SPC Course

Lesson 1 • Understanding Process Variation

Introduces common-cause and special-cause variation as the core concept driving SPC decisions. Connects variation theory to real process behaviour.

Lesson 2 • Basic Statistical Concepts for SPC

Covers descriptive statistics essential for interpreting control charts. Provides the mathematical foundation needed for all subsequent SPC tools.

Lesson 3 • Process Thinking and Systems View

Frames manufacturing and service processes as systems with inputs, outputs, and feedback loops. Prepares learners to identify where SPC monitoring adds value.

Lesson 1 • Attribute Measurement System Analysis

Extends MSA concepts to pass/fail and categorical data using attribute agreement analysis. Prepares learners to validate inspection systems used with attribute charts.

Lesson 2 • Types of Data in SPC

Distinguishes variable data from attribute data and explains how data type determines chart selection. Connects data classification to downstream analysis choices.

Lesson 3 • Measurement System Analysis Basics

Introduces gauge R&R studies to quantify measurement error relative to process variation. Establishes acceptance criteria for measurement system adequacy.

Lesson 4 • Designing a Data Collection Plan

Guides learners through structuring a sampling strategy, frequency, and recording format. Ensures data integrity before any charting begins.

Lesson 1 • Logic and Structure of Control Charts

Explains control limits, centre lines, and the statistical basis for three-sigma limits. Establishes the framework applied to every chart type in this chapter.

Lesson 2 • Xbar-S Chart for Larger Subgroups

Introduces the Xbar-S chart as the preferred alternative when subgroup size exceeds eight. Compares its sensitivity to the Xbar-R chart.

Lesson 3 • Interpreting Variable Chart Signals

Applies Western Electric and Nelson run rules to detect non-random patterns on variable charts. Connects pattern recognition to process investigation actions.

Lesson 4 • Xbar-R Chart Construction and Use

Walks through step-by-step calculation of Xbar and R chart limits using control chart constants. Applies the chart to subgroup data from a process scenario.

Lesson 1 • Attribute Chart Sensitivity and Limitations

Examines the lower detection sensitivity of attribute charts compared to variable charts. Guides decisions on when to convert attribute data to variable measurements.

Lesson 2 • p-Chart and np-Chart

Covers proportion defective and number defective charts, including variable subgroup size handling. Demonstrates limit recalculation for unequal subgroup sizes.

Lesson 3 • Foundations of Attribute Charting

Reviews binomial and Poisson distributions as the statistical basis for attribute charts. Clarifies when attribute charts are appropriate versus variable charts.

Lesson 1 • Capability for Attribute and Non-Normal Data

Extends capability concepts to attribute processes using DPU, DPMO, and sigma level. Addresses capability estimation when normality cannot be assumed.

Lesson 2 • Performance Indices Pp and Ppk

Distinguishes short-term capability from long-term performance using Pp and Ppk. Explains when each index set is appropriate for reporting.

Lesson 3 • Capability Indices Cp and Cpk

Defines and calculates Cp for spread and Cpk for centring relative to specification limits. Interprets index values against industry benchmarks.

Lesson 4 • Normality and Distribution Assessment

Covers normality testing and graphical tools to verify distributional assumptions before computing indices. Introduces alternatives for non-normal data.

Lesson 1 • Reaction Plans and Escalation

Defines structured responses to out-of-control signals, including containment and root cause investigation. Ensures operators know exactly what to do when a signal occurs.

Lesson 2 • Selecting What to Monitor

Applies risk-based thinking and process knowledge to prioritise which parameters warrant SPC charts. Links monitoring decisions to customer and business requirements.

Lesson 3 • Operator Training and Chart Ownership

Covers how to train operators to plot, interpret, and act on control charts at the point of use. Builds chart ownership culture at the process level.

Lesson 4 • Control Plan Development

Guides construction of a control plan that documents monitoring methods, frequencies, and responsibilities. Connects the control plan to process documentation.

Lesson 1 • Linking SPC Signals to Investigation

Establishes the protocol for transitioning from a control chart signal to a formal investigation. Prevents premature conclusions and tampering with stable processes.

Lesson 2 • Corrective Action and Verification

Guides implementation of permanent corrective actions and verification that the process has improved. Closes the loop between SPC signal and sustained improvement.

Lesson 3 • Cause-and-Effect Analysis Tools

Covers fishbone diagrams and the 5-Why technique to systematically identify potential root causes. Applies these tools to SPC signal scenarios.

Lesson 1 • Autocorrelation and Time-Series Processes

Identifies autocorrelation as a violation of SPC independence assumptions and its effect on false alarm rates. Introduces residual charting as a corrective approach.

Lesson 2 • Short-Run and Small-Batch SPC

Addresses the challenge of insufficient data for traditional control limits in short-run manufacturing. Introduces coded charts and target charts for low-volume processes.

Lesson 3 • CUSUM and EWMA Charts

Introduces cumulative sum and exponentially weighted moving average charts for detecting small process shifts. Compares their sensitivity to Shewhart charts.

Lesson 4 • Multivariate Control Charts

Covers Hotelling's T-squared chart for monitoring multiple correlated quality characteristics simultaneously. Explains decomposition methods for signal diagnosis.