MALCOLM MOORE, PHIL KAY
JMP Division os SAS Henley Road,
Medmenham Marlow SL7 2EB, United Kingdom

Abstract

The method of Design of Experiments (DOE) was invented almost one hundred years ago by Sir Ronald Fisher and has since become an invaluable tool in helping improve product and process performance in various scientific and engineering disciplines. Still many academic and commercial organisations prefer to rely on expert judgment alone, or experiment by varying one factor at a time and are not benefiting from the DOE method. We find organizations who have not embraced DOE tend to have more problems than time available to solve them; regularly cut corners, so limiting their understanding of the real drivers of process robustness, effectiveness, and efficiency; and regularly need to fix problems with existing processes which divert resources away from innovating and developing new processes and products. This article will show how recent advances in DOE and predictive modelling methods simplify the application of DOE offering organisations competitive advantage through increased process and product understanding.

HOW CAN DESIGN OF EXPERIMENTS HELP MY BUSINESS?
Client A was unable to predictably scale-up and transfer into production, processes for making new products. This resulted in delayed product launches and poor predictability of supply once in production. Engineering experimental practice was to vary one factor at a time (OFAT) to try and fix problems. They lacked DOE knowledge and considered the learning overhead too high. To reduce the learning threshold and perceived complexity of getting started with DOE, a DOE application customized to their own terminology and engineering language was developed. This enabled their engineering community to adopt DOE easily and they are now scaling-up and transferring processes right first time. Process scale-up and transfer has become predictable since engineers at many of the production plants worldwide have adopted DOE. Compared with the old approach of varying one factor at a time, they are optimizing and scaling up production processes with fewer individual experimental runs and estimate this is saving them €2M per production site per year in reduced experimental effort alone. There is huge upside from getting ...