Statistical Process Control Training Course

Participants can select a process and define measurable characteristics suitable for SPC.

• SIPOC mapping used to identify process boundaries and handoffs
• Critical-to-quality characteristics translated into operational process measures
• Unit, opportunity, defect and defect-rate definitions established consistently
• Leading and lagging measures distinguished for control-chart selection
• Process owners assigned responsibility for data review and response
• Data-collection purpose aligned with customer, regulatory and operational requirements

Participants can classify process data and assess its suitability for common SPC methods.

• Continuous, discrete, count and proportion data classified for SPC
• Histogram shape examined for centre, spread, skewness and outliers
• Normal distribution assumptions tested against process measurement evidence
• Median and range compared with mean and standard deviation
• Time-order preserved when preparing data for process behaviour analysis
• Data transformations considered when distributional assumptions are materially violated

Participants can design rational subgroups and sampling arrangements that reveal process change.

• Rational subgrouping designed to capture short-term process variation
• Within-subgroup and between-subgroup variation interpreted for chart construction
• Sampling frequency matched to process speed, risk and detection needs
• Subgroup size selected using chart sensitivity and collection constraints
• Stratification separates shifts, machines, locations, products and operators
• Sampling bias avoided through defined selection rules and timing

Participants can assess whether measurement data are fit for process-control decisions.

• Measurement-system variation separated from actual process variation sources
• Bias, linearity and stability assessed against reference standards
• Repeatability evaluated through repeated measurements under unchanged conditions
• Reproducibility examined across appraisers, instruments and measurement methods
• Gage R&R study structure matched to measurement-system risk
• Attribute agreement analysis used for visual inspection decisions

Participants can construct the core elements of a control chart from baseline data.

• Centre lines calculated from process averages or proportions
• Three-sigma control limits distinguished from specification limits clearly
• Control-limit formulas linked to data type and subgroup structure
• Phase One baseline studies separated from Phase Two monitoring
• Historical limits reviewed before transferring charts into routine operation
• Chart scales chosen to make meaningful variation visible quickly

Participants can build and interpret I-MR charts for individually observed data.

• Individuals charts selected when subgrouping is impractical or unavailable
• Moving ranges calculated from consecutive time-ordered process observations
• I-MR centre lines and limits calculated using standard constants
• Autocorrelation risks identified in high-frequency continuous process data
• Rare-event service measures monitored using individual observation charts
• Data gaps and changing measurement definitions documented before interpretation

Participants can construct X-bar and R charts for small rational subgroups.

• X-bar charts monitor subgroup averages for process-centre changes
• R charts monitor within-subgroup spread before average-chart interpretation
• Control constants selected according to subgroup size tables
• Average range converted into estimated short-term process sigma
• Range-chart signals investigated before acting on X-bar signals
• Subgroup calculations verified using spreadsheet formulas and audit checks

Participants can use X-bar and S charts for larger subgroup sizes.

• X-bar and S charts selected for larger subgroup samples
• Sample standard deviations calculated for each rational subgroup
• S-chart control limits constructed using subgroup-size constants
• Average-chart limits based on pooled within-subgroup variation estimates
• R and S chart applications compared for data-collection practicality
• Variable-chart selection documented within the process control plan

Participants can select and construct attribute charts for defects and nonconformities.

• P charts monitor changing proportions of nonconforming units
• NP charts monitor nonconforming counts with constant sample sizes
• C charts track defect counts within constant inspection opportunities
• U charts compare defect rates across varying inspection opportunities
• Binomial and Poisson assumptions checked before chart selection
• Overdispersion and underdispersion recognised as chart-performance warnings

Participants can identify meaningful signals and avoid misreading routine variation.

• Western Electric rules applied to identify non-random chart patterns
• Nelson rules compared with organisational signal-detection conventions
• Runs, trends, cycles and mixtures interpreted using process knowledge
• Single-point signals separated from patterns requiring deeper investigation
• False alarms balanced against delayed detection of meaningful shifts
• Annotated charts record events, changes and investigation findings

Participants can investigate control-chart signals and define proportionate response actions.

• Out-of-control action plans specify containment, ownership and escalation steps
• Five Whys used to test plausible causes behind chart signals
• Fishbone diagrams structure investigation across process cause categories
• Is-Is Not analysis narrows the scope of special-cause events
• Verification data confirms whether corrective action restored stability
• Investigation records distinguish evidence, hypotheses, actions and outcomes

Participants can assess whether a stable process can meet specification requirements.

• Cp and Cpk calculated against bilateral specification limits
• Pp and Ppk interpreted using long-term overall process variation
• Process centring assessed against target and tolerance requirements
• One-sided specifications analysed using appropriate capability indices
• Capability studies restricted to stable processes with adequate data
• Non-normal capability approaches considered for skewed process distributions

Participants can assess defect-based performance using appropriate attribute measures.

• Defects per unit calculated for recurring inspection results
• First-pass yield and rolled throughput yield compared carefully
• DPMO estimates linked to defined defect opportunities and units
• Attribute performance baselines separated from variable capability indices
• Pareto analysis prioritises defect categories requiring process improvement
• Inspection effectiveness considered before interpreting reported defect rates

Participants can adapt SPC methods to service, administrative and knowledge-work processes.

• Turnaround times charted for claims, loans and service requests
• Call abandonment proportions monitored using p-chart decision rules
• Backlog volumes analysed alongside demand and processing-capacity measures
• First-time-right rates defined for transactional quality-control monitoring
• Case-mix changes stratified before concluding process performance shifted
• Service-level targets kept separate from statistical control limits

Participants can apply SPC to technical processes with product and equipment constraints.

• Critical dimensions monitored using variable charts and calibrated instruments
• Fill weight variation analysed against target, limits and capability
• Equipment settings linked to chart signals through documented process conditions
• Tool wear patterns investigated using time-ordered measurement data
• Batch, lot and shift effects separated through planned stratification
• Reaction plans coordinate operators, maintenance and quality personnel

Participants can document routine monitoring and response requirements within a control plan.

• Control plans define measures, methods, frequency, limits and responsibilities
• Standard work specifies chart updates, review timing and escalation routes
• Process FMEA links failure modes to monitoring and response controls
• Reaction-plan thresholds aligned with documented quality risk decisions
• Visual management boards display current control status and actions
• Document control preserves approved formulas, definitions and chart revisions

Participants can present SPC findings for operational decisions and management review.

• Control-chart summaries distinguish stability, capability and improvement priorities
• Dashboard measures avoid averaging away important process signals
• Management review packs connect chart evidence to business consequences
• Escalation criteria define when leaders require formal corrective action
• Data governance assigns ownership for source data and calculations
• ISO 9001 monitoring requirements connected to process performance evidence

Participants can position SPC within structured process-improvement work.

• DMAIC Control phase uses charts to sustain verified improvements
• Lean waste analysis complements variation analysis and process control
• Kaizen changes tested against baseline chart behaviour before standardisation
• A3 reports summarise problem definition, evidence, actions and results
• Standardisation follows demonstrated stability rather than isolated performance gains
• Improvement projects prioritised using capability gaps and operational risk

Participants can plan a staged SPC deployment for a priority operational process.

• Pilot scope selected using process risk, data availability and ownership
• Implementation milestones sequence measurement, baseline, training and routine review
• Roles assigned for data collection, chart maintenance and signal response
• Data-system requirements identified for manual, spreadsheet and automated reporting
• Training needs defined for operators, supervisors and process owners
• Audit checks confirm adherence to sampling, charting and reaction procedures

Participants can build, review and present a complete SPC control package for a selected process.

• Process scope and critical measures defined using SIPOC and CTQ logic
• Measurement and sampling plan documented with rational subgroup rationale
• Selected control chart calculated, plotted and interpreted from baseline data
• Capability analysis completed against stated customer or regulatory specifications
• Out-of-control action plan linked to investigation and escalation responsibilities
• Control plan, implementation roadmap and management presentation completed for review