Digital Twin + Industrial Internet for Smart Manufacturing: A Case Study in the Steel Industry
Based on our experience, the feedback first
and foremost comes from product quality,
followed by energy consumption, material
supply, equipment conditions and up-
stream and down-stream sub-processes.
This feedback helps determine the best set
of engineering parameters for meeting
product quality, lower energy and material
consumption and achieve a higher
production rate.
from a fleet of assets that are well-organized
in association with each other. The required
level of complexity in analytics is clearly
increased as a result (Figure 3). The
fragmented data silos—as well as the
absence of a systematic description in the
digital space of complex production
environment analytics found in many
manufacturing
environments
today—
together present a great obstacle to achieve
in-depth collaborative analytics. In other
words, we need a systematic approach to
represent the real world in the digital space
and facilitate these sophisticated analytics.
On the other hand, to realize data-driven
optimization, the analytics become more
involved—progressing from descriptive to
diagnostic, predictive and prescriptive. Its
scope also expands from analyzing a single
asset (e.g. in the case of predictive
maintenance) to a fleet of assets (e.g. in a
production line, or even across production
sub-processes such as sintering and casting
in an iron-and-steel manufacturing process).
This type of analytics relies on data collected
D IGITAL T WIN
The concept of digital twin has garnered
increasing attention in the recent years
because it can be used to systematically
describe the real world, including physical
assets and logical processes, in the digital
space.
Figure 3: Increasing Analytics Complexity
IIC Journal of Innovation
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