Reduce Unscheduled Downtime and Maintenance Costs
Advanced analytics – including condition-based and PRiSM Predictive Asset Analytics solutions – provide early warning notification and diagnosis of equipment problems days, weeks, or months before failure. This change improves maintenance planning and reduces downtime by shifting from reactive to predictive or prescriptive maintenance strategies.
Advanced Analytics Software to Optimise Asset Performance
Apply the right analytics mix to maximise economic return on asset investments.
Condition Management
A new cloud-based solution that is focused on increasing plant asset utilization and operational performance by providing accurate information on equipment downtime and efficiency.
AVEVA Predictive Analytics
Formerly Known As PRiSM Predictive Asset Analytics
Monitor asset health and maximize reliability with purpose-built AI and self-service analytics. Forecast remaining asset life, reduce unplanned downtime, and lower maintenance costs.
AVEVA BI Gateway
A unifying multi-dimensional information model to aggregate data from multiple sources and provide context for use with reporting/ dashboard tools. Improve plant performance in near real-time by enabling rich analytics in your BI tool on industrial data.
Turn Your Data Into Insight
Quickly transform raw data into actionable insights to prevent equipment failure and make smart decisions that improve operations.
Predict Equipment Failures and Reduce Downtime
Catch asset failures days, weeks, or months before they occur, and schedule maintenance operations around the most economically viable time.
Increase Asset Utilisation and Extend Asset Life
When a potential problem is identified, instead of shutting down equipment immediately, the situation can be assessed for more convenient outcomes to optimise asset utilisation.
Improve Safety and Regulatory Compliance
Ensure knowledge capture so that maintenance decisions and processes are repeatable even when organisations are faced with transitioning workforces.
Reduce Operations and Maintenance Costs
Early warning with advanced analytics enables proactive maintenance planning allowing parts to be ordered and shipped without rush and equipment can continue running.
Identify Underperforming Assets
Discover which asset or groups of assets are underperforming to prioritise replacements or optimisation opportunities through fleet-wide monitoring.
Maintenance Engineers
Maintenance engineers are provided with increased situational awareness of their asset's health, allowing them to maximise asset utilisation for the enterprise while reducing maintenance costs due to better planning. Parts can be ordered and shipped without rush, and equipment can continue running.
Operations Managers
Operations management can schedule downtime for asset maintenance at the least economically disruptive time to the enterprise. Keep production lines running and product shipping with improved visibility into how asset performance impacts the enterprise value chain.
Reliability Engineers
Reliability engineers use advanced machine learning built into PRiSM Predictive Asset Analytics to empower them with increased visibility into asset health and operations. This enables them to accurately predict and eliminate the root cause of all failures and plan downtime accordingly.
We found PRiSM Predictive Asset Analytics to be an effective tool in the predictive diagnostics space for detecting functional deviations and impending failures at an early stage for initiating suitable prioritized maintenance actions for enhanced reliability of critical power plant equipment.
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