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

ULUSKAN M., Karşı M. G.

International Journal of Lean Six Sigma, cilt.14, sa.3, ss.630-652, 2023 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 14 Sayı: 3
  • Basım Tarihi: 2023
  • Doi Numarası: 10.1108/ijlss-02-2022-0046
  • Dergi Adı: International Journal of Lean Six Sigma
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, ABI/INFORM, Food Science & Technology Abstracts, INSPEC
  • Sayfa Sayıları: ss.630-652
  • Anahtar Kelimeler: Predictive Six Sigma, Machine learning tools, Artificial neural network, Random forests, Gradient boosting machines, K-nearest neighbors, Multiple linear regression, Turkish manufacturers, RANDOM FOREST, SUCCESS FACTORS, REGRESSION
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

© 2022, Emerald Publishing Limited.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.