Quality problems rarely announce themselves clearly — they show up as small variations, scattered defects, and gut-feeling explanations that turn out to be wrong. The only reliable way to separate real signals from noise is statistics.

This course from the Arab British Fellowship Training Academy gives quality professionals a hands-on statistical toolkit: control charts, process capability, measurement analysis, root-cause techniques and Six Sigma, so every quality decision they make is backed by data rather than guesswork.

Objectives

• Understand why statistical analysis matters for quality management.
• Build skills in control charts, process capability and measurement system analysis.
• Apply statistical techniques to root-cause analysis and process improvement.
• Implement Six Sigma methodology and statistical process control.

Who Should Attend?

• Quality assurance and control professionals.
• Operations and production managers focused on quality improvement.
• Process engineers and analysts working with quality data.
• Team leaders and supervisors responsible for quality systems.

Module 1: Statistical Foundations for Quality Decisions

• Why quality systems need statistical analysis, and key terminology.
• Core statistical concepts: mean, median, standard deviation, variance.
• Distributions, probability, sampling methods and sample-size determination.

Module 2: Monitoring and Measuring Process Performance

• Control charts: purpose, types (X-bar, R, p, np) and how to construct and interpret them.
• Process capability analysis: calculating and interpreting Cp and Cpk against specification limits.
• Measurement System Analysis (MSA): accuracy, precision and gage R&R studies for reliable data.

Module 3: Finding and Fixing the Real Problem

• Root-cause analysis tools: Pareto analysis, fishbone diagrams and the 5 Whys technique.
• Hypothesis testing: t-tests, chi-square tests and ANOVA for data-driven conclusions.

Module 4: Statistical Process Control and Six Sigma in Practice

• Statistical Process Control (SPC) for consistent quality, in production and service settings.
• Six Sigma methodology and the DMAIC (Define, Measure, Analyze, Improve, Control) process.
• Integrating Six Sigma with statistical analysis for continuous improvement.

Module 5: Putting It All Together

• Case studies on statistical analysis in real quality systems.
• Group exercises applying these techniques to real-world scenarios and building a data-driven improvement plan.