AI | Center of Excellence Logistics and IT https://leistungszentrum-logistik-it.de/en/ Wir bringen Technologien in Ihr Unternehmen Mon, 12 Jan 2026 11:33:43 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.1 https://leistungszentrum-logistik-it.de/wp-content/uploads/2018/11/cropped-favicon_512-32x32.png AI | Center of Excellence Logistics and IT https://leistungszentrum-logistik-it.de/en/ 32 32 Driver Knowledge Integration Tool https://leistungszentrum-logistik-it.de/en/project/driver-knowledge-integration-tool/ Thu, 04 Dec 2025 16:58:07 +0000 https://leistungszentrum-logistik-it.de/project/driver-knowledge-integration-tool/ Using an app on their smartphone, they receive situation-specific answers to questions about driver instructions, load securing, hazardous goods or customer-specific requirements. The content is provided from company documents with the help of the Omnistics AI platform and a RAG-based chat system. DRIVE KIT reduces the number of queries to dispatch and control centers, makes it easier to train new drivers and creates a uniform knowledge base in the fleet.

Drive kit as a new feature of the Omnistics platform

Drive-Kit ist Teil der innovativen Plattform Omnistics, die speziell für Speditionsunternehmen entwickelt wurde ohne umfangreiche IT-Infrastruktur funktioniert. Omnistics offers AI-as-a-Service (AIaaS) and significantly lowers the barrier to entry for using artificial intelligence (AI). This allows logistics processes to be designed more effectively and efficiently without having to make high initial investments.

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Martin Friedrich
Fraunhofer IML
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Daniel Hefft
Fraunhofer IML
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]]> Analysis-based evaluation for predictive maintenance strategies https://leistungszentrum-logistik-it.de/en/project/analysis-based-evaluation-for-predictive-maintenance-strategies/ Wed, 03 Dec 2025 11:07:19 +0000 https://leistungszentrum-logistik-it.de/project/analysis-based-evaluation-for-predictive-maintenance-strategies/ Companies receive data-based decision support to determine which machines or individual components are suitable for the introduction of a predictive maintenance strategy. The associated assistance system not only analyzes the reliability of the systems under consideration, but also takes into account the potential effects of an unplanned shutdown. The results obtained are visualized and an individual catalog of recommendations for action and sensible measures is drawn up together with experts.

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Marina Klees
Fraunhofer IML
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]]> Data-driven learning of control rules https://leistungszentrum-logistik-it.de/en/project/data-driven-learning-of-control-rules/ Wed, 03 Dec 2025 09:51:55 +0000 https://leistungszentrum-logistik-it.de/project/data-driven-learning-of-control-rules/ Digital twins create transparency in production and logistics systems and provide support, for example, in the production process. in layout, order and personnel deployment planning thanks to its simulation component. The DaCoR (Data-driven Learning of Control Rules) project focuses on the creation of digital material flow twins based on company data, as part of the open source OpenFactoryTwin (OFacT).

Although simulation-based planning has proven its added value in the past, its creation has so far been complex and time-consuming. This is where DaCoR comes in: Using a (partially) automated approach, not only can static elements such as process times and resource models be mapped, but also dynamic system aspects such as control rules (e.g. FIFO, SPT) can be learned automatically. This enables the efficient creation of realistic models that can be used in planning and control – with significantly reduced effort.

Photos: Fraunhofer IML/Vinzenz Neugebauer

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Jan Cirullies
Fraunhofer ISST
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Christian Schwede
Fraunhofer ISST
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]]> Smart solution for efficient capacity utilization in long-distance transport https://leistungszentrum-logistik-it.de/en/project/smart-solution-for-efficient-capacity-utilization-in-long-distance-transport/ Mon, 24 Nov 2025 13:39:26 +0000 https://leistungszentrum-logistik-it.de/project/smart-solution-for-efficient-capacity-utilization-in-long-distance-transport/ The challenge

Freight forwarders and logistics service providers are facing considerable challenges: EU requirements to reduce emissions are putting the industry under pressure – transport is one of the biggest emitters of greenhouse gases in Germany. However, these stricter targets also offer opportunities to save resources, make processes more efficient and reduce costs.

The solution

This is where the AddRoute (Ad hoc Dynamic Route) project comes in: Haulage companies aim to plan their routes as efficiently and effectively as possible, but often lack the necessary information to do so. On the basis of photos from the truck loading space, freight forwarders receive a basis for decision-making to optimize the use of truck space and to estimate the relocation costs. This allows an ad hoc decision to be made as to whether an additional order would be economical and still fit on the loading area of a truck. Thanks to more efficient capacity utilization in long-distance transport, costs can be saved and additional orders can be implemented. At the same time, emissions are reduced, which leads to a better overall ecological balance.

Photos: Fraunhofer IML/Vinzenz Neugebauer

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Jonas Stenzel
Fraunhofer IML
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Georg Wichmann
Fraunhofer IML
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]]> Real-time ordering of medical products https://leistungszentrum-logistik-it.de/en/project/real-time-ordering-of-medical-products/ Mon, 24 Nov 2025 12:56:24 +0000 https://leistungszentrum-logistik-it.de/project/real-time-ordering-of-medical-products/ AI4Demand is an innovative technical solution for intelligent material requisitioning in hospitals. The time-consuming, manual reordering and stocking of materials in station cabinets can be simplified by the retrofittable system for existing modular cabinets. The solution reduces the effort and time that nursing staff or care assistants have to spend on material removal and allows them to concentrate on the patients. At the heart of AI4Demand is a camera-based hardware and software solution that records and processes data in real time. The technology is based on computer vision processes to detect low stocks and automatically order replenishment.

Photos: Fraunhofer IML/Vinzenz Neugebauer

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Dr.-Ing. Sebastian Wibbeling
Fraunhofer IML
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Sylvia Kaczmarek
Fraunhofer IML
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]]> Optimization of logistical master data https://leistungszentrum-logistik-it.de/en/project/optimization-of-logistical-master-data/ Mon, 24 Nov 2025 11:59:17 +0000 https://leistungszentrum-logistik-it.de/project/optimization-of-logistical-master-data/ The challenge

Logistical master data is often incorrect or inaccurate, which requires tedious manual maintenance and hinders data-driven innovation. Forecasts, analyses or AI models – all of these approaches require precise and reliable information in order to derive strategic decisions or optimizations.

The solution

DSCS is the key to clean master data, which forms the foundation of all logistics processes. It guarantees that information about products, suppliers and stock levels is accurate and up-to-date. With DSCS, wrong decisions that can lead to delays, increased costs and dissatisfied customers can be effectively avoided. It also enables a comprehensive analysis and evaluation of master data quality and identifies valuable optimization potential.

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Hermann Foot
Fraunhofer IML
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Lukas Lehmann
Fraunhofer IML
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