Statistical Process Control Course

Lesson 1 • History and Purpose of SPC

Traces SPC from Shewhart and Deming to modern manufacturing and services. Establishes why controlling variation is central to quality management.

Lesson 2 • Basic Statistical Concepts for SPC

Reviews mean, variance, standard deviation, and distributions as the maths backbone of control charts. Ensures all learners share a common statistical vocabulary.

Lesson 3 • Data Types and Measurement Scales

Classifies data as continuous or attribute and links each type to the correct chart family. Prevents misapplication of control charts in subsequent chapters.

Lesson 4 • Process Thinking and Flow

Introduces SIPOC and process mapping to identify measurement points before charting. Connects process structure to data collection strategy.

Lesson 1 • Attribute Agreement Analysis

Applies Kappa statistics to evaluate consistency in pass/fail or categorical inspection systems. Extends MSA principles to attribute data used in p and np charts.

Lesson 2 • Gage Repeatability and Reproducibility

Covers the crossed Gage R&R study design, data collection, and ANOVA-based analysis. Provides the primary tool for quantifying measurement system variation.

Lesson 3 • Stability and Calibration of Gages

Examines gage stability over time and links calibration schedules to measurement integrity. Ensures measurement systems remain valid throughout long-term SPC programmes.

Lesson 1 • Interpreting Control Chart Signals

Applies Western Electric and Nelson rules to detect non-random patterns on any control chart. Translates statistical signals into actionable process investigation triggers.

Lesson 2 • Recalculating and Updating Control Limits

Explains when and how to revise control limits after process improvements or confirmed special causes. Maintains chart validity as processes evolve over time.

Lesson 3 • X-bar and S Chart for Larger Subgroups

Introduces the S chart as a more efficient spread estimator when subgroup size exceeds four. Builds on R chart logic while improving statistical sensitivity.

Lesson 4 • X-bar and R Chart Construction

Walks through subgroup sampling, centre line calculation, and control limit formulas for X-bar and R charts. Forms the core skill set for monitoring process mean and spread.

Lesson 1 • c Chart for Count of Defects

Applies the Poisson model to monitor total defect counts per inspection unit of constant size. Establishes the foundation for understanding defect-based monitoring.

Lesson 2 • np Chart for Count of Defectives

Constructs np charts when sample size is fixed and the count of defective units is the metric. Highlights when np is preferred over p for operator simplicity.

Lesson 3 • u Chart for Defects per Unit

Extends c chart logic to variable inspection unit sizes using defects-per-unit scaling. Enables defect monitoring when inspection area or volume changes between samples.

Lesson 4 • p Chart for Proportion Defective

Builds p charts for variable and constant sample sizes, including Laney's correction for overdispersion. Covers the most widely used attribute chart in industry.

Lesson 1 • Cp and Cpk Indices

Calculates potential capability (Cp) and actual capability (Cpk) using within-subgroup sigma estimates. Interprets index values against industry benchmarks and sigma levels.

Lesson 2 • Normality and Capability

Tests normality assumptions before applying standard capability indices and applies transformations or non-parametric methods when needed. Ensures valid capability reporting.

Lesson 3 • Pp and Ppk Performance Indices

Distinguishes performance indices using overall sigma from capability indices using short-term sigma. Guides correct index selection for new vs. established processes.

Lesson 4 • Specification Limits vs. Control Limits

Clarifies the fundamental difference between customer-driven specs and statistically derived control limits. Prevents the critical error of confusing the two on control charts.

Lesson 1 • Multivariate Control Charts

Introduces Hotelling's T² chart to monitor multiple correlated quality characteristics simultaneously. Prevents false alarms and missed signals caused by monitoring variables independently.

Lesson 2 • EWMA Charts for Gradual Drift

Builds exponentially weighted moving average charts with tunable lambda to balance sensitivity and stability. Ideal for processes with gradual drift or autocorrelated data.

Lesson 3 • CUSUM Charts for Small Shifts

Constructs tabular and V-mask CUSUM charts optimised to detect mean shifts of 0.5–1.5 sigma. Fills the sensitivity gap left by standard X-bar charts.

Lesson 4 • Adaptive and Zone Control Charts

Covers zone charts and adaptive sampling schemes that adjust subgroup size or frequency based on process signals. Improves detection speed while reducing sampling cost.

Lesson 1 • SPC Deployment Planning

Defines scope, prioritises critical characteristics, and builds a phased rollout plan aligned with business goals. Translates SPC theory into an actionable organisational project.

Lesson 2 • Control Plans and Reaction Plans

Builds control plans that link measurement points, chart types, and response actions into a single document. Formalises the process response system required for sustained SPC.

Lesson 3 • Sustaining and Auditing SPC Programmes

Establishes audit routines, performance metrics, and management review cycles to prevent SPC programme decay. Ensures long-term effectiveness beyond the initial deployment phase.

Lesson 4 • Operator Training and Engagement

Designs role-specific training for operators, technicians, and engineers to ensure correct chart use. Addresses the human factors that determine SPC programme success or failure.

Lesson 1 • Building a Quality Culture with SPC

Examines how consistent SPC use shapes organisational norms around data-driven decision making and continuous improvement. Addresses leadership behaviours that sustain a quality culture.

Lesson 2 • SPC Within Six Sigma DMAIC

Maps SPC tools to each DMAIC phase, emphasising control charts in the Control phase and capability in Analyse. Positions SPC as the measurement and control backbone of Six Sigma.

Lesson 3 • Using SPC Data for Decision Making

Translates control chart trends and capability indices into business decisions on investment, staffing, and equipment. Bridges statistical outputs and executive-level quality strategy.

Lesson 4 • Linking SPC to Lean Operations

Shows how SPC data supports value stream mapping, takt time analysis, and waste elimination decisions. Integrates statistical control with lean flow principles.