No.124 Model-Based Design for Smart Products and Systems: Advanced Capabilities and Challenging Applications


NII Shonan Meeting Seminar 124


Goal: Getting to know one another, identifying and prioritising trends, drivers, enablers and barriers in model-based Smart Systems Engineering
7:30 – 9:00: Breakfast
9:00 – 9:45: Session 1: Introduction Talks: 3 slides per participant (1 on personal background; 1 on current research; 1 giving the view on the Seminar goal)
9:45 – 10:15: Tea Break
10:15 – 11:45: Session 2: Introduction Talks cont’d
11:45 – 13:30: Lunch
13:30 – 15:45: Session 3: Clustering, prioritising of ideas
15:45 – 16:15: Tea Break
16:15 – 17:45: Session 4: Clustering, prioritising of ideas
18:00 – 19:30: Dinner

Goal: Assessment of the trends, drivers, needs, enablers and barriers to progressing the top areas identified in Day 1.
7:30 – 9:00: Breakfast
9:00 – 9:45: Session 1: Opening Plenary (review Day 1; arrangements for Day 2), possibly guest survey presentation
9:45 – 10:15: Tea Break
10:15 – 11:45: Session 2: Parallel Discussion Groups refine the priority topics identified in Day 1
11:45 – 13:30: Lunch
13:30 – 15:45: Session 3: Parallel Discussion Groups refine the priority topics identified in Day 1
15:45 – 16:15: Tea Break
16:15 – 17:45: Session 4: Rapporteurs report back from the group discussions
18:00 – 19:30: Dinner

Goal: Consolidation and identification of potential collaboration opportunities
7:30 – 9:00: Breakfast
9:00 – 9:45: Session 1: possibly guest survey presentation
9:45 – 10:15: Tea Break
10:15 – 11:45: Session 2:
11:45 – 13:30: Lunch
13:30 – 18:00: Excursion
18:00 – 19:30: Banquet

Goal: Plan future reporting and dissemination activities (maybe a book or extended report?)
7:30 – 9:00: Breakfast
9:00 – 9:45: Session 1: possibly guest survey presentation
9:45 – 10:15: Tea Break
10:15 – 11:45: Session 2:
11:45 – 14:00: Lunch


Judith Dahmann The MITRE Corporation USA
Kerstin Eder University of Bristol UK
Gidon Ernst NII Japan
Constance Heitmeyer Naval Research Laboratory USA
Einar Broch Johnsen University of Oslo Norway
Taro Kurita Sony Japan
Kenneth Lausdahl Aarhus University Denmark
Mark Lawford McMaster University Canada
Zhiming Liu Southwest University China
Roy Maxion Carnegie Mellon University USA
Mike Nicolai Siemens Germany
René S.Nilsson Aarhus University, Department of Engineering / AGCO A/S Denmark
Tomohiro Oda Software Research Associates, Inc. Japan
Holger Pfeifer fortiss Germany
Ken Pierce Newcastle University UK
Shin Sahara Hosei University Japan
Sebastian Steinhorst Technical University of Munich Germany
Anders Franz Terkelsen CustomOffice ApS Denmark
Hans Vangheluwe University of Antwerp/Flanders Make and McGill University Belgium


Fuyuki Ishikawa, National Institute of Informatics, Japan
Peter Gorm Larsen, Aarhus University, Denmark
John Fitzgerald, Newcastle University, UK


The objective of the meeting is to identify the opportunities and challenges in developing the methods and tools for model-based engineering of products and systems that are enabled by, and dependent on, networked computing technology. This category of “smart” systems grows ever wider, and ranges from autonomous devices to large-scale infrastructure. The availability of data, and the power to process it at scale, brings enormous opportunities, and considerable challenges. Some of the most fundamental questions relate to the ways in which engineers can be enabled to work across traditional boundaries between disciplines. Our proposed meeting will bring scientists and engineers from this wide range of backgrounds together in order to share the state of the art across these boundaries, and synthesise prioritised sets of challenges for the future.

The rate of technological development ever increasing worldwide, so time-to-market is of the utmost importance to successful innovation of products and systems. At the same time, products also need to be prepared for the future where other products in the eco-system may change they services. This means that closer integration is required, both between design disciplines and between different life cycle phases for products and systems. New technological possibilities for example in electronics, in virtual reality and in 3D printing are emerging and one need to consider how such technologies may disrupt existing solutions[1]. Thus, there is an urgent need to ensure that researchers with different disciplinary backgrounds are enabled to combine their different kinds of models into semantically well-founded but heterogeneous collections[2].

There are different dimensions that are important to consider when one considers increasing the level of smartness in products. One dimension is in relation to the overall ecosystem and here the technology roadmap of the collection of products towards the targeted markets are of main importance. A second dimension is related to how the individual product increase its level of smartness in a step-wise fashion from monitoring, control, optimisation to autonomous behaviour.

Nowadays users simply expect that products from different suppliers will interoperate seamlessly in new ways that were not considered when the individual products were conceived. In particular, in a business-to-business value chain it is of the utmost importance to have a vision for the evolution in interoperability between all relevant products in the same ecosystem and this can be seen at different levels:

  1. Smart Components
  2. Smart Products
  3. Smart Connected Products
  4. Smart Product Systems
  5. Smart (Eco) Systems of Systems

The ecosystem dimension in particular makes use of artefacts produced in one product lifecycle phase in another lifecycle phase. This could either be CAD drawings that can be used for subsequent 3D printing or smart augmented reality support. It could also be monitoring of deployed products that can feed information back to either development or production based on big data analysis.

For each individual product different levels of smartness can be achieved. This forms a kind of a staircase as illustrated in Figure 1 below. For each of the different steps one wish to take on this staircase different enabling technologies are necessary to have success for that appropriate step. Here the four steps and their enabling technologies are:

  1. Monitoring: Here different sensors and external data sources enable monitoring of different aspects of the product (e.g. state, environment and use). Here typically the main enabling technology is the Internet of Things (IoT)[3].
  2. Control: Here 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. Here typically the main enabling technology is the Cyber-Physical Systems (CPSs)[4].
  3. Optimisation: Given that one is able to manage both monitoring and control makes it possible to optimise the performance of products including predictions about forthcoming issues of different kinds. Here typically the enabling technology is based on either big data analysis[5] or machine learning[6].
  4. Autonomy: The ultimate goal for products that today require human interaction 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. Here 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[7].

Figure 1:The staircase of products level of smartness

The meeting gathers scientists and engineers from a wide range of backgrounds to identify the opportunities and challenges in developing the methods and tools for model-based engineering of smart products and systems. The organisers will carefully take the expected output and heterogeneity of the participants into consideration. The meeting will involve two kinds of sessions. One is presentations by each participant and discussions following them. Here the organisers give clear guidance on the presentations: each participant is expected to act as the representative of his/her area and give messages regarding the objective (opportunities and challenges). In other words, the participants should not present only their own work or completed research. The other kind of sessions is group discussions to produce the output: opportunities and challenges. The group discussions will be done following specific methods for idea divergence and convergence such as brainstorming and KJ Method with organized groping[8] – under clear questions defined by the organisers to avoid too free discussions.

[1] Disruptive IoT Innovation, Article No :1274 | July 14, 2014 | by Avi Itzkovitch, UX Magazine,

[2]How Smart, Connected Products Are Transforming Competition”, by Michael E. Porter and James E. Heppelmann, Harvard Business Review, November 2014,

[3] See for example

[4] See for example

[5] See for example

[6] See for example

[7] See for example the predictions for automonous cars in ”Autonomous Vehicle Implementation Predictions Implications for Transport Planning”, Todd Litman Victoria Transport Policy Institute, November 2016,

[8] See