We accompany your innovation process from idea to product by advising on current technologies, conducting maturity analyses or providing the necessary infrastructures to test new developments without major investments.
Maturity level analysis
The digital needs analysis provides skilled crafts businesses with information on the degree of digitalization in your company and on further development potential.
CARE (Cyber Attack Risk Estimation) for SME
The CARE tool offers particularly small and medium-sized enterprises an individual risk assessment with regard to their exposure to various cyberattacks combined with corresponding action recommendations to reduce the greatest risks.
The risk assessment is based on the data set of a representative survey of 5,000 companies in Germany and is carried out after completing a ten-minute questionnaire on various characteristics of the company and security measures already implemented.
The Mittelstand-Digital competence centers have set up an information platform for their demonstrators. In this way, the centers make digitalization a tangible experience and use practical application examples to demonstrate where medium-sized companies can start in their own businesses. The new online platform provides an overview of what can be tried out on site or digitally.
Consulting and knowledge transfer
Consulting in Artificial Intelligence
For many companies, especially those that are medium-sized or small, it is often difficult to navigate the plethora of existing AI technologies. The L3S Research Center offers a wide range of consulting services ranging from data analytics to image recognition to assist companies in selecting and deploying the appropriate technologies.
Start-up incubator StartUpSecure
Comprehensive consulting for start-ups in the IT security industry: market-oriented further development of ideas, creation of business strategies, legal and economic framework conditions for start-ups.
Facilitated exchange with world-class researchers for evaluation of ideas and product development.
Use cases and demonstrators
Demonstrator: Central production control
Many medium-sized companies in the manufacturing sector are organized according to the workshop production principle. In this context, machine assignment and sequencing is a complex optimization problem. (Flexible Job Shop Scheduling Problem). The demonstrator of the Mittelstand 4.0 Competence Center Hannover illustrates how quantum-inspired digital annealing can help to solve this challenge in the shortest possible time and with high quality. Via a user interface, visitors are first shown the simple handling of the optimization tool and an optimization is carried out jointly. Afterwards, a material flow simulation visualizes the result using the example of a Tesa-Roller production line. The direct comparison with widely used allocation and sequence rules immediately shows the productivity potential.
Demonstrator: Learning Visual Control System
The demonstration factory of the Mittelstand 4.0 Competence Center Hanover shows intelligent digitalization solutions from comissioning to manufacturing and assembly to quality control. Visitors can experience digitalization solutions by experiencing first hand the production of a pen in batch size 1. One of the stations - consisting of a lathe and a bar storage system - shows the manufacturing process of the pen's grips made of aluminum bars. Here, an AI-based solution has been implemented to monitor the supply of material by the bar feeder. Workers receive a warning message as soon as a foreign object is detected in the system. In this way, unplanned machine downtime can be avoided.
A learning visual control system was built for monitoring, which provides as output information about the state of the warehouse and the location of the objects in it. The developed deep learning model is able to locate and describe the objects in the aluminum rod bearing. All calculated information (type and position of the object) is visualized and integrated into an image streamed from the camera.
Demonstrator: Quality assurance in the digital transformation
Quality checks along the entire production process can reduce costs, simplify workflows, and increase product and process quality. For this, automated, data-rich, and cross-process quality assurance is a suitable tool.
But not every company is already fully automated and has end-to-end machine data evaluation in real time. In this case, it may be a matter of implementing partial solutions. An example of such a partial solution can be found in the demonstration factory of the Mittelstand 4.0 Competence Center in Hanover: here, quality inspection was implemented with the help of a collaborative robot, sensors and artificial intelligence. The quality inspection can be flexibly integrated into different process cycles.