{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T20:40:41Z","timestamp":1772829641049,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,5]],"date-time":"2021-11-05T00:00:00Z","timestamp":1636070400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21217366","type":"journal-article","created":{"date-parts":[[2021,11,7]],"date-time":"2021-11-07T20:42:54Z","timestamp":1636317774000},"page":"7366","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Continuous Productivity Improvement Using IoE Data for Fault Monitoring: An Automotive Parts Production Line Case Study"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7115-9658","authenticated-orcid":false,"given":"Yuchang","family":"Won","sequence":"first","affiliation":[{"name":"Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seunghyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4807-6461","authenticated-orcid":false,"given":"Kyung-Joon","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2304-7106","authenticated-orcid":false,"given":"Yongsoon","family":"Eun","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 42988, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Park, D., Kim, S., An, Y., and Jung, J.-Y. (2018). LiReD: Light-Weight Real-Time fault detection system for edge computing using LSTM recurrent neural networks. Sensors, 18.","DOI":"10.3390\/s18072110"},{"key":"ref_2","unstructured":"Montgomery, D.C. (2009). Introduction to Statistical Quality Control, Wiley. [6th ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3585","DOI":"10.1109\/ACCESS.2018.2793265","article-title":"Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison","volume":"6","author":"Qi","year":"2018","journal-title":"IEEE Access"},{"key":"ref_4","first-page":"3585","article-title":"The samrt factory as a key construct of industry 4.0: A systematic literature review","volume":"6","author":"Osterrieder","year":"2018","journal-title":"Int. J. Prod. Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2590","DOI":"10.1080\/00207543.2016.1245883","article-title":"Developing performance measurement system for Internet of Things and smart factory environment","volume":"55","author":"Hwang","year":"2016","journal-title":"Int. J. Prod. Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stary, C. (2021). Digital twin generation: Re-conceptualizing agent systems for behavior-centered cyber-physical system development. Sensors, 21.","DOI":"10.3390\/s21041096"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Martinez, E.M., Ponce, P., Macias, I., and Molina, A. (2021). Automation pyramid as constructor for a complete digital twin, case study: A didactic manufacturing system. Sensors, 21.","DOI":"10.3390\/s21144656"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Fera, M., Greco, A., Caterino, M., Gerbino, S., Caputo, F., Macchiaroli, R., and D\u2019Amato, E. (2020). Towards digital twin implementation for assessing production line performance and balancing. Sensors, 20.","DOI":"10.3390\/s20010097"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, S., Wan, J., Li, D., and Liu, C. (2018). Knowledge reasoning with semantic data for real-time data processing in smart factory. Sensors, 18.","DOI":"10.3390\/s18020471"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.icte.2019.04.002","article-title":"A microservice-based framework for integrating IoT management platforms, semantic and AI services for supply chain management","volume":"5","author":"Kousiouris","year":"2019","journal-title":"ICT Express"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Li, J., and Meerkov, S.M. (2009). Production Systems Engineering, Springer.","DOI":"10.1007\/978-0-387-75579-3"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1155\/S1024123X01001776","article-title":"c-Bottlenecks in serial production lines: Identification and application","volume":"7","author":"Chiang","year":"2001","journal-title":"Math. Probl. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1109\/TII.2015.2431232","article-title":"Multiagent system integrating process and quality control in a factory producing laundry washing machines","volume":"11","author":"Leitao","year":"2015","journal-title":"IEEE Trans. Ind. Info."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.procir.2016.11.242","article-title":"Internet-of-Things enabled real-time monitoring of energy efficiency on manufacturing shop floors","volume":"61","author":"Tan","year":"2017","journal-title":"Procedia CIRP"},{"key":"ref_15","unstructured":"(2021, October 28). Ministry of SMEs and Startups, Republic of Korea. Available online: Https:\/\/www.smart-factory.kr\/eng\/introGood?menuId=05."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.cie.2018.12.019","article-title":"A classification and review of timed Markov models of manufacturing systems","volume":"128","author":"Papadopoulos","year":"2019","journal-title":"Comp. Ind. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1080\/07408170701488037","article-title":"Transient behavior of serial production lines with Bernoulli machines","volume":"40","author":"Meerkov","year":"2008","journal-title":"IIE Trans."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1893","DOI":"10.1109\/LRA.2017.2713247","article-title":"Transient performance analysis of closed production lines with Bernoulli machines, finite buffers, and carriers","volume":"2","author":"Jia","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1109\/TASE.2007.893503","article-title":"Lean buffering in serial production lines with nonidentical exponential machines","volume":"5","author":"Chiang","year":"2008","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1080\/00207543.2013.776191","article-title":"Raw material release rates to ensure desired production lead time in Bernoulli serial lines","volume":"51","author":"Biller","year":"2013","journal-title":"Int. J. Prod. Res."},{"key":"ref_21","unstructured":"Burman, M.H. (1995). New Results in Flow Line Analysis. [Ph.D. Thesis, Massachusetts Institute of Technology]."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1109\/TASE.2009.2033568","article-title":"Quality\/Quantity improvement in an automotive paint shop: A case study","volume":"7","author":"Arinez","year":"2010","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/TASE.2015.2433674","article-title":"Finite production run-based serial production lines with Bernoulli machines: Performance analysis, bottleneck, and case study","volume":"13","author":"Jia","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1016\/j.jfoodeng.2012.05.022","article-title":"Modeling, analysis and continuous improvement of food production systems: A case study at a meat shaving and packaging line","volume":"113","author":"Xie","year":"2012","journal-title":"J. Food Eng."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4164","DOI":"10.1109\/LRA.2020.2989658","article-title":"Workforce allocation in motorcycle transmission assembly lines: A case study on modeling, analysis, and improvement","volume":"5","author":"Ma","year":"2020","journal-title":"IEEE Robot. Autom. 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