Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study
This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/21/7366 |
| _version_ | 1797511723950800896 |
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| author | Yuchang Won Seunghyeon Kim Kyung-Joon Park Yongsoon Eun |
| author_facet | Yuchang Won Seunghyeon Kim Kyung-Joon Park Yongsoon Eun |
| author_sort | Yuchang Won |
| collection | DOAJ |
| description | This paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not. |
| first_indexed | 2024-03-10T05:52:06Z |
| format | Article |
| id | doaj.art-9101fb288c24443a8324884d9a3acb55 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T05:52:06Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-9101fb288c24443a8324884d9a3acb552023-11-22T21:40:34ZengMDPI AGSensors1424-82202021-11-012121736610.3390/s21217366Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case StudyYuchang Won0Seunghyeon Kim1Kyung-Joon Park2Yongsoon Eun3Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, KoreaDepartment of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, KoreaDepartment of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, KoreaDepartment of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, KoreaThis paper presents a case study of continuous productivity improvement of an automotive parts production line using Internet of Everything (IoE) data for fault monitoring. Continuous productivity improvement denotes an iterative process of analyzing and updating the production line configuration for productivity improvement based on measured data. Analysis for continuous improvement of a production system requires a set of data (machine uptime, downtime, cycle-time) that are not typically monitored by a conventional fault monitoring system. Although productivity improvement is a critical aspect for a manufacturing site, not many production systems are equipped with a dedicated data recording system towards continuous improvement. In this paper, we study the problem of how to derive the dataset required for continuous improvement from the measurement by a conventional fault monitoring system. In particular, we provide a case study of an automotive parts production line. Based on the data measured by the existing fault monitoring system, we model the production system and derive the dataset required for continuous improvement. Our approach provides the expected amount of improvement to operation managers in a numerical manner to help them make a decision on whether they should modify the line configuration or not.https://www.mdpi.com/1424-8220/21/21/7366internet of everythingproduction systems engineeringcontinuous productivity improvementsmart factoryfault monitoring data |
| spellingShingle | Yuchang Won Seunghyeon Kim Kyung-Joon Park Yongsoon Eun Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study Sensors internet of everything production systems engineering continuous productivity improvement smart factory fault monitoring data |
| title | Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study |
| title_full | Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study |
| title_fullStr | Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study |
| title_full_unstemmed | Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study |
| title_short | Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study |
| title_sort | continuous productivity improvement using ioe data for fault monitoring an automotive parts production line case study |
| topic | internet of everything production systems engineering continuous productivity improvement smart factory fault monitoring data |
| url | https://www.mdpi.com/1424-8220/21/21/7366 |
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