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Predictive Analytics to Help Manage Asset Risk

Infrastructure Maintenance Analytics

What if you could detect risk before it happened? You can. Find out how Infrastructure Maintenance Analytics uses machine learning, predictive analytics, and artificial intelligence to provide clients with advanced notice of emerging risks.

The asset investment expiration and maintenance tipping point

Many power and utility, oil, gas, water, steam, sewer, and other public infrastructure providers and agencies are up against an expiration and maintenance tipping point. They’re grappling with:

  • Aging infrastructure (pipes, machinery, wires, components, structures, transformers, valves, junctions, etc.) and their disrepair
  • Ineffective or imprecise operational risk management and maintenance triage
  • Reinvestment strategies for those assets

This is leading to critical public and worker safety issues and disruption to service.

At the same time, these companies are experiencing increased scrutiny and complexity in the regulatory and customer landscape. That’s why leaders are looking for innovative ways to better manage their infrastructure maintenance, improve their public perception, and manage regulator expectations and/or punitive fines.

Enter Infrastructure Maintenance Analytics. Deloitte Risk and Financial Advisory's Infrastructure Maintenance Analytics offering is changing the way companies approach risk management. Using advanced analytics methodologies and an innovative low-footprint operational solution, we can analyze large amounts of data—internal, external, and synthesized—and detect adverse trends in a company’s asset risk profile that were previously undetected or suboptimally evaluated. We can also alert organizations to indications of potential financial impact from asset losses and provide increased awareness of potential emerging threats.

Infrastructure Maintenance Analytics solutions generate early warning signals, so stakeholders can respond before—or during—the early stages of an infrastructure failure.

Proactive repair triage and targeting can reduce infrastructure failures

Reacting to a major failure is costly. It can also affect a company’s brand, reputation, and worse—from customer service to legal and regulatory ramifications. Why? Just with regards to electric, most transmission and distribution lines were constructed in the 1950s and 1960s with a 50-year life expectancy. It’s clear to see that this aging infrastructure is bound to fail—sooner than later.1 Similarly, most legacy natural gas networks have significant cast iron assets with known operational risks.

How can you identify, target, prioritize, and remediate those assets where there’s the highest probability of repair need or failure?

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Use Infrastructure Maintenance Analytics to move from reactive to proactive management

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Steps to success with Infrastructure Maintenance Analytics

Step 1: Infrastructure maintenance data integration and cleansing
Identify, compile, and integrate multiple data sources and records that constitute the baseline dataset to be used for the downstream analytical effort. The data conditioning effort includes combining data of different formats from independent sources (internal, external, and synthetic) and cleansing and normalizing information containing conflicting, duplicate or invalid data.

Step 2: Data analytics
Leverage the analytic dataset developed in the previous step to identify statistically significant correlations between potential risk factors and the occurrence of repair needs and/or failures in the asset infrastructure. From the individual correlated variables, develop a multivariate scoring model calibrated to rank order geospatial infrastructure locations or specific asset components from lowest to highest risk.

Step 3: Geographic Information System location of risk factors
Incorporate the key risk factors and risk scores into a geospatial representation of the infrastructure. Overlay the risk factors to create positional scores and heat maps.

Step 4: Predictive analytics
This analysis can be used to predict the issues “most likely needing repair or at risk of failure.” This identifies the geospatial positions and components of the asset network and infrastructure. It’s used to triage and guide operations, maintenance, and capital replacement priorities. At the core of the solution is the identification of risk drivers, high-consequence scenarios, the calculation of the impact of the risks, and the determination of probabilities of occurrence and consequences. The solution can then be operationalized in a company’s technology environment and integrated into the business processes.

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Predictive risk Intelligence (PRi) in action

Critical repair priorities target strategic investments

Becoming truly predictive—knowing where the next potential repair need or failure might occur—helps you manage strategic investments to deliver efficiently, with an eye toward meeting stakeholder and customer expectations and safety and regulatory requirements.

Our Infrastructure Maintenance Analytics solution can help you shift your focus to have:

  • A more resilient, reliable system, capable of delivering safer products and services in less time
  • Fewer “reliability” issues by augmenting current operational processes for maintaining, replacing, and retiring infrastructure
  • Improved ability to meet consumer demand for higher quality services enabled by construction and maintenance of a more modern infrastructure

Deloitte Risk and Financial Advisory can help you take the next step. Infrastructure Maintenance Analytics helps our clients become agile—moving from strategy and planning to ongoing performance management. Our experience across energy and resources, from power and utility providers—including natural gas distribution, power generation, and distribution, oil, water, steam, and sewer—positions us to help you rapidly accelerate your performance.

​When big data contains bad data, it can lead to big problems for organizations that use that data to build and strengthen relationships with consumers. Read Predictably inaccurate: The prevalence and perils of bad big data to learn more.

Watch a replay of our recent Dbriefs webcast Asset management 2.0: Driving optimization and performance. Asset management teams in the utility industry are grappling with aging infrastructure and highly visible asset failures. How can emerging technologies help reverse those trends? View the on-demand webcast to learn more.

Let’s talk

John Lucker
Deloitte Risk and Financial Advisory
Global Advanced Analytics Market leader
Deloitte & Touche LLP


Tom Lochbichler
Deloitte Risk and Financial Advisory
Energy, Resources & Industrials
Deloitte & Touche LLP

Brian Murrell
Deloitte Risk and Financial Advisory
Power & Utilities Industry Sector leader
Deloitte & Touche LLP


Neal Gregory
Senior Manager
Deloitte Risk and Financial Advisory
Global Advanced Analytics
Deloitte & Touche LLP

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