Statistical Quality Control (SQC)

Module 1: Introduction to Statistical Quality Control (SQC)

• What is Statistical Quality Control (SQC)?

• The importance of SQC in manufacturing and services.

• Key principles of quality control.

• Overview of quality management systems and their relation to SQC.

Module 2: Types of Data in Quality Control

• Variable data vs. Attribute data.

• Types of measurements: Continuous vs. Discrete.

• Collecting and analyzing data for quality control.

• Data distribution and interpretation.

Module 3: Descriptive Statistics for Quality Control

• Measures of central tendency (mean, median, mode).

• Measures of dispersion (range, variance, standard deviation).

• Interpreting data using descriptive statistics.

• The importance of data visualization in SQC (histograms, box plots).

Module 4: Control Charts

• The concept of control charts.

• Types of control charts:

  • X-bar and R charts (for variables).

  • P charts (for proportions).

  • C charts (for count data).

  • U charts (for rate data).

• Plotting and interpreting control charts.

• Understanding process variation: Common causes vs. special causes.

• Setting control limits and detecting out-of-control conditions.

• Corrective actions based on control chart analysis.

Module 5: Process Capability Analysis

• Understanding process capability and process performance.

• The Cp, Cpk, Pp, and Ppk indices.

• Calculating and interpreting capability indices.

• Identifying whether a process is capable of meeting specifications.

• Setting and monitoring process capability limits.

Module 6: Acceptance Sampling

• Introduction to acceptance sampling.

• Types of acceptance sampling plans (single, double, and sequential sampling).

• Operating characteristic (OC) curve.

• Sampling procedures and decision-making in quality control.

Module 7: Design of Experiments (DOE) for Process Improvement

• The role of DOE in quality control and improvement.

• Basic concepts of experimental design: factors, levels, and response variables.

• Types of experimental designs:

  • Full factorial designs.

  • Fractional factorial designs.

  • Response surface methodology.

• Analysis of variance (ANOVA) for DOE.

• Interpreting DOE results to make decisions for process optimization.

Module 8: Root Cause Analysis and Corrective Actions

• Tools for identifying root causes of quality issues:

  • Fishbone diagram (Ishikawa).

  • 5 Whys.

  • Pareto analysis.

• Corrective and preventive actions (CAPA).

• Implementing process improvements based on SQC analysis.

Module 9: Advanced Topics in Statistical Quality Control

• Multi-Vari charts for analyzing variability across multiple factors.

• Taguchi methods for robust design.

• Six Sigma and SQC: How they complement each other.

• Advanced control charts (EWMA, CUSUM charts).

• Statistical Process Control (SPC) and real-time monitoring.