Three-Stage Industrial Strip-Plot Experiments^*

Abstract

Strip-plot designs are commonly used in situations where the production process consists of two process stages involving hard-to-change factors and where it is possible to apply the second stage to semifinished products from the first stage. In this paper, we focus on three-stage processes. As opposed to the three-stage strip-plot designs in the literature, the third stage does not involve hard-to-change factors but easy-to-change factors that are reset independently for each run. For this scenario, the split-split-plot design is a well-known alternative design option. However, we prefer the more statistically efficient strip-plot designs and, therefore, we construct D-optimal strip-plot designs for three-stage processes with no randomization restriction in the third stage. The coordinate-exchange algorithm we use to construct our designs can handle any type of factor and any number of factor levels, runs, rows, and columns.

• A-Optimality
• D-Optimality
• Easy-to-Change Factors
• Hard-to-Change Factors
• Split-Split-Plot Design
• Strip-Plot Design
• Three-Stage Processes
• Update Formulas

Additional information

Notes on contributors

Heidi Arnouts

Dr. Arnouts is a Postdoctoral Research Fellow of the Research Fund Flanders (FWO) at the Faculty of Applied Economics and StatUa Center for Statistics of the Universiteit Antwerpen. Her email address is [email protected]

Peter Goos

Dr. Goos is Full Professor at the Faculty of Applied Economics and StatUa Center for Statistics of the Universiteit Antwerpen and at the Erasmus School of Economics of the Erasmus Universiteit Rotterdam. He is a Senior Member of the ASQ. His email address is [email protected].

Bradley Jones

Dr. Jones is a Principal Research Fellow at the SAS Institute and a Guest Professor at the Faculty of Applied Economics of the Universiteit Antwerpen. He is a Senior Member of the ASQ. His email address is [email protected].