Use of Symbolic Regression for lean six sigma projects

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

Use of Symbolic Regression for lean six sigma projects

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

Departamento de Ingeniería Mecánica y Eléctrica

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	IIE Annual Conference and Expo 2015
Publisher	Institute of Industrial Engineers
Pages	307-315
Number of pages	9
ISBN (Electronic)	9780983762447
ISBN (Print)	9780983762447
Publication status	Published - 1 Jan 2015
Event	IIE Annual Conference and Expo 2015 - Nashville, United States Duration: 30 May 2015 → 2 Jun 2015

Publication series

Name	IIE Annual Conference and Expo 2015

Conference

Conference	IIE Annual Conference and Expo 2015
Country/Territory	United States
City	Nashville
Period	30/5/15 → 2/6/15

All Science Journal Classification (ASJC) codes

Control and Systems Engineering
Industrial and Manufacturing Engineering

Cite this

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title = "Use of Symbolic Regression for lean six sigma projects",

abstract = "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.",

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N2 - 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.

AB - 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.

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SP - 307

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BT - IIE Annual Conference and Expo 2015

PB - Institute of Industrial Engineers

T2 - IIE Annual Conference and Expo 2015

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ER -

Use of Symbolic Regression for lean six sigma projects

Abstract

Publication series

Conference

All Science Journal Classification (ASJC) codes

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