Industry 4.0 is expanding to the entire manufacturing fabric. Such evolution entails the complete digitalization of industrial processes and products, through the deployment of cyber-physical systems and automation in the shop floors, logistics and business processes. Such digitalization is achieved by extracting value, in the form of insights, decision-supporting information and detailed virtual representations of the physical industrial processes. One prominent example of such digitalization is the advent of Digital Twins, accurate virtual representations of industrial processes and products in the physical world. This work presents the development and deployment phases and procedures of a Big Data-supported Digital Twin for logistics processes in the automotive sector. The Digital Twin enables planning and optimization of logistics processes as, for instance, the optimization of stock and inventory, and planning the arrival of new parts, in order for the production to be as efficient as possible, without the risk of stopping the shop floor, ultimately enabling savings in both idle stored parts and in supplier orders' reductions.