Quantitative and Qualitative Analysis of Enterprise Architecture Models

Context

Enterprise architecture management (EAM) is an established discipline in the information science field. With the digitalization, established enterprises are challenged to face fierce competition, especially from new competitors joining the market in vast amounts of time. These challenges can only be addressed by establishing flexible enterprise architectures that also support efficient management of an enterprise. Albeit the wide adoption of the de-facto industry standard of ArchiMate, only very limited literature focusses on using ArchiMate models as a formalized knowledge base that serves quantitative and qualitative evaluation means.

 

Task

Part of this project is to bridge the strengths of two worlds: On the one hand, there exists a lot of literature on economic models that support enterprises in e.g., making investment decisions or validating profitability of proposing new services as well as literature on the alignment between business and IT, e.g., validating the impact of a technological component on the value proposition. On the other hand, several successful domain-specific modeling languages have been developed that show particular strengths in the mechanisms and algorithms that can be executed on the models. The primary objective of this project is therefore to define a concept that bridges these two worlds by enhancing an existing ArchiMate modeling tool with quantitative and qualitative analysis features.

 

Further Reading (Excerpt)

  • Bork, D., Gerber, A., Miron, E. T., van Deventer, P., Van der Merwe, A., Karagiannis, D., … & Sumereder, A. (2018). Requirements engineering for model-based enterprise architecture management with ArchiMate. In Workshop on Enterprise and Organizational Modeling and Simulation (pp. 16-30). Springer, Cham.
  • Kaczmarek-Heß, M., Ma, Q., & Razo-Zapata, I. S. (2018). Modeling in support of multi-perspective valuation of smart grid initiatives. In 2018 12th International Conference on Research Challenges in Information Science (RCIS) (pp. 1-12). IEEE.
  • Hinkelmann, K., Gerber, A., Karagiannis, D., Thoenssen, B., Van der Merwe, A., & Woitsch, R. (2016). A new paradigm for the continuous alignment of business and IT: Combining enterprise architecture modelling and enterprise ontology. Computers in Industry, 79, 77-86.
  • Razo-Zapata I.S., Shrestha A., Proper E. (2017) On Valuation of Smart Grid Architectures: An Enterprise Engineering Perspective. In: Reinhartz-Berger I., Gulden J., Nurcan S., Guédria W., Bera P. (eds) Enterprise, Business-Process and Information Systems Modeling. BPMDS 2017, EMMSAD 2017. Lecture Notes in Business Information Processing, vol 287. Springer, Cham