Program at a glance
The presentations will take place using ZOOM. The duration of the presentation for full papers is 15 minutes plus 5 min for discussion. The duration of the presentation for short papers is 10 minutes plus 5 min for discussion. We will maintain a Miro conference board available to the registered participants. It will be used for follow-up questions and announcements.
The conference will take place using the Zoom platform. Session participants including the presenters are required to use a registered Zoom account (the e-mail address can be different from the one used in the PoEM’s registration). Links to the Zoom sessions will be provided prior to the conference. You are kindly asked to identify yourself as a speaker in the Zoom session by adding “(speaker)” next to the participant name.
We encourage live presentations. However, if you prefer to pre-record your presentation you can do that. The speaker should still be present to participate in the discussion. In this case, the presentation should be uploaded in Youtube and the link should be forwarded to firstname.lastname@example.org and Kristaps-Peteris.Rubulis@rtu.lv till November 23rd.
The Uncertain Enterprise: Achieving Adaptation through Digital Twins and Machine Learning
by Professor Tony Clark, Engineering & Applied Science Computer Science, Aston University
Abstract: Modern Enterprise Systems are no longer able to operate in a certain world. Such systems must be deployed and maintained in an ever-changing ecosystem and must adapt their goals and operating processes in order to be effective. It is increasingly challenging to be certain about requirements during enterprise system development. This talk proposes that we need new technologies and approaches for the design, development and deployment of modern enterprise systems so that the process is both economic and effective. Simulation, in the form of a Digital Twin can be used to help design, maintain and transform systems where interaction with the real-world is uneconomic or infeasible. Machine Learning can be used to achieve adaptable systems where the requirements are uncertain or where the system must adapt to changes in its ecosystem or its goals. The talk will outline a vision and conceptual approach based on Digital Twins and Machine Learning to improve enterprise systems and will set out challenges for the research community in order to realise this vision.
Bio: Tony Clark is a Professor and Deputy Dean (Education) in the College of Engineering and Physical Science at Aston University. He has experience of working in both Academia and Industry on a range of software projects and consultancies. While at Marconi Research Ltd (1985-1994) he worked in both Software Engineering and Knowledge Based Systems, and was responsible for designing systems for recognising aircraft behaviour, fusing sonar data, and for designing and implementing an AI Toolkit, the first of its kind in the UK. He has worked on languages for Object-Oriented specification and design including contributing to a range of Industry standards (UML 2.0, MDA, QVT, MOF). He co-founded a spin-out company selling UML-based meta-tools and consultancy to a number of blue-chip companies including BT, BAES, BSkyB and Citibank. His recent work addresses the issue of supporting decision making and uncertainty in enterprise systems.
Tony is an editorial board member of the Journal of Software and System Modelling, the Journal of Enterprise Modelling and Information Systems Architectures and the Journal of Object Technology, has edited several special issues including IEEE Software and SoSyM, and was co-chair of the MODELS conference in 2011. He is PC Chair of the Innovations in Software Engineering Conference for 2021.
Industrial Digital Environments in Action: The OMiLAB Innovation Corner
by Dr. Robert Woitsch, Managing Director at BOC
Dr. Robert Woitsch is managing director of BOC Asset Management GmbH and responsible for Innovation- and Knowledge Management. He is involved in innovation projects, mainly funded by the European Commission, in the area of knowledge management, Industry 4.0 as well as cloud computing since 2000.
His project portfolio contains typically between 2-5 Innovation Projects; whereas currently he is involved in three H2020 projects and two Austrian projects in the domain of energy efficiency and digital twin technology for Industry 4.0.
He acts as a speaker and reviewer in scientific conferences and for funding organisations. Dr. Robert Woitsch is also active in customer consultancy projects, in the domain of knowledge and risk- management.
Results are published at about 50 conference publications, some journals and book chapters, mainly in the area of knowledge management and conceptual modelling.
Since 2012 he is responsible for ADOxx.org, a world-wide acting Open Innovation Community that has about 4.300 developers and community members, 37 Universities and research institutions that collaborate in this community in the domain of conceptual modelling.
In 2019, he built the BOC OMiLAB Innovation corner and hence became active member of the world-wide OMiLAB initiative that is coordinated by the German non-profit organisation OMiLAB NPO. After internal setup of the Innvoation Corner, he triggered workshops for digital transformation and academic research and teaching with and for the OMiLAB Innovation Corner for the domains of Digital Engineering, Artificial Intelligence, Digital Transformation mainly in the domain of Industry 4.0.
The goal is to bridge the market-driven needs with the open ideas, novel solutions and innovations for a market-driven and academic sound digital transformation.
Panel: Enterprise Modeling in Digital Age
Chair: Jelena Zdravkovic (Stockholm University)
Enterprise Modeling (EM) is broadly recognized as an approach for the development of IT solutions congruent with the business of their organizations. By a compound modeling of business goals, services, processes, resources, actors, IS capabilities and requirements, EM describes a desired state of the organization integrating business and technology perspectives. The notion closely relates to Enterprise Architecture (EA), which applies various architecture principles through model-based frameworks for guiding organizations towards business and technology changes necessary to execute their strategies.
Ongoing digital transformation, technological disruptions, and business agility require enterprise modeling to evolve to keep up with the pace of change, but moreover - to predict, facilitate, and leverage that change to the advantage of the business. Today’s actual concerns in IS engineering relate to high needs for business integration, setting demands to efficient deployment, connectivity, interoperability, and management of supporting digital components that are of diverse structure, size and dynamics. EM and EA frameworks are increasingly moving the modeling focus from traditional company oriented towards ecosystems orchestrated on digital platforms. This means that to survive and grow, business organizations need to reshape the methods and models for their IS design to consider both internal and external relationships, as well as to by digital means accurately plan, analyze, simulate and integrate desired capabilities; and to facilitate their resilient evolution.
The aim of this panel discussion is to explore the challenges and opportunities that massive digitalization of business data and functions brings to the field of EM&EA:
- How modeling frameworks need to change and extend their concepts to meet the needs for modeling networked organizations in terms of digital business ecosystems?
- How existing modeling methods need to be extended to involve and automate acquisition of massive and diverse digital data and functionalities?
- How can we increase the efficiency of enterprise modeling itself, given current and future business dynamics and interorganizational scopes, to facilitate timely deployment of IS solutions?
- Should advances in underlying technologies and infrastructure, such as cloud, digital twin, IoT, 5G, and other, should be considered for enterprise modeling and methods?