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

Yao-Chin Lin, Ching-Chuan Yeh, Wei-Hung Chen, Wei-Chun Liu and Jyun-Jie Wang
Additional contact information
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

Sustainability, 2020, vol. 12, issue 13, 1-24

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.

Keywords: motor production line; manufacturing; big data; Industry 4.0; life cycle prediction; process monitoring (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/13/5323/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/13/5323/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:13:p:5323-:d:378901

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().