Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target-or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation.
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AbstractAn increasing number of tools are being developed to help academics interact with information, but little is known about the benefits of those tools for their users. This study evaluated academics' receptiveness to information proposed by a mobile app, the SerenA Notebook: information that is based in their inferred interests but does not relate directly to a prior recognized need. The evaluated app aimed at creating the experience of serendipitous encounters: generating ideas and inspiring thoughts, and potentially triggering follow--up actions, by providing users with information related to their work and leisure interests in the form of suggestions. We studied how 20 academics interacted with messages sent by the mobile app at a rate of 3 per day over ten consecutive days.Collected data sets were analyzed using thematic analysis. We found that contextual factors (location, activity and focus) strongly influenced academics' responses to messages. Academics described some unsolicited information as interesting but irrelevant when they could not make immediate use of it. They highlighted filtering information as their major struggle rather than finding information. Some messages that were positively received acted as reminders of activities participants were meant to be doing but were postponing, or were relevant to ongoing activities at the time the information was received.
Serendipity is where unexpected circumstances and an insightful 'aha' moment result in a valuable outcome. We discuss how interactive systems can support the process of serendipity: from making new connections, to projecting and exploiting their potential value. We focus in particular on how technology can support reflectionwhich is an important part of the serendipity process. By considering findings from a set of empirical studies and a set of design principles aimed at encouraging reflection, we present an early stage digital 'Semantic Sketchbook' which was designed with the aim of supporting reflection (as well as other aspects of the process of serendipity). We discuss how our 'Semantic Sketchbook' has the potential to create opportunities for serendipity and the next steps we intend to take in developing it and evaluating its success.
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