Yuan Li, Luiz Cesar Gualberto Veras Inoue, R. Sinha
{"title":"Real-time OEE visualisation for downtime detection","authors":"Yuan Li, Luiz Cesar Gualberto Veras Inoue, R. Sinha","doi":"10.1109/INDIN51773.2022.9976067","DOIUrl":null,"url":null,"abstract":"Unknown and unplanned downtime events during production cause significant disruption and loss of productivity. Investigating, identifying and addressing such events is a pressing need. The primary objective of this study is to examine downtime, performance loss, and quality control in the manufacturing process. Specifically, we propose a solution that provides real-time data processing and visualization of the factory floor. This solution was implemented for a major food manufacturer based in New Zealand. The company provided historical data covering over six years of operation and access to real-time data through their Industrial Internet of Things (IIoT) systems executing on Programmable Logic Controllers (PLCs). Our solution is an Overall Equipment Effectiveness (OEE) standardized Supervisory Control and Data Acquisition (SCADA) system that visualizes the manufacturing process in real-time. Analysis of the data collected during this research shows that by implementing the OEE and employing shift adjustment, there was a significant increase in production output. OEE can help improve manufacturing performance by pinpointing the root of the loss of performance in all areas monitored.","PeriodicalId":359190,"journal":{"name":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 20th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN51773.2022.9976067","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Unknown and unplanned downtime events during production cause significant disruption and loss of productivity. Investigating, identifying and addressing such events is a pressing need. The primary objective of this study is to examine downtime, performance loss, and quality control in the manufacturing process. Specifically, we propose a solution that provides real-time data processing and visualization of the factory floor. This solution was implemented for a major food manufacturer based in New Zealand. The company provided historical data covering over six years of operation and access to real-time data through their Industrial Internet of Things (IIoT) systems executing on Programmable Logic Controllers (PLCs). Our solution is an Overall Equipment Effectiveness (OEE) standardized Supervisory Control and Data Acquisition (SCADA) system that visualizes the manufacturing process in real-time. Analysis of the data collected during this research shows that by implementing the OEE and employing shift adjustment, there was a significant increase in production output. OEE can help improve manufacturing performance by pinpointing the root of the loss of performance in all areas monitored.