Introduction:

Statistical Quality Control (SQC) is a critical component of modern quality management systems. By using statistical methods to monitor and control processes, businesses can ensure product consistency, minimize defects, and continuously improve performance. This advanced training course will focus on leveraging a wide range of statistical techniques to address complex quality issues and enhance decision-making. Participants will gain hands-on experience with advanced tools such as design of experiments (DOE), regression analysis, and multivariate analysis, enabling them to lead quality improvement initiatives and achieve operational excellence.

Course Objectives:

By the end of this course, participants will:

1. Understand the advanced principles and applications of Statistical Quality Control.
2. Gain proficiency in analyzing and interpreting complex process data using advanced statistical methods.
3. Learn how to design experiments (DOE) to optimize processes and improve quality.
4. Use multivariate analysis and other advanced tools for process control and optimization.
5. Develop strategies for managing process variability and reducing defects through statistical techniques.
6. Learn how to apply advanced regression and correlation analysis to improve quality in manufacturing and service environments.
7. Master the use of control charts, process capability analysis, and other tools to monitor and sustain improvements in quality.
8. Be equipped with the skills to apply SQC principles in real-world quality improvement projects.

Who Should Attend?

This course is ideal for:

• Quality Control Engineers and Managers
• Process Improvement Managers
• Production Managers and Supervisors
• Manufacturing Engineers
• Statisticians and Data Analysts
• Six Sigma Green Belts, Black Belts, and Master Black Belts
• Professionals responsible for quality management, process improvement, or product development
• Consultants seeking to deepen their statistical expertise in quality management
• Anyone interested in advanced quality control techniques to drive improvements in their organization

Day-by-Day Outline:

Day 1: Introduction to Advanced Statistical Quality Control

• Overview of Statistical Quality Control (SQC):
  • Definition, objectives, and importance of SQC in modern quality management
  • The role of statistical tools in continuous improvement
  • Review of foundational quality control concepts (e.g., control charts, process capability)
• Advanced Control Charts:
  • Types of control charts for variable and attribute data
  • Control charts for specialized applications (e.g., np, p, c, u charts)
  • Creating and interpreting advanced control charts (e.g., CUSUM, EWMA)
• Process Capability Analysis:
  • Advanced methods for assessing process capability (Cp, Cpk, Pp, Ppk)
  • Understanding capability indices for non-normal distributions
  • Analyzing process stability and performance using advanced techniques
• Hands-On Exercise:
  • Calculating control limits and analyzing process performance using real-world data

Day 2: Regression and Correlation Analysis for Quality Improvement

• Simple and Multiple Linear Regression:
  • Fundamentals of regression analysis for process improvement
  • Interpreting regression coefficients and model fitting
  • Identifying and controlling process variables using regression analysis
• Correlation Analysis:
  • Understanding correlation and its application in quality control
  • Analyzing relationships between process variables
  • Using correlation matrices to identify critical process factors
• Advanced Regression Techniques:
  • Polynomial regression for modeling non-linear relationships
  • Logistic regression for predicting categorical outcomes (e.g., defect vs. non-defect)
• Hands-On Exercise:
  • Building regression models to predict quality outcomes and optimizing process parameters
  • Conducting correlation analysis to identify key drivers of defects

Day 3: Design of Experiments (DOE) for Quality Optimization

• Introduction to Design of Experiments (DOE):
  • Importance of DOE in quality improvement
  • Types of experimental designs (full factorial, fractional factorial, response surface methodology)
  • Choosing the right experimental design for different quality problems
• Planning and Conducting Experiments:
  • Designing experiments to optimize process parameters and reduce variability
  • Randomization, replication, and blocking in experimental designs
  • Interpreting results and determining factor interactions
• Advanced DOE Techniques:
  • Response Surface Methodology (RSM) for optimization
  • Taguchi methods for robust design and parameter optimization
  • Using design of experiments in multivariate settings
• Hands-On Exercise:
  • Planning and conducting a full factorial experiment to optimize a process
  • Analyzing experimental data and identifying optimal conditions

Day 4: Multivariate Statistical Techniques for Quality Control

• Introduction to Multivariate Analysis:
  • Understanding the need for multivariate techniques in complex quality problems
  • Principal Component Analysis (PCA) for dimensionality reduction and identifying patterns in multivariate data
  • Multivariate Control Charts for monitoring multiple process variables simultaneously
• Multivariate Regression:
  • Building multivariate regression models to predict outcomes based on multiple independent variables
  • Interpreting results from multivariate regression analysis in quality contexts
• Cluster Analysis and Discriminant Analysis:
  • Identifying clusters of similar data points to segment quality issues
  • Applying discriminant analysis to classify process data and identify defects
• Hands-On Exercise:
  • Conducting principal component analysis (PCA) on multivariate process data
  • Implementing multivariate control charts to monitor a multi-variable process

Day 5: Advanced Topics and Real-World Applications

• Process Performance and Improvement Techniques:
  • Techniques for monitoring and improving process performance using SQC
  • How to drive continuous improvement using the tools learned in this course
• Handling Special Causes and Variability:
  • Identifying special causes of variation and implementing corrective actions
  • Advanced techniques for managing process variability and preventing defects
• Statistical Techniques for Lean and Six Sigma:
  • Integrating SQC techniques with Lean and Six Sigma methodologies for enhanced process improvement
  • Using SQC tools for waste reduction and efficiency improvement
• Statistical Modeling for Predictive Quality:
  • Building statistical models for predicting future quality issues and outcomes
  • Using predictive analytics to prevent defects and improve customer satisfaction
• Case Study and Project Work:
  • Working in groups to apply advanced SQC tools to a real-world scenario or quality improvement project
• Final Review and Q&A:
  • Reviewing key concepts from the course
  • Open discussion on how participants can apply the techniques in their work environments