Heiner Reinhardt , Mahtab Mahdaviasl , Bastian Prell , Anton Mauersberger , Philipp Klimant , Jörg Reiff-Stephan , Steffen Ihlenfeldt
{"title":"Efficient data acquisition for traceability and analytics","authors":"Heiner Reinhardt , Mahtab Mahdaviasl , Bastian Prell , Anton Mauersberger , Philipp Klimant , Jörg Reiff-Stephan , Steffen Ihlenfeldt","doi":"10.1016/j.procir.2024.01.011","DOIUrl":null,"url":null,"abstract":"<div><p>Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management.</p></div>","PeriodicalId":20535,"journal":{"name":"Procedia CIRP","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2212827124000234/pdf?md5=7eb8466575869b117f5a4121cbe973a4&pid=1-s2.0-S2212827124000234-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Procedia CIRP","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2212827124000234","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
Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management.