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The Use of Big Data for Sustainable Development in Motor Production Line Issues

Author

Listed:
  • Yao-Chin Lin

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

  • Ching-Chuan Yeh

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

  • Wei-Hung Chen

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

  • Wei-Chun Liu

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

  • Jyun-Jie Wang

    (Department of Information Management, Yuan Ze University, Taoyuan 32003, Taiwan)

Abstract

This study explores big data gathered from motor production lines to gain a better understanding of production line issues. Motor products from Solen Electric Company’s motor production lines were used to predict failure points based on big data analytics, where 3606 datapoints from the company’s testing equipment were statistically analyzed. The current study focused on secondary data and expert interview results to further define the relevant statistical dimensions. Only 14 of the original 88 detection parameters were required for monitoring the production line. The relationships between these parameters and the relevant motor components were established to indicate how an abnormal reading may be interpreted to quickly resolve an issue. Thus, a theoretical model for the monitoring of the motor production line was proposed. Further implications and practical suggestions are also offered to improve the production lines. This study explores big data analysis and smart manufacturing and demonstrates the promise of these technologies in improving production line efficiency and reducing waste to promote sustainable production goals. Big data thus constitute the core technology for advancing production lines into Industry 4.0 and promoting industry sustainability.

Suggested Citation

  • Yao-Chin Lin & Ching-Chuan Yeh & Wei-Hung Chen & Wei-Chun Liu & Jyun-Jie Wang, 2020. "The Use of Big Data for Sustainable Development in Motor Production Line Issues," Sustainability, MDPI, vol. 12(13), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:13:p:5323-:d:378901
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    References listed on IDEAS

    as
    1. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    2. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
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    Cited by:

    1. Dominique Lepore & Alessandra Micozzi & Francesca Spigarelli, 2021. "Industry 4.0 Accelerating Sustainable Manufacturing in the COVID-19 Era: Assessing the Readiness and Responsiveness of Italian Regions," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    2. Meir Russ, 2021. "Knowledge Management for Sustainable Development in the Era of Continuously Accelerating Technological Revolutions: A Framework and Models," Sustainability, MDPI, vol. 13(6), pages 1-32, March.
    3. Yongmao Xiao & Wei Yan & Ruping Wang & Zhigang Jiang & Ying Liu, 2021. "Research on Blank Optimization Design Based on Low-Carbon and Low-Cost Blank Process Route Optimization Model," Sustainability, MDPI, vol. 13(4), pages 1-21, February.

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