Course in Statistical Quality Control (SQC)

In today's competitive business environment, quality management is no longer based on intuition or experience alone. It requires accurate, data-driven methods that ensure consistency, reduce variation, and identify inefficiencies. Statistical Quality Control (SQC) is among the most effective approaches to monitor and improve processes, relying on measurable indicators and statistical techniques to maintain high standards of quality.
With this in mind, Geneva Institute of Business Administration presents the “Statistical Quality Control (SQC)” course, designed to equip professionals with the necessary skills and knowledge to apply statistical tools for monitoring process performance, identifying defects, and ensuring continuous improvement in both manufacturing and service sectors.

Target Group

• Quality engineers and inspectors working in production lines or process control.

• Quality assurance professionals in industrial and service organizations.

• Specialists in continuous improvement and operational development units.

• Production managers aiming to reduce waste and optimize efficiency.

• Process analysts and data engineers involved in performance monitoring.

• Technicians and supervisors responsible for measuring and ensuring product or service quality.

Objectives

• To introduce participants to the concepts and importance of Statistical Quality Control in modern quality management.

• To build competency in using essential statistical tools to monitor quality in various operations.

• To develop the ability to interpret operational data to detect and control deviations.

• To enable accurate reading and analysis of different types of control charts.

• To reinforce understanding of core statistical concepts such as variation, mean, and standard deviation.

• To support the implementation of proactive monitoring systems based on real-time data.

• To contribute to reducing operational defects and improving customer satisfaction.

• To foster preventive thinking rather than reactive troubleshooting in quality assurance.

Course Outline

• Introduction to Quality Concepts and Statistical Control

  • Defining quality and its core principles.

  • The origin and evolution of Statistical Quality Control.

  • Differences between traditional control and data-based control.

  • The connection between quality and operational cost.

• The Seven Basic Quality Tools

  • Histograms and frequency distributions.

  • Pareto charts for identifying major contributing factors.

  • Cause-and-effect diagrams (fishbone diagrams).

  • Scatter diagrams to understand variable relationships.

• Descriptive Statistics in Quality Management

  • Mean, variance, and standard deviation.

  • Measures of central tendency and dispersion.

  • The significance of the normal distribution.

  • Basic data analysis using graphical tools.

• Understanding Variation and Process Control

  • Natural vs. assignable causes of variation.

  • Characteristics of stable versus unstable processes.

  • Impact of variation on final product or service quality.

  • Collecting reliable data for process assessment.

• Control Charts for Averages and Ranges (X̄ - R)

  • Steps to construct X̄ - R control charts.

  • Calculating control limits and interpreting charts.

  • Recognizing process shifts and trends.

  • Applying X̄ - R charts in production monitoring.

• Attribute Control Charts (P, C, np)

  • When and how to use attribute control charts.

  • Creating P charts to monitor defect percentages.

  • Utilizing C and np charts to count nonconformities.

  • Evaluating process stability in non-continuous outputs.

• Interpreting and Analyzing Control Charts

  • Identifying out-of-control signals and special causes.

  • Detecting patterns and shifts in process behavior.

  • Diagnosing potential root causes from chart trends.

  • Linking findings to preventive actions.

• Process Capability and Performance Indices

  • Understanding Cp and Cpk indicators.

  • Measuring process capability against specifications.

  • Implications of capability analysis on customer satisfaction.

  • Strategies to enhance capability over time.

• Monitoring and Improving Processes Using Data

  • Selecting appropriate sampling points in workflows.

  • Designing a data collection plan for consistent tracking.

  • Using spreadsheets or digital tools to analyze data sets.

  • Handling unexpected outcomes with statistical logic.

• Integrating SQC with Total Quality Management (TQM)

  • Positioning SQC within broader quality frameworks.

  • How statistical tools support continuous quality culture.

  • Encouraging leadership support for quality monitoring.

  • Creating a sustainable quality control environment.