A more holistic approach to APM: The future of asset performance goes beyond reliability
Posted: March 09, 2026
A more holistic approach to asset performance management (APM)—built around integrated data, digital twins, and AI—helps organizations move from reactive maintenance to proactive performance management across the life cycle.
In this blog, you’ll learn:
In industrial operations today—whether in energy, manufacturing, metals, mining, minerals, utilities, or chemicals—leaders are being asked to do more with less. They need to maximize uptime, reduce operational costs, shrink their carbon footprint, and support compliance, all while navigating volatile markets and workforce shortages. And yet, experts estimate that unplanned downtime continues to cost the global economy around $50B each year.[1] Traditional asset performance management (APM) has long been the backbone of operational reliability, but the expectations placed on operators are changing. To meet the moment, APM itself must evolve.
What is APM, and why does it matter?
At its core, APM ensures physical assets—everything from turbines to compressors to pumps to entire production lines—perform at their best throughout their life cycle. For decades, APM has been key to improving reliability, reducing maintenance costs, and minimizing unplanned downtime. A good APM strategy helps operators with:
- Cost savings:
APM helps organizations prevent failures before they occur. The result is fewer emergency repairs, lower labor costs, optimized spare parts management, and reduced production interruptions. - Efficiency and throughput:
APM boosts operational efficiency by ensuring assets run as designed—using the right amount of energy, generating consistent output, and avoiding bottlenecks. - Health, safety and environment (HSE):
Effective APM reduces safety risks, environmental incidents, and regulatory non-compliance by keeping assets within safe operating limits and catching anomalies early. - Overall equipment effectiveness (OEE):
A strong APM program drives higher OEE by increasing availability, improving performance, and reducing quality losses.
Traditional APM has delivered tremendous value. But with rising ESG pressures, more complex asset portfolios, and growing digital expectations, operators now need a more holistic approach.
Increasingly, leading organizations are operationalizing APM through a digital reliability center model. Rather than managing reliability in silos, a digital reliability center brings together the entire value chain of asset management including data management and digital twins, asset strategies, predictive analytics, monitoring and visualization, and mobile workforce enablement. Teams work in a shared environment where asset health, risk, and performance insights are continuously monitored, prioritized, and acted upon.
How is APM changing?
One of the major challenges companies face with their APM strategy is that their data is trapped inside separate systems or oriented in a non-intuitive way that makes it difficult to access. For APM to move beyond maintenance and reliability, organizations must move from isolated systems to a coordinated operating model. This is where the digital reliability center becomes essential. By unifying asset data, analytics and workflows across disciplines, the digital reliability center enables teams to shift from reactive decision-making to proactive performance management that feeds back into the full asset life cycle.
At the same time, generative AI is changing how teams interact with APM insights. Rather than relying solely on dashboards and alerts, organizations are beginning to use AI assistants and agents to interpret asset health data, surface risks, explain anomalies, and guide next best actions. This shifts APM from a system of record to an active decision partner.
Due to increasing pressure for companies to meet decarbonization goals, sustainability metrics are also being incorporated into this more holistic APM strategy. APM is no longer only about asset availability.
Organizations are now adding ESG metrics, such as:
- Energy consumption by asset and site
- Carbon emissions from equipment operations
- Life cycle risk and end-of-life planning
- Resource efficiency, including water use and waste generation
These expanded KPIs allow operators to make decisions that optimize not only asset health, but also sustainability—and often both at the same time. When APM programs incorporate ESG considerations, companies can mitigate long-term environmental and operational risks while unlocking new value streams, such as energy savings, reduced material consumption, lower carbon taxes, and improved equipment longevity.
Many today are struggling to progress in their digital transformation and in their digital maturity, and a key contributor to this is that many are getting trapped by point-level software solutions that are only solving a very singular problem for the business.
A digital reliability center addresses this challenge by replacing fragmented point solutions with a shared environment where insights are contextualized, prioritized, and translated into coordinated action.
How technology is transforming the APM landscape
In a digital reliability center, technology is not just integrated; it is orchestrated. Generative AI assistants and AI agents extend the digital backbone, continuously analyzing operational maintenance and engineering data to help teams understand what matters most and why it matters. With an open, agnostic architecture, companies can then choose the specific strategy and tools they need along their APM journey.
CONNECT visualizations can unify data streams—financial, production, 3D models, GIS maps, and asset status—into one contextualized view.
IoT and real-time condition monitoring
Connected sensors and industrial IoT platforms enable continuous monitoring of vibration, temperature, pressure, performance, and energy usage. Operators get real-time visibility into asset health, enabling early detection of anomalies before they escalate into failures. Within a digital reliability center, these insights are shared across roles and functions, enabling faster prioritization, clearer accountability, and more consistent execution.
SCG Chemicals, Thailand’s largest petrochemical company, created a new Digital Reliability Platform to monitor plant operations in real time and catch asset failures before they occur. This data-driven APM solution predicts equipment health, monitors performance, and enables advanced maintenance to eliminate unplanned downtime. Using a mix of on-premises and cloud-based applications, the solution integrates online and offline equipment data to visualize plant performance. The platform also applies AI for predictive maintenance and issue resolutions. Since implementing its Digital Reliability Platform, SCG Chemicals has seen plant reliability increase from 98% to nearly 100% through avoided asset failures and reduced maintenance costs by 40%.
Digital twins and advanced analytics
Digital twins provide a dynamic, virtual replica of assets or entire facilities. They enable operators to simulate performance, anticipate failures, evaluate energy consumption, and optimize operations without disrupting production.
In the past, Saudi Aramco’s operators had to optimize each control loop of refineries individually. By introducing a process simulation twin, operators now simulate and predict optimal operating scenarios. A process simulation twin provides an online, high-fidelity replica of refinery units, so Saudi Aramco can run predictive modeling, KPI calculation, and what-if studies. Integrated with AVEVA™ Predictive Analytics, it delivers fault diagnostics, time-to-failure insights, and prescriptive actions. The unified refinery model supports automated unit monitoring, economic opportunity evaluation, planning accuracy, and engineering tasks like troubleshooting and debottlenecking.
In one major use case at Saudi Arabia’s largest refinery, Saudi Aramco used real-time blending optimization to improve fuel quality and performance recovery, with initial studies suggesting about a 10% production increase of that line.
Generative AI
Generative AI is redefining how industrial teams engage with asset performance insights. AI assistants allow users to interact with complex operational and maintenance data through natural language, quickly answering questions such as which assets are at highest risk, what is driving performance degradation, or where energy losses are occurring.
Beyond assistants, AI agents can operate continuously in the background monitoring conditions, correlating signals across systems, and proactively surfacing emerging risks or optimization opportunities. These agents can recommend maintenance actions, highlight trade-offs between reliability, cost, and sustainability, escalate issues to the right teams, and autonomously respond before failures occur.
Within a digital reliability center, generative AI assistants and agents help teams move faster with greater confidence by reducing cognitive load, aligning insights across roles, and ensuring decisions are based on a shared operational context.
Automated ESG and sustainability reporting
Modern APM platforms increasingly include dashboards for tracking and reporting ESG metrics—such as emissions reductions, energy savings, or compliance requirements. This helps organizations meet regulatory expectations and public commitments. Generative AI assistants help contextualize these insights for different roles, while AI agents continuously monitor patterns and flag conditions that require attention within the digital reliability center.
TotalEnergies
In its digital transformation, TotalEnergies used a digital twin with analytical models and enterprise visualization to track and mitigate its greenhouse gas emissions. Using dashboards to see an overview of total site emissions or drill down into specific equipment and KPIs, the team can calculate emissions per equipment as well as the specific energy efficiency for each asset.
As a result, TotalEnergies is tracking about 85% of its scope 1 emissions from operations in real time. The models the team created of rotating and firing equipment provide up-to-date information that helps the team identify and prioritize emissions reduction opportunities. In one use case, in which the company set out to optimize power delivery configuration of a wind farm, it reduced CO2 emissions by 15% annually. And now that these models are built, TotalEnergies can apply them to hundreds of other equipment and processes without additional capital expenditures.
Holistic APM creates smarter, cleaner, more profitable operations
As the industrial world continues to evolve, companies must rethink how they manage asset performance. The future of APM is holistic—tightly integrated with enterprise data, supported by real-time insights, aligned with ESG priorities, and powered by advanced digital technologies. As asset environments grow more complex, the role of generative AI becomes increasingly critical. AI assistants and agents enable organizations to scale expertise, close skills gaps, and ensure that insights are translated into action consistently across sites and teams. A digital reliability center plays a critical role in making end-to-end APM actionable. By providing a single operational view of asset health, performance risk, and sustainability metrics, it enables organizations to align day-to-day decisions with long-term business objectives.
Organizations that embrace this shift will see not just better reliability, but stronger financial performance, reduced operational risk, and a clearer path to sustainable growth.
Is your APM ready for an upgrade?
Sean Gregerson, AVEVA Vice President of Asset Performance Management, shares the trends shaping asset reliability.
Reference:
Gregerson, Sean. (2025). APM | Data Intelligence and Generative AI: Unlocking productivity, performance and profitability. https://www.aveva.com/en/perspectives/presentations/2025/apm---data-intelligence-and-generative-ai--unlocking-productivity--performance-and-profitability/
[1] Pragma. (2025). Trends in Asset Management Report 2025. chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://www.pragmaworld.net/wp-content/uploads/Pragma_Trends-in-Asset-Management-Report_2025.pdf
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