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Statistics Spotlight: Why Not Sequential DoEs?
  • Manufacturing, Quality, Service

Statistics Spotlight: Why Not Sequential DoEs?

Approach maximizes knowledge and avoids costly, wasteful mistakes

Publication:
Quality Progress
Date:
November 2020
Issue:
Volume 53 Issue 11
Pages:
pp. 44-47
Author(s):
Anderson-Cook, Christine M. , Lu, Lu
Organization(s):
Los Alamos National Laboratory, Los Alamos, NM, University of South Florida, Tampa, FL

Abstract

Data collection is expensive, and time- and labor-intensive. The sequential approach to experimentation allows for maximal knowledge to be used to guide choices at each stage, which can help avoid costly and wasteful mistakes. It allows a calculated, thoughtful response to surprises—both good and bad. In addition, it helps to break the often- complicated designed experiment process into more manageable, easy-to-implement steps. 

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