APM Alliance

Asset Performance Management



By analyzing patterns and trends, Failure Prediction tools preemptively identify potential asset failures. Early detection allows for proactive maintenance, reducing costly breakdowns and improving asset longevity.

Artificial Intelligence /
Machine Learning / IoT

Leveraging AI, ML, and IoT technologies, asset management becomes predictive, automated, and intelligent. This integration means enhanced operational efficiency, improved asset utilization, and the ability to harness real-time data for immediate decision-making.

Data Analytics

Weibull analysis, a trusted method in reliability engineering, aids in understanding failure modes and asset life distribution. Insights from this analysis drive better maintenance planning and improve overall asset reliability.

Reliability Availability
Maintainability Model

This RAM model provides a comprehensive view of an asset's performance attributes utilising Reliability Block Diagrams. By understanding these three core areas, organizations can develop strategies to optimize asset output, extend asset life, and reduce maintenance costs.

Life Cycle

Considering the total cost of ownership of an asset, from acquisition to disposal, Life Cycle Costing ensures that organizations achieve maximum value from their assets, making informed decisions based on long-term financial projections.


Balancing inventory levels with demand, Inventory Optimisation reduces carrying costs, minimizes stockouts, and ensures that maintenance activities are never hampered by a lack of necessary parts.

Remaining Useful

By predicting the duration an asset can operate before requiring replacement, organizations can plan replacements or overhauls strategically, optimizing budget allocation and minimizing disruptions.

Supervisory control
and data acquisition

SCADA systems provide real-time monitoring and control over assets. This ensures immediate response to anomalies, enhanced process control, and improved asset performance.