Automation's Next Step

AUTOMATION & OPTIMIZATION
screw proved superior to the Robertson
head screw as it was compatible with
automated screwdrivers used at the time.
The technical development of the screw
and screwdriver aside, both standardization
and the first two industrial revolutions are
ultimately what propelled the screw and
screwdriver to their current prominence in
manufacturing, our everyday life, and likely
one of your kitchen drawers.
HAND-IN-HAND WITH DATA
The screw, on its own, struggled to be
anything more than a niche technology.
History shows us that without the invention
of the screwdriver, the development of
standards, and a move towards efficiency,
the screw probably would have struggled to
gain mainstream acceptance.
Data, like the screw, on its own, has limited
applications. In 2012, it was estimated that
only 0.5% of data was analyzed. While we
couldn’t find an updated estimate of this
number for 2020, it is difficult to imagine
it has jumped significantly, especially when
you consider that we’ve increased the
amount of data available to us 40-fold.
The question is no longer, “Do we have
enough data to analyze to make informed
decisions?” In fact, it isn’t even, “Is the data
available to us good or not?” We’re living in
a time when the quality of data has never
been higher. In 2012, it was estimated that
the quantity of “useful data” – data that
could be analyzed – accounted for 22%
of the one zettabyte of data that existed.
Today, that number has risen to 37% of
the 40 zettabytes of data available. In case
you’re wondering (we were), a “zettabyte”
is “1” with 21 “0s” behind it! You’re
probably comfortable today talking about
terabytes, or a “1” with 12 “0s” behind it.
The real question is one of computational
power. With so much data available to us,
the human brain alone is incapable of
comprehending the sheer quantity, much
less weeding out the inherent quality and
subsequently finding insights therein. Like
the screw, the story of data requires a
counterpart to enable its real potential to
shine. That “tool” is AI, and its “head” of
choice is machine learning.
A STORM IS BREWING
Time doesn’t stand still. Many argue
whether we’re living today in the third or
fourth industrial revolution. Some argue
the third is/was typified by the emergence
and prevalence of the digital revolution.
Case in point – when was the last time you
argued whether you require a TOS to run
your terminal? The fourth revolution is
also no stranger to the maritime industry,
focusing on IoT and automation. Clearly,
we’re tracking strongly into this era with
highly automated maritime terminals
becoming increasingly commonplace.
All of this data is presenting new questions
to the maritime sector around standardization,
data ownership, and data exchange. We’ve
seen progressive conversation herein over
the past 24 months.
OPPORTUNITIES FOR MACHINE LEARNING
IN CONTAINER TERMINALS
At INFORM, we’ve seen the value of data
growing over our 25-plus years in the
industry as a software supplier. Our AI and
OR-based algorithms render a wide range
of terminal processes more efficient, from
train loading operations to vehicle, and
container movements. In 2018, we asked
whether we could apply INFORM’s broader
experience and machine learning (ML)
algorithms to terminal operations and set
off on the first in a series of ongoing ML
assessments to identify ways in which we
could assist terminal operations and port
ABOUT THE AUTHORS
Dr. Eva Savelsberg is Senior Vice President of
INFORM’s Logistics Division. She specializes
in Optimization Software that renders
a wide range of operational processes
more productive, agile, and reliable. Eva
is also lecturer at the University of Aachen
(RWTH), where she received her PhD in
mechanical Engineering in 2002. Eva has
published 5 books and over 40 papers on
innovation in freight transportation.
Matthew Wittemeier is responsible for
Marketing at INFORM's Logistics Division
where he’s become a thought-provoking
contributor to many industry publications
and conferences. He’s co-author of the
award winning 2038: A Smart Port Story
– a story about the future of technology
and the social challenges it may bring.
He’s a member and judge at The Academy
of Interactive and Visual Arts (AIVA), the
authorities to leverage their data to improve
operational performance. In our paper
featured in Port Technology’s Edition 83,
Machine Learning in Terminal Operations
we delved into multiple applications areas
of ML, and we’re putting that to use in
customer ports even as we write.
It is no longer a question of whether data
has value. In the same way that we view
the screw and screwdriver as commonplace
today, we will soon view data and
ML as part of our everyday business
toolkits. And, like Robertson’s screw and
screwdriver paradigm shift, or McLean’s
concept of containerization, we’re now at
the precipice when understanding that AIenabled
software and ML will transform
how we engage with data.
Today, the real question is whether you
will use these “digital screws” to build the
foundation of your future or stick with the
flat-head screwdriver approach?
senior producer and host of the buzzITtalk
podcast, as well as an award-winning
designer.
ABOUT THE ORGANIZATION
INFORM specializes in AI and optimization
software to improve operational decision
making. Based in Aachen, Germany, the
company has been in the optimization
business for 50 years and serves a wide span
of logistics industries including maritime,
intermodal, and inland terminals. With
a broad range of standalone and add-on
software modules, INFORM’s unique blend
of algorithmic based software expertise,
rich industry experience, and big world
thinking delivers huge value for their
customers.
ENQUIRIES
https://infrm.co/screwed
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