TY - GEN
T1 - Development of a Predictive Process Monitoring Methodology in a Self-organized Manufacturing System
AU - López Castro, Laura María
AU - Martínez, Sonia Geraldine
AU - Rodriguez, Nestor Eduardo
AU - Lovera, Luna Violeta
AU - Santiago Aguirre, Hugo
AU - Jimenez, Jose Fernando
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - This paper presents a methodology for the introduction of a predictive model in a flexible, self-organized manufacturing system, allowing the system to make improved decisions. Taking into consideration the high efficiency required for product manufacturing, predictive models provide valuable insights for accurate decision-making to optimize ongoing processes for key indicators such as process execution time. Furthermore, these models can be used to generate an appropriate response to possible perturbations in the system. Data analysis tools such as process mining allow developing a methodology that enables predictive process modelling in flexible self-organized manufacturing systems. The simulated system in this study is based on the manufacturing cell AIP-PRIMECA, which is located at Polytechnic University Hauts de France in Valenciennes. The process mining tools Apromore, Nirdizati, and ProM were used for the development of the proposed methodology and its implementation in the simulated system. It is expected that applying the proposed methodology will make the manufacturing system more efficient.
AB - This paper presents a methodology for the introduction of a predictive model in a flexible, self-organized manufacturing system, allowing the system to make improved decisions. Taking into consideration the high efficiency required for product manufacturing, predictive models provide valuable insights for accurate decision-making to optimize ongoing processes for key indicators such as process execution time. Furthermore, these models can be used to generate an appropriate response to possible perturbations in the system. Data analysis tools such as process mining allow developing a methodology that enables predictive process modelling in flexible self-organized manufacturing systems. The simulated system in this study is based on the manufacturing cell AIP-PRIMECA, which is located at Polytechnic University Hauts de France in Valenciennes. The process mining tools Apromore, Nirdizati, and ProM were used for the development of the proposed methodology and its implementation in the simulated system. It is expected that applying the proposed methodology will make the manufacturing system more efficient.
KW - Apromore
KW - Celonis
KW - Flexible manufacturing system
KW - Netlogo
KW - Nirdizati
KW - Predictive process monitoring
KW - Process mining
KW - Self-organized system
UR - http://www.scopus.com/inward/record.url?scp=85113772838&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-80906-5_1
DO - 10.1007/978-3-030-80906-5_1
M3 - Conference contribution
AN - SCOPUS:85113772838
SN - 9783030809058
T3 - Studies in Computational Intelligence
SP - 3
EP - 16
BT - Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future - Proceedings of SOHOMA LATIN AMERICA 2021
A2 - Trentesaux, Damien
A2 - Borangiu, Theodor
A2 - Leitão, Paulo
A2 - Jimenez, Jose-Fernando
A2 - Montoya-Torres, Jairo R.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 1st Latin-American Workshop on Service-Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future, SOHOMA LATIN AMERICA 2021
Y2 - 27 January 2021 through 28 January 2021
ER -