Predictive Maintenance in Pharma Manufacturing: Challenges and Strategic Directions ^†

Abstract

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and organizational constraints. This paper presents a structured technical analysis of recent academic and industrial works addressing PdM implementation in pharmaceutical manufacturing systems. The analysis examines applied AI and machine learning techniques, sensor and data acquisition strategies, implementation maturity, and regulatory considerations relevant to highly regulated environments. The findings indicate that, while PdM solutions can significantly improve equipment availability and reduce maintenance-related costs, major barriers persist, including limited failure data, validation and re-validation requirements, and organizational resistance to data-driven maintenance practices. Based on this analysis, the paper argues that hybrid approaches combining physics-based models (PBMs) with data-driven methods and explainable artificial intelligence (XAI), supported by digital twins and robust data governance frameworks, represent a practical and regulation-aware pathway for the broader adoption of predictive maintenance in pharmaceutical manufacturing

1. Introduction

Pharmaceutical manufacturing plays a critical role in public health by ensuring the continuous production of safe, effective, and high-quality medicinal products, including small-molecule drugs, biologics, vaccines, and radiopharmaceuticals. Given the direct impact of manufacturing performance on patient safety and regulatory compliance, equipment reliability and availability are central concerns within pharmaceutical production facilities. Unplanned equipment downtime may lead not only to significant financial losses but also to deviations in product quality and potential regulatory non-compliance [1]. The pharmaceutical industry operates under one of the most stringent regulatory frameworks worldwide, governed by standards such as Good Manufacturing Practices (GMP), FDA 21 CFR Part 211, and EMA guidelines. Within this context, maintenance activities are not merely operational tasks but regulated processes that must ensure traceability, validation, and auditability throughout the entire equipment lifecycle. Traditional maintenance strategies, including corrective and time-based preventive maintenance, remain widely adopted; however, these approaches often result in unnecessary downtime, inefficient resource utilization, and delayed failure detection [2]. Predictive maintenance (PdM), a key pillar of Industry 4.0, has emerged as a promising strategy to overcome these limitations by leveraging condition monitoring data, advanced analytics, and artificial intelligence (AI) techniques to anticipate equipment failures before they occur [3]. In industrial sectors such as aerospace, automotive, and energy, PdM has demonstrated substantial benefits, including reduced downtime, optimized maintenance scheduling, and improved asset reliability [4].

Despite these demonstrated advantages, PdM adoption within pharmaceutical manufacturing remains comparatively limited. Several studies highlight that this slower adoption is primarily driven by sector-specific constraints, including restricted access to failure data due to small-batch production, limited sensor intrusiveness in validated environments, and the need for costly and time-consuming re-validation when modifying existing systems [5,6]. Additionally, concerns related to the interpretability of AI-driven models and their acceptance by regulatory authorities continue to hinder large-scale deployment [7].

Rather than presenting an exhaustive systematic review, this paper provides a structured technical analysis of predictive maintenance approaches applied to pharmaceutical manufacturing systems. By critically synthesizing recent academic and industrial contributions, this study aims to identify prevailing technical trends, implementation challenges, and strategic directions for PdM adoption in regulated environments [8]. Particular emphasis is placed on AI and machine learning methodologies, sensor and data acquisition strategies, equipment criticality, regulatory implications, and implementation maturity.

Furthermore, this paper argues that broader PdM adoption in pharmaceutical manufacturing requires a shift away from purely data-driven approaches toward hybrid strategies that integrate physics-based models (PBMs), explainable artificial intelligence (XAI), and digitalization technologies such as digital twins. Such approaches offer improved interpretability, reduced data dependency, and enhanced regulatory alignment, making them particularly suited for highly regulated pharmaceutical production systems [9].

2. Analytical Framework and Scope of the Study

This study adopts a structured analytical framework to examine the technical landscape of predictive maintenance (PdM) in pharmaceutical manufacturing. Rather than following a formal systematic review protocol, the analysis is based on a critical synthesis of representative academic and industrial studies addressing PdM deployment in regulated manufacturing environments. The selected contributions were published between 2015 and 2025, a period marked by the increasing adoption of Industry 4.0 technologies within pharmaceutical production systems [10,11].

The scope of the analysis is intentionally oriented toward industrial applicability and regulatory relevance. Studies were considered if they addressed at least one of the following aspects: implementation of predictive maintenance on pharmaceutical equipment, use of data-driven or hybrid modeling approaches, integration of sensor-based condition monitoring, or explicit consideration of regulatory and validation constraints [5,12]. Conceptual works lacking technical implementation or studies focusing exclusively on general maintenance strategies without predictive components were not emphasized.

To ensure a coherent comparative analysis, the selected studies were examined along five primary analytical axes, which reflect the key technical and organizational factors influencing PdM adoption in pharmaceutical environments:

• Artificial Intelligence and Machine Learning Techniques: Evaluation of statistical methods, machine learning algorithms, deep learning architectures, and hybrid approaches used for anomaly detection, fault diagnosis, and remaining useful life estimation [6,13,14].
• Targeted Equipment and Process Criticality: Analysis of the types of pharmaceutical equipment addressed, including discrete manufacturing assets such as tablet presses, continuous and biopharmaceutical systems such as bioreactors and aseptic filling lines, and highly complex systems including cyclotrons [4,8].
• Sensor Technologies and Data Acquisition Strategies: Assessment of data sources ranging from existing process sensors to dedicated condition-monitoring instrumentation, with particular attention to sensor intrusiveness, data quality, and compatibility with validated environments [5,15].
• Implementation Maturity and Real-Time Capabilities: Classification of studies according to their deployment level, from conceptual frameworks and offline validation to real-time industrial implementation with operational decision support [13,14].
• Regulatory and Organizational Constraints: Identification of challenges related to system validation, data integrity, model interpretability, and organizational readiness, which significantly influence PdM feasibility in pharmaceutical manufacturing [7,11].

By structuring the analysis around these dimensions, this paper aims to highlight both recent technical advancements and persistent barriers affecting the adoption of predictive maintenance in regulated pharmaceutical environments. This analytical framework enables a comparative interpretation of diverse approaches while facilitating the identification of strategic gaps and future research directions aligned with industrial and regulatory realities.

The following sections apply this framework to analyze the current technical landscape, examine key implementation challenges, and propose strategic pathways for the broader and sustainable adoption of predictive maintenance in pharmaceutical manufacturing systems.

3. Current State and Advancements in Predictive Maintenance for Pharmaceutical Manufacturing

This section presents a comparative technical analysis of predictive maintenance (PdM) approaches reported in recent academic and industrial studies related to pharmaceutical manufacturing. The examined works cover a broad spectrum of methodologies, industrial contexts, and implementation levels, reflecting the diversity and complexity of PdM applications in regulated production environments. Given the pharmaceutical sector’s stringent requirements in terms of reliability, compliance, and operational efficiency, understanding the evolution and maturity of PdM strategies is of particular importance.

The analysis aims to identify prevailing trends, strengths, limitations, and innovative elements observed in current PdM implementations. To this end, the discussion is structured around several critical dimensions, including artificial intelligence and machine learning techniques, targeted equipment and process complexity, sensor technologies and data acquisition strategies, real-time monitoring capabilities, and implementation maturity with associated industrial benefits. The following subsections provide a detailed analysis across these dimensions.

3.1. AI/ML Techniques: Evolution and Comparison

3.1.1. Technique Evolution

(a)

Foundational Methods: Early predictive maintenance implementations in pharmaceutical environments relied primarily on statistical techniques such as Principal Component Analysis (PCA) and Slow Feature Analysis (SFA) for anomaly detection and dimensionality reduction [5]. While these approaches are effective in identifying deviations from normal operating conditions, they exhibit limitations in modeling complex failure mechanisms or estimating remaining useful life (RUL).

(b)

Mainstream Machine Learning Methods:

• Unsupervised learning techniques such as DBSCAN have been employed to manage unlabeled datasets, enabling the clustering of operational states and anomaly detection in processes such as Clean-In-Place (CIP) systems [6].
• Supervised learning models, including Support Vector Machines (SVM) and Artificial Neural Networks (ANN), have been applied for fault classification and RUL prediction when labeled datasets are available [16].

(c)

Advanced Deep Learning Approaches:

• Deep learning architectures such as Long Short-Term Memory (LSTM) networks, Recurrent Neural Networks (RNN), and Transformers have demonstrated strong potential for modeling time-dependent degradation phenomena in complex pharmaceutical equipment, including bioreactors and cyclotrons [7,11,13].
• In particular, Transformer-based models enable the capture of long-range temporal dependencies, improving degradation trend modeling in highly dynamic systems [13].
• Hybrid deep learning approaches combining AutoEncoders for feature extraction with supervised models have also been proposed to enhance scalability and prediction performance [17].

3.1.2. Supervised vs. Unsupervised Learning

(a)

Unsupervised Learning: Unsupervised approaches are particularly effective for early anomaly detection in environments where labeled failure data are scarce, which is a common constraint in pharmaceutical manufacturing [5,6].

(b)

Supervised Learning: Supervised models offer higher accuracy for fault classification and RUL estimation; however, they require high-quality historical failure data, which are often limited due to small-batch production and stringent validation requirements [10,11,13].

3.1.3. Hybrid Models

Hybrid modeling strategies have been increasingly explored to balance data efficiency and predictive performance. For instance, the combination of unsupervised AutoEncoders with supervised classifiers enables effective feature learning while maintaining robust fault prediction capabilities [17,18]. Additionally, federated learning approaches have been investigated to support decentralized model training across distributed and privacy-sensitive datasets, which is particularly relevant in regulated pharmaceutical contexts [14].

3.1.4. Non-AI Approaches

Not all predictive maintenance implementations rely on artificial intelligence. Several studies demonstrate that effective process automation and condition monitoring can be achieved through sensor-based control logic alone, highlighting that simpler approaches may still deliver significant value in specific industrial scenarios [15].

3.2. Targeted Equipment and Process Complexity

(a)

High-Risk and High-Value Equipment

• Discrete Manufacturing: Tablet presses and pelletizers are critical assets in solid dosage form production, where PdM enables early detection of wear and leakage phenomena [4,15].
• Continuous and Biopharmaceutical Systems: Bioreactors, aseptic filling lines, lyophilizers, and cyclotrons are highly sensitive to process disturbances and downtime, making them prime candidates for PdM deployment [3,5,8,16].
• Generic Components: Bearings and dry gas seals represent fundamental yet critical components where PdM has proven effective for degradation stage prediction [16,17].

(b)

Specific versus General Frameworks

• Several studies focus on detailed monitoring of specific components or subsystems to achieve high diagnostic precision [3,6,8,15,16].
• In contrast, broader PdM platforms aim to enhance scalability across diverse asset types and production lines [7,10,11,13].

(c)

Comparative Insights

• The intrinsic complexity of cyclotron systems poses significant challenges in data acquisition and model validation due to multiple interacting subsystems [3,8].
• Practical case studies, such as powder leak detection in tablet presses using level sensors, illustrate that relatively simple PdM implementations can deliver substantial industrial benefits [4].

3.3. Sensor Technologies and Data Acquisition Strategies

(a)

Use of Existing Process Sensors

• Leveraging existing process sensors is a cost-effective and regulation-friendly approach; however, such measurements often provide indirect indicators of equipment health rather than direct condition monitoring [5,7,10].

(b)

Specialized Condition Monitoring Sensors

• Vibration sensors are widely used for rotating machinery monitoring [7,10,14,16,17].
• Acoustic emission techniques enable early crack and defect detection, particularly in slowly rotating components [16].
• Load sensors facilitate real-time operational adjustments in equipment such as pelletizers and cyclotrons [8,15].
• Flow-rate measurements serve as indicators of CIP process efficiency [6].
• Integrated industrial IoT sensors provided by equipment manufacturers enable comprehensive condition monitoring in advanced PdM implementations [14].

(c)

Regulatory Constraints and Sensor Intrusiveness

• Non-intrusive and easily validated instrumentation, such as portable data loggers, is often preferred to minimize the need for system re-validation [16].

(d)

Data Volume and Processing

• High-frequency data acquisition and edge-processing architectures generate large data volumes, introducing challenges related to storage, processing, and data governance in PdM systems [14].

3.4. Real-Time Versus Retrospective Monitoring

(a)

Evolution toward Real-Time Monitoring

• Early PdM implementations primarily relied on retrospective and offline analysis [5,6].
• More recent studies report real-time monitoring solutions incorporating continuous dashboards, edge-based alerts, and direct control integration [4,7,8,14,15,16].

(b)

Enabling Technologies

• Edge computing architectures enable low-latency anomaly detection and local decision-making [8,14].
• Real-time visualization dashboards provide immediate operational feedback to maintenance teams [4,16].

(c)

Comparative Perspective

• Among the reported implementations, advanced edge–cloud architectures demonstrate the highest level of real-time functionality and operational impact [14].

3.5. Implementation Status and Measurable Benefits

(a)

Implementation Maturity

• Conceptual frameworks focus primarily on methodological development without full industrial deployment [3].
• Validated yet non-operational implementations demonstrate model feasibility using experimental or historical datasets [16,17].
• Fully operational PdM systems report real-world industrial deployment and measurable performance improvements [4,5,6,7,8,11,14,15].

(b)

Quantifiable Benefits

• Significant improvements in anomaly detection speed and downtime reduction have been reported in industrial deployments [14].
• Other reported benefits include increased equipment uptime, reduced maintenance costs, and waste minimization [11].
• Early degradation detection contributes to yield preservation and avoidance of unplanned production interruptions [4].
• Continuous monitoring of aseptic production lines supports compliance with Good Manufacturing Practices [5].

(c)

Practical Implementation Insights

• Simple, data-driven approaches such as real-time mass balance analysis can effectively support leakage detection [4].
• The use of portable and non-intrusive data acquisition systems represents a pragmatic strategy for addressing regulatory constraints [16].

4. Key Challenges in Implementing Predictive Maintenance in the Pharmaceutical Sector

The implementation of predictive maintenance (PdM) in the pharmaceutical sector offers significant potential benefits; however, it is confronted with a unique combination of technical, regulatory, and organizational challenges. Addressing these challenges is essential to enable large-scale adoption and to fully leverage data-driven maintenance strategies in regulated manufacturing environments.

4.1. Data-Related Challenges

An effective predictive maintenance strategy relies on the availability of high-quality and relevant data. In pharmaceutical environments, however, data acquisition, management, and interpretation remain major obstacles. Several studies highlight limited sensor coverage and a strong reliance on indirect process measurements, which are often designed for process control rather than direct equipment health monitoring [5,6]. As a result, equipment degradation is frequently inferred indirectly, complicating fault isolation and diagnostic accuracy.

Moreover, relying on a restricted number of signals, such as flow-rate measurements, poses challenges for constructing precise physical models or detecting localized degradation in complex equipment such as lyophilizers [6]. This limitation leads to signal ambiguity, where a single measurement may reflect the behavior of multiple components.

Pharmaceutical production systems generate heterogeneous and complex datasets originating from diverse equipment and processes. Several works emphasize the need for extensive data preprocessing to integrate these heterogeneous sources effectively [5,10]. Data quality issues, including noise, batch-to-batch variability, incomplete records, and manual data entry, further complicate PdM model development and may degrade prediction accuracy [5,7,11]. These challenges are often described as “messy” or “inconsistent” data, representing a major barrier to reliable analytics.

Certain equipment types introduce additional data-related complexities. Highly complex systems such as cyclotrons present significant difficulties in acquiring high-quality, synchronized data suitable for predictive algorithms [3,8]. Furthermore, large-scale real-time monitoring generates substantial data volumes, requiring robust data management architectures, advanced processing capabilities, and scalable storage solutions [13,14].

4.2. Regulatory and Compliance Hurdles

Pharmaceutical manufacturing operates under strict regulatory frameworks, including GMP, FDA 21 CFR Part 11, and EMA guidelines, which significantly influence PdM deployment. Numerous studies report that any modification to validated systems, such as adding sensors or deploying predictive software—typically triggers a costly and time-consuming re-validation process [5,7,10,11,14,15,17]. This constraint often drives a preference for non-intrusive sensors and portable data acquisition solutions that minimize interference with validated production zones [16].

Additionally, integrating data from heterogeneous sources such as sensors, PLCs, ERP, and SCADA systems introduces compliance challenges related to data integrity, audit trails, and traceability [16]. Regulatory authorities increasingly require that AI and machine learning models used for PdM be transparent, auditable, and demonstrably reliable. However, the limited interpretability of certain advanced AI models complicates regulatory approval and validation processes [10,13]. These constraints highlight the growing importance of explainable and validation-friendly PdM solutions.

4.3. Model- and Algorithm-Related Challenges

Developing robust, scalable, and reliable AI/ML models for predictive maintenance in pharmaceutical environments presents additional challenges. Ensuring algorithm accuracy and adaptability to evolving operating conditions, such as data drift or changes in raw materials, remains a critical concern [3,14]. Models trained for specific equipment often struggle to generalize across different assets or production lines without extensive re-engineering.

Furthermore, the inherent complexity of pharmaceutical systems, particularly cyclotrons, introduces multiple interacting components and failure modes, limiting the availability of failure data for supervised learning approaches [3,8]. Predicting multi-stage degradation processes rather than binary failure events further increases modeling complexity [17]. In addition, the long-term maintenance of predictive models—covering performance monitoring, retraining, and updating—is frequently underestimated, despite being critical for sustained PdM effectiveness [7].

4.4. Operational and Organizational Challenges

Beyond technical barriers, operational and organizational factors significantly affect PdM adoption. Several studies emphasize the need for cultural change, workforce upskilling, and the mitigation of resistance to transitioning from traditional maintenance practices to data-driven approaches [7,11]. Integrating PdM insights into existing maintenance workflows and production planning systems requires careful change management. Additional concerns related to production scheduling conflicts, data security, and privacy further complicate implementation [10].

4.5. Cost and ROI Justification

The initial investment associated with PdM systems—including sensors, software platforms, and specialized personnel—can be substantial. Demonstrating a clear return on investment (ROI) remains challenging, particularly during early adoption phases [3,11]. The complexity and cost of large-scale PdM solutions may limit accessibility for certain organizations, despite their long-term benefits [3].

These challenges are not isolated but deeply interconnected. For example, data quality limitations directly affect model reliability, while regulatory constraints influence technology selection and organizational processes. Addressing PdM challenges therefore requires a holistic and coordinated strategy tailored to the pharmaceutical context.

5. Strategies for Overcoming Challenges and Future Directions: A Proposed Pathway for Pharmaceutical Predictive Maintenance

Although predictive maintenance presents clear advantages, its adoption within the pharmaceutical sector remains constrained by technical and regulatory complexities. Nevertheless, these barriers are not insurmountable. This section outlines a strategic pathway for PdM deployment that emphasizes pragmatic, regulation-aware, and scalable approaches.

5.1. Re-Contextualizing Data Strategies for a Cautious Industry

While advanced analytics and comprehensive data governance represent long-term objectives, initial PdM adoption should prioritize extracting value from existing or minimally enhanced data sources. Non-intrusive sensors provide a practical entry point; however, greater emphasis should be placed on targeted feature engineering and the identification of critical failure modes, where even limited data can yield actionable insights [16].

5.2. Physics-Based and Hybrid Models: The Key to Broader PdM Adoption

This work suggests that the perceived intricacy and reliance on data in predictive maintenance, frequently associated exclusively with complex AI and machine learning, has impeded its adoption within the pharmaceutical sector. This viewpoint neglects a significant and more approachable path: physics-based models (PBMs) [9]. These models, grounded in well-established engineering principles regarding equipment functionality and degradation, do not require extensive historical failure datasets at the outset, addressing a significant challenge highlighted in the literature [5]. Their intrinsic interpretability effectively corresponds with regulatory requirements for transparency, addressing the “black box” issues linked to certain AI systems, which remains particularly challenging for complex systems such as cyclotron applications [3]. Utilizing established principles of material science and equipment design, PBMs are able to delineate standard operating parameters and identify irregularities that indicate the onset of wear or malfunction. This methodology offers prompt benefits while establishing a fundamental comprehension.

The genuine catalyst for predictive maintenance in the pharmaceutical sector nonetheless resides in the strategic implementation of hybrid models. In this context, PBMs can function as a foundational element, with their outputs improved and polished through data-driven methodologies as operational data is accessed and confidence increases. An example of this would be a PBM identifying essential parameters for monitoring, allowing simpler ML models to subsequently learn the intricate patterns present within these parameters. This structured methodology renders predictive maintenance less challenging, enhances transparency, and gradually increases its efficacy. It is essential to foster the comprehension that PdM is not solely reliant on data-driven AI; it includes a range of methodologies. Initiating with PBMs or more straightforward hybrid systems can serve as a practical and effective approach to facilitate broader acceptance within the sensitive pharmaceutical environment.

After pharmaceutical companies gain confidence in initial predictive maintenance techniques, particularly those based on engineering principles like physics-based models, they can progress to more sophisticated AI approaches. This phase necessitates the application of explainable AI (XAI) to clarify AI decision-making processes, thereby fostering trust and facilitating easier regulatory compliance. Methods such as federated learning facilitate the collaborative enhancement of AI models among various groups without the need to exchange sensitive data, whereas MLOps (Machine Learning Operations) guarantees the sustained reliability and accuracy of these AI models over time.

Adhering to rigorous pharmaceutical regulations necessitates the development of predictive maintenance systems that are transparent and readily verifiable from the outset. The implementation of tools such as digital twins, which serve as virtual representations of equipment for testing purposes, alongside adherence to established protocols like GAMP 5 for computerized systems, is crucial for ensuring compliance.

For predictive maintenance to gain widespread adoption, organizations must foster an innovative mindset regarding maintenance practices. This entails a clear demonstration of the advantages, beginning with minor, successful pilot projects on critical machinery, integrating predictive maintenance systems with current factory software (MES/CMMS), and ensuring the engagement of all stakeholders, particularly the maintenance teams.

In conclusion, the effective utilization of contemporary technologies such as digital twins, edge/cloud computing for rapid local analysis and robust central processing, along with explainable artificial intelligence (XAI), is essential. These technologies enable organizations to progressively embrace more sophisticated and reliable AI-driven predictive maintenance, beginning with basic, comprehensible models, and advancing from that foundation.

6. Discussion

The analyzed studies collectively demonstrate that predictive maintenance can deliver substantial benefits in pharmaceutical manufacturing, including improved equipment reliability, reduced unplanned downtime, enhanced process efficiency, and cost reduction [4,5,11,14]. Successful applications span a wide range of equipment, from tablet presses to bioreactors, highlighting PdM’s broad industrial relevance.

Despite these successes, PdM adoption in pharmaceutical manufacturing remains less mature than in sectors such as aerospace or energy. This gap is largely attributed to stringent regulatory requirements, data-related challenges, model interpretability concerns, and organizational resistance [5,7,10]. A narrow perception of PdM as exclusively data-driven further contributes to this hesitation.

This work argues that physics-based and hybrid modeling approaches offer a pragmatic entry point for PdM adoption in regulated environments. By combining interpretability, engineering insight, and incremental data-driven refinement, these approaches align well with pharmaceutical validation cultures while preserving predictive performance.

Overall, predictive maintenance should be viewed as a spectrum of methodologies rather than a single technological solution. Broadening this perspective can facilitate wider acceptance and accelerate the transition from pilot implementations to sustainable industrial practice.

7. Conclusions

This systematic review indicates that, although predictive maintenance (PdM) shows notable effectiveness in improving reliability and efficiency in specific pharmaceutical case studies, its wider implementation in the industry faces considerable obstacles related to data, regulatory, and organizational issues. As a result, PdM is still not as widely adopted in the pharmaceutical industry when compared to other manufacturing sectors.

An important conclusion drawn from this analysis is the necessity to advocate for physics-based models (PBMs) and hybrid methodologies as practical starting points for predictive maintenance. This approach has the potential to alleviate early data dependencies and address transparency issues, thereby promoting broader acceptance. In conclusion, realizing the significant, yet often overlooked, potential of PdM in the pharmaceutical sector necessitates a comprehensive strategy: adopting interpretable AI such as XAI, utilizing facilitating technologies, proactively addressing regulatory challenges, and fostering a conducive organizational environment. Embracing a more inclusive and expansive perspective on PdM enables the industry to progress more effectively towards achieving highly efficient, reliable, and compliant manufacturing operations.