Dagstuhl Seminar 16192: Supporting Organisational Efficiency and Agility: Models, Languages and Software Systems Stijn Hoppenbrouwers Model-Based Information Systems research group, Faculty of Engineering, HAN University of Applied Sciences, Arnhem, the Netherlands

Personal Background Dr. Stijn Hoppenbrouwers, 1970, married, 3 kids; English (MA Utrecht, 1993), Linguistics (MA Bangor, 1994) 1996 Tilburg University (projects on conceptual modelling and information extraction; Language Action Perspective) 1998 Ordina: research consultancy and start PhD (part time) Radboud University Nijmegen: Information Science; PhD 2003 (promotor: Proper); “Freezing Language”; language and language patterns as the basis of IS design; also, ArchiMate; ORM 2005: Assistant Professor at Radboud; language & communication aspects of the process of IS design (RE), including modelling; collaborative modelling, pragmatics of modelling; business rules 2012: professor at HAN UAS; information modelling (Fact Oriented; book); practice oriented; very broad scope on IT and Media 2016 rekindling specialism: looking for industrial links with Model-Based Information Systems and Data-Driven Applications in SMEs (Business Engineering, Business Intelligence)

Perspective To me, the core of business modelling for IS purposes is patterns of communication (syntax, semantics and pragmatics): comm. is what models do and what they are largely about. Generally, I’m on board with Jan Dietz’s EO concepts. However, working with these (or others) in a traditional design context does not seem to suffice to closely and actively involve the business. For agile, more evolutionary/emergent ISs, we may look more at how business stakeholders already communicate in and about their organisation in their everyday work, and tap into that at runtime. (I have some ideas on this I’d like to elaborate on) And yet, up-front design and solid IS engineering (BPM, Data) cannot be whisked away; how to combine this with ‘organic’ organisational communication? Building blocks, ‘expression mechanisms’, ‘creation mechanisms’? What does a blend of ‘social media’ and ISs look like? Indeed this still calls, somehow, for continuously dealing with domain specific concepts and language (language engineering?); connections with Dutch Tax Office, business rules (more design than emergence)

Expectations State of the Art overview and issues Looking for a beter connection with industry; vendors as well as consultants; application mostly in SMEs; lowering threshold for ‘ISs without Programming’ Do we stick to modelling as we know it, or can we (also) integrate it in runtime systems & operations? Making ISs more personal, even in business? Link between IS and (big) data; they are inseperable, but now different currents?

Modelling  What kind of models of organizations do you find useful for what purpose?  Who are/should be primary addressees of such models?  What do you relate to the idea of models being repositories of organizational knowledge?  Should modelling languages (DSMLs) rather aim at fitting a wide range of organisations or should they be tuned to the specific needs of one organisation? To me, the core of business modelling for IS purposes is patterns of communication (syntax, semantics and pragmatics): comm. is what models do and what they are largely about. Generally, I’m on board with Jan Dietz’s EO concepts. However, working with these in a traditional design context does not suffice to closely and actively Involve the business. We should look more at how business stakeholders already communicate about their business in their everyday work and tap into that at runtime. Indeed this calls for continuously tuning concepts and language to domain specifics (language engineering?). Generic aspects should be covered, however, at meta-level; generic and specific are both crucial.

Agility  What are key aspects of making an organisation agile, that is, adaptable to a changing environment?  How can an organisation's ability to collaborate with other organisations be supported?  What kind of abstractions are required to make an organisation more agile? 1. Simple, re-usable, stakeholder-oriented building blocks can be quicly put together and form complex yet easily changeable wholes 2. Underneath, his will require some very solid architectural principles 3. Both (1. and 2.) will help interconnect between domains, inter-org and intra-org. 4. Standardization will always be both required and a huge problem. If standardization is hard, standardized ways of negotiating, sharing or coupling models are needed, but will not prevent actual work to be necessary for this (until very clever AI comes about). 5. Abstractions at all levels where business concepts are input for or touch upon engineering concepts; also, never lose the link between abstractions and ‘real life’ concepts and communication.

Competitiveness What are driving forces of competitiveness in the era of the digital transformation?  What is the role of models for representing an enterprise in a digital economy? It still is a comprehensive package, both robustness and agility have to be on board. Monolithic approaches to enterprise systems will still be there because of the need for extensive integration and standardization, but links with other domains/systems will become even more important. This means the monoliths will also need to be very easily extended ,adapted, linked at an operational level. Detailed models (data still at the core) will still be required despite the need for higher level models (architectural, strategic). Lose that (loer) level and lose everything. Standardization and automation (generation) may help here to some extent.

Enterprise Software  How would you deal with the ambivalent role of standards (on the one hand, they protect investments and enable reuse, on the other hand they are likely to inhibit progress)?  How could/should future enterprise software be designed to empower users, that is, to better enable them with adapting the system to their/the organisation's needs?  What kind of architecture is required to increase the level of reuse in enterprise software?  How would you assess the idea of enhancing enterprise software with models (of organisational goal systems, of the IT infrastructure, of business processes ...) at runtime? See above See above See above See above

Decision Making  What kind of models is required to support managerial decisions in organisations?  What are the prospects of large scale data sets ("big data") for the inductive creation of decision models? Real decision making is unlike problem solving in mainstream computation. For supporting it, we should tap into concepts and (possibly) models stemming from decision making as such; closest link may be advanced AI (Watson). Rationality is not the only aspect here. Should it be? Again, this mosty involves advanced AI, which I am still sceptical about to begin wih. We should be very careful about this. And yet, cleverly handled big data combined with raw computing power seem to have brought us most AI-like breakthroughs so far, so may be unavoidable.

Automation  Are comprehensive models of an enterprise suited to diminish the limitations of automation?  More specific: What kind of models would be required to substantially increase the level of automation of administrative/managerial work?  Is it realistic to aim at self-adaptive organizational (software) systems that adapt their underlying goal model to changes in their environment and subsequently adapt their systems and operations to the revised goal system? Assuming fullautomation is aimed for: much detail will be needed, requiring very detailed modelling bordering on programming. So at the moment: no. Powerful, dependable links between models at a higher abstraction and ‘filling in the details’ are (also) required: not just modelling but also data/software for engineering ‘processing’ the models. AI may play a role (but scary); declarative paradigm (functional programming?)seems the one here, but how to operationalise?? At the moment, I don’t think so. Let’s first deal with ‘adaptive’ before evolving to ‘self-adaptive’? Unless some major AI breakthrough occurs and we can skip all that tedious software development stuff right away 