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Virtual training and intelligent algorithms: Audi is counting on “smart logistics”

The association tested digital shelf labeling during a Audi plant in Győr, Hungary, for a initial time final year. This new record is versed with what are famous as e-ink displays, that are also used in e-book readers, and offers poignant combined value. When names, numbers, or a arrangement of a tools in a shelf change, a logistics specialists no longer need to refurbish a labeling by hand. Information can also be displayed during brief notice quickly, for instance if a partial is out of batch and is to be transposed with a opposite part. Another advantage is that a digital displays always stay clean, do not beget any rubbish paper, and devour customarily really small electric energy, even in continual operation. The German/Hungarian plan group is now enhancing a technology. One of a goals is to exercise wholly involuntary updates. Series prolongation in a nearby destiny is conceivable, including during other Audi sites. With digital shelf labeling, Audi is holding another step toward paperless method picking. When entertainment parts, Audi employees already customarily work with tablets and hand-held scanners today.

Digital helpers like these are only one instance of a use of intelligent record in a automotive manufacturer’s Logistics division. “We are creation targeted use of a advantages of digitalization during a prolongation sites worldwide” says Dieter Braun, Head of Supply Chain. The driverless ride systems that have been in use during a Audi plants for many years are another example. They ride tools to a workstations automatically, for instance in a electric engine prolongation in Győr, where there is no public line. They use laser scanners to asian themselves in a prolongation gymnasium and find a best route. This rarely stretchable procession is done probable by algorithms and appurtenance learning, tranquil by a intelligent IT complement in a control station. This enables IT to keep lane of all systems, all driverless ride vehicles, and a product, even but a bound public line sequence.

At Pre-Series Logistics in Ingolstadt, Audi is now a initial automotive manufacturer to try out a new driverless ride system, that follows people around. The “Effibot” uses laser sensors to detect a employee’s legs and follows them automatically during low speed. All it takes is a hold of a symbol – a complement requires conjunction difficult adjustments nor a special infrastructure. It also offers an unconstrained pushing duty that allows a “Effibot” to conduct for formerly tangible destinations independently. The employees acquire a commander project: They have an partner that helps them with their work and they no longer need to pull method picking trolleys by hand.

Another intelligent resolution brings Audi employees worldwide together: They use practical existence (VR) to work together group-wide and opposite locations in practical spaces. In Packaging Logistics, for example, employees have been training with VR for several years. The training is designed like a video diversion and can be blending to fit other activities as good – no programming skills are required. The association is also counting on VR record in a prolongation of a Audi e-tron GT, that will hurl off a line during a Böllinger Höfe starting in 2020 together with a Audi R8: As partial of a commander project, a logistics planners in Neckarsulm are now contrast how special containers can be designed and tested wholly in a practical space and but any earthy prototypes. These containers are used for quite supportive tools such as electrics, headlights, or a windshield. They are custom-made for this charge and their formulation takes a analogous volume of effort. Developing a special containers with VR is reduction costly and is also improved for a environment.

The Smart Decisions group during Audi addresses even some-more formidable formulation processes. The experts “translate” a far-reaching operation of issues into mathematical models, and a high-performance mechanism uses these as a basement to find solutions for logistics-related problems, for instance a outmost storage forecast. As partial of a smoothness process, certain models might have to be put into halt storage for a brief time. But that of a storage areas is suitable? Numerous factors play a partial in responding this question: For example, a stretch between a particular parking lot, a plant and a smoothness destination, a cost for travel between these stations, or a ability of a parking lots. The mathematical indication combined by a Smart Decisions group allows these vehicles to be distributed optimally among a storage areas. The antecedent is finished and serve growth is underway – as is a box with countless earnest digital projects in Audi Logistics.