Practical of Measurement System Analysis (MSA) and Statistical Process Control

Course Duration:

2 Days (16 Hours)

Target Audience:

• Quality Engineers
• Process Engineers
• Production Supervisors
• Manufacturing and Quality Control Personnel
• Six Sigma Practitioners
• Anyone involved in data collection and process improvement

Course Objectives:

By the end of this course, participants will:

1. Understand the fundamental concepts of Measurement System Analysis (MSA) and Statistical Process Control (SPC).
2. Learn how to evaluate and improve measurement system accuracy, precision, and stability.
3. Apply MSA tools such as Gauge Repeatability & Reproducibility (Gauge R&R), Bias, Linearity, and Stability Studies.
4. Gain hands-on experience in conducting MSA studies and analyzing measurement system performance.
5. Learn how to implement SPC techniques, including control charts, process capability studies, and variation analysis.
6. Interpret control charts to differentiate between common cause and special cause variation.
7. Develop the ability to make data-driven decisions for process improvements.

Learning Outcomes:

Upon successful completion, participants will be able to:
✅ Conduct Measurement System Analysis (MSA) studies and interpret results.
✅ Perform Gauge R&R analysis using software tools such as Minitab or Excel.
✅ Identify sources of measurement variation and take corrective actions.
✅ Set up and use Statistical Process Control (SPC) tools, including control charts and capability analysis.
✅ Monitor and analyze process performance using SPC methods.
✅ Make informed quality control decisions based on statistical analysis.

Course Outline:

Day 1: Measurement System Analysis (MSA)

Module 1: Introduction to Measurement System Analysis (MSA)

• Definition and Importance of MSA
• Types of Measurement Errors
• Key MSA Terminologies

Module 2: Gauge Repeatability and Reproducibility (Gauge R&R)

• Understanding Repeatability vs. Reproducibility
• Conducting a Variable (Continuous Data) Gauge R&R Study
• Conducting an Attribute (Discrete Data) Gauge R&R Study
• Interpreting Gauge R&R Results
• Hands-on Practice with Real Data

Module 3: Other MSA Studies

• Bias Study: Assessing systematic measurement error
• Linearity Study: Evaluating measurement variation over a range
• Stability Study: Checking measurement consistency over time
• Workshop: Conducting Bias, Linearity, and Stability Studies

Day 2: Statistical Process Control (SPC)

Module 4: Introduction to Statistical Process Control (SPC)

• Definition and Benefits of SPC
• Variation in Processes: Common Cause vs. Special Cause
• Types of SPC Charts (Variable & Attribute Data)

Module 5: Control Charts for Variables (Continuous Data)

• X̄-R and X̄-S Charts
• Individual Moving Range (I-MR) Charts
• Interpreting Control Charts
• Hands-on Exercise: Plotting and Analyzing Control Charts

Module 6: Control Charts for Attributes (Discrete Data)

• P-Charts (Proportion of Defectives)
• NP-Charts (Number of Defectives)
• C-Charts (Defects per Unit)
• U-Charts (Defects per Sample Size)
• Case Study: Application in Real-World Scenarios

Module 7: Process Capability Analysis

• Cp, Cpk, Pp, Ppk Analysis
• Using SPC to Improve Process Capability
• Hands-on Practice: Calculating and Interpreting Cp & Cpk

Module 8: Implementing SPC in Industry

• How to Sustain SPC in the Workplace
• Linking SPC with Continuous Improvement (Six Sigma & Lean)
• Case Studies and Group Discussion

Training Methodology:

🔹 Interactive Lectures – Conceptual explanations with real-life industry examples
🔹 Hands-on Workshops – Practical exercises using measurement tools, Minitab/Excel
🔹 Case Studies – Analyzing real-world data to apply MSA and SPC concepts
🔹 Group Discussions & Problem Solving – Encouraging peer learning
🔹 Live Demonstrations – Conducting Gauge R&R and SPC control charting in a practical setting
🔹 Assessment & Certification – Quiz, practical exercises, and final evaluation