Paper Info
Title
Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study
Abstract
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.
Year
DOI
Venue
2021
10.3390/s21217366
SENSORS
Keywords
DocType
Volume
internet of everything, production systems engineering, continuous productivity improvement, smart factory, fault monitoring data
Journal
21
Issue
ISSN
Citations 
21
1424-8220
1
PageRank 
References 
Authors
0.36
0
4
Name
Order
Citations
PageRank
Yuchang Won121.72
Seunghyeon Kim210.36
Kyung-Joon Park327036.78
Yongsoon Eun47723.26