Automation and Internet Cloud Computing are evolving
and merging into a space allowing for Artificial Intelligence or AI programming
to facilitate improvements in productivity and efficiency
with many economic benefits.
Along with Internet Cloud computing and other advancements with the Internet,
we are now in the middle of the next industrial revolution
mining and turning data into information.
This period of evolution is called Industry 4.0
or the Fourth Industrial Revolution
which includes Industrial Internet of Things (IIoT)
and Digital Transformation mechanisms
that facilitate this evolved 4th generation period of Automation.
In this lesson, we will learn about,
“What is the Industrial Internet of Things or IIoT”?
While the Industrial Internet of Things
does fall under the Internet of Things or IoT area of influence,
the key difference is IIoT
concentrates on the connection of machines and devices in industries like manufacturing,
healthcare, and logistics.
IoT is commonly used to define consumer-based devices like Fitbits,
and a wide range of smart home devices for example refrigerators,
ring video doorbells, lighting, thermostats, and alarm systems.
IIoT is about getting information that every single consumer needs in their hands
when they want it
and Digital transformation
is the digitization of a business
in how we must move to a unified organize data space
using the International Society of Automation standard
for developing an automated interface between enterprise and control systems or ISA 95.
ISA 95 essentially provides a hierarchical model for the Enterprise,
Site, Area, Line, and Cell.
Each level of this architecture uses a specific software system
to gather data for that part of the organization needs,
in which creates a problem for communication between these disparate applications.
With most organizations,
the data is not real-time and lacks efficiency
inhibiting key stakeholders to make informed decisions.
IIoT is about making decisions from information received real-time
and not from a report created from yesterday’s data.
The Industrial Internet of Things is all about interconnected instruments,
sensors, and many other devices that are networked together
and communicating with computer-driven industrial applications
for many manufacturing and energy management areas of business.
IIoT is an evolution of a control system
that allows for a significant improvement in automation
by using cloud computing to enhance and optimize process controls.
Industrial IoT depends on many technologies
but the key technologies primarily consist of Artificial intelligence,
Cybersecurity, Cloud Computing, Edge computing, and Data Mining.
Artificial intelligence or AI and machine learning or ML
are fields that are part of computer science.
AI is where intelligent machines are developed and respond like humans
and ML is where machine learning is a part of AI predicting
and a more accurate outcome delivered without programming.
Cybersecurity technology becomes an important basic platform for IoT and IIoT
enabling disconnected machines to physically connect and communicate in a secure method.
Cloud computing is basically using IT services
and the files to be uploaded and downloaded from Internet-based servers
opposed to using local extranet-connected servers.
Edge computing is a distributed computing model
bringing data storage closer to the location where it is needed
and optimizes sensors, industrial computers, and devices
that are part of the IIoT system
for publishing and consuming data for faster processing.
Data mining and analytics are about
collating and examining large amounts of data stored from various parts of the enterprise.
OK, so why should we go through this somewhat major transformation,
and what are the benefits?
Companies want to be more competitive,
increase efficiency with just-in-time manufacturing in meeting higher customer demand
with better inventory control.
We can do all of this
with the creation of the organizations’ overall automation a digital twin.
A digital twin is a virtual representation
referring to a digital replication of the actual companies’ physical assets,
processes in place, automation systems, and devices.
The twin uses real-time data to enable learning
and reasoning for improved decision making.
The digital twin allows experimentation
with new information that is generated by cloud-based AI functions
without having to shut-down production,
or be concerned with personal safety because testing is performed in a virtual space.
Also, the digital twin could be used as a training ground for new employees
without impacting the live system.
And when comparing IIoT and IoT failures,
IIoT create much higher risks than IoT.
Life-threatening situations or major financial losses
could happen from an actual system failure or downtime for example.
While we may benefit from a long list of benefits through the IIoT transformation,
there are some concerns and risks we should be aware of.
Some of the potential risks of adopting IIoT
are expense of the cost of data integration,
lack of experience, and difficulty of implementation,
and the devastating cyber threats.
Data Integration is one of the biggest obstacles to IIoT implementation.
You could be looking at building a system with
thousands of existing connected sensors and devices,
adding new equipment and software systems,
and interfacing legacy equipment to communicate using standard IIoT protocols.
There is a high cost of integration preparing for IIoT,
requiring new software, hardware, and equipment.
You will need to consider the costs of associated training your existing workforce
and hiring new staff
and the time needed to get workers productive.
And the lack of expertise with integrating IIoT
requiring automation companies to be fully experienced
with an organizations layers of systems
starting with the plant floor transmitters,
process PLCs, operation HMI and SCADA,
reporting based on Database Administration,
manufacturing execution systems or MES for warehouse control
and Enterprise Resource Planning or ERP or accounting systems.
Now integrators are required to have expertise in machine learning,
data science, and real-time analytics.
In the long term, IIoT could more than pay for itself,
but many organizations are still justifiably concerned about investing so much in IIoT.
On the bright side,
the benefits of IIoT in manufacturing,
pharmaceuticals, agriculture,
mining, Oil and Gas, and retail are gaining ground
and many companies are embracing this new paradigm for solutions in optimizing operations
and personnel productivity.
Many manufacturers have already achieved process performance
and have reductions in downtime.
And while manufacturing is certainly leading the way with IIoT adoption,
other industries are becoming open to the idea of embracing IIoT for example.
This concludes the video,
“What is the Industrial Internet of Things IIoT?”
If you have any questions about the “Industrial Internet of Things” in general
feel free to add them in the comment section.
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