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Data analytics for quality management in Industry 4.0 from a MSME perspective

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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.

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Authors and Affiliations

  1. Department of Business, Yasar University, İzmir, Turkey

    Gorkem Sariyer

  2. Operations Management, Jindal Global Business School, O P Jindal Global University, Haryana, India

    Sachin Kumar Mangla

  3. Department of International Logistics Management, Yasar University, İzmir, Turkey

    Yigit Kazancoglu

  4. Independent Researcher, İzmir, Turkey

    Ceren Ocal Tasar

  5. Department of Mechanical Engineering, Ch. Ranbir Singh State Institute of Engineering & Technology, Jhajjar, Haryana, 124103, India

    Sunil Luthra

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  1. Gorkem Sariyer
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  2. Sachin Kumar Mangla
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  3. Yigit Kazancoglu
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Correspondence to Sunil Luthra.

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Sariyer, G., Mangla, S.K., Kazancoglu, Y. et al. Data analytics for quality management in Industry 4.0 from a MSME perspective. Ann Oper Res (2021). https://doi.org/10.1007/s10479-021-04215-9

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