Statistical Process Control

Course Overview

This course is a great place to learn about what really drives product quality and how to monitor processes to pro-actively drive quality improvement. Participants gain the fundamental knowledge necessary to implement Statistical Process Control and learn to avoid the common mis-applications in practice. Knowledge of basic algebra is helpful but not required.

Seminar Content

  1. SPC Fundamentals
    • Concept of Variation
    • The Normal Distribution
    • Control Limits vs. Specification Limits
    • Definition of Control/Stability
    • Definition of Quality
    • Quality Control vs. Process Control
  2. A Central Limit Theorem
    • Introduction to Non-Normal Data
    • The Central Limit Theorem
  3. Conceptual Implementation of SPC
    • Measurement Systems Issues
    • Monitoring Process Behavior
    • Xbar and R Chart Concepts
  4. Sources of Variation
    • Common and Special Cause Sources
    • Detecting Special Cause Sources
  5. Xbar and R Charts
    • Differences Between Measurements and Averages
    • Computing Control Limits and Charting
  6. Chart Interpretation
    • Type I and Type II Errors
    • Guidelines for Analysis of Charts
    • Out of Control Signals
  7. Basic Statistics
    • Population versus Sample
    • Notation
    • Measures of Central Tendency (Mean, Mean)
    • Measures of Variation (Range, Standard Deviation, Variance)
  8. Sampling
    • Random, Systematic, and Rational Samples
    • Importance of Rational Sampling
  9. Sensitivity
    • Impact of Sample Size on Chart Sensitivity
    • Determining Sample Size
  10. Process Capability
    • Stability vs. Capability
    • The Standard Normal
    • Z Values
    • Computing Proportion Defective
    • Capability Indices: Cp, Cpk, Pp, Ppk
  11. Other Charts
    • Individuals & Moving Ranges
    • Xbar and S Charts
    • Charts for Short Production Runs
    • Attribute Charts (p, np, c, u)