Lean Six Sigma Problem Solving Training Course

Participants can translate a business concern into measurable customer and process requirements.

• Voice of customer data gathered from complaints, interviews and service records
• Critical-to-quality requirements translated into measurable operational specifications
• Problem statements separated from presumed causes and preferred solutions
• SMART project objectives linked to customer, financial and risk measures
• Operational definitions established for defects, opportunities and unit counts
• CTQ trees connecting broad customer needs with measurable requirements

Participants can prepare a project charter with scope, targets, governance and quantified benefits.

• Project charter sections covering problem, goal, scope, risks and milestones
• Baseline financial impact calculated from waste, rework, delay and capacity loss
• Benefit types distinguished between hard savings, avoidance and service gains
• In-scope and out-of-scope activities agreed with process owners
• Milestone plans aligned with DMAIC tollgates and sponsor review points
• Assumptions, constraints and dependencies recorded within the benefits case

Participants can map a process at appropriate levels of detail to identify data and improvement opportunities.

• SIPOC diagrams define suppliers, inputs, process, outputs and customers
• Swimlane flowcharts reveal handoffs, roles, approvals and decision points
• Process boundaries tested against customer demand and downstream consequences
• Rework loops, queues and escalation paths captured within current-state maps
• Value-added analysis separates customer value, business value and waste
• Gemba observation records actual work rather than documented procedures

Participants can identify flow constraints and prioritise waste for further investigation.

• TIMWOODS categories identify transport, inventory, motion, waiting and defects
• Value stream maps quantify lead time, touch time and queue time
• Takt time compares customer demand with available productive capacity
• Little's Law links work-in-progress, throughput and cycle time
• Bottleneck analysis identifies constraints limiting end-to-end process output
• Pull systems and work limits reduce excessive queues and overproduction

Participants can design a data collection plan that produces usable evidence for analysis.

• Data collection plans specify variables, sources, owners, timing and sampling
• Operational definitions remove ambiguity from defect and cycle-time measures
• Discrete and continuous data distinguished before selecting analytical methods
• Sampling approaches balance confidence, practical effort and operational disruption
• Stratification fields capture shift, channel, product, location and customer type
• Data dictionaries document field meaning, format, range and transformation rules

Participants can assess whether measurement methods are sufficiently reliable for decision making.

• Measurement error separated into accuracy, precision, bias and stability
• Attribute agreement analysis evaluates consistency between assessors and standards
• Gage repeatability and reproducibility concepts applied to continuous measurements
• Calibration records reviewed for instruments affecting critical process measures
• Data validation checks identify missing values, duplicates and impossible entries
• Measurement risks addressed before drawing conclusions from process data

Participants can describe process performance and establish a defensible baseline.

• Frequency distributions show common values, spread, skewness and unusual patterns
• Mean, median and mode selected for appropriate data distributions
• Range, standard deviation and interquartile range quantify process variation
• Run charts display performance changes over time and operational events
• Baseline measures calculated for defects, yield, cycle time and backlog
• Data segmentation compares performance across meaningful process categories

Participants can prioritise problem categories using visual evidence and contribution analysis.

• Pareto charts rank defect categories by frequency, cost or customer impact
• Cumulative percentages identify vital categories requiring focused investigation
• Histograms reveal distribution shape, spread, gaps and multiple populations
• Scatter plots test apparent relationships between potential causes and outcomes
• Box plots compare variation and outliers across process segments
• Data visualisation choices match the question, audience and decision required

Participants can distinguish common-cause variation from signals requiring investigation.

• Common-cause and special-cause variation distinguished using process behaviour
• Control limits differ from specification limits and performance targets
• Individuals and moving-range charts monitor continuous operational measures
• P charts track proportions defective where opportunity counts vary
• Control-chart signals interpreted using runs, trends and out-of-control points
• Reaction plans specify investigation, containment and escalation after signals

Participants can assess whether a stable process can meet customer specifications.

• Specification limits translated from customer requirements and regulatory thresholds
• Cp and Cpk measure potential and actual capability against specifications
• Pp and Ppk compare overall performance with long-term variation
• Defects per million opportunities calculated from defect opportunities and volume
• First-pass yield exposes hidden rework within multi-step processes
• Capability findings linked to customer risk, cost and improvement priority

Participants can generate and organise evidence-based hypotheses about process causes.

• Fishbone diagrams organise potential causes across people, process and technology
• Five Whys probes causal chains while checking each answer against evidence
• Cause-and-effect matrices rank input variables against critical output requirements
• Is-Is Not analysis clarifies where, when and whom problems affect
• Process walk-throughs test stated hypotheses at the point of work
• Root causes distinguished from symptoms, contributing factors and conditions

Participants can use basic hypothesis tests to evaluate suspected differences and changes.

• Null and alternative hypotheses stated for operational improvement questions
• P-values and confidence intervals interpreted without overstating statistical certainty
• Two-sample tests compare cycle times, costs or error rates
• Paired tests assess before-and-after results from the same process
• Chi-square tests examine relationships between categorical process variables
• Practical significance considered alongside statistical significance and implementation cost

Participants can evaluate relationships between process inputs and performance outcomes.

• Correlation coefficients quantify association without establishing causal direction
• Simple linear regression estimates outcome changes from a measurable input
• Scatter plots check linearity, outliers and unusual data clusters
• Residual patterns identify poor model fit and missing explanatory factors
• Regression assumptions reviewed before using predictions for operational decisions
• Findings translated into testable improvement hypotheses and process controls

Participants can develop countermeasures that address verified causes and operational constraints.

• Countermeasures linked directly to verified causes and failure mechanisms
• Brainstorming rules separate idea generation from early evaluation and criticism
• Solution selection matrices score impact, cost, risk and implementation effort
• Poka-yoke designs prevent errors or make errors immediately visible
• Standard work defines sequence, timing, quality checks and escalation points
• Kaizen opportunities distinguish rapid fixes from larger process redesign

Participants can plan and evaluate a controlled pilot before wider implementation.

• Pilot scope defines location, population, duration, measures and decision criteria
• Before-and-after comparisons account for demand, seasonality and process changes
• Implementation plans assign actions, owners, dates, dependencies and approvals
• Communication plans address affected teams, leaders, customers and support functions
• Training needs identified from revised roles, standards and system changes
• Pilot results documented with benefits, limitations, risks and next actions

Participants can assess implementation risks and design preventative controls for revised processes.

• Failure mode and effects analysis identifies potential process breakdowns
• Severity, occurrence and detection ratings prioritise prevention and control actions
• Process risks reviewed before releasing new procedures or system changes
• Mistake-proofing options assessed for prevention, detection and containment strength
• Contingency actions specify triggers, decision authority and customer communication
• Residual risk accepted only with accountable owner and review date

Participants can establish controls that maintain gains after project closure.

• Control plans specify critical measures, methods, frequency, limits and owners
• Visual management makes current performance and abnormal conditions readily visible
• Standard operating procedures incorporate revised work sequence and controls
• Layered process audits check adherence to critical operating standards
• Control charts trigger defined response actions before customer impact expands
• Benefits tracking confirms realised savings, service improvements and risk reduction

Participants can secure ownership and governance for an improvement beyond the project team.

• Stakeholder analysis identifies supporters, resisters, decision-makers and affected groups
• Sponsor updates communicate progress, decisions required, risks and expected benefits
• RACI matrices clarify accountability for implementation and ongoing process control
• Resistance patterns addressed through involvement, evidence, training and local feedback
• Tollgate reviews assess readiness to proceed using agreed project criteria
• Project closure documents lessons, ownership transfer and remaining actions

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

• Project charter presents problem, scope, target, benefits and stakeholder commitments
• Current-state map and baseline establish the process performance evidence
• Root-cause analysis links verified drivers with supporting data and observations
• Improvement plan prioritises countermeasures, pilot measures, risks and implementation actions
• Control plan assigns measures, limits, response actions and accountable owners
• Sponsor presentation communicates findings, recommendations, benefits and decisions required