Efficient Plant June 2022 - 17

feature | digital transformation
Manufacturers should strive for better
sustainability for the future of the planet and
for the future of their facilities. Th e PricewaterhouseCoopers,
New York City (pwc.com)
2021 Global Investor Survey revealed that
reducing scope one and two emissions-
direct emissions from owned or controlled
sources and indirect emissions from purchased
electricity generation-is the top
priority for 65% of investors. How can leveraging
the industrial edge make it happen?
MONITORING PERFORMANCE
As smart-manufacturing facilities grow
in number, the quantity of data produced
by the industry is growing exponentially.
Th is data is typically stored in data centers
which, according to the International Energy
Agency (IEA), Paris (iea.org), consumed
between 200 and 250 terawatt hours (TWh)
of electricity in 2020, which is 1% of total
global demand. With vast quantities of data
available, it's important for manufacturers to
learn how to use it to improve their facility's
sustainability.
Edge analytics allows businesses to process
data close to its source. Aſt er collecting
data from various machines on a factory
fl oor, manufacturers can employ edgeanalytics
tools to process it. Th ey can adopt
smart fi ltering to remove any irrelevant,
invalid, or " dirty " data and only send relevant
information to the data center.
Accessing and using this data in the correct
way allows manufacturers to unlock its
power and monitor environmental key performance
indicators (KPIs). Environmental
KPIs typically relate to emissions, such as
CO2
, and resource use such as water or
material waste. Sensors on each machine
can calculate individual emissions and
resource usage, which can then be stanOpposite.
Using edge analytics to improve
machine effi ciency will play a major role in
manufacturers realizing increases in operational
effi ciency from IIoT investments.
JUNE 2022
dardized to create one uniform data set,
generating KPIs in real time.
Once machine data has been obtained,
fi ltered, and standardized, manufacturers
are leſt with deep insight into asset performance,
making it easier to identify key
contributors to emissions or a particular
resource drain. Further actions can then be
taken to improve the facility's sustainability
credentials, particularly through improved
energy effi ciency and reduced waste.
EDGE-ENABLED EFFICIENCY
Once the machine's data is available, it can
be used to reduce energy consumption.
Industry accounts for 39% of the world's
fi nal energy use, according to the IEA.
Making small improvements to individual
asset performance can result in incremental
energy savings that build to a big impact.
Manufacturers can streamline M2M
(machine-to-machine) communication by
using the data from one machine to gain
an actionable insight into its performance
and generate an output that determines the
confi guration of the next machine.
Using edge analytics to improve machine
effi ciency will be a major reason behind the
technology's adoption in 2022. According
to Deloitte, 45% of manufacturers expect
increases in operational effi ciency from
IIoT investments. Although the economic
benefi ts of improved effi ciency are clear,
embracing the edge also transforms a facility's
sustainability status.
Identifying energy ineffi ciencies in real
time presents manufacturers with an opportunity
to act quickly. For example, edge
analytics can be used to monitor the speed
at which a machine is running or the
quantity of a material it dispenses. In
response to the data collected, each
machine's confi guration can be modifi ed
immediately according to a predetermined
algorithm for streamlined operations. By
optimizing several individual machine
processes that are signifi cant energy consumers,
edge analytics controls and reduces
a facility's overall energy consumption.
DECREASED DEFECTS
Industrial waste is another huge element of
manufacturing's sustainability problem. Th e
World Bank has estimated that, globally,
industrial-waste generation is almost 18
times more than municipal solid waste. Th e
two greatest contributors to industrial-waste
generation are overproduction and defective
output.
If a machine begins to malfunction, its
output will be aff ected, meaning all of the
defective parts it produces will ultimately
end up as industrial waste. By analyzing
machine data immediately and continuously,
manufacturers can identify performance
anomalies before they become a problem.
Once an anomaly has been identifi ed, it
can trigger a warning to inform machine
operators that direct action must be taken.
Production can be temporarily paused, the
machine repaired, and output remains as
intended.
If manufacturing is to reach net zero
and align its activities with international
climate goals, it must act now. Forrester
Research, Cambridge, MA (forrester.com),
has predicted sustainability-related services
powered by edge technology will grow in
the near future. With the urgency of the
crisis clear, there's no time to waste in adopting
environmental, social, and governance
goals. Embracing the edge is crucial to
making it happen. EP
Johan Jonzon is CMO and Co-Founder of
Crosser, Stockholm, Sweden (crosser.io),
producer of the Crosser Flow Studio edgeanalytics
platform. Jonzon has more than
15 years experience working in various roles
in the technology sector. He currently leads
Crosser's product-user interface design and
development program.
EFFICIENTPLANTMAG.COM | 17
http://www.pwc.com http://www.iea.org http://www.forrester.com http://www.crosser.io http://www.EFFICIENTPLANTMAG.COM
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