Trans-disciplinary systems as complex systems

The system concept is a widely-used concept in research and practice. Already in the 50s of the previous century, a community was created to investigate interrelationships between domains and create a theory surpassing and comparing domains. The General Systems Theory (GST) community has tried to come up with such a theory for several decades. The ambition has grown more realistic in the years after, recognizing that an all-encompassing theory would not be possible. Since then, systems research was aimed at generating useful and usable approaches to compare and interrelate domains, thus creating a trans-disciplinary approach to enable description and analysis of large, and even complex, systems. The concept of systems, however, is often loosely used. Levels of abstraction are neglected, and interrelationships between systems ignored. In this paper, the concept of system is put in historical context, and further elaborated upon in the context of complex and trans-disciplinary systems. Two examples of transdisciplinary systems are presented and discussed to illustrate the use of the system concept

CE Challenges Work to Do

CE has been used for more than two decades now. Despite many successes and advantages, there are still many challenges to be addressed. These challenges are both technical and organisational. In the paper we will address the current challenges of CE. Many challenges are related to the exchange of data and knowledge and to the systems that make data and knowledge exchange possible. Although much progress has been made in enabling extensive data and knowledge exchange and use, much remains to be wished. For example, there are still barriers to data exchange. Technically, these barriers may consist of different formats, differences in infrastructures and systems, and different semantics. There are also organisational and political barriers. For example, investment in information system may heavily impact upstream suppliers, while revenues of better information exchange may predominantly be gained by downstream actors. Without sharing costs and revenues, chain-wide information exchange will not be easily realised. Another barrier is the possible lack of willingness to share information, because of potential misuse of knowledge and loss of power. The paper is organised as follows. First we will describe the current manifestation of CE as described in a recent book. Second, we will list current trends in CE. Third, we will present some Critical Success Factors (CSFs) that are considered relevant for implementing and adapting CE practices. Last, we indicate some research and practical questions to be addressed, especially for areas that have a high potential and actual impact

Special issue on ‘transdisciplinary approaches to digital manufacturing for industry 4.0’

The concept of Industry 4.0 (I4.0) outlines the vision of a smart factory characterised by the complete networking of all production parts and processes, consisting of real-time control via cyber-physical systems, increased use of robots, intelligent and adaptable production systems, which should contribute to greater productivity through resource efficiency. The convergence of production and interaction, work and communication requires increasingly transdisciplinary competencies for creating a smart factory, which is economically successful and competitive. These competencies consist, among others, of divers expert knowledge, flexibility, and creativity for moving toward I4.0

CE Challenges: Work to Do

CE has been used for more than two decades now. Despite many successes and advantages, there are still many challenges to be addressed. These challenges are both technical and organisational. In the paper we will address the current challenges of CE. Many challenges are related to the exchange of data and knowledge and to the systems that make data and knowledge exchange possible. Although much progress has been made in enabling extensive data and knowledge exchange and use, much remains to be wished. For example, there are still barriers to data exchange. Technically, these barriers may consist of different formats, differences in infrastructures and systems, and different semantics. There are also organisational and political barriers. For example, investment in information system may heavily impact upstream suppliers, while revenues of better information exchange may predominantly be gained by downstream actors. Without sharing costs and revenues, chain-wide information exchange will not be easily realised. Another barrier is the possible lack of willingness to share information, because of potential misuse of knowledge and loss of power. The paper is organised as follows. First we will describe the current manifestation of CE as described in a recent book. Second, we will list current trends in CE. Third, we will present some Critical Success Factors (CSFs) that are considered relevant for implementing and adapting CE practices. Last, we indicate some research and practical questions to be addressed, especially for areas that have a high potential and actual impact. Aerospace Transport & Operation

Trans-Disciplinary Systems as Complex Systems

The system concept is a widely-used concept in research and practice. Already in the 50s of the previous century, a community was created to investigate interrelationships between domains and create a theory surpassing and comparing domains. The General Systems Theory (GST) community has tried to come up with such a theory for several decades. The ambition has grown more realistic in the years after, recognizing that an all-encompassing theory would not be possible. Since then, systems research was aimed at generating useful and usable approaches to compare and interrelate domains, thus creating a trans-disciplinary approach to enable description and analysis of large, and even complex, systems. The concept of systems, however, is often loosely used. Levels of abstraction are neglected, and interrelationships between systems ignored. In this paper, the concept of system is put in historical context, and further elaborated upon in the context of complex and trans-disciplinary systems. Two examples of transdisciplinary systems are presented and discussed to illustrate the use of the system concept. Aerospace Transport & Operation

Knowledge-based engineering

The handling of knowledge represents the key to competitiveness, with company-specific product and process knowledge marking a unique position with respect to competition. Knowledge-based engineering (KBE) is a comprehensive application of artificial intelligence in engineering. It facilitates new product development by automating repetitive design tasks through acquisition, capture, transform, retention, share, and (re-)use of product and process knowledge. The idea behind KBE is to store engineering knowledge once by suitable, user friendly means and use it whenever necessary in a formal, well documented, repeatable and traceable process. It works like design automation. This chapter begins with the definition of knowledge in an engineering context and subsequently addresses the state-of-the-art in KBE research. Three particular areas of research are discussed in detail: knowledge structuring, maintainability of knowledge and KBE applications, and the technological progress and weaknesses of commercial KBE applications like KBE templates. From case study examples, various recent developments in KBE research, development and industrial exploitation are highlighted. By the resulting sequence optimization of the design process a significant time saving can be achieved. However, there are still notable drawbacks such as the complexity of KBE implementation and the adaptability of developed applications that need to be researched and solved. A view on KBE systems within the Concurrent Engineering context is synthesized, leading to the identification of future directions for research