Predictive Analytics for
Utilities
Quickly respond to demand and supply changes.
Utilities need to improve strategic alignment, strengthen customer focus and gain visibility into the impact of the customer, supply and financial decisions.
Eight out of 10 telecommunication services companies on the S&P 500 use software from SPSS.
Predictive Analytics for Utilities
In order to anticipate, or quickly respond to, demand and supply changes, utilities need to improve strategic alignment, strengthen customer focus and gain visibility into the impact of the customer, supply and financial decisions.
Version 1 Predictive Analytics solutions for water, electricity and other utility suppliers help them:
- Identify and understand customer behavioural patterns
- Forecast water consumption and energy usage
- Align supply and distribution to customer demands
- Predict maintenance issues and reduce the impact
Primary Analytics Solutions for Utilities
Design the analytical solution that is best for your business.
Predictive Maintenance
Predictive maintenance solutions from Version 1 can predict when and where equipment failures are likely to occur, avoiding unanticipated equipment downtime and reducing maintenance costs.
- Predict when and where asset failures are likely to occur
- Leverage from the Internet of Things (IoT) sensors readings on instrumented assets
- Combine external data with internal data, such as environmental and weather data with data from Asset Management and Supervisory Control Systems
- Avoid asset downtime and reduce maintenance cost
- Perform root-cause analyses of asset and process failures
- Mine maintenance logs to determine the most effective repair procedures and failure patterns
- Visualize maintenance and operational insights and ensure Operational Efficiency through alarms prioritization and recommendations
Planning & Customer Analysis
Drive revenue growth, increase competitiveness and improve customer loyalty by better understanding customer needs and aligning your supply and distribution to their demand.
Utilities Case Studies
Case Study – Santos
When a critical asset fails in one of Santos’ operations, the result can be lost revenues and more time on the road for engineers. Santos needed to find a way to identify faults before failures occurred. To maximize production uptime and promote safe, efficient maintenance, Santos predictively models data from assets connected to the Internet of Things, providing early warnings of equipment failure.
Case Study – Israel Electric
Israel Electric Corporation (IEC) generates 95 percent of Israel’s electricity. To meet peak demand, its turbines need to run at full capacity – so it is vital to keep them online and running efficiently. IEC uses predictive maintenance and other technologies to model the behavior of its turbines and monitor their performance in real time. When anomalies are detected, it can quickly trigger maintenance resources to fix problems before outages occur or efficiencies are reduced
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Discover More Industry-Specific Solutions
Version 1’s SPSS experts can consult and deliver a wide variety of analytics solutions across a broad range of industry sectors. Find out more at the links below.
