In production logistics, the supermarket serves as an interim warehouse for articles which will be needed in upcoming production steps. Since increasingly flexible production processes are making advance long-term planning of demand more and more difficult, the state-of-the-art factory solution is to monitor the dolly hubs inside the supermarket. Intelligent sensors automatically detect when a dolly is removed from a hub or fed into a monorail track and communicate this information in real time, providing a digital visualisation of the stock. Requisitions are signalled automatically on the basis of preconfigured criteria, guaranteeing that the necessary parts are always available.
The permanent availability of necessary articles in the supermarket is essential: if parts run out, production could grind to a halt. Precise monitoring of the individual parking slots in a dolly hub enables a requisition to be triggered automatically as soon as a dolly is removed. A sensor is positioned in each relevant parking slot, reliably detecting the occupancy of the slot and automatically communicating any changes to the control system.
Flexible positioning of sensors in the hubs means that different hub types can be easily combined within a single application. Dolly removal can be visualised, as can the moment when a dolly is fed into the hub – independently of whether it is added on the same side or, following the FIFO principle, from the other side. Trolleys can be moved manually or with the help of automated guided transport systems.
Through precise monitoring of the hubs using multiple sensors, not only can replenishments be requested in good time: requisitioning can also be configured differently for each parking slot, making prioritisation possible. If the supermarket stock of available articles of a certain type falls below a predefined minimum quantity, specific automatic escalation measures are triggered accordingly. A digital production twin also means that the timing of the next removal can be calculated in advance, making it possible to predict the probability of a material request even more accurately.