Continuous process improvement through designed experiments and multi-attribute desirability optimization

Abstract

Process optimization is a problem with many dimensions. Attributes of interest compete with one another and are affected by a host of variables. It is impossible to achieve the best possible values for all process outputs simultaneously. For this reason, it is important to define what should be achieved from the process. Once the objectives are known, statistically designed experiments can be used effectively to determine the optimal levels of controllable process variables that will produce the desired result and make the process robust to variations in the influential parameters that cannot be controlled. This paper describes an approach to establishing values for process variables to consistently achieve the optimal set of process outputs. It is an iterative process that produces continuous improvement. Principlpes of statistical experimental design and multi-attribute desirability optimization methodology are employed. The benefits of this approach include better products, less variability, lower costs, and more efficient process definition.