In 2025, there’s been a marked increase in FDA warning letters. During the second quarter of 2025 alone, the U.S. Food and Drug Administration (FDA) issued 172 warning letters. A notable enforcement surge occurred in September 2025 when the FDA released 80 warning letters in a single week. Although this surge has been attributed in part to the FDA’s expanded use of artificial intelligence—specifically the internal Elsa tool—it’s continued evidence that the FDA is focused on its mission of enforcement.

Warning letters are issued for many reasons. What often doesn’t get the focus it deserves are issues related to calibration and maintenance, such as missing calibration records, lack of preventive maintenance planning, and fractured audit trails. According to Blue Mountain research on recent warning letters citing calibration and maintenance issues, key observations included incorrect calibrations, lack of records and inadequate evidence, lack of validation, and lack of routine calibration and maintenance programs.

These lapses signal a deeper problem: When assets fail or data integrity falters, patient safety and regulatory compliance hang in the balance. It’s a clear call for life sciences companies to upgrade how they manage critical instrumentation and production systems.

Why asset performance management can no longer be optional

For years, many companies treated equipment as background infrastructure, intervening only when failures occurred. Later, preventive maintenance became standard practice, with organizations scheduling work at fixed intervals to minimize breakdowns. Yet neither approach is sufficient in an environment where regulators scrutinize every record, and downtime can derail entire supply chains.

Asset performance management (APM) shifts the focus from reactive fixes and arbitrary schedules to continuous monitoring, risk assessment, and optimization. It blends compliance, quality, and efficiency into a single discipline. Regulators like the FDA have made clear that they expect not just documentation, but also proof that organizations understand and can control the health of their assets.

The case for APM is compelling. It strengthens regulatory assurance by generating validated, auditable records automatically. It reduces downtime, protecting patients from therapy delays, and companies from costly batch losses. Financially, APM may lower costs by preventing emergencies and reducing unnecessary maintenance. Predictive maintenance is now one of the most frequently discussed strategies for asset reliability across manufacturing sectors, signaling its movement into the mainstream.

APM vs. asset life cycle management

Some executives conflate APM with asset life cycle management (ALM). But the distinction matters. ALM is the overarching process, encompassing everything from procurement and commissioning through decommissioning and disposal. In contrast, APM focuses specifically on the functional operation over the life of the asset—keeping equipment reliable, compliant, and efficient once it’s deployed.

If ALM determines what assets to acquire and when to retire them, APM ensures that those assets deliver their intended performance day after day. Together, the two disciplines form a holistic framework for asset stewardship. But APM is the one most directly tied to quality outcomes and regulatory inspections.

The six phases of APM maturity

Moving toward APM maturity is rarely a leap; it’s more often a progression through six distinct phases.

The journey begins with reactive maintenance, where equipment is repaired only after it fails. This “run to failure” model results in high downtime, soaring repair costs, and frequent compliance exposure. One pharmaceutical site reported that nearly 30% of its downtime was reactive before it began shifting toward predictive.

Preventive maintenance comes next, with work scheduled at fixed intervals based on time or use. Although this approach reduces unexpected breakdowns, it often leads to overmaintenance and wasted resources.

Companies then evolve into condition-based maintenance, where IoT sensors monitor indicators such as vibration, pressure, or temperature. These sensors trigger service only when thresholds are exceeded, saving unnecessary interventions and catching some failures earlier. The limitation is that rule-based systems can’t anticipate problems beyond the conditions they are designed to monitor.

The fourth stage, connected asset management, integrates disparate systems into a single platform, unifying maintenance data, quality records, and compliance information into one version of the truth. This holistic view improves visibility and streamlines regulatory reporting, but it also demands overcoming the challenges of legacy-system integration and cross-departmental governance.

Predictive maintenance follows, powered by analytics and machine learning. Algorithms forecast failures before they occur, enabling organizations to intervene proactively. The returns are significant: Across industries, predictive maintenance has been shown to reduce unplanned downtime by up to 50% and cut maintenance costs by as much as 40%. In one pharmaceutical case, predictive modeling identified more than 30 latent issues within two months of implementation and cut reactive downtime from 29% to just 9%.

Finally, organizations reach asset performance optimization, where AI, digital twins, and prescriptive analytics continuously refine strategies across the enterprise. At this stage, the focus shifts from predicting failures to determining the optimal balance of risk, cost, and performance. It represents the pinnacle of digital maturity but requires robust infrastructure, strong leadership buy-in, and a culture committed to continuous improvement.

Overcoming the barriers to adoption

Despite the clear road map, many organizations remain stuck in the early phases of maturity. Data quality is often a stumbling block, because predictive models depend on accurate and complete historical records and clean data structure. Legacy systems present another obstacle, because older platforms might not integrate with modern analytics tools. Cultural resistance also plays a role; maintenance teams accustomed to “fix it later” approaches may distrust AI-driven recommendations. Finally, resource constraints—from sensor deployment to data science expertise—can slow progress.

A systematic review of predictive maintenance adoption found that even organizations with strong pilot programs often struggle to scale them enterprisewide due to these very barriers. Yet those that push through consistently see improvements in compliance, cost savings, and operational resilience.

Linking APM to regulatory outcomes

The most compelling case for APM lies in its alignment with regulatory expectations. Many of the FDA’s most common findings—missing calibration logs, incomplete maintenance schedules, or retrospective data entries—are precisely the weaknesses that APM systems address. In fact, the calibration-related citations included in FDA warning letters are directly mitigated by digital APM platforms, which capture calibration events automatically, enforce scheduling, and generate secure, contemporaneous audit trails.

By embedding reliability and traceability into day-to-day operations, APM helps organizations tell a story of control, accountability, and continuous improvement. For regulators, that story signals compliance. For patients, it ensures that therapies are delivered safely and consistently.

Looking ahead: APM, Industry 4.0, and beyond

As life sciences moves through Industry 4.0, APM will be the foundation of smart factories and continuous manufacturing. Digital twins already enable organizations to simulate asset performance and refine strategies in real time. AI-driven prescriptive analytics are evolving from pilots into practical tools.

Organizations that adopt these innovations will gain a competitive edge not just in efficiency but also in regulatory credibility. Those that delay risk falling behind on both counts.

Conclusion: Turning assets into strategic allies

The journey from reactive maintenance to asset performance optimization is demanding. But the rewards are substantial. Companies that make the transition achieve higher uptime, lower costs, improved audit readiness, and stronger resilience. They also position themselves for future innovations in digital manufacturing.

As one industry veteran put it, “Assets aren’t liabilities waiting to fail—they’re strategic nodes in your quality story.” In the tightly regulated world of life sciences, that story must now be digital, data-driven, and continuously optimized.