Enterprise BIM: A Holistic Approach to the Future of Smart Buildings

This paper presents Enterprise BIM (EBIM), which is the utilization of Building Information Modeling (BIM) in a holistic organizational aspect structure during the entire life cycle of a building. EBIM acts as a virtual comprehensive representation of the buildings and infrastructure that is aimed to optimize andimprove business management, knowledge sharing, digital interaction and connection in the different phases of the building life cycle. This study shows how EBIM could support an organization’s core business operationally and strategically. In fact, EBIM is aimed to support the building’s entire life cycle and in this approach facility management (FM) is considered as a necessity. This paper demonstrates that FM is not a dedicated or a separate system and it uses exactly the same data as those required for business operations. Hence, we have surveyed the role of EBIM at St. Olavs Hospital as a case study for further consideration. St. Olavs Hospital uses EBIM to realize the goals of better and cheaper construction projects and real estate operations management with the aim of more efficient use of resources. The case study highlights the details and benefits of this approach both with respect to processes, tools and techniques. Furthermore, we demonstrate how EBIM may support interoperability, transparent communication and collaboration among the core business, facility management, the project organization and the different stakeholders throughout the building life cycle

ICT-Architecture applied on pilot building

This memo follows up earlier theoretical reports on a possible ICT architecture for a collection of zero emission neighbourhoods described in ZEN Report 34 – 2021 (ZEN Data Management and Monitoring – Requirements and Architecture). A case study has been performed, taking the data visualization need for following up the adherence of the building operations to selected ZEN KPI, as described in ZEN-report 44. The case used the ZEB laboratory, which is already heavily instrumented making it possible to capture live data related to a number of the dynamic KPIs on a building in operation. By developing dashboards for the different KPIs, we get insight into the needed underlying data pathways from building sensors to the web. The focus on the existing work was purely on technical aspects, and we have enhanced this by including all levels in an enterprise architecture, adopting the approach from the +CityxChange project. As expected, the original architecture was overly general, although the main technical levels (edge, fog, cloudlet, cloud) makes sense also in the case, even if there is no cloudlet-layer here since we have only data from one building and not a full-fledged neighbourhood. Data is captured, processed and made available at different levels, rather than all collected in a cloud to be made generally available for all, which is the general approach e.g. in EU data spaces and the smart building hub project.1 The case is driven by the need for access to data on the current state, and to some design targets, but not a combination of current data and simulated data of the future, which could be relevant for a building manager. We see in the ArchiMate enterprise architecture models how one can combine the technical levels and business levels (e.g., showing how some data can go straight from edge to cloud, other is going first to the fog). The movement of data (from real-time, to last recent, to historical must be looked into in more detail. As observed from the dashboards. the dividing line between last-recent and historical data is not clear-cut. One can also more clearly define boundaries of who owns / is responsible for the various parts of the infrastructure using e.g., ArchiMate for depicting the whole Enterprise Architecture, include the ICT architecture. It was also noted that the current solution did not support the management of meta-data, and there are plans to adopt a standardized meta data schema. Meta-data is both a traditional data-model and a data movement plan (what is done with the data from it is collected to it is used and visualized, and more subtle aspect on a different granularity level (e.g., that some apparatus is not working appropriately in a certain time-period) Given the large activity nationally and internationally on such ICT and data architecture, it is advised to coordinate further work on this with the SBHub-project, where both SINTEF and NTNU collaborate, which uses the ZEB lab as a case. The case is simple compared to the scope of ZEN since the focus has been on an individual building as part of a smart neighbourhood, and not a total neighbourhood. Given that, not all KPIs are relevant, thus the next step is to investigate this on an area level. Missing areas in the ZEN KPIs are also identified, e.g. on the quality of the working environment such as air quality that one should have ways of following up. Through energinet2 one can access additional buildings at Gløshaugen. Still, starting small has given a good testbed providing relevant results both for the ICT Architecture and for the development of the KPIs themselves. Next, we need additional feedback from those responsible for the different KPI-areas. • How is it best to represent the different KPIs in a dashboard? A work on representing emission and other data for a building manager will be done in spring 2024. • How should one investigate the interactions between KPIs? • How to capture missing KPI-areas • How to integrate illustrating design targets, historical data, and simulated future data? For those needing a more holistic view, e.g. from the different entrepreneurs and technology providers questions might be overlapping with the above. • How should one investigate and illustrate the interactions between KPIs? • How to capture missing KPI-areas (if any)? • How to integrate illustrating baselines/reference data, design targets, historical data, and simulated future data? Similarly, researchers on different areas might want to have tailored views on the overall dataset.publishedVersio

Enhancing Learning and Collaboration in Organisations through In-house Crowdsourcing

Learning and innovation are central to organisations’ development. Insights and innovative ideas occur to individuals. However, learning in organisations takes place at several levels which include individuals, groups, and the organisation itself. Thus, there is a need to enhance the transfer of insights, ideas, and concerns from individuals to groups and to the organisation. This paper explores the role of in-house crowdsourcing and the design of interactive technologies for organisational learning. We build upon our earlier work on the use of interactive technologies for organisational learning. The main research contribution of this work is the conceptualisation of in-house crowdsourcing scenarios to support the design and development of interactive technologies for organisational learning

Masters in Serious Games Curriculum Framework

Thin, A. G., Lim, T., Louchart, S., De Gloria, A., Mayer, I., Kickmeier-Rust, M., Klamma, R., VeltKamp, R., Arnab, S., Bellotti, F., Boyle, L., Prada, R., Westera, W., Nadolski, R., & Abbas Petersen, S. (2013). Masters in Serious Games Curriculum Framework. Deliverable 5.3 of the Games and Learning Alliance Network of Excellence. Available at http://www.seriousgamessociety.org/download/SGMastersFwk.pdf.This report outlines a European Masters of Science programme on serious gaming.This report is a deliverable of the GALA project, which is sponsored by the the FP7 Programme of the European Commissio

Virtual enterprise formation and partner selection: an analysis using case studies

Abstract: A Virtual Enterprise (VE) can be described as an organisational form that emerges when individual entities form a team of partners to achieve a specific goal. The ability to assemble the best team is critical to the success of the VE and this imposes strong demands on its formation. In this paper, we present an agent-based model of a VE, where the partners of a VE are represented by software agents. We show how this model can support the different processes that are used in industry for selecting the partners. Industrial case studies have been used to illustrate the different partner selection processes that are used in industry. The selection processes are analysed using Agent Interaction Protocols (AIP) to describe the interactions that take place between the different entities