
Ready for a challenge?
INNOVATION CHALLENGE FOR STUDENTS IN DINGOLFING/LANDAU
Ready for a challenge in the high-tech hub of Dingolfing? Join our 24-hour innovation challenge and tackle real-world problems in the fields of lean, green & digital. Collaborate with an interdisciplinary team to develop cutting-edge prototypes for regional companies, cities and global industry leaders. Expect exciting prizes, exclusive insights into the Ecosystem Smart Ostbayern—a dynamic network of regional business, science and society—and invaluable networking opportunities. After the Innovation Challenge, a party will take place, followed by a free overnight stay in a four-star hotel. The next day will begin with a business breakfast attended by companies from the region. Who knows? You might even land your dream job along the way! Are you up for it?

Register now!
When: 08.05.2025 to 10.05.2025
Where: Dingolfing and Landau
- Due to limited capacity, the number of participants for the Innovation Challenge is limited. Secure your place and apply now!
- You can find the descriptions of each use case below. Take a look to see which challenge you would like to work on and what requirements you already have.
Register by 30 April 2025
Not an IT-Nerd? No problem! Whatever you study – please apply! We are looking forward to having various perspectives on our Use Cases!
Simply complete the survey. Registration will end automatically once all places are filled.
WHAT TO EXPECT AT OUR INNOVATION CHALLENGE
Use Cases
Host and Location
- This use case is hosted by: SAR
- This use case is hosted at: SAR office
Motivation
Target
Approach
- Introduction, planning, definition of objectives
- App development and technical implementation
- Validation of the app using an exemplary system model including PLC
- Preparation and planning of the presentation
Distribution of Activities
Useful knowledge
- There are no technical requirements for participants to consider. For information on the candidate profile, see the next point.
Additional information
Host and Location
- This use case is hosted by: DE software & control GmbH
- This use case is hosted at: DE office
Motivation
Target
Approach
- Identification and Selection: Analyze relevant process types and select the three most useful processes.
- Requirements Analysis: Conduct a detailed analysis of the requirements for creating work instructions.
- Process Description: Develop a generic process description considering various conditions and customer requirements.
- Prototype Implementation: Implement and test a prototype for at least one selected process using the Halerium AI platform.
Distribution of Activities
Useful knowledge
- Basics in GenAI and Prompt Engineering
- Understanding how to create documentation and work instructions
- Experience with AI platforms, ideally Halerium
- Interest in user interface design and user experience
Additional information
Host and Location
- This use case is hosted by: City of Landau
- This use case is hosted at: City of Landau
Motivation
Target
Approach
- Data Discovery
- Data Integration
- Use Case Exploration
- Implementation
Distribution of Activities
Useful knowledge
- Cloud Services: Microsoft Azure
- Programming Languages: Python, JavaScript/TypeScript, SQL, REST APIs
Additional information
Host and Location
- This use case is hosted by: Karl Mossandl GmbH & Co. KG
- This use case is hosted at: Offices of Karl Mossandl GmbH & Co. KG
Motivation
Target
Host and Location
- This use case is hosted by: HORSCH LEEB Application Systems GmbH
- This use case is hosted at: Offices of HORSCH LEEB Application Systems GmbH
Motivation
Target
Approach
- You are receiving 50 spare parts from Horsch with article numbers. Develop a tool within 24 hours to identify and assign the parts to their article numbers. Use pretrained neural networks. Adapt and train them with additional pictures of our specific parts. Identify parts from various perspectives and conditions, including new, used, and assembled states. Develop a solution for parts that appear too similar and cannot be distinctly identified.
Distribution of Activities
Additional information
Host and Location
- This use case is hosted by: City of Dingolfing
- This use case is hosted at: Landshut University, TZ PULS, Bräuhausgasse 33, 84130 Dingolfing
Motivation
Target
Approach
- Applications should be able to be submitted directly via a web application and a corresponding grant notification should be generated automatically by the program. The program should be able to independently check various eligibility criteria, such as the date on the offer. The program also creates a clear database of all applications received and enables real-time evaluation of the various program items. For example, it should be possible to call up how many electricity storage systems have been installed in the city and their total output in kilowatt hours.
Distribution of Activities
Useful knowledge
- Technical requirements: Open source programs: All programs used should be openly accessible, usable and secure for the administration.
- Candidate Profile: - Experience with web applications, AI or data processing - Ability to create a clear user interface - Basic understanding of energy technology, climate protection and renewable energies - Bachelor's degree or Master's degree
Additional information
Host and Location
- This use case is hosted by: BMW
- This use case is hosted at: BMW Bildungszentrum: Mengkofener Straße 13, 84130 Dingolfing
Motivation
Target
Approach
- Plant 2.2 -> Can be expanded to all BMW plants for the inventory as best practice
Distribution of Activities
Useful knowledge
- Technical requirements: Linking of PowerApp or similar application with Excel and possibly Sharepoint.
- Candidate profile: Anyone who can program well and has skills in linking PowerApp or a similar application with Excel and possibly Sharepoint.
Additional information
Host and Location
- This use case is hosted by: BMW
- This use case is hosted at: BMW Bildungszentrum: Mengkofener Straße 13, 84130 Dingolfing
Motivation
Target
Approach
- Plant 2.2 -> Scalable for all TEM plants worldwide -> MEX, HU, USA, China, DE. The tool can also be used by the city for any event - or for internal organization / DGF Ecosystem.
Distribution of Activities
Useful knowledge
- Technical prerequisites: APEX is a good fit for our project. An AI-supported decision-making aid in execution would be an advantage.
- Candidate profile: Anyone who is well versed in APEX programming and can integrate AI help on this platform.
Additional information
Host and Location
- This use case is hosted by: BMW
- This use case is hosted at: BMW Bildungszentrum: Mengkofener Straße 13, 84130 Dingolfing
Motivation
Target
Approach
- We can use various methods, including generative AI (generative adversarial networks or diffusion models) and classical image processing methods, automated with Python.
- Metaverse- or simulation-based approaches should not be considered.
Distribution of Activities
Useful knowledge
- Technical prerequisites: AWS Cloudroom / JUMA, GitHub
- Candidate profile: Basic Python Programming, Image Processing & Machine Learning Skills
Additional information
Host and Location
- This use case is hosted by: BMW
- This use case is hosted at: BMW Bildungszentrum: Mengkofener Straße 13, 84130 Dingolfing
Motivation
Target
Distribution of Activities
Useful knowledge
- Technical prerequisites: Programming language Python
- Candidate profile: Bachelor students, Ability to abstract, Understanding of Data Processing & AI-Knowledge
Host and Location
- This use case is hosted by: BMW
- This use case is hosted at: BMW Bildungszentrum: Mengkofener Straße 13, 84130 Dingolfing
Motivation
Target
Approach
- Use deep computer vision techniques to determine the exact type of container in a supervised manner.
Distribution of Activities
Useful knowledge
- Technical prerequisites: Software engineering (Python), Image processing (pillow, cv2), Machine Learning (pytorch)
- Candidate profile: IT students
Host and Location
- This use case is hosted by: i-LogiX - Capgemeni
- This use case is hosted at: TZ-PULS, Bräuhausgasse 33, 84130 Dingolfing
Motivation
Target
Approach
- Using a provided dataset, we will apply AI algorithms to optimize route planning, considering time windows, delivery cycles, and fleet constraints. The system will aim to reduce idle times, enhance scheduling efficiency, and lower overall operational costs.
Useful knowledge
- Technical prerequisites: AI-based optimization algorithms (e.g., Machine Learning); Data processing & analysis (e.g., Python, NumPy); Cloud-based or on-premise computing infrastructure
- Candidate profile: Experience in AI/ML optimization techniques; Proficiency in programming (Python, SQL, Java)