The future of industrial statistics: A panel discussion

David M. Steinberg, Søren Bisgaard, Nicholas Fisher, Gerald Hahn, Jon Kettenring, Douglas C. Montgomery, Necip Doganaksoy, Bert Gunter, Sallie Keller-Mc nulty, William Q. Meeker, C. F.Jeff Wu

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


Technometrics was founded in 1959 as a forum for publishing statistical methods and applications in engineering and the physical and chemical sciences. The expanding role of statistics in industry was a major stimulus, and, throughout the years many articles in the journal have been motivated by industrial problems. In this panel discussion we look ahead to the future of industrial statistics. Ten experts, encompassing a range of backgrounds, experience, and expertise, answered my request to share with us their thoughts on what lies ahead in industrial statistics. Short biographical sketches of the panelists are provided at the end of the discussion. The panelists wrote independent essays, which I have combined into an integrated discussion. Most of the essays were written as responses to a list of 10 questions that I provided to help the participants direct their thoughts. I have organized the discussion in that same fashion, stating the questions and then providing the related responses. Several discussants added remarks on the role of statistics journals, particularly of Technometrics, and I have added that as a final question. We see this article, not as the end of the story, but rather as the takeoff point for further discussion. To that end, we are initiating an open discussion forum; to participate, go to and click on Networking and Events. The American Society for Quality will host the forum and Bert Gunter has graciously agreed to serve as moderator.

Original languageEnglish
Pages (from-to)103-127
Number of pages25
Issue number2
StatePublished - 1 Jan 2008


  • Computer models
  • Design of experiments
  • Financial statistics
  • Industrial research groups
  • Information technology
  • Innovation
  • Massive data sets
  • Process improvement
  • Quality
  • Quality management
  • Reliability
  • Six Sigma


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