Towards Smart Campus Management: Defining Information Requirements for Decision Making through Dashboard Design

At universities worldwide, the notion of a ‘smart campus’ is becoming increasingly appealing as a response to the multitude of challenges that impact campus development and operation. Smart campus tools are widely used to support students and employees, optimise space use and save energy. Although smart campus tools are supposed to support campus managers in their decision-making processes, the use of the information delivered by smart campus tools and their application in organisational processes has received little attention. In this paper, we focus on the use of dashboards in the connection of IoT information to strategic decision-making processes in the management of university campuses. To this end, we developed a briefing approach for dashboards that expresses the needs of campus management and matches the structure of decision-making processes. In two cases, dashboards based on this approach were use-tested by stakeholders for defining information requirements for IoT applications. The results suggest that users are able to use dashboards for assessing portfolio performance and determining interventions. Through iteration the usability of the dashboard is improved and information requirements are refined, resulting in a brief for a campus management dashboard. The results suggest that the briefing approach can be used to determine IoT information requirements, though further research is required to study indications and contra-indications of the proposed method.

Digital World Spawns Identical Twins

This article explores the concept of digital twins and reasons why manufacturers prefer digital replicas of products, machines, processes, or even entire factories. A digital twin models the robotic line with such high fidelity that the engineer can do all this in the virtual world. Digital twins are the foundation of tomorrow’s smarter workplace. A factory’s digital twin must be robust enough to capture those changes, plus all relevant data from each operation. Smart factories, such as GE’s Brilliant Factory and Siemens’ competing Industrie 4.0, need both types of digital twins—product and process—to work. Digital product models contain each component that goes into a product, from screws and welds to plastic shapes and machined metals. Digital twins also support greater automation. As artificial intelligence (AI) systems learn more about specific machines, they will use their digital twins to help engineers run plants more efficiently. AI can analyze it to see if a screw is loose or a bearing is starting to fail. The better the AI knows the machine, the more accurately it can predict when that failure is likely to happen.