什么是资产性能管理 (APM)？

Asset Performance Management (APM) is the practice of tracking the use and condition of fixed assets throughout all company locations to optimize their efficiency and effectiveness. APM processes are critical to asset-intensive organizations where asset idleness and degradation threatens business goals and poor asset maintenance jeopardizes environmental, health & safety compliance. Additionally, underperforming assets contribute to unplanned downtime which is often vastly more expensive than the alternative plan of regularly scheduled maintenance, some reports warn it could be up to 10 times more costly.

To combat this and reduce total cost of ownership, many companies have turned to Asset Performance Management software. Asset Performance Management software approaches optimizing asset lifecycle performance using a data-driven methodology. Collecting data, interpreting, optimizing, and visualizing are basic features, with more sophisticated APM applications able to predict and prescribe courses of actions based on current conditions of the asset (real-world condition at the moment).

The best APM apps will offer prognostic features, which anticipate if, when, and how the asset/machine/component will fail, and then prescribing a plan of maintenance before the critical failure. Data variables that contribute to prognostic software can be exceptionally detailed, such examples can include the monitoring of bearings, gearboxes, tires, electrical load, vibrations, road conditions, etc. This translates, for example, in understanding which vehicles within a fleet are undergoing more stress and in need of sooner maintenance.

Asset performance management software connects disparate data sources and leverages analytics to turn data into insights. Using data management, pattern recognition, predictive analytics, artificial intelligence, and machine learning, APM packages can look across multiple sites, finding familiar patterns, and suggest optimizations accordingly. 

These features are incredibly powerful for efficiency and optimization, with Digital Twin modeling, a new approach and technology for modeling real-world objects with a virtual representation, raises the bar. With Digital Twins, assets can be modeled, put under stress tests, and detect even hyper-subtle opportunities of failures that potentially could save high-value assets, or assets far from reach such as spacecraft or submersibles.

Asset Performance Management software benefits asset-intensive organizations, so they can achieve operational efficiency and ultimately improve profitability. To achieve this, APM has several key benefits:

• Anticipates Risks and Resolves Before Critical Events Occur — APM software makes major improvements in asset performance by reducing unplanned downtime, a critical challenge for maintenance teams and asset managers. Using predictive and prognostic analytics, APM software can anticipate critical events based on real-time conditions combined with other data indicators and determine a resolution before risking downtime. 
• Maximizes Time Management — The benefit of using insights about future downtime and anticipating critical security risks with their appropriate resolution allows teams to eliminate many time-consuming practices while retaining essential routines that maximize worker schedules. 
• Extends Useful Life of Assets — Because asset performance becomes more efficient, their economic life is extended thereby reducing capital expenditures over time as fewer assets need to be replaced as often. 
• Data Optimizations — A repository of structured data captured from assets creates a historic record that smooth job transitions, much like patient records, teams can refer to an accurate chronicle of jobs performed on the asset. Further analytics on this data could suggest asset optimizations that can improve its efficiency and usable life.
• ·Streamlines Compliance Management — Compliance management also improves with streamline workflows and embedded reporting.

Asset performance management is the set of practices a company undertakes to improve company-wide asset usage while reducing costs. Companies can implement any number of approaches based on their resources. The following are some of the strategic approaches that best-in-class organizations have found have led to their successes.

• Advanced asset performance management and analytical capability — Implement a tech stack that captures data from assets, then draws analytical insights using the latest analysis techniques such as with AI. Best-in-class companies invest in monitoring assets with hyper detail, e.g., indicators like machine vibration can help predict future downtime, and call for preventative maintenance.
• Continuous improvement programs in place — Adopting a culture of rapid incremental improvements can help deliver value quickly after performance bottlenecks are discovered. Intermittent breakthrough improvements, although useful like when installing APM software where there was none before, are important, however they can leave long stretches of time open to underperformance.
• Improve effectiveness of maintenance operations — Continuous improvement depends on improving overall maintenance operations. Tracking maintenance metrics, maintenance team training, maintenance planning and scheduling, improving worker morale, and improved basic maintenance systems are just some ways maintenance operations can be improved. The key is to measure and improve, raise the bar, and not allow teams to settle for good enough.

Asset Performance Management software approaches optimizing asset lifecycle performance using a data-driven methodology. Collecting data, interpreting, optimizing, and visualizing are basic features, with more sophisticated APM applications able to predict and prescribe courses of actions based on current conditions of the asset (real-world condition at the moment). 

The best APM apps will offer prognostic features, which anticipate if, when, and how the asset/machine/component will fail, and then prescribing a plan of maintenance before the critical failure. Data variables that contribute to prognostic software can be exceptionally detailed, such examples can include the monitoring of bearings, gearboxes, tires, electrical load, vibrations, road conditions, etc. This translates, for example, in understanding which vehicles within a fleet are undergoing more stress and in need of sooner maintenance.

For software to be considered an asset performance management solution it must have the following functionality:

• Consolidate fixed asset data from multiple sources and/or locations
• Provide standardized KPIs to accurately track asset performance
• Analyze performance data and identify trends and risks that impact assets
• Deliver responses and optimizations to improve asset usage
• Track the financial performance of fixed assets