H-SEIF - Harvesting value from big data and digitalization through a Human Systems-Engineering Innovation Framework

H-SEIF (Human Systems Engineering Innovation Framework) is a collaborative framework that helps engineers and designers develop innovative systems for and by people. The project focuses on Digitalization and Data-Driven Decisions. In H-SEIF 2, the project has developed a framework that enhances effective system development, with a particular focus on data-supported early decisions and collaboration. The project has explored how we can create value from data and information, and how we can collaborate effectively based on data. Collaboration with Industry is important. Nine industry partners have been actively involved in the project, contributing their own resources, providing case studies, and making projects and operational environments available to the researchers. This has made the research highly relevant and practical. Academic Contribution is significant. Much of the development and research has taken place at the University of South-Eastern Norway (USN) and the Oslo School of Architecture and Design (AHO). The project has involved two full-time scientific resources, two PhD students who are about to submit their dissertations at the end of the project, and more than 20 master's theses have been written in collaboration with industry partners. The framework is building on systems thinking. The H-SEIF framework includes methods and techniques based on the fields of systems engineering, system architecture, system-oriented design, and participatory design. These methods aim to promote radical, groundbreaking, and disruptive innovations. Project Goals were ambitious. The Kongsberg-based development house Semcon has led the project, with the goal of strengthening Norwegian industry's competitiveness and relevance within innovation and new thinking in the data-driven economy. The project has contributed to making Norwegian industry more robust in the face of future challenges and emphasizes the importance of close collaboration between academia and industry. Future Perspective and Sustainability has been an important part of the project. The project has identified the current situation and established a common future vision. The climate and nature crises will place greater demands on regenerative processes at all levels to reduce footprints and reuse resources. Integration of Big Data and Personal Insight has been investigated. The project highlighted the importance of a balanced approach that combines the efficiency of big data and digitalization with personal insight. This approach is crucial for creating sustainable system solutions that benefit industry, society, and global communities. Future Opportunities lies withing holistic thinking. The project recognizes future challenges, including the increasing shortage of technologists and the difficulties companies face in finding and retaining talent. Promoting effective and interdisciplinary collaboration is a priority. By bridging different fields, we can develop robust methods for sustainable systems development. Systems engineering and systems thinking techniques promise to ensure human responsibility when implementing systems that include artificial intelligence. However, there is a need to further develop next-generation product development approaches that seamlessly blend human insight and adaptive tools. We see the potential for artificial intelligence to improve efficiency and foster a collaborative environment with an emphasis on human insight and participation.

The overall objective of H-SEIF 2 is to increase the speed of product development, develop new services, and increase customer satisfaction. The project has focused on unlocking the potential of data, embracing collaboration, and demonstrating the practical value of research driven innovation. Key outcomes has been: Knowledge exchange Bring together 2 Academic researchers and 10 industry partners to share expertise and co-develop practical solutions. Long term impact Demonstrate how systematic approaches to data management can drive efficiency, innovation, and strategic decision-making. Focus on relevance Helping organizations process and interpret vast datasets to identify and leverage the most critical and actionable information.

Today’s large-scale, highly complex systems, such as telecom, space, transportation and energy, depend on each other and interact in ways unimagined until recently. At the same time, the Norwegian high-tech industries face rapidly changing market needs. The customers demand new and integrated systems that are attractive to use. The partner companies, supplying complex systems in challenging markets, must increase the effectiveness of their engineering and innovation processes. These high-tech industries search methodologies that increase their value proposition, cope with complexity, and at the same time reduce development risk. A mismatch late in the project, between unspoken stakeholder needs and the engineered solutions, normally leads to costly reworks and un-attractive systems. This is a reason for why collaborative methodologies and co-creation is highly recommended. Large-scale complex systems are hard for people to envisage, especially before they are made. Such systems, though, have abundant data from multiple sources. Despite the high potential for increasing speed and quality of product development, these data rarely come to use. A common reason is that the industrial actors find it challenging to generate business and customer value from the myriad of digital data, initiatives, and opportunities. This project will enable data-supported early decisions into a collaborative framework, and use industry as laboratory in a series of cases to give cross-industry learning and cross-company validation for the new framework. The project is utilizing a wide range of data sources. Many of the partners are more and more interested in using sustainability data to improve systems and services.