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Accelerating Six Sigma Research with the Definitive Screening Design (DSD) Technique

By Bart Peeters, Guido Desmarets, Marc Roels and Sam Van Aeken |

DSD is a new design-of-experiments (DOE) technique that is expected to bring huge benefits when using a Six Sigma optimization strategy Statistical tools are deeply ingrained in the Six Sigma methodology to optimize processes and products during chemical process industries (CPI) operations. In fact, Six Sigma has been an important contributing factor for the widespread use of statistics in many different industrial sectors over the past several decades [ 1]. In Six Sigma’s DMAIC (define-measure-analyze-improve-control) roadmap, many statistical methods are pivotal for the proper collection of data and the ability to translate gathered data into useful information and actionable knowledge. In particular, the design of experiments (DOE) methodology appeals to many chemical engineers (see, for example, Chem. Eng., Nov. 2014 and Sept. 2016 issues [ 2,3]) as a methodology for systematically quantifying cause-and-effect relationships between input and output variables during both manufacturing processes and laboratory research and development (R&D) efforts. DOE is also widely used during the Improve phase of Six Sigma projects. This article discusses the importance of DOE in R&D, and the new definitive screening design (DSD)…
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