Instrumented Interconnecteds Intelligent

Deepak Advani, Vice President, Business Analytics, IBM

Deepak Advani, General Manager, Cloud and Smarter Infrastructure, IBM

By Deepak Advani

The Internet Age has made it possible for dramatic amounts of information to be available at our fingertips. And as capacity expands and accessibility grows, we push ever closer to the  Internet-of-things, where our physical and digital worlds are tightly coupled and leveraged.

With the ability to generate, share, store and access increasing amounts of data – Big Data – the challenge soon becomes one of management and analysis. Left alone, the mountains of seemingly disparate information are useless. But when mined intelligently, they become treasure troves of insight that can unlock benefits, such as improved customer service, equipment-saving predictive maintenance, and new business opportunities, to name a few.

Take for example, the second largest commodity rail carrier in the European Union, PKP Cargo. The company manages 63,000 wagons and 2,400 locomotives that transport more than 110 million tons of cargo every year. PKP Cargo offers one-stop logistical services that include 10 multi-system locomotives, ensuring continuous journeys along routes electrified by more than one system. These trains and wagons are used for international transport in Poland, Germany, Czech Republic, Slovakia, Austria and Belgium. On average, the company runs about 1,000 trains a day, servicing several thousand customers.

SP PKP Cargo 2013

The amount of data being generated and needing to be tracked was difficult for PKP to sift through. The company realized it wasn’t just the task of tracking their wagons and trains that was vexing; it was the lack of analytics that it could turn into actionable information. Through IBM Maximo Asset Management software, PKP can now better predict when and where cargo will arrive, making sure each train is on schedule and meeting customer expectations. Just as important as tracking their assets, analytics empowers more than 10,000 of PKP’s employees with information to make more intelligent decisions, which ultimately enables PKP to expand its business. By also tracking the usage of each physical asset, PKP is using IBM analytics to predict potential repairs before failures occur and to perform preventative maintenance on its rolling stock.

PKP’s use of analytics is not uncommon and neither is the concept of gleaning intelligence from data to create new opportunities. Consider Pakistan. The country is quickly outpacing its current power generation capacity, with energy consumption growing by almost 80 percent in the last 15 years. The Pakistan Water and Power Development Authority (WAPDA) forecasts the country’s electricity demand will increase to around 40,000 megawatts (MW) by 2020. The country’s current power generation capacity is at 23,538 MW.

To meet these growing demands, Pakistan created its first hydroelectric power plant through its operator, Tenaga Nasional Bhd (TNB) Remaco. The recently-constructed $235 million plant will help meet Pakistan’s growing energy needs by adding an estimated net annual energy of 540 GWh to the national grid and enhance the hydro power generation capacity of Independent Power Producers (IPP) by 40 percent. The addition of this power source will replace about 135,000 tons of imported oil, reducing carbon emissions and saving Pakistan approximately US$100 million per year in fuel costs.

The inherent challenge, however, was managing the hydro plant operations in a way that could scale to meet business demands. Through IBM’s asset management software, TNB Remaco now employs a single, converged view of all energy data, as well as asset and service management data. That enables employees to identify problematic equipment and make better informed decisions that are critical to the operations of the plant

The moving parts of a hydroelectric plant or railroad system serve as good examples of how our world is dependent on the mechanics and operations of physical objects. But today, we can do more than just monitor these systems and machines. We can collect data from them and apply analytics to make better, more strategic decisions on how to use them. The next step will be finding ways to create “cognitive” systems that can learn, adapt and hypothesize answers.

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