The manufacturing sector is facing an important stage with Industry 4.0. This paradigm
shift impulses companies to embrace innovative technologies and to pursuit near-zero
fault, near real-time reactivity, better traceability, and more predictability, while working
to achieve cheaper product customization.
The scenario presented addresses multiple intra-logistic processes of the automotive factory
Volkswagen Autoeuropa, where different situations need to be addressed. The main
obstacle is the absence of harmonized and integrated data flows between all stages of the
intra-logistic process which leads to inefficiencies. The existence of data silos is heavily
contributing to this situation, which makes the planning of intra-logistics processes a
challenge.
The objective of the work presented here, is to integrate big data and machine learning
technologies over data generated by the several manufacturing systems present, and
thus support the management and optimisation of warehouse, parts transportation, sequencing
and point-of-fit areas. This will support the creation of a digital twin of the
intra-logistics processes. Still, the end goal is to employ deep learning techniques to
achieve predictive capabilities, all together with simulation, in order to optimize processes
planning and equipment efficiency.
The work presented on this thesis, is aligned with the European project BOOST 4.0, with
the objective to drive big data technologies in manufacturing domain, focusing on the
automotive use-case
