Manufacturing Maps, a Novel Tool for Smart Factory Management Based on Petri Nets and Big Data Mini-Terms

Javier Llopis, Antonio Lacasa, Eduardo Garcia, Nicolás Montés, Lucía Hilario, Judith Vizcaíno, Cristina Vilar, Judit Vilar, Laura Sánchez and Juan Carlos Latorre
Additional contact information
Javier Llopis: Ford Spain, Polígono Industrial Ford S/N, Almussafes, 46440 Valencia, Spain
Antonio Lacasa: Ford Spain, Polígono Industrial Ford S/N, Almussafes, 46440 Valencia, Spain
Eduardo Garcia: Ford Spain, Polígono Industrial Ford S/N, Almussafes, 46440 Valencia, Spain
Nicolás Montés: Department of Mathematics, Physics and Technological Sciences, University CEU Cardenal Herrera, C/San Bartolome 55, Alfara del Patriarca, 46115 Valencia, Spain
Lucía Hilario: Department of Mathematics, Physics and Technological Sciences, University CEU Cardenal Herrera, C/San Bartolome 55, Alfara del Patriarca, 46115 Valencia, Spain
Judith Vizcaíno: Faculty of Mathematics, University of Valencia, Blasco Ibáñez Avenue, 13, 46010 Valencia, Spain
Cristina Vilar: Faculty of Mathematics, University of Valencia, Blasco Ibáñez Avenue, 13, 46010 Valencia, Spain
Judit Vilar: Faculty of Mathematics, University of Valencia, Blasco Ibáñez Avenue, 13, 46010 Valencia, Spain
Laura Sánchez: Faculty of Mathematics, University of Valencia, Blasco Ibáñez Avenue, 13, 46010 Valencia, Spain
Juan Carlos Latorre: Department of Mathematics, Physics and Technological Sciences, University CEU Cardenal Herrera, C/San Bartolome 55, Alfara del Patriarca, 46115 Valencia, Spain

Mathematics, 2022, vol. 10, issue 14, 1-22

Abstract: This article defines a new concept for real-time factory management—manufacturing maps. Manufacturing maps are generated from two fundamental elements, mini-terms and Petri nets. Mini-terms are sub-times of a technical cycle, the time it takes for any component to perform its task. A mini-term, by definition, is a sub-cycle time and it would only make sense to use the term in connection with production improvement. Previous studies have shown that when the sub-cycle time worsens, this indicates that something unusual is happening, enabling anticipation of line failures. As a result, a mini-term has dual functionality, since, on the one hand, it is a production parameter and, on the other, it is a sensor used for predictive maintenance. This, combined with how easy and cheap it is to extract relevant data from manufacturing lines, has resulted in the mini-term becoming a new paradigm for predictive maintenance, and, indirectly, for production analysis. Applying this parameter using big data for machines and components can enable the complete modeling of a factory using Petri nets. This article presents manufacturing maps as a hierarchical construction of Petri nets in which the lowest level network is a temporary Petri net based on mini-terms, and in which the highest level is a global view of the entire plant. The user of a manufacturing map can select intermediate levels, such as a specific production line, and perform analysis or simulation using real-time data from the mini-term database. As an example, this paper examines the modeling of the 8XY line, a multi-model welding line at the Ford factory in Almussafes (Valencia), where the lower layers are modeled until the mini-term layer is reached. The results, and a discussion of the possible applications of manufacturing maps in industry, are provided at the end of this article.

Keywords: manufacturing; Petri nets; big data; Industry 4.0; smart manufacturing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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