The Enterprise Design and Engineering Network (EDEN) is a community of academics and practitioners that aims to develop the field that gives its name to the network. It results from the merger and evolution of the ciao and enterprise engineering networks, both previously focused on the theme of enterprise engineering. Its’ members interact in diverse bilateral and multilateral collaborations and seek collaborations with/contributions of other groups addressing the same domains. EDEN promotes the yearly Enterprise Design and Engineering Working Conference (EDEWC) an evolution of the EEWC organized between 2011 and 2022.
The last decade has seen an increased digitization of enterprises. This has resulted in a substantial change in the nature and structure of enterprises. Driven by a desire to make enterprises more efficient in the production of a widening range of services and products, combined with a need to become more agile and resilient due to increased global competition, the balance between the social and technical aspects has shifted substantially towards the latter.
As part of this evolution, (traditional) information systems have now been supplemented with, or even supplanted by, advanced systems involving IoT, Big Data, Digital Twins, and AI, as well as (model-driven) no-code/low-code platforms. This has resulted in the emergence of “digital enterprise”, in which information systems no longer merely provide a passive mirror of reality but have become a primary driver of its business activities. In such enterprises, information systems tend to not only support key business decision making, but increasingly take these decisions in a (semi-)autonomous way that shapes and innovates business reality rather than only mirroring it. These technical evolutions have also influenced the human and organizational side of enterprises. Concurrently with this evolution, the organizational structure of some enterprises has become increasingly formal, with strict governance, risk, and compliance principles and procedures in place which are regularly audited at unprecedented scale, whereas others have thrived as ultra-agile startups with informal new and flat adhocracies.
The combination of both of these evolutions constitute an unprecedented challenge that needs to be addressed in a systematic way with rigour and relevance.
Looking towards the future, considering the breakthroughs of AI, IoT, Digital Twins, Distributed Ledgers, Quantum Computing and other technologies, one can expect far more complex IT systems to be built. This will result in enterprises of an unprecedented complexity and agility, that are part of value chains/networks that are of increasing importance to all aspects of economic and societal life.
In the context of such complex and agile interconnected socio-technical systems, we believe there is a general need for a new holistic and integrated approach to manage the development of enterprise integrating different fields and approaches, including enterprise governance, enterprise architecture management, organizational design, information systems engineering, software engineering, change management, etc, as well as specializations towards specific aspects or concerns, such as human resources management, business process management, risk and compliance management, and quality management.
The discipline of enterprise design and engineering aims to take an integrative and engineering oriented perspective to enterprise development management. As such, it considers enterprises as purposefully designed systems where all relevant aspects should be designed in coherence. In doing so, it does not claim an (implemented) enterprise to be a fully engineered artifact per se, but rather takes on the ambition of enabling the development of enterprises with instruments, and engineering principles, to make evidence-based decisions regarding their future development. Part of this ambition is aimed at investigating how design and engineering approaches should evolve to fit the new, fast changing, equilibrium between the social and technical factors. This includes the investigation of how existing approaches should be adapted to this new context as well as exploring new approaches, with explicit attention to the drawbacks of over-engineering with negative consequences for human factors. These ambitions not only include attention to the “as-designed” (by multiple actors) enterprise, but explicitly also (traceability to the) actual “as-implemented” enterprise, thus fully embracing its socio-technical reality.
Within this scope, it is assumed that (conceptual) modeling and ontologies are of primary importance, as these allow an implementation-agnostic, semantically rich, and human-aligned encoding of enterprise systems and knowledge. It is also assumed that the implementation of (conceptual) modeling and ontologies in mission-critical platforms will be crucial, as well as the link between these models and the actual socio-technical implementation of the enterprise. All these models and implementations, including the traceability between both, need to be highly evolvable at the product- and the design process-level to deal with unseen rates of change. Therefore, contributions on (conceptual) modeling, ontologies and their implementation and evolution, are especially welcomed when a combination of theory and empirical validation is presented.
Key topics include:
- Modeling and ontologies, including their (human) usability.
- Implementation of conceptual modeling and ontologies, a.o. machine-actionability and code generation in new platforms (eg. Blockchain, …).
- Traceability (between models and implementations).
- Evolvability/Agility in all its dimensions, including (technical and business) scalability.
- Design and engineering methods for enterprises and their (computerized) information systems, preferably with a focus on agility/evolvability in realistic contexts.
- Integration of methods, aimed at amongst others, finding synergies between methods.
- Domain-specific methods.
- Human aspects of engineering enterprises, including usability/understandability of modeling
- Relationship between rigorous engineering and design science methodology (theory) and best practices/heuristics from practice.