Asset Performance Management (APM) Defined

Asset performance management (APM) systems act to improve the reliability and availability of physical assets while minimizing risk and operating costs.  APM typically includes condition monitoring, predictive maintenance, asset integrity management, reliability-centered maintenance, and often involves technologies such as asset health data collection, visualization, and analytics.

Asset Performance Management involves information sharing and application integration among operations and maintenance to provide a comprehensive view of production, asset performance, and product quality.  APM improves integration between production management (making the product) and asset management (ensuring the capability to produce).  Goals and objectives become more clearly communicated and shared.  The ramifications of APM extend into business processes, technology, and organizational structure. 

Asset Performance Management APM
Asset Performance Management (APM) Model with KPIs

Asset Performance Management synchronizes production and maintenance with information sharing and application integration among enterprise asset management, manufacturing execution systems/manufacturing operations management, plant asset management, asset integrity management (inspections), and other solutions to provide a comprehensive view of production and asset performance.  This integration increases cross-functional visibility, collaboration, and communication for better productivity, reliability, safety, quality and return on assets. 

Industrial IoT (IIoT) and Industrie 4.0 provide new opportunities to improve overall business performance, particularly for APM.  For owner-operators, this includes operational improvements mostly through improved asset reliability in the process industries and higher quality in the discrete industries.  For original equipment manufacturers (OEMs), IIoT offers new sources of revenue by extending the company’s business model into aftermarket services for higher reliability and quality.  For both end users and suppliers, this incorporates IIoT, analytics, and other predictive and prescriptive technologies to bring performance to a higher level. 

Optimization using APM spans functional silos – like operations, maintenance and quality management - including between silos where significant inefficiency, waste, and sometimes dysfunction often reside.  This APM approach becomes a means to systematically improve key metrics like uptime, mean time to repair (MTTR), asset longevity, cost, quality/yield and safety.  Success with these metrics leads to improvements in executive metrics like revenue, margin, customer satisfaction, and work-in-process (WIP) inventory. 

Proactive Asset Performance Management with IIoT and Analytics

The Industrial Internet of Things (IIoT) with advanced analytics, offers new opportunities to improve the reliability of industrial assets, enabling owner-operators to progress toward no unplanned downtime, which many consider to be the ultimate objective for maintenance and operations.

Preventive maintenance assumes a failure pattern that increases with age or use. Unfortunately, this applies to only 18 percent of assets. The other 82 percent of assets display a random failure pattern. In contrast, predictive maintenance (PdM) approaches employ condition monitoring data to predict failure. Proactive maintenance goes further by combining multiple variables with analytics to predict failure with a higher degree of confidence and fewer false positives.

Typical benefits of proactive maintenance include improved uptime, asset longevity, maintenance cost control, and safety. Industrial organizations should review their asset management strategy and consider increased adoption of condition monitoring and predictive maintenance solutions. ARC Advisory Group recommends that users consider a pilot project for proactive maintenance with analytics – especially for complex assets or a common asset type.

Comparing Maintenance Strategies & Approaches

Building the case for proactive maintenance requires a review of other maintenance strategies along with strengths and weaknesses.

Preventive Maintenance: Appropriate for Just 18 Percent of Assets

Preventive maintenance schedules inspections and/or repairs are typically based on calendar time, run time, or cycle count. This approach assumes that the probability of equipment failure increases with use. However, data on failure patterns compiled by NASA and the US Navy show that only 18 percent of failures are age related, and 82 percent have a random pattern. Based on these data, preventive maintenance (PM) provides a benefit for just 18 percent of assets. Doing a PM on the other 82 percent may well cause failures by placing the asset at the beginning of the Type B curve for early life failures.

Predictive Maintenance: Simple Math

PdM uses condition monitoring to provide advanced notice of a failure so appropriate maintenance can be scheduled and performed to prevent the unplanned downtime. The three common approaches for the condition monitoring include:

  1. Monitoring a process data value associated with the asset from the plant historian with trend charts and/or a mathematical calculation
  2. A plant asset management (PAM) system with sensors (commonly vibration) typically applied to rotating equipment
  3. Periodic inspections and evaluation involving infrared, ultrasonic, oil analysis or corrosion typically used for stationary plant equipment such as steam boilers, piping and heat exchangers

Strategy

Description

Asset Attributes

Car Analogy

Reactive

Run to failure, and then repair

Failure is unlikely, easily fixed/replaced, or non-critical

Radio

Preventive

Service in a fixed time or cycle interval

Probability of failure increases with asset use or time

Replace engine oil every 5,000 miles

Condition Monitoring

Alerts for bad trends or other rules-based logic using a single data value

Assets where a component failure cascades into big $ losses

Oil pressure, coolant temperature indicators

Predictive (PdM) Equipment specific algorithms or machine learning. Multivariable Critical assets where unplanned downtime has business impact Battery Management System in electric cars

Prescriptive

Model and knowledgebase identifies an issue and what to do for repair

Complex assets requiring advanced skills

Dealership-level diagnostic equipment

ARC Coverage Areas

Engage with ARC Advisory Group

Representative End User Clients