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Logistics Insight Asia: Cognizant’s AVP of Manufacturing, Logistics, Energy and Utilities Consulting Says Predictive Analytics can Help Utilities Reduce Asset Lifecycle Cost and Improve Decision-Making

“At present, preventive maintenance schedules prescribed by manufacturers are not enough to help utilities avoid asset failures,” writes Badrinath Setlur. “In order to improve customer satisfaction, utility organizations need to work towards avoiding unexpected outages, managing asset risks and maintaining assets before failure strikes.” Excerpts:

“By applying predictive analytics to smart asset management, utilities can realize asset lifecycle cost reduction while improving the accuracy of their decision-making, allowing them to plan and prioritize maintenance activities.

Improve customer satisfaction and reliability of power: Customer satisfaction and power reliability are two important measures of a utility’s performance. Customers expect planned outages to be communicated in advance for the purposes of planning for electricity consumption. As a result, utilities also require proactive maintenance of assets prior to failure, so as to avoid penalties governed by strict outage regulations.

Prioritize maintenance activities and reduce TCO: By preventing key equipment failure, utilities can save a sizeable amount of money through predictive maintenance practices. Accurate modelling techniques utilize historical data from multiple sources, enabling the generation of predictions and risk scores. They also produce interpretable information to allow the understanding of implications of events, thereby enabling the right response to be implemented.

Optimize field crews with better route planning: A comprehensive understanding of asset health can serve utilities well in terms of work planning, prioritization and scheduling. Unexpected equipment failure often requires reallocation of crews from other work locations to restore the outage, hiring of extra labor and often, an entire rescheduling of other planned maintenance activities. The percentage of work done in reactive activities can be effectively applied for predictive maintenance—improving crew response time and utilization, while also reducing total maintenance duration and asset downtime.

Improve overall safety and compliance: Predictive asset analytics proactively addresses potential safety risks by integrating data from multiple sources—SCADA (supervisory control and data acquisition), EAM-GIS (enterprise asset management—geographic information system), online monitoring systems, weather channels along with nonoperational data, and so on. They enable utilities to identify safety risks and deploy suitable operational actions to mitigate these risks in a shorter span of time.

Clearly define the immediate objectives of the solution; understand future business requirements; and assess the scalability prerequisites to support additional applications. It is imperative to improve business processes and upgrade IT infrastructure to support any analytics solution before it is deployed.

In order to mitigate the implementation risk for comprehensive end-to-end predictive solutions, an effective way is to harvest the best-in-class solution from multiple providers—data management, systems integration, analytics engines and operational technology integration.”

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