Models for workforce planning and scheduling have been studied in operations research for decades. Driven by the Industrial Internet of Things new data sources have become available that have not yet been used to improve field service management. This paper proposes a research agenda towards leveraging this potential in the context of industrial maintenance. By combining predictive analytics (e.g. forecasting demand) with prescriptive analytics (e.g. determining optimal maintenance schedules) companies can decrease uncertainties in their maintenance planning, increase the availability of machines, decrease overall maintenance costs, and ultimately develop new business models.
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