A Bivariate Dead Band Process Adjustment Policy

TL;DR

This paper extends the feedback adjustment problem to a bivariate setting with a dead band policy, optimizing costs of adjustments and deviations, with applications in manufacturing and healthcare.

Contribution

It introduces a bivariate dead band control policy with analytical formulas for cost minimization, applicable beyond machine tools to areas like medicine.

Findings

Derived formulas for the loss function and optimal dead band boundaries

Demonstrated the policy's application in semiconductor manufacturing

Provided insights into balancing adjustment costs and process deviations

Abstract

A bivariate extension to Box and Jenkins (1963) feedback adjustment problem is presented in this paper. The model balances the fixed cost of making an adjustment, which is assumed independent of the magnitude of the adjustments, with the cost of running the process off-target, which is assumed quadratic. It is also assumed that two controllable factors are available to compensate for the deviations from target of two responses in the presence of a bivariate IMA(1,1) disturbance. The optimal policy has the form of a "dead band", in which adjustments are justified only when the predicted process responses exceed some boundary in $\mathbb{R}^2$. This boundary indicates when the responses are predicted to be far enough from their targets that an additional adjustment or intervention in the process is justified. Although originally developed to control a machine tool, dead band control policies have application in other areas. For example, they could be used to control a disease through the application of a drug to a patient depending on the level of a substance in the body (e.g., diabetes control). This paper presents analytical formulae for the computation of the loss function that combines off-target and adjustment costs per time unit. Expressions are derived for the average adjustment interval and for the scaled mean square deviations from target. The minimization of the loss function and the practical use of the resulting dead band adjustment strategy is illustrated with an application to a semiconductor manufacturing process.