Beyond the Algorithm: Industrializing Predictive Maintenance in the Pharmaceutical Industry
By Miguel Ángel Rodríguez, Domain Lead of Manufacturing at SDG Group
For more than fifteen years, the industry has focused on sensors, prognosis models, and algorithms capable of preventing failures before they occur. This discipline, known as predictive maintenance, reduces the impact of breakdowns and improves the functional availability of assets. In the pharmaceutical sector, these solutions range from analyzing the degradation of bearings and motors in plant equipment, to monitoring the health of complete manufacturing lines and identifying patterns hidden in thousands of work orders from global production networks.
From a technical perspective, the message is clear: the algorithms are sufficiently proven. The true challenge lies in industrialization - ensuring these solutions perform reliably within the fluid, ever-changing reality of a pharmaceutical plant.
Recent reviews on predictive maintenance show that when companies move from purely preventive schemes to data-driven approaches, both unplanned downtime and maintenance costs are reduced. However, these analyses also reveal that many initiatives fail to integrate stably into day-to-day operations, often surviving as pilot projects only.
In the pharmaceutical sector, the context is even more demanding. Beyond standard availability and cost objectives, manufacturers must navigate critical factors such as product criticality, GMP regulations, and stringent data integrity requirements (ALCOA+). Research focused specifically on AI in pharmaceutical manufacturing identifies predictive maintenance as one of the primary levers for improvement, alongside process optimization and real-time quality control.
Many of the most successful predictive maintenance solutions model the normal behavior of assets and detect deviations. However, process adjustments, maintenance interventions, or product changes continuously alter the operational footprint of equipment. If models do not incorporate this evolution, their predictive accuracy degrades.
This challenge cannot be solved solely with better algorithms. It's essential to have integrated, cross-referenced data sources that combine sensor signals with configuration data, interventions, technical modifications, and associated quality deviations. Without this integrated and traceable data foundation, it becomes difficult to explain accuracy loss and maintain validated models in a regulated environment.
Maintenance-oriented Digitalization
In many pharmaceutical companies, digitalization has focused primarily on operations. Maintenance has often been relegated to work-order management systems, spreadsheets, and tacit knowledge. However, the real value leap occurs when operations, maintenance, and quality data are consistently integrated within a unified data architecture.
This means structurally integrating condition monitoring data, maintenance interventions, and quality deviations, while designing data architectures centered on assets and reliability use cases. Predictive maintenance is a continuous analytical capability supported by an integrated digital foundation that keeps it operational and relevant.
At SDG Group, we offer a set of AI-driven capabilities for predictive maintenance aimed at consolidating and enriching asset data, extracting operational knowledge, and improving decision-making throughout the asset lifecycle:
- Automatically integrate and structure technical and operational information from multiple sources, combining condition data with plant signals.
- Extract knowledge from work orders and historical records using advanced language analysis, improving fault identification and classification.
- Improve the quality and availability of field-captured data, reducing operational friction in documentation processes.
- Generate new signals from automated inspections to enable early detection of failures, degradation, anomalies, or defects.
- Support reliability and inventory strategies through historical analysis, lifetime models, and cost-risk evaluation.
- Continuously compare real asset behavior with reference models to estimate remaining useful life and plan preventive actions.
- Accelerate diagnosis and decision-making by correlating process information, alarms, and maintenance history in near real time.
Predictive maintenance relies on a broader ecosystem of capabilities beyond the forecasting model itself. Pharmaceutical assets often have lifecycles spanning decades, and much of their historical knowledge - modifications, recurring failures, and effective solutions - resides with a small number of experts. If this knowledge is not captured, it may be lost when people change roles, forcing new technicians to rebuild diagnostic criteria with inconsistent results.
Recent advances in language-based AI offer a practical solution to this challenge: specialized conversational agents trained on technical manuals, failure histories, condition reports, and internal guidelines. These systems guide less experienced staff and standardize documented best practices regarding root causes and corrective actions. This transforms individual expertise into a reusable organizational asset, fully aligned with modern data ecosystems and digital twin strategies.
Ultimately, predictive maintenance is a mature discipline; the primary objective now is its sustainable integration into operations within GMP environments. The primary bottleneck lies in industrialization: absorbing plant variability, maintaining traceability of changes, and ensuring models evolve at the same pace as assets and processes. This requires a digital transformation that fully integrates maintenance, aligning operational and quality data under robust governance and clear reliability objectives.
In this context, the combination of multiple AI capabilities enables organizations to anticipate, diagnose, and continuously learn, industrializing predictive maintenance as a stable, auditable practice aligned with availability, quality, and sustainability goals.
Original article translated from spanish here.