Please use this identifier to cite or link to this item: http://hdl.handle.net/10739/4965
Title: Data analytics for quality management in Industry 4.0 from a MSME perspective
Authors: Sariyer, Gorkem
Kumar Mangla, Sachin
Kazancoglu, Yigit
Ocal Tasar, Ceren
Luthra, Sunil
Keywords: MSME
Machine learning
Re-work and root causes of defect
Association rule mining
Industry 4.0
Quality control and Manufacturing
Issue Date: Aug-2021
Publisher: Annals of Operations Research, Springer, Netherlands
Citation: Sariyer, 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/s10479-021-04215-9
Abstract: Advances 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.
URI: https://doi.org/10.1007/s10479-021-04215-9
http://hdl.handle.net/10739/4965
ISSN: 0254-5330
1572-9338
Appears in Collections:JGU Research Publications

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