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Predictive Method Proposal for a Manufacturing System with Industry 4.0 Technologies

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Applied Computer Sciences in Engineering (WEA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1685))

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Abstract

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.

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Author information

Authors and Affiliations

  1. Pontificia Universidad Javeriana, Bogotá, Colombia

    Santiago Aguirre & Lina Zuñiga

  2. Universidad de Costa Rica, San Pedro, Costa Rica

    Michael Arias

Authors
  1. Santiago Aguirre
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  2. Lina Zuñiga
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  3. Michael Arias
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Corresponding author

Correspondence to Santiago Aguirre .

Editor information

Editors and Affiliations

  1. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad del Rosario, Bogotá, Colombia

    Carlos Franco

  3. Universidad de La Salle, Bogotá, Colombia

    Yesid Díaz-Gutierrez

  4. National University of Colombia, Bogotá, Colombia

    Germán Hernández-Pérez

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Aguirre, S., Zuñiga, L., Arias, M. (2022). Predictive Method Proposal for a Manufacturing System with Industry 4.0 Technologies. In: Figueroa-García, J.C., Franco, C., Díaz-Gutierrez, Y., Hernández-Pérez, G. (eds) Applied Computer Sciences in Engineering. WEA 2022. Communications in Computer and Information Science, vol 1685. Springer, Cham. https://doi.org/10.1007/978-3-031-20611-5_10

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  • DOI: https://doi.org/10.1007/978-3-031-20611-5_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20610-8

  • Online ISBN: 978-3-031-20611-5

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