Reduce unscheduled downtime, increase reliability, and improve safety by applying the right analytics to your APM strategy
Reduce Unscheduled Downtime
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 Optimize Asset Performance
Apply the right analytics mix to maximize economic return on asset investments.
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 Utilization 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 optimize asset utilization.
Improve Safety and Regulatory Compliance
Ensure knowledge capture so that maintenance decisions and processes are repeatable even when organizations 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 to continue running.
Identify Underperforming Assets
Discover which asset or groups of assets are underperforming to prioritize replacements or optimization opportunities through fleet-wide monitoring.
Maintenance engineers are provided with increased situational awareness of their asset’s health, allowing them to maximize asset utilization 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 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 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.
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