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Predictive Six Sigma for Turkish manufacturers: utilization of machine learning tools in DMAIC by Meryem Uluskan; Merve Gizem Karşı is a Business, Management and Accounting article available to read on EtoBox.

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## 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 “m

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Author

Meryem Uluskan; Merve Gizem Karşı

Publisher

Emerald

Published

2022

Language

EN

Field

Business, Management and Accounting (Social Sciences)