Aim higher, reach further.
Risk & Compliance Journal
Risk management, strategy and analysis from Deloitte

How Predictive Analytics Can Help Power & Utility Companies Detect Risks

Predictive analytics can provide power and utility companies a new set of tools to help identify issues with their infrastructure, including underground pipeline networks.

Monitoring and maintaining assets as complex as gas pipelines and distribution infrastructures is a costly and time-consuming effort for power and utility (P&U) companies, requiring intense risk management knowhow and practices. The possibility of an aging stretch of buried pipeline becoming unstable one day, perhaps after new above-ground construction moves ever closer, presents a level of risk few other industries face.

Aging or damaged networks of gas pipes, clamps, gauges, valves, machinery, and other critical components can lead to service disruptions, hazardous leaks, or worse—explosions—that can cause injuries or loss of life. In recent years, following several system failures that involved fatalities, state regulators in California, Kansas, Colorado, and elsewhere have imposed more stringent infrastructure repair and replacement requirements.

With so many factors that can contribute to the eroding of a gas pipeline, determining which sections and components are most urgently in need of repair is a difficult process, especially in urban areas where pipes can be located far below busy street or rail transport levels. New cognitive data-based technologies combined with predictive analytics, however, are helping to improve the odds of probabilistically identifying and triaging dangerous conditions so that pipelines can be better repaired and replaced in a timely manner.

John Lucker
John Lucker

“Capabilities powered through the use of predictive analytics and benchmarking tools can be adapted to a number of problems for asset management and maintenance in the P&U sector and elsewhere,” says John Lucker, a principal and a global advanced analytics market leader with Deloitte Risk and Financial Advisory for Deloitte & Touche LLP. “Such innovations can allow engineers to be more granular and more precise in how they think through where they need to focus their repair crews’ time and attention and better focus on those parts of the infrastructure that could pose the greatest threat.”

By creating predictive algorithms based on historical and current internal, external, and synthetic data, and using risk modeling to rank risk levels across geospatial locations, predictive analytics takes operational risk management well beyond traditional methods. Similar predictive tools and techniques have applications in other industries, including financial services, insurance, and life sciences, where such methods have successfully been used to better identify desired customers, underwrite insurance risks and price loans, and evaluate pharmaceutical efficacy.

Brian Murrell
Brian Murrell

“P&U companies take the safety of employees, customers, and the public very seriously. However like most organizations, they are constrained in the amount of capital they can use for testing and monitoring their gas infrastructures,” says Brian Murrell, Deloitte Advisory Power & Utilities industry sector leader for Deloitte & Touche LLP.

“The goal is to efficiently and effectively get ahead of problems in the network proactively. New technologies can help operations and maintenance teams be more prescriptive and predictive in risk evaluation and thus improve planning around asset maintenance, repairs, or infrastructure replacement,” he adds.

A Supplement to Human Decision-making

Part of the power of predictive analytic engines is their ability to draw on numerous information sources, both internal to the organization and publicly available externally via vendors or open source, and to use robotic process automation (RPA) and statistical processing to create a revised narrative of priorities through risk scoring and real-time alerts. RPA and cognitive intelligence are used to assimilate, cleanse, standardize, and aggregate various formats and types of data from multiple sources.

Throughout the process, the human factor remains essential. “Predictive analytic solutions are not a replacement for people and their expertise,” says Lucker. “These are tools that can add to the arsenal of intelligence and experience of existing engineers and other service staff, while giving them access to a carefully collated and prioritized set of data that can help them triage situations more effectively and more accurately.”

Internal data sources can include geospatial infrastructure mapping, system performance and repair metrics, pipeline sectional age and service history, organizational business systems and applications, customer emails and voice mails, call center records, as well as information from machine sensors. External sources can include traffic density statistics, weather and soil compensation, roadway repair data, customer complaints and social media and news feeds, and government alerts and regulatory notifications at the local and federal levels.

The structured narrative on conditions and risk-based ranking of priorities can be used to build real-time service decisions across many thousands of locations. Is a maintenance crew in the process of digging up and replacing an area of pipeline on a busy street that is most, or disproportionately, risky? If so, it would be helpful to know that a potentially hazardous condition exists in a nearby section of pipeline, which could be simultaneously and more cost effectively examined and replaced before an adverse event could occur.

Predictive results can be continuously evaluated against actual results to determine the power of the models and enhance the accuracy of outcomes. Risk reporting dashboards, heat maps, and mobile smart technologies can provide alerts on threshold breaches and anomalous conditions, as well as risk mitigation recommendations, including corrective actions, templates, and training, as well as support from management. Formal reports can also be generated to describe the evolving risk environment for the C-suite and board to assist them in key decisions and overall enterprise risk governance.

“Having data and risk models that can better help anticipate where there is a service need, and reprioritize service calls and repairs based on that need, is very powerful in terms of safety management and driving down maintenance costs and waste of time and resources,” Lucker adds.

As highly regulated entities, P&U companies are required to submit capital plans to regulators in the states in which they operate, including replacement plans. “That can be challenging as gas pipelines are very complicated networks to maintain; still the industry must comply with regulators’ requests,” says Murrell. “Predictive analytics, when combined with other technologies and operational enhancements, can help that process by giving companies more evidence for why they need to make repairs, when they should be done, and what they need to spend in order to maintain the safety of the infrastructure.”

Putting the Tester to the Test

A utility industry pilot project by Deloitte & Touche LLP shows an example of how predictive analytics can offer a more robust way to identify infrastructure problems. Advanced analytics provided over 450 percent more predictive power than the company’s current state-of-the art methods to predict the asset failure of the worst 10 percent of assets and geolocations. For the worst and riskiest one percent of performing assets, the predictive scoring solution provided a greater than 10 times improvement in predictive repair needs.

Because predictive analytic capabilities rely in part on historical repair and performance records, a testing phase should be used to calibrate the model for accuracy. “Once the analytics, correlations, and probabilistic scoring algorithms are developed and focused on the specific issues we are trying to predict, we test them against prior repair history using blind holdout data to find the degree to which the models correctly and accurately foresee what actually ended up occurring and what geolocations ended up needing repair,” Lucker adds.

“Utilities have always had lots of operational data, and with the increase in ‘industrial internet of things’ connected devices, even more potentially useful data is increasingly available for analysis,” says Murrell. “This enables companies to test the system using actual historical information to see how accurately the algorithm can predict repair needs versus actual prior events to understand how reliably predictive a tool this is, as an important first step before testing in a live environment.” With some fine tuning, a validated predictive scoring model can be calibrated for field use. Final testing using information from prior period inputs allows developers and business leaders to become comfortable with the predictions and necessary actions, and appreciate the significant potential value available by adding this capability to a utility’s asset maintenance tool chest.

By John Labate, Deloitte Services LP, senior writer, Deloitte Insights for executives with risk oversight

About Deloitte Insights
