SESG Webinar: Systems Engineering, Artificial Intelligence, and Big Data

Big data. Illustration.

The Systems Engineering Study Group (SESG) is organizing an online dialogue session for systems engineers & architects under the topic: Systems Engineering, Artificial Intelligence, and Big Data.


14 Apr

Practical information

Many companies, small and large, gather much data through their installed base of systems and solutions. They hope to use that data to improve their systems, the operation, maintenance, etc. Technologies such as machine learning and data analytics sound promising.
In this SESG, we are inviting the authors of INCOSE symposium papers to present their findings on these topics.

Register for free here

Idea Development Method, Applying Systems Design Thinking in a Very Small Entity

Abstract: Very Small Entities (VSE) delivers a substantial percentage of the world’s products and services. These agile and innovative companies approach their market in a flexible, informal, and human-centered approach. Their transformation from the startup phase to an established company can be problematic. As they grow, the VSE must put more emphasis on incorporating formal organizational structure and processes to cope with the increased size and complexity. Failure in adapting often results in a negative market effect. Systems Design Thinking (SDT) is a combination of Systems Engineering, Systems Thinking, and Design Thinking. There is an increased awareness that the perspectives of SDT bring value to organizations. However, the advantage of SDT is not well understood for VSEs that are in the maturing phase. In this work, the researchers explore the value of SDT for very small enterprises transitioning from infancy to adolescence. The article presents a new method for idea development based on SDT principles and tailored to the needs of a VSE. Case-based research is applied in a small company to understand the needs and verify the desirability, feasibility, and viability of our proposed method. The results suggest that an SDT approach improves the company’s ability to capture and develop ideas and can help grow the company.

Tommy LangenTommy Langen is a Research Assistant at the University of South-Eastern Norway (USN). He holds a Master of Science in Systems Engineering with Industrial Economics and a Bachelor’s in Mechanical Engineering with Product Development from USN. He has several years’ experience in the Subsea Oil & Gas and the Defence industry, working from early concept to testing of complex systems. Currently, he is performing research on the use of data in early-phase systems engineering with focus on innovation and human factors.

 

Unlocking the power of big data within the early design phase of the new product development process

Abstract: The aim of this study is to investigate through a real industry problem how to exploit big data. Through this exploitation, we strive to increase knowledge in the early design phase within new product development. There is little research, and an especial scarcity of empirical studies, in utilizing big data within new product development in manufacturing industries. Shorter design cycles demand rapid decision-making and the need for data-driven information is evident. An increase of knowledge through big data analytics, closing the loop with a knowledge base, has become a critical success criterion within the various industries.

Haytham AliHaytham B. Ali is an Assistant Professor at the University of South-Eastern Norway (USN), where he is working at connecting engineering with science, with a focus on mathematics. He holds a Master of Science in Systems Engineering with Industrial Economics degree and a Bachelor degree in Mechanical Engineering with a specialization in Product Development, both from USN.

 

How the use of Artificial Intelligence can boost knowledge reuse

Abstract: We observed in our company that several quality issues surfaced during the product commissioning phase causing a negative impact on project cost, delivery time, and customer satisfaction. By using root cause analyses, this research found several links between poor quality and lack of proper knowledge management. With better knowledge management, most of these quality issues could be addressed and solved at an earlier stage of the product life cycle. Today different barriers are preventing organizations from taking full advantage of previously generated valuable knowledge. This thesis explores how the use of Artificial Intelligence can boost knowledge reuse. The goal is to empower faster and more informed decision-making based on lessons learned in the past to minimize waste, rework, re-invention and redundancy.

Sajjad LRSajjad Sarwar has been working in the oil industry for 10 years. In 2009 he received his bachelor’s degree in Mechanical engineering with a specialization in mechatronics from the University of Agder. He has been working as a Service Engineer in various complex projects in the drilling market. In 2020 he earned a Master’s degree in Systems Engineering from the University of South-Eastern Norway.