dc.contributor.author | Sariyer Ataman, G. | |
dc.contributor.author | Mangla, S.K. | |
dc.contributor.author | Kazancoglu, Y. | |
dc.contributor.author | Tasar, C.O. | |
dc.contributor.author | Luthra, S. | |
dc.date.accessioned | 2021-12-20T10:50:30Z | |
dc.date.available | 2021-12-20T10:50:30Z | |
dc.date.issued | 2021 | |
dc.identifier.issn | 0254-5330 | |
dc.identifier.uri | https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/18519 | |
dc.description.abstract | Advances in smart technologies (Industry 4.0) assist managers of Micro Small and Medium Enterprises (MSME) to control quality in manufacturing using sophisticated data-driven 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.language.iso | English | en_US |
dc.publisher | Springer | en_US |
dc.rights | info:eu-repo/semantics/openAccess | en_US |
dc.subject | MSME | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Quality control | en_US |
dc.subject | Industry 4.0 | en_US |
dc.subject | Data analytics | en_US |
dc.subject | Manufacturing | en_US |
dc.title | Data analytics for quality management in Industry 4.0 from a MSME perspective | en_US |
dc.type | Article | en_US |
dc.relation.journal | Annals of Operations Research | en_US |
dc.identifier.doi | 10.1007/s10479-021-04215-9 | en_US |
dc.contributor.department | Department of Business | en_US |
dc.contributor.department | Department of International Logistics Management | en_US |
dc.identifier.wos | https://www.webofscience.com/wos/woscc/full-record/WOS:000682405200001?AlertId=fc1c72a7-b080-4d60-92a3-edbe4aa40157&SID=F48o6GDghiBrbTGOtCo | en_US |
dc.identifier.scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85112624139&origin=resultslist&sort=plf-f&src=s&st1=Data+analytics+for+quality+management+in+Industry+4.0+from+a+MSME+perspective&sid=dc3fbe8f19957c886997ae5b5b93e203&sot=b&sdt=b&sl=92&s=TITLE-ABS-KEY%28Data+analytics+for+quality+management+in+Industry+4.0+from+a+MSME+perspective%29&relpos=0&citeCnt=0&searchTerm= | en_US |
dc.contributor.yasarauthor | 0000-0001-9199-671X: Yiğit Kazançoğlu | en_US |
dc.contributor.yasarauthor | 0000-0002-8290-2248: Görkem Ataman Sarıyer | en_US |