A Review and Critique of Auxiliary Information-Based Process Monitoring Methods

TL;DR

This paper critically reviews auxiliary information-based process monitoring methods, highlighting their assumptions, limitations, and potential for undetectable shifts, and advises caution in their application.

Contribution

It provides a comprehensive critique of AIB methods, revealing their assumptions and limitations, and demonstrates their potential pitfalls in quality control.

Findings

Violations of auxiliary variable assumptions can impair detection.

AIB methods are a special case of linear regression monitoring.

Strong assumptions limit AIB methods' practical use.

Abstract

We review the rapidly growing literature on auxiliary information-based (AIB) process monitoring methods. Under this approach, there is an assumption that the auxiliary variable, which is correlated with the quality variable of interest, has a known mean, or some other parameter, which cannot change over time. We demonstrate that violations of this assumption can have serious adverse effects both when the process is stable and when there has been a process shift. Some process shifts can become undetectable. We also show that the basic AIB approach is a special case of simple linear regression profile monitoring. The AIB charting techniques require strong assumptions. Based on our results, we warn against the use of AIB approach in quality control applications.