Model Driven Development and Decision Support | 2013- Product Development Research Lab

Building an international research profile in the area of Sustainable Product-Service System Innovation at Blekinge Institute of Technology

This Knowledge Foundation (KKS) profile+ project application is based on a clear vision and objective regarding model driven development and decision support for sustainable product-service system (PSS) innovation and is building on the further deepening of the ongoing profile work with perspectives of raising international recognition through further presence in top publications, partnering with academics internationally, and also affecting educational programmes at BTH. The ongoing digitalisation is an already integrated part of the work, while the profile+ is extending the Data Driven Design approach based on Internet of Things progress and access to live data from solutions in the field.

The research focus is model-driven sustainable product-service system innovation. The concept of model-driven means that throughout the development process using models (virtual representation of reality) as communication media in order to shorten development cycles and improve multidisciplinary understanding. By also adding capability to collect live data from machines via connected sensors (Internet of Things – IOT) and finding patterns and solutions using big data analytics (including AI and Machine Learning), the aim is to increase the profiling in this research.

Vision

An internationally leading research environment and the first-hand choice of partners who want to lead the way to a sustainable society through competitive product-service systems supporting a circular economy.

Objective

In co-production mode develop, disseminate, and integrate relevant, user-friendly and efficient computer aided support methods and tools for sustainable product-service system innovation into business leaders’, business developers’ and product developers’ working environments that enable industry to thrive in the changing global context, supported by both simulated and real data using internet of things and AI/machine learning.

Driving research question

How can we design a simulation, and data, driven PSS process where societal challenges are used as a driver for innovation, and methods and tools from diverse disciplines (engineering, sustainability, economics etc.) are linked in a decision environment, enabling value creating life-cycle solutions?

Research areas

The industrial relevance is threefold:

increased capabilities to mitigate risks, explore opportunities, and take informed decisions early on,
increased opportunities to bring new sustainability-driven innovations to the market and
research findings, methods and tools that help industry undertake the changes on several levels that are necessary to stay competitive.

The driving research question is broken down into several sub-questions that are being dealt with in the research tracks of MD3S+ as presented below.

RT1 – Value Driven Design

(mechanical engineering, innovation engineering)

How can value models iteratively translate customers’ desires into terms that are meaningful for PSS design decision-making?
How can a PSS process look like that supports a system of systems perspective and different component life-cycles over the course of PSS deployment?
How to drive innovation in multi-stakeholder environments (both internal/external stakeholders)?

RT2 – Sustainable Product Development

(sustainable development, mechanical engineering)

How can sustainability criteria and indicators guide the finding of generic LCA data to be used in a model-based decision support for value and sustainability assessment?
How can a risk perspective be applied to identify, assess, and manage potential consequences of sustainability-related decisions?
How to define a methodology to identify and assess how strategic sustainability criteria can be integrated in requirements and model how they affect value criteria?

RT3 – Simulation Driven Design

(mechanical engineering, data science, electrical engineering)

How to assess model maturity levels: confidence in models and model generated results as a basis for decision making?
How to use/asses IoT data-driven design and machine learning configurators for conceptual design?
How to perform design automation tasks in a multi-discipline environment for decision making?

Marco Bertoni (PhD)

Associate Professor

Research Track leader RT1

Sophie Hallstedt (PhD)

Associate Professor

Research Track leader RT2

Johan Wall (PhD)

Assistant Professor

Research Track leader RT3

Research partners

Research publications

A role for physical prototyping in Product-Service System design: Case study in construction equipment

August 23, 2019 Publications, Research

Abstract Using a case study methodology to exploring an ambitious experimental combination of a construction equipment manufacturer’s products tailored to…