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5 Steps to Operational Intelligence

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BY BILL MEEHAN, ESRI

At 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-related 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 companies 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 Intelligence

For infrastructure-dependent industries-including power distribution, manufacturing, mining, oil and gas, telecom, transportation and water-today's technologies deliver more data than operations managers can possibly handle at once.

Throughout these industries, operations veterans have been retiring in large numbers. In their absence, infrastructure companies, like utilities, need a system that can replicate those retirees' collective wisdom. They need operational intelligence.

Even the short list of supporting technologies is long: automatic vehicle location (AVL), distributed control system (DCS), manufacturing execution system (MES), programable logic controllers (PLCs), SCADA and workforce management (WFM). Because these systems often originate from different vendors and use disparate proprietary data models, integration is difficult. Point-to-point data integration is complex, costly, time-consuming, and often doesn't happen-leaving operations personnel with a fragmented picture of business operations.

Most executives have seen this in practice. The operations manager sits in front of a bunch of monitors, each with a different 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 a SCADA display. The desk is filled with paper reports from the field, detailing work that needs to be scheduled.

The manager must synthesize that information into three kinds of insight:

• What is happening?

• Where it is happening?

• What should be done about it?

It is rare to find a manager who has that ability.

Wanted: A Digital Stanley

Years ago, when I ran the operations division of a northeast utility, one of my favorite employees was Stanley, the regional crew manager. Every night, Stanley had to make a decision. He had to choose whether to send the day crews home or keep them on overtime. If he dismissed them before a bad storm hit, for instance, it would mean longer outages, unhappy customers and a flood of complaints to the regulator. If he kept the crews on overtime and no storm appeared, he would have wasted company money.

Here is what Stanley did: He stood at the loading dock to smell the air. He checked the weather forecast. He considered our vulnerabilities-trees that hadn't been trimmed, poles that were leaning and overstressed. He knew exactly where customers had been complaining. To make his decision, he organized this data in his head, by location. He assessed the risks. Then, he would come into my office and tell me he was sending the crews home or keeping some on overtime. Stanley was doing spatial analysis.

In the years I worked with Stanley, he was almost always right. His deep knowledge of his workers, local weather patterns and our infrastructure-mixed with intuition-influenced his decisions and formed the core of our utility's operational intelligence.

Then Stanley retired. All that knowledge, intuition and understanding simply walked out the door.

Some Will Struggle To Survive

Many infrastructure-focused companies 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.

My company wasn't unique. Throughout the industry, people like Stanley have been retiring in large numbers. In their absence, we need a system that can replicate their collective wisdom. We need operational intelligence: technology that shows real-time asset performance, infrastructure locations, trouble tickets, weather conditions, customer calls and tweets, and field crew activity. In short, utilities need technology that will help them monitor the many threats they face. In addition to the ever-present threat of storms, utilities must monitor aging assets, whose failure could lead to outages. Utilities must guard against malicious attacks-both physical and cyber. And, they must keep a wary eye on workers' safety.

If we'd had the digital version of Stanley-one system dedicated to operational intelligence, easily viewable by location-we would have been much quicker to diagnose issues and launch appropriate responses.

Five Steps to Operational Intelligence

Many organizations struggle on the path to operational intelligence. Yet, companies can (and have) improve their decision-making by building a better picture of what events are occurring in their service territory-and where.

Event and location information can have a huge impact on operations. Following is a plan designed to help organizations improve their operational intelligence:

1. Map Assets

A digital map is the baseline of a true common operating picture. When operations personnel understand the lay of the land, they enjoy a unified picture of everything inside and outside the operation. That becomes the foundation for decisions that lower costs, improve service and promote safety.

2. Connect Data Across the Organization

A company looking to extract value from operational information must first bridge the gaps among data sources. A geographic information system (GIS) can be a valuable bridge for an infrastructure-based company, because GIS pulls data from all operational systems: SCADA, AVL, IoT-based sensors in the field, and more. GIS technology arranges that data on a map and makes the relationships among operational activities vividly apparent to managers and executives.

3. Define Operating Rules

Not all operational information must be regularly monitored, and much of it must be refined before it can be useful. With GIS technology in place, a company can implement rules that filter noncritical data. Information that should be monitored monthly, for instance, won't be displayed on a real-time dashboard, and conditions that indicate failure will appear in the operating picture only when they cross predefined thresholds.

4. Share Intelligence

Decision-making occurs at regular and irregular intervals throughout an organization. Executives and managers must ensure that those decisions are based on consistent, reliable and up-to-date information. Utilities must dismantle the information silos that plague many organizations and open data to create a broader view of the operating picture. Here, too, GIS plays a central role, distributing information and insight to decision-makers when they need them.

5. Optimize Fieldwork

The final step in creating true operational intelligence is to optimize field operations by eliminating duplicate work and operational blind spots. Through a location-based information platform like GIS, fieldworkers see their assignments on any device, get turn-by-turn directions to work locations, and collect data about the work they perform-all of which feeds into the operations center for real-time monitoring.

Intelligence in the Field

This five-step approach is helping infrastructure-based companies become more efficient, provide better experiences for their customers and protect and expand revenue streams.

By using GIS to understand the location and status of assets, Tennessee's White House Utility District saves $1 million annually through early leak detection and more efficient use of personnel.

Seattle City Light, the 10th-largest public power system in the United States, feeds social media information into its GIS platform to better predict and respond to storms, boosting customer satisfaction.

A multibillion-dollar oil exploration and production company eliminates costly travel by using sensors to remotely monitor oil wells and analyzing that information with GIS.

A California county of more than one million residents analyzes traffic information in real time and optimizes signals to keep drivers moving, improving the area's quality of life.

No one can say for sure what the world will look like in 2027. But one thing we can say is that infrastructure-based companies that synthesize information more effectively will make better operational decisions-and increase their chances of thriving in the next decade.


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; Empowering Electric 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.

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