Optimum Process Target for Two Quality Characteristics Using Regression Analysis

Abstract

The selection of the optimum process target has become one of the key research areas for increasing productivity and improving product quality. Although the quality engineering literature contains a vast collection of work related to this issue, several questions still remain unanswered. First, a quality loss function approach using conventional quality loss functions, such as step loss and quadratic loss functions, has been extensively used to determine the optimum process target largely due to mathematical convenience. When historical data concerning customer loss associated with product performance are available, a quality loss function using a well-established statistical method, such as regression analysis, might be a more practical alternative procedure. Second, many researchers have carried out their studies based on a single quality characteristic. From the customer's viewpoint, however, products are often judged based on more than one characteristic. In this article, we consider two quality characteristics and develop a bivariate empirical loss function based on the historical data associated with the product performance and its associated customer loss. We then propose an optimization scheme for the most economical process target.