As the pharmaceutical industry pushes toward smarter, leaner operations, maintenance is increasingly recognised as a key lever for quality, not just cost control. At the Pharmaceutical Maintenance Symposium 2025, leaders from across the sector shared insights on how sites can evolve their maintenance strategies.
This fuelled immensely fruitful discussions in two deeply interrelated topics:
1. How to digitalise maintenance practically and effectively
2. How data maturity can help maintenance strategies evolve
1. Digitalising Maintenance: A Journey of Small Steps
Many speakers in the Symposium have echoed the point that digitalisation in pharmaceutical maintenance isn’t about adopting the most expensive or complex system—it’s about solving real problems with the right level of technology. Darren McDonnell of SCRI-IS shared the sobering statistic that up to 90% of digital transformation projects fail because they lack clear business metrics or try to do too much, too soon.
Start with the Pain Points
Instead of an ambitious full-scale digital overhaul of existing systems, identify within the maintenance process where the most time or information is lost. Linda Dalfovo of Endress+Hauser shared that manufacturing teams spend 70% of their time just finding the right information before they can act. In this case, the digitalisation effort then should focus on making what goes on in the site more transparent, especially in aspects tied to risks and performance.
Fit-for-purpose
The solution to these problems doesn’t have to be a completely new CMMS or AI solution. Lim Meng Chong of Novartis brought up the example of validated spreadsheets, which are more than sufficient to perform calculations required for calibration (e.g., USP <41> Min Weight).
Gerard Carton of PlantQuest highlighted that a data maturity assessment is required to determine the right digitalisation solution for the organisation in question. What is interesting is that for many sites, digitalisation efforts don’t necessarily need to start from scratch. Based on his experience, asset data for many sites do exist but they are typically left to rot as sites start operating. The challenges are then to create proper data frameworks that utilise this information to create insights that inform Maintenance decisions, and to incentivise updating asset information.
2. Evolution of Maintenance Strategies
While digital tools improve efficiency, the real power lies in the data they produce. As plants build data maturity, they open the door to shifting their maintenance strategy—from traditional to risk-based to predictive. However, having a good technical groundwork done is not the only factor in implementing successful maintenance strategies.
Maintenance Strategies
Giacomo Mastrorocco and Giacomo Rinaldi of Fedegari provided a holistic view of the different maintenance strategies that can be adopted:
- Traditional Methods (Reactive, Corrective or Preventive)
- Advanced Strategies (Proactive, Total Productive Maintenance (TPM), Risk-based)
- Modern Methods (Predictive, Prescriptive)
These strategies vary in the volume of data required. A minimal amount of data on the typical failure rate of parts is required to enact a basic preventive maintenance plan, while large volumes of data organised into a coherent data framework is required to build a machine learning model for predictive maintenance. Understandably, knowing what data to collect is equally crucial.
Understanding the data
To move from purely preventive to predictive maintenance, Darren McDonnell of SCRI-IS and Pieter van Camp of I-Care provided examples on how better understanding of the assets they work with (elastomers and machinery respectively) have helped their clients optimise their maintenance plans. This starts from understanding what data to collect, then providing means to collect this data efficiently, providing expertise for correct analysis of the data, and finally to use this data to drive maintenance decisions.
Holistic Support from All Stakeholders
Implementing the advanced strategies requires support from the entire organisation, be it from engineers and quality personnel who understand the process criticality of parts, instruments and equipment to implement a risk-based approach, or input from the entire manufacturing site in TPM. Nicholas Scully of Novartis provided his insights on how a site can begin to adopt TPM, beginning with defining the overall structure, followed by breaking down the workstream into stages over years, then procedurising and finally executing the strategy.
Final Thought: From Insight to Action
Digital maintenance is a series of smart, practical moves rooted in business reality. And data maturity is less about high-end tech than it is about knowing your assets and your people.
Whether you’re just getting started or scaling your strategy, the time to act is now.
Start small. Build smart. Think long-term.
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