Situational Intelligence and Smart Cities: Water


Previously, we looked at how progressive use cases can help cities evolve their smart infrastructure, and how that might work for city transportation. Let’s look at how this evolution might work for municipal water systems.

As a first step, situational intelligence supports mapping water flows through meters across the city. By comparing projected and actual water flows, situational intelligence can locate water mains most in need for repair or replacement. Critical and proactive repairs can help address the 10 percent or more of water lost in most distribution systems due to leaks. Reclaiming lost water raises production, which saves money by either increasing sales or holding down operating costs.

With the savings from repairing leaks, a city can add a customer service module to their situational intelligence platform to provide better service to customers at lower operating costs. At the same time, customer satisfaction with city water service can rise.

Once a city can map water flows across the distribution system and increase customer service and satisfaction, it is in position to implement a water conservation program to increase sustainability and ensure water supply for future economic development. Situational intelligence supports water conservation by flagging anomalies in water consumption patterns governed by conservation program rules.

In a future blog post, we’ll see how this progression can work for energy.


Virtual Reality and Visual Analytics: Context for the Internet of Things


A related post explains how virtual reality revolutionizes visual analytics by providing a virtual presence combining data and the work environment. But a virtual presence is just one advantage of virtual reality.

Consider other, more analytical advantages of a 3D environment:

  • More space for information: because virtual reality offers a 3D immersive experience, there’s practically unlimited space for exploring your data. Compare this with the desktop metaphor, where you get more space by either adding more monitors or opening additional windows and moving between them.
  • A third dimension for information: because virtual reality is 3D, it offers an additional axis for data display and manipulation. In virtual reality, you can have a cube of data; on the desktop, you can have a table or spreadsheet. Many datasets are inherently three-dimensional. For instance, make, model, and year of cars in a fleet of vehicles.
  • Context for data from the Internet of Things: An explosion of devices are becoming connected to the Internet and generating data: thermostats, appliances, cars and more. Having an immersive environment that replicates the physical one makes it easier to place all that data in a familiar context, making correlation and analysis much more intuitive.

Yes, you’d look silly in your office tomorrow morning, working at your desk with an Oculus headset covering half your head. Just remember that the original computer mouse was a block of wood with sensors, and computer monitors used to require cathode ray tubes that occupied half your desk. It’s quite possible that virtual reality will have more impact on how we work than the mouse and the monitor.


Virtual Reality: A Revolution in Visual Analytics


A previous post asserts that visualization cannot be a commodity, because visualization leads to visual analytics that improve, simplify and speed data-driven decision making. Virtual reality is poised to move visual analytics, and many other aspects of how we work, beyond commodity and to the edges of science fiction.

Virtual reality is a computer generated, three-dimensional environment that people can interact with and explore. Oculus is one virtual reality company you may recognize. (In March 2014, Facebook announced that it would acquire Oculus for $2 billion, before the company had even shipped a consumer product.)

With virtual reality, nearly any environment, factual or fantastic, can be generated—including the familiar environs of our workaday world. This means virtual reality has the promise of helping workers resolve issues faster and more safely with less expense.

Oculus 2
Virtual reality is changing the way we do our jobs and interact with the world.

For example, one environment that can be modeled in virtual reality is an electrical substation. That model can display IT, operational, and external data related to the substation. When an operator receives notice of a malfunction at the substation, he or she could perform a practical walk-through of the substation using virtual reality correlated, analyzed and visualized (situational intelligence) with actual data read from the real-world environment. This allows easy, contextual inspection of the problem without the expense of rolling a service truck to the site just to identify the problem.

One company offering a glimpse into how virtual reality combined with visual analytics might look is Space-Time Insight, and the 2015 DistribuTECH event. They’ve offered a summary of what you can see if you stop by their booth.

Using the virtual walkthrough, operators can either identify how to remedy the malfunction without sending a crew, or fully diagnosis the problem so that crews can repair the malfunction with a single trip. (This applies as well to mobile assets, such as planes, trains, and ships–a virtual environment allows an operator or technician to virtually board the vehicle while it is moving, to diagnosis issues during operation.)

In dangerous environments, such as hazardous waste disposal and sites rendered unstable by natural disasters, virtual reality provides unprecedented safe access to inspect conditions systems. By combining virtual reality with advanced data analytics and robotics, remote operators can inspect and repair locations and equipment without jeopardizing workers.

Until now, virtual reality has been the province of research labs, video gamers, and sci-fi writers. Industrial applications, such as visual analytics, will quickly move virtual reality into the mainstream.


Situational Intelligence and Smart Cities: Transportation


In the opening of this series of posts on situational intelligence and smarter cities, I outlined how cities can progress through solving different types of use cases to build their smart infrastructure of the future while also addressing current needs. Let’s look at how this might work for transportation in an evolving city.

As a first step, city officials can use situational intelligence to score and rank the most congested portions of city roadways and test possible solutions. Cities can also score and rank the risk posed by aging and overworked bridges, overpasses, and tunnels. Quantifiable evidence of the probability of asset failure, and the consequences should failure happen, provides strong evidence for seeking new funds to infrastructure improvement.

After addressing the most congested and riskiest transportation corridors, city planners can use situational intelligence to study and improve public transportation speed and capacity through major corridors. This helps to further reduce congestion and degradation of city streets.

Using situational intelligence to bring together traffic information, city planning, environmental data, economic development, and population growth data, city planners can design future neighborhoods and transportation corridors that provide increased convenience and safety with decreased noise and air pollution and traffic congestion.

In future blog posts, I’ll lay out similar use case progressions that can help evolve smarter cities today.


How is Situational Intelligence Different From Situational Awareness?


In the heat of battle, you need to keep your wits about you.

Situational awareness is the approach developed by military leaders to identify, process and apply critical elements of information about their team and its progress towards the stated goal. Simply put, situational awareness is knowing what’s going on around you.

Situational awareness encompasses the team, the objective, equipment and supplies, the physical terrain, the time, the weather, the enemy and anything else that’s relevant. To make optimal decisions, you need to be aware of all the relevant information related to your decision process.

In situational awareness, bad decisions happen because the team does not recognize a threat or risk, is not aware of a lack of equipment or resources, or cannot adequately foresee the consequences of previous actions.

But even with perfect situational awareness you can make a bad decision. One source of bad decisions is the limitation of the human mind to simulate future outcomes based on the current state. When the volume, variety or velocity of data about the current state exceeds our human abilities, we need additional help to identify, process and apply critical information related to our stated goal.

Situational intelligence helps us and our teams exceed their normal human limits for situational awareness to develop an optimal response to current and future conditions. It augments the discipline of situational awareness with the power of machine intelligence. With the assistance of sensors, computers, analytics and machine learning, we can comprehend and respond to situations made enormously complex due to petabytes of information, milliseconds of time, multitudes of network relationships and numerous external data sources.

As technology becomes increasingly integral to, and integrated with, our decision making, it seems likely that situational intelligence will supersede the concept of situational awareness.


Situational Intelligence Offers a Path Toward a Smarter City


Like other large organizations, cities become smarter over time, in an evolutionary more than a revolutionary process.

The experiences of vendors and their clients implementing smart technologies for utilities, telecommunications, and other large enterprises show that simple, successful early use cases can build infrastructure, revenues, and organizational support for subsequent and more complex use cases. Later, higher level use cases emerge that justify and help pay for new investments.

For instance, early use cases may focus on lowering operational costs and building common tools for future projects. Using those common tools, later use cases can improve existing city services and support new services.

Smart City use cases

Kicking off smart city evolution in a smart way requires beginning with identifiable, achievable use cases with definable results that also help establish enabling infrastructure for future smart city use cases. Implementing a situational intelligence platform for analytics and visualization supports level one use cases for a smart city and forms a foundation for future level two and level three use cases, as well.

This post is the first in a series that explores how situational intelligence helps make a city smarter today and leads to the Smart City of the future.


Optimizing Crew Dispatch


The service call seems like a dying art. For repairs at your home, it can be hard to pin down a repair technician to a four-hour arrival window. When the technician arrives, will he or she have the right training, tools, and supplies to diagnosis and repair the problem? If not, you’re stuck with follow-up appointment sometime within another four-hour arrival window. Magnify this problem for mission-critical equipment costing thousands or millions of dollars, and service calls become a critical event in asset-intensive industries.

Field service embodies complexity that can benefit from situational intelligence. One large utility in Canada found that it could save millions of dollars a year simply by better scheduling of field work crews. The utility supplies power to a sprawling, rural service territory. There are more power poles than people, and drive times are long. When you’re paying repair crews by the hour to drive great distances, reducing the number of trips quickly produces savings.

The first way to optimize crew dispatch is accurately locating and diagnosing the problem. This is easy when a homeowner calls to say that their dishwasher doesn’t work. It’s much harder with geographically large and diverse networks such as utilities and transportation. The growing ubiquity of sensors leading to the Internet of Things can assist in location and diagnosis. Sensor data benefits from situational intelligence’s enrichment with IT and external data, such as financial information and weather data.

Once the problem has been located and properly diagnosed, optimization depends on sending the right personnel at the right time with the right tools and supplies. This requires correlating information from multiple data sources such as personnel records (to determine skill sets of repair technicians), workforce management, customer service, fleet management, inventory management and more. Many of these sources could be manually queried and correlated given enough time and staff; because repairs are often rush jobs, situational intelligence simplifies data correlation to arrive at the optimal repair strategy.

For proactive organizations, optimizing crew dispatch means doing double duty. As long as a crew is repairing a fault on a particular device, is there any other upcoming maintenance that they should perform? This would save dispatching another crew to the same spot in the near future. Likewise, are there any other repair or maintenance needs near the item to be fixed? Doing work while in the vicinity increases efficiency, and possibly reliability as well. The only way to quickly spot opportunities for doing double duty is having situational intelligence in place for your asset maintenance and operations. And of course, serendipitous work needs to be factored into planning personnel, tools, and supplies.

How could, or does, your organization use situational intelligence to restore the lost art of service calls?




Analytics and Holiday Shipping


How did the holiday supply chain hold up in 2014?

Parcel shipping company UPS predicted that they would handle 585 million packages during the holiday season. The company expected to handle more than 30 million packages on at least six days during the 2014 holiday season.

Rival shipper FedEx projected volume of 290 million holiday packages.

According to a recent Washington Post article, both FedEx and UPS delivered 98 percent of their 2014 holiday packages on time by Christmas Eve. This is an improvement from 2013, when FedEx delivered 90 percent on time and UPS only 83 percent.

The 2013 holiday season was hit with larger-than-forecasted package volumes and last-minute snowstorms. Using situational intelligence to correlate and analyze multiple sources of retail sales forecasts along with weather data and vehicle capacity could help improve the performance of large shippers like FedEx and UPS, throughout the year and especially during the holiday season.

In the article, a spokesperson for logistics company ShipMatrix notes that much of the 2013 shipping troubles originated with retailers overpromising on shipping guarantees. Retailers would guarantee a customer delivery by Christmas Eve, but then purchase less expensive, non-guaranteed shipping services. Without analytics, it might be easy to lay all the blame on the shipping companies. Correlating service delivery rate with the services that retailers promised to customers, services the retailers actually purchased, carrier capacity and weather data helps tell a more complete story.


Utilities: Making Sense of Phasor Data


Synchrophasors present electric utilities with a particularly challenging combination of data volume and velocity.  A synchrophasor measures electrical waves on the grid up to 30 times per second, using a common time source to synchronize with other measurement devices on the grid. Suppose a utility places one synchrophasor at each of its 50 most critical substations–that’s potentially 90,000 data points streaming into the utility each minute, or nearly 130 million data points a day.  Traditional visual data analytics applications for utilities can’t keep up with that pace.

Data from synchrophasors becomes more meaningful and valuable when enriched with information from GIS, mobile workforce management, weather, and other internal and external systems.  However, this variety of data further complicates the analytics and visualization challenge.

Utility operators need an intuitive and informative user experience that also transforms big data into little data by simplifying the complexity of synchrophasor systems. Situational intelligence brings together information from several systems in real-time and display it within the context of the service territory map.  Operators can call up multiple views and dashboards, focusing their efforts on maintaining voltage stability and reducing oscillation, instead of wrestling with the overwhelming volume and velocity of data.

This video from the 2104 NASPI Oscillation Detection and Voltage Stability Workshop shows an example of a situational intelligence application helping utility operators make sense of synchrophasor data.

Synchrophasor video 01

To learn more about using situational intelligence to make the most of synchorphasors, request this recorded webinar or read this article from Transmission & Distribution World.