  The Internet of Things adoption in the manufacturing industry allows
enterprises to monitor their electrical power consumption in real time and at
machine level. In this paper, we follow up on such emerging opportunities for
data acquisition and show that analyzing power consumption in manufacturing
enterprises can serve a variety of purposes. Apart from the prevalent goal of
reducing overall power consumption for economical and ecological reasons, such
data can, for example, be used to improve production processes.
  Based on a literature review and expert interviews, we discuss how analyzing
power consumption data can serve the goals reporting, optimization, fault
detection, and predictive maintenance. To tackle these goals, we propose to
implement the measures real-time data processing, multi-level monitoring,
temporal aggregation, correlation, anomaly detection, forecasting,
visualization, and alerting in software.
  We transfer our findings to two manufacturing enterprises and show how the
presented goals reflect in these enterprises. In a pilot implementation of a
power consumption analytics platform, we show how our proposed measures can be
implemented with a microservice-based architecture, stream processing
techniques, and the fog computing paradigm. We provide the implementations as
open source as well as a public demo allowing to reproduce and extend our
research.
