TY - GEN
T1 - Predictive Method Proposal for a Manufacturing System with Industry 4.0 Technologies
AU - Aguirre, Santiago
AU - Zuñiga, Lina
AU - Arias, Michael
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Cyber-physical manufacturing systems with industry 4.0 technologies have the ability to generate real-time data on the behavior of the system in each of its components, so predictions can be generated from this data. This article presents a method for the development of a predictive model where process mining techniques and data mining algorithms are combined. Through the discovery techniques of process mining, a descriptive analysis of the system is carried out to subsequently develop a predictive model with predictive data mining algorithms that provide information on the time remaining for a product that is in process to be completed. This prediction allows decision makers to reconfigure the manufacturing system variables and its schedule to optimize its performance. The method was applied in a production system that is currently installed in the Computer Integration Manufacturing Lab at Pontificia Universidad Javeriana.
AB - Cyber-physical manufacturing systems with industry 4.0 technologies have the ability to generate real-time data on the behavior of the system in each of its components, so predictions can be generated from this data. This article presents a method for the development of a predictive model where process mining techniques and data mining algorithms are combined. Through the discovery techniques of process mining, a descriptive analysis of the system is carried out to subsequently develop a predictive model with predictive data mining algorithms that provide information on the time remaining for a product that is in process to be completed. This prediction allows decision makers to reconfigure the manufacturing system variables and its schedule to optimize its performance. The method was applied in a production system that is currently installed in the Computer Integration Manufacturing Lab at Pontificia Universidad Javeriana.
KW - Data mining
KW - Industry 4.0
KW - Predictive monitoring
KW - Process mining
UR - http://www.scopus.com/inward/record.url?scp=85144166804&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-20611-5_10
DO - 10.1007/978-3-031-20611-5_10
M3 - Conference contribution
AN - SCOPUS:85144166804
SN - 9783031206108
T3 - Communications in Computer and Information Science
SP - 109
EP - 121
BT - Applied Computer Sciences in Engineering - 9th Workshop on Engineering Applications, WEA 2022, Proceedings
A2 - Figueroa-García, Juan Carlos
A2 - Franco, Carlos
A2 - Díaz-Gutierrez, Yesid
A2 - Hernández-Pérez, Germán
PB - Springer Science and Business Media Deutschland GmbH
T2 - 9th Workshop on Engineering Applications on Applied Computer Sciences in Engineering, WEA 2022
Y2 - 30 November 2022 through 2 December 2022
ER -