The output of maintenance analytics creates new decision-making challenges for maintainers, engineers, and managers. The most significant risk is that the information is simply not used in Organisations. This theme will adopt a multilevel perspective to build a comprehensive picture of the factors that support decision making when using complex data. This aspect of the project translates the core research programs into business practice by integrating user issues hierarchically at three levels of analysis: | UI Steps |
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Cognitive - is particularly important for maintainers using information delivered through novel media and in a context that is different to traditional maintenance systems, |
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Task demands - is particularly important for engineers designing models, and for the maintainers using models. Use of data will be inhibited if the output of models is not a meaningful part of the overall task requirements of maintainers or generates tasks that cause fatigue, boredom, or distress |
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Organisational culture. is particularly important for managers implementing decisions based on analytic models. For example, if an underlying cultural belief places primacy on the intuition and implicit skill of senior managers, then analytics models can undermine the role of managers and limit the use of data output |
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This translation project will work with the industry partners to implement a plan based on research at each level to integrate across the three levels. A framework around “person, tasks, and culture” will be developed that is specific to the maintenance context of the organisation and involves stakeholders from the maintainer, the engineer, and the manager groups. It is essential that the overall system, not just individual elements, supports the translation of analytic models into business practice. | 
