Two batches from the same production line can look identical on the surface and still hide very different failure rates — the only way to tell the difference is by measuring variation statistically, not by inspecting finished output alone. That is the core idea behind Statistical Quality Control (SQC).

This training programme from the Arab British Fellowship Training Academy equips participants to design control charts, build performance indicators, and read the data behind production and service processes, so that quality decisions are backed by evidence rather than guesswork. The course also covers process analysis, defect reduction, and waste minimisation to raise both quality and customer satisfaction.

Who Should Attend?

• Quality engineers and quality-control officers in industrial and service organisations, alongside supervisors and department managers responsible for production and operational processes.
• Professionals in planning and production seeking to raise operational efficiency.
• Analysts and specialists working in statistical evaluation and performance improvement.

Knowledge and Benefits

After completing the programme, participants will be able to:

• Build control charts and analyse variation across production and service processes.
• Apply statistical methods to improve performance and cut defects and waste.
• Understand the fundamentals of statistical quality control and its tools for evaluating processes.
• Strengthen data-driven decision-making, and use performance indicators to track and monitor quality improvements over time.

Module 1: Statistics as a Quality Tool

• Definition and importance of SQC in quality improvement, and its role in production and service processes.
• The difference between traditional and statistical quality-monitoring methods.
• Core statistical concepts: variables, qualitative vs quantitative data, distribution, variance, and standard deviation.

Module 2: Building and Reading Control Charts

• Types of control charts, their applications, and how to set control limits.
• Using charts across manufacturing and service operations to monitor performance and flag variation.
• Analysing results and taking corrective action.

Module 3: Digging Into the Data

• Root-cause analysis of quality problems.
• Process-capability analysis.
• Using charts and graphs to interpret performance data.

Module 4: Indicators That Matter

• Identifying key quality metrics and linking them to organisational objectives.
• Using indicators to track continuous improvement.

Module 5: From Data to Decisions

• Collecting and analysing data using statistical methods to spot weaknesses and opportunities.
• Making data-driven decisions to enhance quality.
• Using statistical tools and continuous-improvement techniques to minimise defects and waste, and monitoring outcomes to keep improvements sustainable.

Module 6: Making Statistical Control Part of the Culture

• Developing organisational quality policies using statistical tools.
• Building teams that reinforce continuous quality monitoring.
• Promoting institutional commitment to ongoing improvement.

Module 7: Where Statistical Quality Control Is Headed

• Emerging trends in statistical quality monitoring.
• Leveraging technology and digital analytics for quality management.
• Strategies for sustainable process improvement and excellence.