Integrating Statistical Process Control and Engineering Process Control

Abstract

Statistical process control (SPC) is traditionally applied to processes that vary about a fixed mean, and where successive observations are viewed as statistically independent. Engineering process control (EPC) is usually applied to processes in which successive observations are related over time, and where the mean drifts dynamically. Thus, EPC seeks to minimize variability by transferring it from the output variable to a related process input (controllable) variable, while SPC seeks to reduce variability by detecting and eliminating assignable causes of variation. This paper shows through simulation that when using EPC it is always better to have an SPC system in place that monitors and acts properly on the root cause of the assignable change.

• Autoregressive Integrated Moving Average Process
• Engineering Process Control
• Minimum Mean Square Error Controller
• Statistical Process Control

Additional information

Notes on contributors

Douglas C. Montgomery

Dr. Montgomery is a Professor in the Department of Industrial and Management Systems Engineering. He is a Fellow of ASQC.

J. Bert Keats

Dr. Keats is an Associate Professor in the Department of Industrial and Management Systems Engineering and Director of the Statistical and Engineering Applications for Quality Laboratory. He is a Member of ASQC.

George C. Runger

Dr. Runger is a Quality Consultant. He is a Member of ASQC.

William S. Messina

Dr. Messina is a Statistician in Sales and Marketing Research. He is a Member of ASQC.