QC Course

Lesson 1 • History and Evolution of QC

Traces QC from craft inspection to statistical methods and modern systems. Provides context for why current practices exist.

Lesson 2 • Key QC Roles and Responsibilities

Maps QC functions across organisational levels from operator to manager. Clarifies accountability structures students will operate within.

Lesson 3 • QC Documentation Basics

Introduces standard QC documents: specifications, records, and reports. Proper documentation underpins traceability and compliance throughout the course.

Lesson 1 • Common Measurement Instruments

Surveys calipers, micrometers, gauges, and digital instruments used in QC. Correct instrument selection prevents measurement error at the source.

Lesson 2 • Gauge Repeatability and Reproducibility

Introduces Gauge R&R studies to quantify measurement system variation. Students learn to determine whether a measurement system is fit for purpose.

Lesson 3 • Data Collection Planning

Teaches sampling strategies, check sheets, and data recording protocols. Structured collection plans ensure data integrity for downstream analysis.

Lesson 4 • Measurement System Fundamentals

Covers accuracy, precision, bias, and resolution in measurement. These concepts are prerequisites for all data-driven QC decisions.

Lesson 1 • Other Key Distributions

Surveys binomial, Poisson, and other distributions relevant to attribute data. Selecting the correct distribution improves defect modelling accuracy.

Lesson 2 • Probability Concepts in QC

Introduces probability rules, distributions, and their relevance to defect rates. Probability thinking enables risk-based QC decision-making.

Lesson 3 • Normal Distribution and QC

Explains the normal distribution, z-scores, and the empirical rule. Most variable QC data follows this distribution, making it central to process analysis.

Lesson 4 • Descriptive Statistics Essentials

Covers mean, median, mode, range, variance, and standard deviation. These measures summarise process behaviour and form the basis of QC analysis.

Lesson 1 • SPC Implementation and Response

Guides the practical rollout of SPC on the shop floor and response protocols. Students can design a control plan and lead corrective action when signals appear.

Lesson 2 • Variation and Process Stability

Defines common cause and special cause variation and their management implications. Understanding variation types is the conceptual foundation of SPC.

Lesson 3 • Control Charts for Attribute Data

Introduces p, np, c, and u charts for defect and defective monitoring. Attribute charts extend SPC to non-measurable quality characteristics.

Lesson 4 • Control Charts for Variable Data

Covers X-bar, R, and S charts for monitoring continuous measurements. These charts are the primary SPC tools for variable QC data.

Lesson 1 • Non-Normal Process Capability

Addresses capability analysis when data does not follow a normal distribution. Transformation and non-parametric methods extend capability analysis to real-world data.

Lesson 2 • Performance Indices Pp and Ppk

Distinguishes short-term capability (Cp/Cpk) from long-term performance (Pp/Ppk). Students learn when each index is appropriate and how to compare them.

Lesson 3 • Capability Indices Cp and Cpk

Calculates and interprets Cp for spread and Cpk for centring relative to specs. These indices are the standard language of process capability reporting.

Lesson 4 • Capability Improvement Strategies

Links low capability indices to root causes and improvement actions. Students can prioritise process changes that yield the greatest capability gains.

Lesson 1 • Attribute Sampling Plans

Applies single, double, and multiple sampling plans for attribute inspection. Students select and execute plans matched to quality risk and lot size.

Lesson 2 • Inspection Planning Principles

Covers inspection objectives, timing, and scope within a production flow. A well-designed inspection plan balances detection effectiveness with resource cost.

Lesson 3 • Variable Sampling Plans

Covers sampling plans based on measured data rather than pass/fail counts. Variable plans achieve equivalent protection with smaller sample sizes.

Lesson 4 • Inspection Effectiveness and Errors

Examines inspector error, missed defects, and false rejections in inspection systems. Quantifying inspection effectiveness drives improvements in detection reliability.

Lesson 1 • Problem Definition and Scoping

Teaches problem statements, IS/IS-NOT analysis, and scope boundaries. A precise problem definition prevents wasted effort on symptoms rather than causes.

Lesson 2 • Corrective and Preventive Actions

Distinguishes containment, corrective, and preventive actions and their sequencing. Students design actions that address root causes and prevent recurrence.

Lesson 3 • Data-Driven Root Cause Verification

Uses Pareto analysis, scatter plots, and hypothesis tests to confirm root causes. Verification prevents corrective actions based on assumptions rather than evidence.

Lesson 4 • Cause-and-Effect Analysis Tools

Covers fishbone diagrams and the 5-Why technique for cause exploration. These tools structure team thinking and surface potential root causes systematically.

Lesson 1 • Continuous Improvement Methodologies

Introduces PDCA, DMAIC, and kaizen as structured improvement cycles. Students select the appropriate methodology based on problem complexity and scope.

Lesson 2 • QC Metrics and Performance Dashboards

Defines key QC metrics—defect rate, yield, Cpk trends—and dashboard design. Visible metrics drive accountability and focus improvement efforts.

Lesson 3 • Quality Management System Frameworks

Surveys process-based QMS frameworks, their structure, and QC's role within them. Understanding the system context helps QC professionals align their work with broader goals.

Lesson 4 • Control Plans and FMEA

Covers control plan development and Failure Mode and Effects Analysis for risk prevention. These tools proactively identify and mitigate quality risks before defects occur.