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Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC

Meryem Uluskan (Department of Industrial Engineering, Eskişehir Osmangazi Üniversitesi, Eskisehir, Turkey)
Merve Gizem Karşı (Department of Industrial Engineering, Eskişehir Osmangazi Üniversitesi, Eskisehir, Turkey)

International Journal of Lean Six Sigma

ISSN: 2040-4166

Article publication date: 9 November 2022

Issue publication date: 12 April 2023

490

Abstract

Purpose

This study aims to emphasize utilization of Predictive Six Sigma to achieve process improvements based on machine learning (ML) techniques embedded in define, measure, analyze, improve, control (DMAIC). With this aim, this study presents selection and utilization of ML techniques, including multiple linear regression (MLR), artificial neural network (ANN), random forests (RF), gradient boosting machines (GBM) and k-nearest neighbors (k-NN) in the analyze and improve phases of Six Sigma DMAIC.

Design/methodology/approach

A data set containing 320 observations with nine input and one output variables is used. To achieve the objective which was to decrease the number of fabric defects, five ML techniques were compared in terms of prediction performance and best tools were selected. Next, most important causes of defects were determined via these tools. Finally, parameter optimization was conducted for minimum number of defects.

Findings

Among five ML tools, ANN, GBM and RF are found to be the best predictors. Out of nine potential causes, “machine speed” and “fabric width” are determined as the most important variables by using these tools. Then, optimum values for “machine speed” and “fabric width” for fabric defect minimization are determined both via regression response optimizer and ANN surface optimization. Ultimately, average defect number was decreased from 13/roll to 3/roll, which is a considerable decrease attained through utilization of ML techniques in Six Sigma.

Originality/value

Addressing an important gap in Six Sigma literature, in this study, certain ML techniques (i.e. MLR, ANN, RF, GBM and k-NN) are compared and the ones possessing best performances are used in the analyze and improve phases of Six Sigma DMAIC.

Keywords

Citation

Uluskan, M. and Karşı, M.G. (2023), "Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC", International Journal of Lean Six Sigma, Vol. 14 No. 3, pp. 630-652. https://doi.org/10.1108/IJLSS-02-2022-0046

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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