Use of Symbolic Regression for lean six sigma projects

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

Resultado de la investigación

Resumen

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.

Idioma originalEnglish
Título de la publicación alojadaIIE Annual Conference and Expo 2015
EditorialInstitute of Industrial Engineers
Páginas307-315
Número de páginas9
ISBN (versión digital)9780983762447
EstadoPublished - 1 ene 2015
EventoIIE Annual Conference and Expo 2015 - Nashville
Duración: 30 may 20152 jun 2015

Serie de la publicación

NombreIIE Annual Conference and Expo 2015

Conference

ConferenceIIE Annual Conference and Expo 2015
PaísUnited States
CiudadNashville
Período30/5/152/6/15

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

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  • Citar esto

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