A Practical Framework To Turn IoT Technology Into Operational Capability
The data stream leverages the integration
library described in the previous section.
Data from streaming IoT data sources and
other business applications are brought in
with listeners. In this example, data from a
DDS control system (streaming data) is
combined with SAP asset information
(context) and additional information is
added to the event stream from a
commercial historian system. All the
temporal and contextual information is
joined based on a timestamp. This data
Report or NCR with a corresponding Root
Cause Analysis workflow is generated in a
BPMS tool.
The second leg of the original data stream
calls a cloud-based predictive model to
predict the change in other KPIs and a
chatbot interacts with the operations team
on the impact of the event data. This
example combines IoT and application
integration with orchestration, advanced
analytics, actuation, contextualization and
real-time data transformation in a
Figure 8 - Example of an event stream that integrates IoT data into operational workflows
stream is then multi-casted to two parallel
operations. The first identifies KPIs that are
out of bounds (based on defined business
rules) and this is again multi-casted to a
standard dashboard visualization tool as well
as a parallel task to create a work order in
the ERP or EAM solution for a field crew to
investigate the issue. A Non-Conformance
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distributed deployment environment. It
extends beyond visual models, as the event
stream is executed by the “play” button at
the top of the user interface.
The example demonstrates how event
information from IoT data sources is pre-
processed before it is integrated to existing
workflows for systems of transaction such as
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