared with demand rate and available capacity
• Queue analysis identifying accumulation points, rework loops and batch delays
• Waste categories: defects, waiting, motion, overprocessing and unused capability
• Current-state observations tested against written procedures and performance data

Participants can create a data collection plan that produces relevant, traceable and usable baseline evidence.

• Operational definitions specifying units, counting rules, exclusions and timing
• Data collection plans linking measures, sources, owners, frequency and purpose
• Stratification variables revealing differences by product, channel, team and period
• Sampling approaches balancing confidence, workload, cost and operational disruption
• Data checks for missing values, duplicates, outliers and coding errors
• Baseline dashboards combining quality, speed, cost and customer measures

Participants can summarise process data and establish a defensible baseline for improvement decisions.

• Mean, median and mode selected for appropriate process distributions
• Range, variance and standard deviation describing process spread
• Histograms revealing distribution shape, clustering, gaps and extreme observations
• Run charts showing performance patterns over time and operational events
• Percentiles used for service-time commitments and customer experience measures
• Baseline statements quantifying defect rate, cycle time, backlog and cost

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

• Measurement system analysis separating process variation from measurement variation
• Accuracy, precision, bias and stability in operational measurement systems
• Attribute agreement analysis for inspection, classification and audit decisions
• Repeatability and reproducibility concepts for variable measurement data
• Calibration records and reference standards supporting traceable measurements
• Data collection improvements when measures cannot support reliable conclusions

Participants can prioritise significant problem categories and structure evidence-based root-cause investigation.

• Pareto charts ranking defect categories by frequency, cost and customer impact
• Stratified Pareto analysis exposing patterns hidden in aggregate results
• Cause-and-effect diagrams structured around people, process, technology and policy
• 5 Whys analysis tested against evidence rather than assumed explanations
• Problem statements distinguished from causes, symptoms and proposed solutions
• Root-cause verification using process observations, data and stakeholder interviews

Participants can use control charts to distinguish common-cause variation from special-cause signals.

• Common-cause and special-cause variation interpreted through control-chart signals
• Individuals and moving-range charts for continuous operational measures
• p charts tracking proportion defective across varying sample sizes
• c and u charts monitoring counts of defects and nonconformities
• Control limits distinguished from customer specifications and performance targets
• Response plans defining investigation, escalation and corrective-action triggers

Participants can assess whether a stable process is capable of meeting defined customer specifications.

• Specification limits derived from CTQs, contracts, policy and customer requirements
• Cp and Cpk indices interpreted for stable continuous processes
• Pp and Ppk indices used for overall observed performance
• Capability assumptions checked for stability, distribution and measurement quality
• Defects per unit and first-pass yield for transactional processes
• Capability findings translated into operational risk and improvement priorities

Participants can test whether observed differences are likely to support an operational improvement decision.

• Null and alternative hypotheses framed from operational improvement questions
• Confidence intervals communicating uncertainty around estimated process performance
• Two-sample tests comparing teams, suppliers, channels or process designs
• Paired tests assessing before-and-after changes on matched observations
• Chi-square tests examining associations between categorical process variables
• Practical significance distinguished from statistical significance and business value

Participants can generate, assess and select process changes that address verified causes and customer requirements.

• Solution generation using brainwriting, benchmarking and constraint removal
• Impact-effort matrices prioritising feasible changes with measurable value
• Pugh matrices comparing alternatives against weighted selection criteria
• Future-state maps showing revised flow, roles, controls and information
• Standard work specifying sequence, timing, quality criteria and escalation
• Pilot hypotheses linking proposed changes to expected performance measures

Participants can redesign workflow to improve flow, reduce queues and manage operational constraints.

• Little's Law relating work-in-progress, throughput and cycle time
• Pull systems releasing work according to downstream capacity and demand
• Bottleneck analysis locating constraints that limit end-to-end throughput
• Drum-buffer-rope principles protecting constrained resources from disruption
• Batch-size reduction shortening feedback loops and reducing waiting
• Visual management making workload, priorities, abnormalities and capacity visible

Participants can plan controlled tests that generate evidence before wider implementation.

• PDCA cycles structuring small tests, learning reviews and adjustments
• Pilot plans defining scope, duration, measures, safeguards and decision criteria
• A/B testing principles for comparable service, communication and workflow options
• Factor screening identifying variables most likely to influence performance
• Change backlogs sequenced by dependencies, learning value and implementation risk
• Review cadences converting pilot results into adoption, revision or abandonment decisions

Participants can assess process risks and prioritise preventive controls for failure-prone activities.

• FMEA structure linking failure modes, effects, causes and controls
• Severity, occurrence and detection ratings applied with defined scoring criteria
• Risk priority decisions focused on high-consequence and weakly controlled failures
• Mistake-proofing methods preventing errors or detecting them at source
• Control ownership assigned to roles with authority and process knowledge
• Residual risk reviewed after proposed preventive and detective controls

Participants can design controls that maintain gains, detect deterioration and assign clear response ownership.

• Control plans specifying CTQs, measures, limits, frequency and ownership
• Standard operating procedures revised to reflect approved process changes
• Layered process audits checking adherence to critical control points
• Visual controls supporting correct sequence, status recognition and escalation
• Training plans addressing role changes, competence checks and handover
• Benefits tracking linking verified gains to financial and service measures

Participants can plan stakeholder engagement, decision gates and adoption measures for sustained change.

• Stakeholder analysis mapping influence, impact, concerns and engagement actions
• RACI matrices clarifying decision rights, responsibilities and escalation paths
• Communication plans tailored to executives, operators, customers and control functions
• DMAIC tollgates reviewing evidence, risks, decisions and next-stage readiness
• Resistance diagnosis separating capability, capacity, incentives and confidence barriers
• Adoption measures tracking use, compliance, feedback and performance stability

Participants can quantify improvement benefits and report project status credibly to decision-makers.

• Cost-of-poor-quality categories: scrap, rework, returns, delay and complaints
• Hard and soft benefits distinguished through agreed financial definitions
• Benefit calculations using volume, unit cost, avoided loss and timing
• Project dashboards showing baseline, target, actual results, risks and decisions
• A3 reports presenting problem, analysis, countermeasures, results and follow-up
• Executive summaries connecting operational evidence to customer and financial outcomes

Participants can build, review and present a complete Agile Six Sigma improvement project with analysis, improvement actions and control measures.

• Project charter integrating business case, scope, CTQs and governance
• Current-state analysis combining SIPOC, process map, baseline and root causes
• Prioritised improvement backlog linked to verified causes and expected benefits
• Pilot design defining tests, measures, safeguards, owners and review points
• Control plan specifying standards, dashboards, response actions and accountability
• Stakeholder presentation defending evidence, decisions, risks and implementation sequence