Statistical Engineering and Variation Reduction

Statistical engineering as proposed by Hoerl and Snee (Citation2010a) aims to develop a discipline devoted to better understanding how to use statistical tools to support project goals. Existing examples abound but more work is needed. We discuss the use of statistical engineering to improve problem solving—that is, reducing variation in processes—and note that this requires a series of empirical investigations where we should use information gained to help plan subsequent investigations. The systematic use of prior/existing information, especially baseline information, in problem solving is illustrated using a crossbar dimension case study. The baseline results are used to help plan and analyze all subsequent investigations both when looking for a dominant cause of the variation and when assessing a possible solution. The effective use of prior statistical information and the consequences of its use in the variation reduction context are not commonly taught and thus opportunities for more efficient problem solving are lost.