Abstract
This research project consists of bringing innovation to the shop floor in such a way that it will allow its approach to the Industry 4.0 concept. The main aim includes integrating the present installed systems in order to provide its user with data as if it was a unique system. More concretely, this study intends to unify the information that comes from different systems: Manufacturing Execution System (MES); Enterprise Resource Planning (ERP); Supervisory Control and Data Acquisition (SCADA); Product Lifecycle Management (PLM); Computerized Maintenance Management Systems (CMMS); Quality Management System (QMS). Integrating this data will enable the creation of automatic procedures which can eliminate the existing gaps within the communication among the different systems. Furthermore, this will allow a real-time view of the whole plant so that immediate decisions can be made in case of any occurrence. In order to provide data fusion from the distinct systems previously mentioned, machine learning (ML) methodology will be applied. This document presents the research done and the reviewed literature, as well as the technologies and methodologies needed in this project.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hernandez-de-Menendez, M., Morales-Menendez, R., Escobar, C.A., McGovern, M.: Competencies for Industry 4.0. Int. J. Interact. Des. and Manuf. (IJIDeM) 14(4), 1511–1524 (2020). https://doi.org/10.1007/s12008-020-00716-2
Sardjono, W., Aditya, S.: Use of enterprise resource planning in industrial revolution 4.0. ICIC Exp. Lett. 15(1), 75–85 (2021)
Beric, D., Havzi, S., Lolic, T., Simeunovic, N., Stefanovic, D.: Development of the MES software and Integration with an existing ERP Software in Industrial Enterprise. In: 19th International Symposium INFOTEH-JAHORINA, INFOTEH - Proceedings, March, pp. 1–6. IEEE, Bosnia and Herzegovina (2020)
Govender, E., Telukdarie, A., Sishi, M.N.: Approach for implementing Industry 4.0 framework in the steel industry. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 1314–1318. IEEE, China (2019)
Traini, E., Bruno, G., Awouda, A., Chiabert, P., Lombardi, F.: Integration between PLM and MES for one-of-a-kind production. In: Fortin, C., Rivest, L., Bernard, A., Bouras, A. (eds.) PLM 2019. IAICT, vol. 565, pp. 356–365. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-42250-9_34
Lopes, I., et al.: Requirements specification of a computerized maintenance management system - a case study. In: Procedia CIRP, vol. 52, pp. 268–273. Elsevier BV, UK (2016)
Ko, M.J., Cho, Y.J.: Development and application of standard data schema for interoperability between manufacturing application systems. In: Procedia Manufacturing, vol. 51, pp. 926–931. Elsevier BV, Greece (2020)
Birtel, M., David, A., Hermann, J., Mohr, F., Ruskowski, M.: FutureFit: a strategy for getting a production asset to an Industry 4.0 component - a human-centered approach. In: Procedia Manufacturing (FAIM 2019), vol. 38, pp. 1000–1007. Elsevier BV, Ireland (2019)
Diez-Olivan, A., Ser, J.D., Galar, D., Sierra, B.: Data fusion and machine learning for industrial prognosis: trends and perspectives towards Industry 4.0. Inf. Fus. 50, 92–111 (2019)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Oliveira, A., Filipe, V., Amorim, E.V. (2022). Data Integration in Shop Floor for Industry 4.0. In: González, S.R., et al. Distributed Computing and Artificial Intelligence, Volume 2: Special Sessions 18th International Conference. DCAI 2021. Lecture Notes in Networks and Systems, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-86887-1_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-86887-1_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-86886-4
Online ISBN: 978-3-030-86887-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)