Learning objectives
- With the right asset management strategy, improved availability and reduced maintenance costs can be achieved simultaneously.
- Predictive maintenance costs less than reactive maintenance due to increased uptime and less unneeded servicing of assets in good condition.
- Assets running at peak performance reduce energy costs, while driving increased throughput and higher quality.
Asset management insights
- Asset management has become a strategic priority for reliability teams because it enables continuous operations, higher availability and lower maintenance costs by shifting from reactive and schedule-based maintenance to predictive, data-driven decision-making.
- A successful asset management strategy aligns monitoring and sensing technologies to asset criticality, focuses early efforts on high-risk or problem assets and uses expert guidance and enterprise-ready data infrastructure to deliver measurable ROI and long-term operational advantage.
Modern reliability teams have come to understand that asset management is now a strategic imperative. Consequently, there is more urgency than ever to ensure that continuous operations remain truly continuous, with no unplanned downtime due to asset failures.
Global markets are increasingly volatile. To stay competitive, organizations need continuous operations, peak availability and lower maintenance costs — simultaneously. On the surface, accomplishing both cost reduction and improved availability may seem to be a contradiction, especially in the face of shrinking teams and few reliability experts.
However, with the right asset management strategy, improved availability and reduced maintenance spend reinforce each other. Predictive maintenance simply costs less than reactive maintenance and assets running at peak performance reduce energy costs and drive increased throughput and higher quality.
Forward-looking teams are exploring ways to implement and/or improve their asset management strategies. However, in the wake of a digital transformation boom, executives face an overwhelming technology landscape with too many point solutions and seemingly unclear pathways to return on investment (ROI). As a result, many teams stall before they even begin implementation.
Fortunately, there is a viable path forward and it is not nearly as challenging as many people think. By approaching asset management with strategic clarity, clear technology alignment and expert-driven deployment, reliability teams can shift from isolated experiments to an ROI-driven strategy.
Aligning assets and budgets
Many organizations already have a preventive maintenance strategy in place for their reliability teams, following standard manufacturer recommended practices to ensure their assets receive the attention they need. However, relying solely on preventive maintenance comes with risks.
First and foremost, in the era of workforce shortages and few available expert personnel, it can be easy for preventive maintenance schedules to slip when the right people are not available. If preventive maintenance is delayed or skipped, unnoticed asset issues can quickly turn into asset failures.
Even if a plant stays perfectly on top of its preventive maintenance schedule, the reliability team may be spending too much time and money by over-maintaining assets. If an asset is overhauled on a regular cadence, regardless of need, the team may be using parts and initiating outages that are completely unnecessary, as the asset may have months or years of remaining lifespan in its current condition. Worse, disassembling an asset that is running well runs the risk of introducing issues where there were none before — and if that asset is not maintained again until the next scheduled cycle, that issue could become a failure before anyone can intervene.
Reliance on traditional reliability strategies is compelling, but failure potential is a powerful business case for predictive monitoring and analytics.
Consider the example of one large refinery that was vacillating on whether it wanted to invest in predictive maintenance technologies. Over the course of many meetings with the automation solutions provider, the team struggled to come to a final decision — until a major asset failure forced them to confront the cost of inaction. The team could see that the reactive nature of their maintenance strategy was incredibly expensive and more importantly, could use the asset failure example to demonstrate to management what the cost avoidance would have been if they had implemented the predictive monitoring they were considering.
The real-world impact was enough to drive the team to action to initiate a proof-of-concept project. They instrumented approximately 10% of their assets, mostly bad actors and used those pilot projects to demonstrate fast ROI.
The key lesson? Real failures often reveal what models and spreadsheets cannot: reactive maintenance is far more expensive than predictive optimization.
Moving away from manual data collection
What is the first step toward implementing a modern predictive maintenance program? Teams must move beyond the structural limitations of infrequent, manual data-gathering approaches. Manual data collection every 30 to 90 days is too infrequent to detect early faults, degradation or root causes. Issues can develop quickly and teams need instant access to asset health to help them prioritize action. Manual rounds are also expensive in terms of personnel costs and can put employees in harm’s way.
Modern reliability strategies depend on automated collection of data via continuous condition monitoring. By adding intelligent sensing devices to assets around the plant, teams can ensure data is collected at a regular cadence to power analytics tools and provide effective insight into machinery health (see Figure 1).
Continuous condition monitoring is particularly valuable for lean teams. As more assets around the plant are monitored automatically and removed from manual rounds, personnel are freed to focus on more valuable tasks. Moreover, if an organization employs fit-for-purpose condition monitoring tools, the data will be clean and contextualized, ensuring it is ready to power machine learning and prescriptive analytics tools.
Asset management program success
Most asset management programs begin with a proof-of-concept pilot program, but without a clear ROI model going into that pilot, many will stall or fail early. Yet, because many organizations are just beginning their modern asset management journey, they often struggle to define clear ROI goals that will help them succeed.
This is where working with an expert automation solutions provider can be a helpful first step. A partner with decades of expertise in an organization’s specific industry can help identify industry-specific strategies the reliability team can replicate.
For example, at one large North American refinery, the reliability team knew it needed more data to stay ahead of chronic asset failures. The team relied on its information technology department, but the department identified low-cost, limited-capability sensors for implementation. But before committing to the solution, the team reached out to its automation solution provider, which connected them with expert personnel at another refinery in the refinery’s own enterprise who had implemented a similar program years before. The expert shared how the low-capability sensors would not deliver the actionable insight and effective ROI the team was seeking and they helped steer them toward more advanced sensors with predictive value.
Asset criticality
In addition to an effective focus on ROI, teams must also know what assets to instrument and how to instrument them. Assets vary drastically; for example, a cooling tower pump, boiler feed pump and compressor each require different monitoring frequency. Monitoring every process variable is neither practical nor cost-effective, so teams must follow a criticality-based strategy to ensure effective monitoring.
Monitoring typically ranges from:
- Monthly manual routes: low criticality
- Hourly wireless sensors: medium criticality
- Continuous monitoring (once per minute or per second): high criticality
- High-speed sub-100 millisecond monitoring: assets with catastrophic risk
An effective, holistic asset management program will use multiple modalities matched to asset criticality instead of a single universal solution. By doing so, the team can focus its limited resources where failure generates the highest risk, ultimately delivering the highest ROI (see Figure 2).
Sensors to manage the assets
In addition to ensuring the right assets are monitored, teams must also use the right sensing technologies. The most basic wireless sensors deliver only raw data, leaving teams drowning in unprocessed information. Even if a team has a deep bench of analysts — and most do not — the time spent turning that data into actionable information could be much better spent on other tasks.
To address these and related issues, forward-thinking teams are letting the sensors process the data, using high-capability intelligent sensing devices that turn data into actionable guidance before delivering their reports. These tools use edge analytics and on-board artificial intelligence (AI) to identify the most common causes of issues and to then deliver decision support alongside the data (Figure 3).
In practice, these tools save reliability teams significant time. At one specialty chemical site where the reliability team installed a series of intelligent wireless vibration sensors, upon activating the sensors the technicians immediately received notification of low lubrication on a rotating asset. When the team inspected the asset and checked their records, they discovered the asset was not only near failure, but it also had never been lubricated.
At another plant, an asset monitoring device reported a lubrication issue to the reliability team and they assumed the device was faulty because they had just lubricated the asset the week before the notification. Upon manual inspection, the team discovered they had used the wrong lubricant, an issue they likely would have missed had they relied upon manual rounds or low-capability sensors delivering only raw data.
The right intelligent sensing devices reduce the need for specialized analytics skill, a valuable differentiator when expertise is scarce.
Determine which assets to monitor
A well-functioning asset may take years to begin degrading to the point where intervention saves resources. As such, instrumenting the plant’s best assets is not an ideal strategy for demonstrating the ROI of an asset management program.
Most plants without an asset management program have problem assets. These are the perfect targets for monitoring and analytics. If a team focuses on assets with known issues and includes enough assets to demonstrate ROI, typically in the range of 10% of all assets and then monitors and reports the data thoroughly to capture early wins from preventable failures, the likely result will be a high-quality, proof-of-concept pilot.
Advanced asset management technology
Asset management success requires both advanced tools and human expertise. Teams not only need to identify the right sensing technologies, but also to implement them effectively based on asset criticality. Yet not every organization has the expert personnel on staff to accomplish those goals.
This is another area where many teams drive successful programs by building expert partnerships with automation solution providers with a broad portfolio of sensors, software and services covering all asset classes. These organizations typically have internal Category 3 and 4 analysts – personnel qualified for advanced process and system analysis, optimization and problem-solving – whose expertise is essential for high-value diagnostics. Expert partners can help teams determine criticality, provide analysis support, coach teams during implementation and help continually optimize a program as the reliability team demonstrates success.
Another key advantage of working with an expert partner is their ability to help the organization future proof its investments. Today’s most successful reliability programs are built as part of an enterprise operations platform strategy, focused on seamless data mobility from the intelligent field, through the edge and into the cloud. Based on a unified data fabric, an enterprise operations platform strategy not only helps ensure solutions are seamlessly integrated for easier deployment and maintenance, but also prepares the team for future technological advancement.
As teams begin to demonstrate success at the plant level, many will want to move toward centralized machinery health management, first at the plant and ultimately across the enterprise. An expert partner can help accomplish this goal, designing with an enterprise operations platform strategy from the earliest stages to avoid silos of data that limit visibility and impede integration (see Figure 3).
Moreover, those same competencies will also deliver value as emerging artificial intelligence (AI) technologies elevate asset management capabilities. The most advanced reliability tools already leverage AI for automated vibration analysis, pattern recognition, failure mode identification and prescriptive recommendations.
Yet that is only the beginning. Near-future AI will predict time to failure with increasing accuracy — but only if long-term, high-quality data exists. Teams building an asset management solution based on an enterprise operations platform model are building the powerful data foundation necessary to unlock those capabilities and deliver competitive advantage in the years ahead.
Strategic asset management delivers increased success
It is possible to simultaneously accomplish both cost reduction and improved availability and performance. The most effective asset management programs target the right assets, use the right tools, collect the right data, leverage expert partnerships and scale to enterprise analytics. This approach builds the data foundation for actionable decision support today and AI-driven reliability in the years ahead.
Teams that delay modernization risk widening competitive gaps as other organizations build predictive and, eventually, prescriptive advantage. Asset management is no longer just a maintenance program; it is instead a strategic component of operational excellence and enterprise competitiveness.
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