Today many products are digitalised in order to make them ”smart” so they will be able to produce new functionalities for their users. For each product, different levels of smartness can be achieved. This is illustrated in the Porter and Heppelmann ladder illustrated below (The "smart systems" ladder).

For each of the steps on this ladder, different enabling technologies are necessary to succeed on that particular step.

The four steps and their enabling technologies are:

• Monitoring: Different sensors and external data sources enable monitoring of different aspects of the product (e.g. state, environment and use). Typically the main enabling technology is the Internet of Things (IoT)
• Control: Embedded software inside the product and/or in connection with a cloud solution has the ability to personalise and control some of the functionality of the product. Typically the main enabling technology is the Cyber-Physical Systems (CPSs).
• Optimisation: If you are able to manage both monitoring and control, it is possible to optimise the performance of products, including predictions about forthcoming issues of different kinds. For example digital twins. Typically the enabling technology is based on either big data analysis or machine learning.
• Autonomy: The ultimate goal for products that require human interaction today is to achieve autonomy where configuration and coordination can be established in collaboration with external systems. Such products are typically able to optimise performance and carry out self-diagnostics in a safe manner. Typically speciality fields such as safety, security and dependability in general are needed, and certification needs to be involved if the product has an ability to damage vital objects such as human beings.

Our research aims to improve the methods and tools for the development of smart products for the three highest levels in the ladder. We use a model-based approach and incorporate technologies in particular from machine learning and big data analysis in order to intelligently optimise the behaviour of smart systems as well as shorten the time-to-market for such products.

More recently, we conduct research on exploiting models of CPSs and systems of CPSs (CPSoSs) in a digital twin context. A digital twin is a digital replica of physical assets, processes, people, places, systems or devices^[1], created and maintained in order to answer questions about its physical counterpart. We focus on how digital twins can be created from models developed during the engineering of a CPS, and can be used during its deployment. Aarhus University's Centre for Digital Twins is led by Peter Gorm Larsen.

Our research also includes incorporating the human-in-the-loop to determine the best approach for autonomy in different situations, for example in connection with vehicles.

[1] Gartner group put digital twins as number four as the 10 strategically most important technologies in 2019