5At the dawn of 2007, Apple hadn’t released the iPhone, although Time magazine would later name
it—along with The Palm
Centro—a “gadget of the
year.” The Glo Pillow debuted
that year, sporting LED lights
that gradually woke sleepers—but it did not connect
to the Internet of Things
(IoT). Today, even our eggs do.
(Google EggMinder if you want to learn
more about that.)
In early 2007, consumers hadn’t met
Amazon’s Kindle, which would go on to
upend the publishing business. Boeing’s
787 Dreamliner was four years from
entry into service.
How will our world change in the next
10 years? Will we see more sensors on
more devices and more operations-re-lated information? Almost certainly. Will
customers’ demands on businesses grow?
Very likely. Will your business survive if
it doesn’t innovate? Don’t bet on it.
In infrastructure-based industries, upstarts
are beginning to write the story of the
next decade. Renewable energy and
distributed-generation companies are
changing the way the grid operates.
Telecoms are investing in networks to
support autonomous transportation and
manage billions of devices connected to
the IoT, all of them generating data that
can be converted into insight.
Many infrastructure-focused compa-
nies will struggle to survive against the
upstarts. Data, along with the intelligence
it provides, may be their salvation, but
only if they know how to synthesize it.
THE CASE FOR OPERATIONAL
For infrastructure-dependent indus-
tries—including power distribution, man-
ufacturing, mining, oil and gas, telecom,
transportation and water—today’s tech-
nologies deliver more data than operations
managers can possibly handle at once.
Throughout these industries, operations
veterans have been retiring in large num-
bers. In their absence, infrastructure com-
panies, like utilities, need a system that
can replicate those retirees’ collective wis-
dom. They need operational intelligence.
Even the short list of supporting tech-
nologies is long: automatic vehicle loca-
tion (AVL), distributed control system
(DCS), manufacturing execution system
(MES), programable logic controllers
(PLCs), SCADA and workforce manage-
ment (WFM). Because these systems often
originate from different vendors and use
disparate proprietary data models, inte-
gration is difficult. Point-to-point data
integration is complex, costly, time-con-
suming, and often doesn’t happen—leav-
ing operations personnel with a fragment-
ed picture of business operations.
Most executives have seen this in prac-
tice. The operations manager sits in front
of a bunch of monitors, each with a differ-
ent view of the business. On one screen,
a feed from the AVL system pinpoints the
location of remote workers. On another
is the local weather forecast. To the left
are the day’s work orders, above which is
BY BILL MEEHAN, ESRI
Steps to Operational
Bill Meehan, P.E., heads the worldwide utility
industry solutions practice for Esri. He is the
author of Enhancing
Electric Utility Performance with GIS; Modeling Electric Distribution
Performance with GIS;
and Gas Utilities; Power System Analysis by
Digital Computer, and numerous papers and
articles. Meehan has lectured extensively and
taught courses at Northeastern University and
the University of Massachusetts. Bill is a registered professional engineer.