Digital Twin + Industrial Internet for Smart Manufacturing: A Case Study in the Steel Industry
business context would be sent back to the
real-world object to be executed.
I NDUSTRIAL I NTERNET P LATFORM AS
AN I NDUSTRIAL D ATA O PERATING
S YSTEM
With a digital twin, therefore, we can
describe, simulate and predict the state and
behavior of its real-world counterpart based
on analytics on historical and real-time
data—and we can consequently optimally
respond to changing conditions of the real-
world counterpart.
An industrial internet platform that is built
with the latest advanced technologies—
including Cloud Computing, Big Data and
machine learning/Artificial Intelligence—
offers great potential to rethink traditional
digital architecture in the manufacturing
environment, find new ways to bridge the
application islands and channel data silos as
described previously, enable holistic data-
driven optimization across manufacturing
applications and processes and more
importantly enable a new breed of data-
driven smart industrial applications.
Furthermore, if we define a common
construct (data, models and service API) for
digital twins, we can build digital twins for
components and from them construct digital
twins for equipment, production lines,
workshops, factories and even enterprises—
just like we construct these entities in the
real world. Digital twin thus offers a
systematic approach to represent complex
real-world systems—including those in the
manufacturing environment and digital
space—building comprehensive digital
factories, as depicted in Figure 5.
For example, cloud computing technologies
built on the foundation of virtualization—
including containerization and dynamic
workload orchestration technologies—
enable large-scale computation capabilities
on demand with unprecedented scalability,
Figure 5: Digital Factory Representation built from Digital Twin
IIC Journal of Innovation
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