CONTROL & AUTOMATION
DIGITAL FOOD FOR THOUGHT
MITSUBISHI
Smart factories and the use of AI from an automation perspective
Factory automation has traditionally provided
the food industry with faster, more reliable
and cleaner production capacity - however,
with the advent of the smart factory and
commercial AI the factory of the very near
future is starting to make production efficiency
improvement decisions itself.
The first stage of becoming a smart factory
is digital network communication, having the
right data infrastructure allows companies
to create, move and use that data efficiently.
With the swift movement and processing
of data comes homogeneous control and
fast responsive manufacturing, which in-turn
justifies investment in the latest automation
technology.
Getting the most from a Smart factory is then a
case of being aware of the possibilities, this is
where using state-of-the-art technology such
as AI can already improve the performance
and efficiency of factory equipment and
human resources.
THE ROLE OF THE INDUSTRIAL
INTERNET OF THINGS (IIOT)
The role of the IIoT in today’s factory is to
connect customer demand to a fast and
flexible production facility. Once a purchase
decision is made then any increase in the
speed of response from the manufacturer
is a competitive advantage. If the IIoT offers
us one thing, then it is the ability to define
customer demand instantly and adapt
production to suit.
Companies that can be flexible enough to
move away from large batch production can
also avoid the cost of large stock holding,
both at the manufacturer and throughout the
distribution chain. Customisation is already
a unique selling point for a large number
of consumer goods. The food industry is
following suit with individual printing and
marking options being designed into many
new products.
Customisation equals profitability in both
cases. The transfer of data from a sales
operation to a manufacturing site, out to the
suppliers and then simultaneously back to the
distribution and retail network is the key to
responsive, flexible manufacturing. To achieve
‘batch size one’ profitably and efficiently we
must have the connectivity that the IIoT offers.
The ability to generate, record, transfer and
process a large amount of data reliably and
efficiently has other benefits. It enables a
higher degree of traceability for example,
serialisation is already essential for many
food, pharmaceutical and consumer
products. Better information also allows
for continuous improvement and process
optimisations at a micro and macro level,
generating multiple opportunities for
increased efficiency and cost reduction.
ARTIFICIAL INTELLIGENCE (AI) IN
CONTEXT
AI is still at the beginning of its journey but
we can expect it to have a substantial impact
on the industrial environment over the next
few years. AI is a perfect fit for manufacturing
and leading companies are now integrating
various AI functions into factory automation
equipment.
Advanced Analytics (AA) and Artificial
Intelligence (AI) technologies are extending
traditional machine control architectures with
more advanced data processing, learning
and decision-making capacity. The objective
is to deliver increased productivity, efficiency,
reliability and accuracy, as well as opening
up new possibilities for machine control.
AI can, for example, be a driver for increased
productivity. Today, most machines are
still built to work within defined margins of
capability – perhaps to allow for different
loads or speeds or safety ranges. AI
technology using deep learning algorithms
within the control system enables machines
to be driven right up to and even beyond
today’s margins, significantly boosting
productivity without compromising reliability
and quality.
Applying AI principles to individual machine
processes can already help to reduce autoadjustment
times, synchronise increasingly
complex systems and offer helpful
suggestions to operators. It can even enable
autonomous decisions to be made based on
measured data in real-time, further optimising
the process.
Making reliable predictions based on
experience, evidence and guidelines is a
fundamental function of human intelligence.
AI is no different in this respect, it can
contribute toward more effective predictive
maintenance, monitoring the condition of
components to enable replacement before
damage occurs, so preventing unplanned
downtime.
We see this already but deep learning
algorithms are pushing the boundaries
further, calculating with more accuracy
how long a component can run before
replacement. Maybe even compensating
for delivery times on replacement parts
by slowing the machine down slightly to
increase longevity rather than stopping the
production line completely.
28 PECM Issue 45