Conceptual Framework for Statistical Process Control Based on Ch. 5 from Statistical Quality Design and Control Authors: Devor, Chang and Sutherland Presented by Dr. Joan Burtner, Certified Quality Engineer Associate Professor of Industrial Engineering and Industrial Management Mercer University

Origins and Nature of Variability Outer Noise External factors that affect the product when it is operating Measured during field use under actual operating conditions Inner Noise Internal change in product parameters Caused by such things as mechanical wear and aging Variational Noise Variation in product parameters from one unit to the next Manufacturing imperfection ISE 327 Chapter 5 Dr. Joan Burtner, Associate Professor of Industrial Engineering

Countermeasures to Functional Variation Engineering design Outer noise Inner noise Statistical process control Variational noise ISE 327 Chapter 5 Dr. Joan Burtner, Associate Professor of Industrial Engineering

Two Types of Process Variation Common cause Problems within the system that are ever-present Correctable typically by management Special cause  Assignable sporadic problems  Correctable locally ISE 327 Chapter 5 Dr. Joan Burtner, Associate Professor of Industrial Engineering

Dr. Joan Burtner, Associate Professor of Industrial Engineering Deming’s Observation Common cause 80% -85% Special cause 15%- 20% Similar to the Pareto Principle which is also known as the 80-20 Rule 80% of the complaints come from 20% of the customers 80% of the defects can be attributed to 20% of the faults ISE 327 Chapter 5 Dr. Joan Burtner, Associate Professor of Industrial Engineering

Time Evolution of Process Behavior When a system is subject to common causes, data collected over time may be used to develop a statistical model for the process. The most common changes in process performance over time are changes in the process mean level and changes in the amount of variation in the process. Through periodic sampling, it is possible to detect changes in the process model. ISE 327 Chapter 5 Dr. Joan Burtner, Associate Professor of Industrial Engineering