Please use this identifier to cite or link to this item: http://hdl.handle.net/10739/4965
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dc.contributor.authorSariyer, Gorkem-
dc.contributor.authorKumar Mangla, Sachin-
dc.contributor.authorKazancoglu, Yigit-
dc.contributor.authorOcal Tasar, Ceren-
dc.contributor.authorLuthra, Sunil-
dc.date.accessioned2021-08-23T18:15:16Z-
dc.date.available2021-08-23T18:15:16Z-
dc.date.issued2021-08-
dc.identifier.citationSariyer, G., Mangla, S. K., Kazancoglu, Y., Ocal Tasar, C., & Luthra, S. (2021). Data analytics for quality management in Industry 4.0 from a MSME perspective. Annals of Operations Research, (In Press), DOI: https://doi.org/10.1007/s10479-021-04215-9en_US
dc.identifier.issn0254-5330-
dc.identifier.issn1572-9338en_US
dc.identifier.urihttps://doi.org/10.1007/s10479-021-04215-9-
dc.identifier.urihttp://hdl.handle.net/10739/4965-
dc.description.abstractAdvances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated datadriven techniques. This study presents a 3-stage model that classifies products depending on defects (defects or non-defects) and defect type according to their levels. This article seeks to detect potential errors to ensure superior quality through machine learning and data mining. The proposed model is tested in a medium enterprise—a kitchenware company in Turkey. Using the main features of data set, product, customer, country, production line, production volume, sample quantity and defect code, a Multilayer Perceptron algorithm for product quality level classification was developed with 96% accuracy. Once a defect is detected, an estimation is made of how many re-works are required. Thus, considering the attributes of product, production line, production volume, sample quantity and product quality level, a Multilayer Perceptron algorithm for re-work quantity prediction model was developed with 98% performance. From the findings, re-work quantity has the highest relation with product quality level where re-work quantities were higher for major defects compared to minor/moderate defects. Finally, this work explores the root causes of defects considering production line and product quality level through association rule mining. The top mined rule achieves a confidence level of 80% where assembly and material were identified as main root causes.en_US
dc.formattexten_US
dc.language.isoenen_US
dc.publisherAnnals of Operations Research, Springer, Netherlandsen_US
dc.subjectMSMEen_US
dc.subjectMachine learningen_US
dc.subjectRe-work and root causes of defecten_US
dc.subjectAssociation rule miningen_US
dc.subjectIndustry 4.0en_US
dc.subjectQuality control and Manufacturingen_US
dc.titleData analytics for quality management in Industry 4.0 from a MSME perspectiveen_US
dc.typejournal-articleen_US
dc.typeScopusen_US
dc.typejournal-articleen_US
dc.typeScopusen_US
dc.institutionJindal Global Business School (Co-author)en_US
dc.rightlicenseden_US
Appears in Collections:JGU Research Publications

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