A four-step way out of pharma manufacturing’s asset management struggles

Warren Buffett once said he bought stocks “on the assumption that they could close the market the next day and not reopen it for five years”. After a 2025 dominated by tariffs and short-term pressures, that mindset may serve pharmaceutical manufacturers well.

By decade’s end, drugmakers will face several major headwinds, among which the upcoming patent cliff will loom large. Beginning in 2026, drugs worth roughly $180 billion in annual revenue, or 12 percent of the global market, will lose exclusivity. Companies such as Merck & Co, Bristol Myers Squibb and Pfizer will face rising pressure to protect revenue.

Why the full lifecycle view matters

The wave of upcoming patent expirations will add to the pressure felt in manufacturing plants across the world, already grappling with shorter product lives and revenue that increasingly depends on adapting to the complexity of new modalities and continuous manufacturing.

Today’s plants and teams already struggle with inefficiencies, compliance requirements and siloed data. According to Hexagon’s Pharma & Life Sciences Operations report based on the research conducted with Forrester Consulting, 68 percent of pharma manufacturers report data silos and 80 percent say that improving data and document management is a high priority.

Forrester’s survey also shows that plant-level struggles lead to group-level ones: When asked about their most pressing challenges, the top answer among pharma executives (cited by 57 percent) was “scaling processes from one manufacturing plant to others”.

That challenge will only get more complex as groups engage in a wave of mergers and acquisitions, with Novartis’ recent 12-billion-dollar acquisition of Avidity Biosciences being the latest example. Each acquisition brings its own systems and assets, and multiplies the volume and complexity of operational data, from engineering models to equipment validation and maintenance records.

This makes several strategies indispensable. First, developing a core, replicable set of tools across plants to acquire visibility over performance and asset lifecycle. Second, developing data-driven strategies to detect and remediate inefficiencies that tend to occur across plants.

Understanding the challenges

On that journey, let’s start at the most common starting point.

Every pharmaceutical facility relies on capital-intensive assets. Autoclaves, isolators, mixing vessels and HVAC systems cost millions to acquire and install but their real cost emerges over time. Maintenance, modifications, energy use and retirement drive around 70 percent to 80 percent of the actual total cost of ownership (TCO).

In many plants, calculating the actual figure is difficult because a full record does not exist. Assets are tracked across fragmented systems, capital budgets sit apart from maintenance logs and energy bills, and some components are captured only in spreadsheets, if at all.

As a result, managers see purchase price in detail but lack visibility of the larger costs that accrue over decades. Without a consolidated lifecycle view, choices to repair, replace or upgrade rely on incomplete evidence, locking in inefficiencies and exposing the business to higher long-term risk.

Building the foundation and tracking everything, from everywhere, all at once

So, what does better look like? Let’s start with the foundation.

To achieve a comprehensive lifecycle record, each asset needs a unique identifier capturing investment, downtime and energy costs. Managers can then assess whether preventive maintenance is worth the expense, if spare parts inventory matches risk, or if replacement is cheaper than repair.

This typically requires an Enterprise Asset Management (EAM) system connected to ERP and other key systems such as MES, energy and emissions monitoring, and LIMS/HPLC data. In pharma, it must be validated and compliant with 21 CFR Part 11.

A modern EAM combines reliability management, preventive and performance-based maintenance, calibration, inspection and labour tracking within one system. A precise asset hierarchy links pumps, valves and HVAC units to systems and locations with each carrying its full financial and operational history. Calibration ties directly to equipment, failed inspections generate corrective work orders and costs roll up from machine to plant level.

This unified asset registry simplifies GMP compliance through audit trails, electronic signatures and validated workflows. It also represents a major advance over traditional CMMS platforms, which often focus narrowly on scheduling and risk wasting resources through excessive “just in case” maintenance.

Creating a replicable core across plants old and new

This foundation brings a key benefit for an industry transforming its manufacturing footprint, pursuing M&A and shifting to digitally enabled continuous manufacturing: replicability.

For large manufacturers, achieving efficiency at scale requires a core set of capabilities across plants. That’s the approach Pfizer followed to drive faster integration of acquisitions. As Mike Tommasco, former Vice President at Pfizer Digital, noted in a recent webinar: “Pfizer would buy companies and our job was to help them become more efficient. One of our strategies was to put in core solution capabilities, such as maintenance management and calibration. This capability set was one of the first global models.”

A common core simplifies compliance across the board. Facilities evolve constantly and each change risks data drift if new assets, calibration routines or limits are not updated. A replicable asset management or digital twin platform enables controlled change: when equipment is modified or replaced, engineering documentation and calibration data are updated in the same validated environment, ensuring traceability.

This continuous validation loop aligns with regulatory expectations and operational goals. Inspectors gain confidence in asset traceability while site leaders can track performance without the constant revalidation that fragmented systems demand.

AI and asset performance management as steps further

This foundation also supports the reasoned and documented use of artificial intelligence (AI) in GMP environments.

The operational potential of AI in pharma manufacturing has long been uncertain. The European Federation of Pharmaceutical Industries and Associations (EFPIA) has described its “immense potential” in manufacturing, but the regulatory framework remained unclear.

That changed with the EMA’s draft Annex 22. It sets expectations for AI in GMP systems and focuses on static, auditable models that do not keep learning in production, thus steering teams toward modest, practical cases that fit current rules.

A potential use case that stands out is asset performance management, or APM. It can offer pharma manufacturers significant value by overcoming the limits of classic predictive maintenance, which forecasts failures but cannot weigh business impact. APM integrates data across equipment, lines and systems, embedding context such as which assets are GMP-critical, which failures risk batch loss and which downtime threatens supply obligations. The result is guidance that makes operational and business sense and is truly prescriptive.

In the complexity of pharma’s manufacturing environments, APM’s strength lies in integration. It can stream sensor data from SCADA or PLCs into anomaly models, then validate predictions against data-historian and maintenance logs. If a pump seal failure risk is detected, APM checks MES schedules for critical batches, the EAM for spare-parts availability and the CAPA system for past causes.

Implementation will still face hurdles. Annex 22 allows only static or periodically retrained models, which limits adaptive learning and AI projects must follow full validation discipline.

Still, the prize is clear. In recent research by Hexagon, 66 percent of pharma executives agreed that “the lack of available data on asset performance is impacting the financial performance of the business”, and 60 percent cited asset unreliability as a key challenge. In a cost-conscious manufacturing environment, reliable assets and accessible data will decide who benefits first from compliant AI and stands to win in the period ahead.