TY - CHAP
T1 - Big data analytics in the manufacturing sector
T2 - Guidelines and lessons learned through the Centro Ricerche FIAT (CRF) case
AU - Alexopoulos, Andreas
AU - Becerra, Yolanda
AU - Boehm, Omer
AU - Bravos, George
AU - Chatzigiannakis, Vassilis
AU - Cugnasco, Cesare
AU - Demetriou, Giorgos
AU - Eleftheriou, Iliada
AU - Fotis, Spiros
AU - Genchi, Gianmarco
AU - Ioannidis, Sotiris
AU - Jakovetic, Dusan
AU - Kallipolitis, Leonidas
AU - Katusic, Vlatka
AU - Kavakli, Evangelia
AU - Kopanaki, Despina
AU - Leventis, Christoforos
AU - Martínez, Miquel
AU - Mascolo, Julien
AU - Milosevic, Nemanja
AU - Montanera, Enric Pere Pages
AU - Ristow, Gerald
AU - Ruiz-Ocampo, Hernan
AU - Sakellariou, Rizos
AU - Sirvent, Raül
AU - Skrbic, Srdjan
AU - Spais, Ilias
AU - Spennacchio, Giuseppe Danilo
AU - Stamenkovic, Dusan
AU - Vasiliadis, Giorgos
AU - Vinov, Michael
N1 - Publisher Copyright:
© The Author(s) 2022. All rights reserved.
PY - 2022/4/28
Y1 - 2022/4/28
N2 - Manufacturing processes are highly complex. Production lines have several robots and digital tools, generating massive amounts of data. Unstructured, noisy and incomplete data have to be collected, aggregated, pre-processed and transformed into structured messages of a common, unified format in order to be analysed not only for the monitoring of the processes but also for increasing their robustness and efficiency. This chapter describes the solution, best practices, lessons learned and guidelines for Big Data analytics in two manufacturing scenarios defined by CRF, within the I-BiDaaS project, namely 'Production process of aluminium die-casting', and 'Maintenance and monitoring of production assets'. First, it reports on the retrieval of useful data from real processes taking into consideration the privacy policies of industrial data and on the definition of the corresponding technical and business KPIs. It then describes the solution in terms of architecture, data analytics and visualizations and assesses its impact with respect to the quality of the processes and products.
AB - Manufacturing processes are highly complex. Production lines have several robots and digital tools, generating massive amounts of data. Unstructured, noisy and incomplete data have to be collected, aggregated, pre-processed and transformed into structured messages of a common, unified format in order to be analysed not only for the monitoring of the processes but also for increasing their robustness and efficiency. This chapter describes the solution, best practices, lessons learned and guidelines for Big Data analytics in two manufacturing scenarios defined by CRF, within the I-BiDaaS project, namely 'Production process of aluminium die-casting', and 'Maintenance and monitoring of production assets'. First, it reports on the retrieval of useful data from real processes taking into consideration the privacy policies of industrial data and on the definition of the corresponding technical and business KPIs. It then describes the solution in terms of architecture, data analytics and visualizations and assesses its impact with respect to the quality of the processes and products.
KW - Advanced analytics and visualizations
KW - Big Data
KW - Die-casting
KW - Maintenance and Monitoring
KW - Manufacturing
KW - Self-service solution
UR - http://www.scopus.com/inward/record.url?scp=85159870911&partnerID=8YFLogxK
U2 - 10.1007/9783030783075_15
DO - 10.1007/9783030783075_15
M3 - Chapter
AN - SCOPUS:85159870911
SN - 9783030783068
SP - 321
EP - 344
BT - Technologies and Applications for Big Data Value
PB - Springer International Publishing
ER -