Project overview

Factory planning projects require an abundance of information in a wide variety of formats and
variance is required for the individual elements of the planning object. On the one hand, this is available in
systems, forms of presentation and software applications or can only be obtained through discussions. Sorting and evaluating heterogeneous data volumes with regard to suitable quality and completeness takes a lot of time. With the help of AI approaches (clustering and classification methods), this effort can be significantly reduced compared to the current manual or partially automated approach.
As part of the GenAI4FFD project, an assistance system based on Generative Artificial Intelligence
(GenAI), an assistance system is being developed that provides support in the early phases of the factory planning process
from the beginning of target definition to the completion of detailed planning. In the first step, a factory planning-specific GenAI-based solution will be developed, which will be used as a prototype for three central task areas of factory planning as part of the project:

1. Requirements recording and analysis,
2. Generation of factory designs,
3. Model creation and evaluation
.

Existing European GenAI models are compared, evaluated and specialized for factory planning knowledge. For this purpose, specialist literature, relevant standards and guidelines and, above all, data from completed factory planning projects with operational information on their performance and framework conditions are used. This forms the basis for the three fields of application, which are then implemented prototypically, firstly via the digital room book and secondly as an independent web instance and aggregator in planning and simulation software. At the same time, it will be examined how the solution can be transferred to other planning issues, such as housing development, and how it can be easily used for SMEs.
This text was translated with DeepL on 18/12/2025

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GreenCityMind uses artificial intelligence to create an advanced digital twin of urban environments designed to improve the microclimate and develop more livable spaces that can withstand the challenges of climate change, such as extreme heat events. Our integrated approach combines natural language AI interfaces, virtual reality visualization, and precise microclimate modeling in a comprehensive digital twin. The AI system directly controls the planning tools and implements optimized urban design solutions based on both expert knowledge and stakeholder input. The system features a parameterized microclimate model based on historical climate data, seamlessly integrated with geographic information systems and additional data sources in an immersive, VR-accessible 3D environment.

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Problem definition

Local public transport in rural areas is often poorly developed. Mobility stations can make public transport more attractive by providing a flexible and easily accessible transfer point between demand-oriented modules such as bikes, cars, buses and trains. However, the planning of these modules and the provision of mobility information for users has so far been individualized. Digitally available information on the stations (e.g. number of free car and bicycle parking spaces) is hardly available and cannot be retrieved in a standardized way. This delays the switch to environmentally friendly modes of transport on the supply and data side.

Project objective
A modular concept is being developed for a smart mobility station in the district of Mansfeld-Südharz. This is to consist of a total of three core components.
Smart mobility station - these will be equipped with an infotainment system that can be used to obtain tourist or timetable-related information.
Modular mobility station - the decisive advantage of the system for municipalities is that the modules are standardized and can be exchanged if necessary.
Open source planning tool - the digital data and the modular structure are to be made available in this tool.
A prototype is to be built and tested at a site in the Mansfeld-Südharz district.

Implementation

In the project, regional partners in the application region work together with supra-regional partners on an interdisciplinary basis. The first step is to define the requirements and interfaces for a smart mobility station in order to achieve the objectives. Subsequently, the partners will work in teams to develop the smart components, the modular station and the planning tool for it in parallel. In the final phase of the project, the prototype will be implemented in a field test with the construction of a functional model and the validation of the smart components and data exchange.
This text was translated with DeepL

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