Use of Symbolic Regression for lean six sigma projects

Daniel Moreno-Sanchez, Jacobo Tijerina-Aguilera, Arlethe Yari Aguilar-Villarreal

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Lean Six Sigma projects and the quality engineering profession have to deal with an extensive selection of tools most of them requiring specialized training. The increased availability of standard statistical software motivates the use of advanced data science techniques to identify relationships between potential causes and project metrics. In these circumstances, Symbolic Regression has received increased attention from researchers and practitioners to uncover the intrinsic relationships hidden within complex data without requiring specialized training for its implementation. The objective of this paper is to evaluate the advantages and drawbacks of using computer assisted Symbolic Regression within the Analyze phase of a Lean Six Sigma project. An application of this approach in a service industry project is also presented.

Original languageEnglish
Title of host publicationIIE Annual Conference and Expo 2015
PublisherInstitute of Industrial Engineers
Number of pages9
ISBN (Electronic)9780983762447
Publication statusPublished - 1 Jan 2015
EventIIE Annual Conference and Expo 2015 - Nashville, United States
Duration: 30 May 20152 Jun 2015

Publication series

NameIIE Annual Conference and Expo 2015


ConferenceIIE Annual Conference and Expo 2015
CountryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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    Moreno-Sanchez, D., Tijerina-Aguilera, J., & Aguilar-Villarreal, A. Y. (2015). Use of Symbolic Regression for lean six sigma projects. In IIE Annual Conference and Expo 2015 (pp. 307-315). (IIE Annual Conference and Expo 2015). Institute of Industrial Engineers.