Most societal and economic improvements incorporate software-intensive high-tech systems or system technologies in one form or another. The challenge for the European high-tech systems industry is to accommodate these technologies.

Who is it for?

The challenge for the European high-tech systems industry is to accommodate this emerging role, pursuing the position of world-leader in their respective markets. In that process, industry is confronted with several emerging trends and concerns with respect to the life-cycles of high-tech systems :

  • Increase of complexity due to changing business models. There is an increasing demand for customizations (a.k.a. series of 1)4,5 and increasing demand for integration in the customer environment and business activities. Systems more and more evolve into ‘as-aservice’ propositions. Shorter product life-cycles require rapid responsiveness to customer demands.
  • Need for evolvability, systems increasingly are expected to become smart networked solutions6,7,8,9 and consequently need to deal with changes in their operational environment.
  • Increase of OPEX, in a more globalized and competitive market customer expectations.
    and demands are continuously increasing, whereas skilled personnel able to satisfy these expectations is scarce to find.
  • Globalization of the supply chain, information flows become more critical due to the involvement of large number of partners, suppliers, re-sellers as a result of the globalization of European industry. Closely related, there is an increase in variety and diversity of components, devices, applications, services to deal with.
  • Emerging data, the volume of operational data increases exponentially due to the growing availability and embedding of sensors. Next to that, systems get more and more software intensive which results in a massive increase of operational logging. Availability of (cloud) infrastructures nowadays ensures system data becomes accessible for actual analysis and exploitation. Unfortunately, availability of data still does not guarantee impact. Typically captured data is coming from a variety of sources which are noisy, unstructured, heterogeneous, etc. Valorisation of (historic) data sources requires coordinated filtering and processing across diverse expertise domains.