Within the DIH4AI project, we actively collaborate with companies on use cases that use the services provided by the Digital Innovation Hub.
Local Use Cases
Assessment of a business plan
We synergized the knowledge of digital technologies with the know-how of the largest Czech bank, Česká spořitelna. We have created a special methodology that evaluates the extent to which planned investments in digital technologies are useful for the economic activity of the company in the future – that is, the creation of higher profits, added value or cost reduction. Nowadays, when deciding which innovative project to finance, banks rely more and more on analyzes of future revenue, the rate of increase in the company’s innovativeness and the increase in its overall competitiveness. The experiment proved the functionality of the methodology. The company will not only receive very useful feedback regarding its investment decisions. The resulting report can increase the rating of the relevant financial investment and support the acquisition of the necessary investment funds. The final report is also a suitable basis for applying for subsidies, the subject of which is to increase the digital maturity of the company.
Data analysis and process mining for industrial systems
Management of manufacturing operations requires detailed insight into industrial processes followed by meaningful optimization of their layout and parameters. This technological experiment is aimed at supporting this kind of business intelligence. The goal is to provide a general environment for recording and processing data aimed at improving and optimizing production processes from the user’s point of view. Detailed data science knowledge/skills are not required/expected as data scientist positions are not often found in today’s small and medium industrial manufacturing companies.
Semantics for production Feasibility
This experiment presents how automated reasoning can be used to verify manufacturing feasibility. It thus provides answers to questions such as whether the new order is manufacturable with the available set of production machines? The experiment further shows how genetic programming based on specified key performance indicators makes it possible to find an optimal (or at least a very good) sequence of production steps. The experiment uses semantic web technologies to describe related processes, including production dependencies between operations as well as specification of required production activities. It also semantically describes the relevant resources related to production, i.e. work cells, production machines and the production operations provided by them.
AI-based monitoring of production processes
The experiment prepared small and medium-sized enterprises for the introduction of artificial intelligence methods in the management of their production processes. It informed companies about the advantages and obstacles and helped them better prepare for the implementation of artificial intelligence methods in process monitoring and control. We used our experience gained from cooperation with large companies, and after discussion with many small companies, we adjusted the developed solutions and proposed the form of successful deployment in small and medium-sized plants. The results of the experiment should help representatives of small and medium-sized companies better navigate the complex world of AI methods and their applications and avoid obstacles such as an ambiguously specified goal, insufficient economic benefits of using AI, problems with integrating the AI project into business processes, insufficient involvement of all important stakeholders and affected workers in the enterprise, insufficient data collection and storage processes, time-consuming phase of data preparation and setup, low acceptance by employees or the need for continuous evaluation and updating of deployed AI models.
Simulation and Digital Twinning for Industrial Systems
The increasing level of connectivity in the cyber-physical production systems of Industry 4.0 enables new possibilities for data recording, analysis and processing. Server or cloud solutions are capable and suitable for soft sensing of unmeasured quantities from the production hall. Some process variables are difficult, expensive, or even impossible to measure directly, so it is better to calculate them from other measured variables. Mathematical-physical relationships need to be expressed or learned for this calculation. Such calculation is carried out using simulation models. In this technological experiment, it is not necessary for the simulation software to be installed on-site, near the industrial components on industrial computers (IPC), but it can be deployed on a server in the industrial area (on-premise) or in the cloud. This approach saves computational resources (equipment) in the field of operational technology (OT), reduces the number of licenses required, since simulations for multiple variables can be calculated in parallel within one simulation environment, and streamlines maintenance processes. The experiment is being implemented with a Lego partner that has a packaging facility near Prague. The proposed simulation is focused on the energy behavior of packaging machines.
AI Business Plan Assessment
The goal of this experiment was to find out whether our Digiaudit is also functional for foreign companies and brings them the same level of benefits as Czech companies. We were led to this attempt by the interconnectedness of the Czech industry with other countries of the European Union and the increasing pressure of foreign suppliers to increase the digital maturity of their Czech suppliers. One of the advantages of digitization is the increased degree of complementarity of business models with foreign partners, and the fact that our Digiaudit was also appreciated by companies in Germany and the Netherlands proves that thanks to it, Czech companies can achieve a greater degree of symbiosis with the digital systems of their foreign partners.
International Use Cases
AI and Industry 4.0 readiness assessment of companies
Partners: CIIRC, TNO, Frauenhofer IFF
We verified a new section of our DigiAudit dedicated to the issues of deploying machine learning and artificial intelligence methods and their effective use in practice. The goal was to prove that the given methodology can identify other areas for development even in a technically advanced area and direct companies to invest and develop areas in artificial intelligence where it is most advantageous for them in terms of the overall functioning of the company. The methodology is independent, developed by a consortium of partners and recognized by the Ministry of Industry and Trade as one of the two best methodologies in the Czech Republic.
AI winter school
Partners: CIIRC, Frauenhofer IFF, Fortiss
The Prague, Munich and Saxony-Anhalt DIH are jointly organizing a winter artificial intelligence school in January 2023, at which they will combine their complementary expertise in the field of artificial intelligence and related sub-disciplines and techniques. The main topic of this winter school will be the introduction of specific industrial solutions based on the use of AI methods. The basic theoretical concepts will be presented in the individual lectures and then their specific application to the task at hand. Another topic of the winter school will be the introduction of the issue of technology transfer from the research environment to industrial practice. The main pitfalls and recommended practices will be discussed. At the same time, the different financial instruments that can support technology transfer will be presented. Thanks to the fact that this winter school will take place at CTU, the program will also include excursions to individual laboratories, where participants will be able to see live demonstrations of the use of AI for specific production applications.
Pan-EU AI Adopters Ecosystem
Partners: Digihall, Frauenhofer IFF
DIGIHALL and Saxony-Anhalt DIH bridge their communities to exchange best practices as well as obstacles of using the AI4EU platform (Home Page | AI4EU (ai4europe.eu)) and its available components, and more generally using AI, by organizing a webinar for AI adopters on AI ethics. The Pan-EU AI adopters ecosystem will raise awareness among SMEs within the respective ecosystems about ethics in AI, and help demystifying the concept by giving concrete examples. It is also expected that this experiment will bring new contacts and generate ideas among participants for potential future collaboration, for instance in EU-funded projects relating to AI and ethics.
AI Testing and Experimental Facility in Manufacturing
Partners: TNO, CIIRC
This experiment provides a federated deep learning solution which allows multiple DIHs to collaborate on an AI solution by locally training a neural network on their data and then combining these into one larger, more efficient solution. Providing an AI Testing and Experimental Facility in Manufacturing through DIHs enables combining different dispersed technologies and datasets in Europe. Therefore, enabling use-cases such as AI training on local data and then combining the Ais in a single neural network results in a more powerful and practically usable solution then couldn’t be obtained otherwise. This combines the ideas of data sovereignty and federalization present in Europe with the technical reality that larger datasets are needed to keep up with the global AI development speed. Providing a federated deep learning solution positions the DIHs in a key position regarding AI developments and gives the DIHs partners a key competitive advantage in the development and usage of AI applications.
Quick Check – Maturity Assessments
Partners: Fortiss – Munich Innovation Hub for applied AI, DIHs Paris, Saxony-Anhalt, CIIRC
The DIHs Munich, Paris, Saxony-Anhalt and CIIRC/CTU jointly developed a quick check of maturity assessments available on the European market, starting from the services each of the 4 partner DIHs does provide. The quick check is working with categories upon which interested end users will be able to find the most suitable maturity assessment available for them. The scope of this quick check covers not only AI related maturity assessments but also a wider range of assessments, for instance in the field of digitization, industry 4.0 or software development, since many SMEs which ask about AI solutions are still facing challenges of earlier digitization stages.
AI Awareness Raising Skills for DIH
Partners: Fortiss – Munich Innovation Hub for applied AI, DIHs Paris, Saxony-Anhalt, CTU
The experiment addresses the needs of DIH directly focusing their efforts on SME to raise awareness of benefits of digitalization and AI. The aim is to raise awareness among SME and supporting them in unlocking the technological and economic potential of digitization. Especially the expansion of an DIH’s ecosystem to approach traditional / manufacturing SME as potential technology adopters is addressed. The service is, therefore, also relevant for DIH, with predominantly technology providers in their ecosystem, who want to approach SME to specifically raise awareness for AI topics, e.g. in predictive maintenance. The experiment aims to complement DIH skills and knowledge base towards including awareness raising, formulation of SME-focused messages and establishment of SME-oriented Demonstrators. Fraunhofer IFF will use its experience in addressing manufacturing SME in its regions to transfer skills to the receiver DIH and help adapt it to the contextual/cultural environment.
Platform-as-a service for accountable evidential transactions
Partners: Fortiss, TNO
The experiment – as a bundle of “small” experiments” – will contribute with PIANAI as a technology service to the DIH4AI platform (odkaz na platformu) and benefit from the latter as follows. First, regarding the DIH4AI platform as an on-demand platforms for innovative AI solutions “made in Europe”, the experiment will integrate #4 regional AI Assets. Second, regarding the envisioned regional-European platforms interoperability framework, the experiment focuses on analyzing and designing functionalities along IDS and GAIA-X principles towards the integration of the regional platform PIANAI integrated. This effort includes the topic of cloud-edge interoperability in terms of containerized services provided. Third, the experiment will contribute to the objective of creating Innovation and Collaboration Platform for DIHs in terms of providing #2 Assets to be accessed by the DIH4AI platform: the PIANAI service as well as a federated learning service. Fourth, the experiment will serve as a basis for operationalizing AI Applications Ethical Assessment and Certification by assessing #4 AI applications and providing verifiable claims for their auditing. Fifth, the Intra-DIH Regional experiment will involve #2 SMEs and potentially engage up to #4 in follow-up activities. Finally, our experiment will include cross-DIH collaborative activities, where a joint provision and development will take place together with TNO and their testbed.
AI EU Consortia
Partners: Digihall, Frauenhofer IFF
DIGIHALL engages in a matchmaking collaboration initiative with DIH Saxony-Anhalt (Fraunhofer IFF) in order to support SMEs in the identification of partners with unique AI expertise to establish an effective consortium for applying to EU funded call for proposals and hence develop together successful project proposals. This will be achieved through the organisation of a joint matchmaking event on Horizon Europe opportunities in the field of AI, mainly focused on Cluster 4 Work Programme 2023-24. DIHs will use of the DIH4AI (Dihiware – Collaboration Hub (eng.it) and AI4EU Platforms to disseminate the planned event and identify potential AI partners/experts in line with the matchmaking request.