Digital Twin in Industrial Application – Requirements to a Comprehensive Data Model
This situation led the manufacturer to decide
to provide (digital) services for its mixers. In
doing so, it had two aims in mind: first, to
provide supplemental services (such as
maintenance and spare part sales) for its
mixing machines in order to better meet cus-
tomer demands and strengthen the cus-
tomer relationship. Second, to gain more ac-
curate insights from field operations to ena-
ble it to improve its next generation of mix-
ers by means of “closed loop engineering”
(feedback round trip from operations to de-
sign).
The results of this study provide a good gen-
eral impression of the goals companies asso-
ciate with IIoT: benefits are envisaged pri-
marily at the level of broader processes like
customer service optimization, whereas, ac-
cording to the respondents to the study, spe-
cific applications have less potential.
Consequently, the following real-world sce-
narios are selected from our customers' fo-
cus on broader business cases and various
interrelations within the supply chain. By
means of this selection, we try to cover a
range of typical industrial applications in or-
der to give us a representative basis for the
proposals that follow.
The business-critical requirements for IIoT in
this case are to:
Case 1: Customer support and design
loop
The first example is provided by an equip-
ment manufacturer that sells batch reactors
for mixing processes in the chemical indus-
try. The manufacturer’s customers usually
install these reactors in machine lines, with
various upstream and downstream pro-
cesses being integrated around the mixers.
The end products are chemicals that are very
sensitive to production parameters such as
temperature, pressure or humidity. Insuffi-
cient control of such parameters may lead to
the scrapping of entire product batches and
require the conduct of maintenance checks
at the mixers. However, since the batch pro-
cesses running on the machine lines are
something of a black box, it is difficult to lo-
calize the actual faults and thus prevent the
rejection of subsequent batches, thereby re-
sulting in considerable costs due to product
nonconformities and machine downtimes.
IIC Journal of Innovation
Track control parameters (tempera-
ture, speed, etc.)
Consider environmental influences
(humidity, temperature, etc.)
Identify production jobs (e.g. for
quality feedback)
Identify spare parts
Case 2: Overall fleet monitoring and pre-
dictive maintenance
The second example comes from a large-
scale logistics supplier that operates various
sites for transshipping freight from global to
local transport routes. The company oper-
ates a fleet of different-sized container carri-
ers, fork lifts and cranes to address the dif-
ferent reloading situations. The critical goal
when reloading is to minimize the downtime
of ships, trucks and trains. The logistics sup-
plier’s performance is measured on the basis
of the transfer time, and penalties are in-
curred if deadlines are missed. Therefore,
the availability of the corresponding logistics
equipment is a business-critical factor and
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