VP Embedded Systems Innovation ESI 2023-2026

The target of the Vraaggestuurde Programma (VP) ESI is to drive advances in high-tech systems development by embedding cutting-edge engineering methodologies in the Dutch high-tech systems industry to cope with the ever-increasing complexity of their products: (i) creating impactful and industrially applicable methodologies and (ii) providing support to the high-tech industry to apply the results. The Dutch high-tech industry is responsible for a large portion of the Dutch private R&D expenditures. Many companies are worldwide market and innovation leaders. They bring systems to the market with ever-higher performance and dependability, more and more functionality, better cost performance ratios, and tighter integration in customer processes. To strengthen their market position, the industry needs to deliver innovations continuously (shorten time-to-market) and dependably.
The complexity of high-tech systems grows steeply due to product diversification and customer-specific products, tight integration and optimization in customer processes (system of systems), AI, etc. Combining the growing complexity with the need for continuous innovation and high dependability asks for (i) systems engineering methodologies to improve the efficiency, effectiveness, quality and cost of product development and (ii) a highly qualified workforce capable to apply such methodologies in the industry, addressing aspects such as:
• multi-disciplinary architecting and design;
• efficient and effective product innovation process (e.g., model-based methodologies, AI-for-engineering, virtualization and simulation, etc.);
• the full product life-cycle context (continuous updates and upgrades during the full product life-cycle);
• creation of systems in industrial eco-systems (OEMs, suppliers, innovation partners, service providers, etc.);
• integrating of systems into systems-of-systems (i.e., tight integration in customer-specific workflows and system);
• continuous an lifelong knowledge and skill development (human capital development).
Updated ambitions for 2026
The program of VP ESI is based on the needs of the industry partners of ESI. It is a multi-year program that evolves as the needs of the industry and the state-of-the-art evolves. In 2022, an extensive process was performed to align the research agenda with its industry and academic partners. The process confirmed the directions of the VP, strengthening the focus on platforms and system diversification, and on the integration of systems in customer-specific systems (of systems) and processes. A specific challenge and ambition was added, regarding integration of artificial intelligence in high-tech equipment and the application of artificial intelligence for their development:
• Engineer4AI: Methodologies to deal with the opportunities and challenges of the integration of AI/ML, adaptivity and autonomy in high-tech equipment (e.g., dependability consequences);
• AI4Engineering: Applying AI to optimize the efficiency and effectiveness of R&D-teams (hyper-automation for R&D).
Results for 2023
In summary, the following results are targeted for in 2023:
Agenda Setting:
• reflect on the roadmap developed in 2022 with ESI’s international partners;
• review the HTSM SE Roadmap 2020 and update it if applicable in view of the results from the exploration of industry needs performed in 2022.
Research Projects:
• Submit a proposal for an academic research program on the topic of AI4Engineering, as successor of the MASCOT program (NWO project proposal Zorro);
• Participate in European programs to address strategic challenges. Currently proposals are prepared for the KDT program on two topics:
o Efficient and effective verification and validation of diversified product portfolios;
o Applying artificial intelligence and models to perform root cause analysis of system/software performance issues.
ESI is participating in European projects that will continue in 2023:
o ASIMOV: to analyze the value of applying Digital Twin based training of Artificial Intelligence for automatic calibration of high tech equipment;
o Transact: to analyze system performance across the system-edge-cloud continuum;
o Vivaldy: analyzing the application of model-based change impact analysis to optimize system verification and validation.
• In 2021 and 2022, a study was performed on the application of Model-Based Systems Engineering (MBSE) together with the industry and academic partners (1). A successor study will run to dive deeper into industry MBSE-pilots and to align with international MBSE practitioners.
Research targets per strategic program line for projects in 2023:
• Performance: Systematic reasoning on system performance (to diagnose and to optimize) covering functional, software, and platform levels, including execution architectures and systems-of-systems;
• Dependable Systems: Effective verification and validation of diversified product portfolios (customer specific configurations, covering many versions) using models and change impact analysis to optimize V&V efficiency while assuring the quality of released systems (updates).
Design methodologies for system diagnostics (including further strengthening of our program on diagnostics with Twente/UT);
• Evolving Systems: System and software architectures addressing the modularity needs in business processes across the full product life cycle (incl. manufacturing, service, legacy challenges, etc.);
• Systems in Context: Methodologies and architectures to integrate high-tech equipment into customer processes and systems of systems, addressing system-of-system level concerns such as performance, dependability, system evolution etc.
Systems Architecting: Model-based systems architecture and systems engineering methodologies (e.g., MBSE) to realize customer and business value through customer value modelling, modularity, platforms, system variant management, aligning systems and SW architectures, etc.;
In all running programs lines, we foresee a growing trend to extend the application of AI-techniques in these programs [AI4Engineering] and to study the consequences of applying AI in high-tech equipment [Engineer4AI].
Sharing, professionalizing, dissemination and competence development:
• Exploiting the added value by seeking synergies in research with various partners and exchanging experiences and results;
• Building a network of service providers as implementation partner of the results of the VP, organized in an Implementers Council;
• New courses, course offerings, and updating existing courses.
International positioning and visibility:
• Strengthen the international cooperation network of systems engineering research centres by aligning strategies, roadmaps, exchange of results and exploring opportunities for joint initiatives.
• Participate in European projects together (as we do with DLR, KDT and Fraunhofer in new KDT proposals).
Systems Architecting: Model-based systems architecture and systems engineering methodologies (e.g., MBSE) to realize customer and business value through customer value modelling, modularity, platforms, system variant management, aligning systems and SW architectures, etc.;
(1) https://publications.tno.nl/publication/34639873/jgHNmz/TNO-R2022-R11504.pdf