A Digital Twin for Intra-Logistics Process Planning for the Automotive Sector Supported by Big Data Analytics

Abstract

One of the areas that can heavily benefit with Industry 4.0 is the logistics, namely with the association of sensing technologies and the application of techniques such as Big Data Analytic, Data Visualization, prediction algorithms, and especially 3D simulation. The association of real data, prediction techniques, and 3D models, allow the creation of realistic Digital Twins that emulate factory processes, making possible the experimentation and testing of new ideas and different scenarios by tweaking key variables, without stopping production. However, there are many challenges in order to handle and compute all fast-growing, multi dimension data generated, so that all this production related data can be quickly used for defect control, preventive maintenance, advanced analytics for production and resources management, or even later simulation. The work presented in this paper focus in this “in between” processing work, presenting an easily deployable and self-reconfigurable Big Data architecture, where different technologies can work together to extract, transform, load, apply analytics, and then feed a 3D Digital Simulation model. The work presented in this paper is funded by the EU project BOOST4.0 and focus in a specific logistic process of car manufacturing.

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