Industrial data is often siloed and difficult to use. This blog shows how Merit’s engineering-led approach and agentic AI unify fragmented data into actionable insights that improve asset reliability, reduce downtime and enable practical predictive maintenance within real operational constraints.
Manufacturers generate vast volumes of operational data every day, yet turning that fragmented, multi-system data into reliable, actionable decisions remains a major challenge, especially across UK and European plants that rely on a mix of legacy SCADA environments, PLC networks and historian systems. This is where Merit provides a tangible, differentiated advantage.
Merit’s core strength lies in industrial data engineering: unifying heterogeneous sensor feeds, cleaning inconsistent historian logs and establishing accurate operational baselines that most predictive maintenance programmes struggle to achieve. This foundation has consistently enabled manufacturers to move from reactive maintenance to repeatable, data-driven decision cycles, improving early detection of equipment degradation and reducing unplanned downtime in measurable ways.
Crucially, Merit’s approach is built around the operational realities of regulated industrial environments. Agentic AI workflows are designed to integrate directly with existing enterprise systems while respecting safety controls, compliance requirements and site-specific operating protocols. Rather than promoting futuristic ideas such as fully autonomous or “self-healing” factories, Merit focuses on practical, auditable steps that help maintenance teams act earlier, make safer decisions and maintain continuity without disrupting established processes.
Unplanned downtime remains one of the most material and measurable risks in manufacturing. Across UK and European operations, the cost of halted production can range from £20,000–£80,000 per hour in automotive assembly and even higher in tightly regulated pharmaceutical environments where product loss and batch integrity add further financial impact. Beyond direct costs, equipment failures disrupt schedules, trigger overtime, delay customer orders and can introduce quality deviations that undermine long-term reliability.
Even modest reductions in unplanned downtime can deliver substantial savings for medium and large facilities - benefits that extend beyond cost. Predictive maintenance directly supports wider ESG objectives by improving energy efficiency, reducing material waste from scrap or rework and minimising the environmental footprint associated with repeated component failures.
Shifting from reactive maintenance to condition-based, data-driven interventions also aligns with established operational frameworks such as ISO 55000, which emphasise asset reliability, lifecycle performance and risk-aligned decision-making. By detecting emerging issues earlier, manufacturers can prevent catastrophic failures, stabilise production cycles and create more predictable, resource-efficient operations, making predictive maintenance a high-value, strategically aligned opportunity for European industrial leaders.
Most factories already capture critical telemetry from their equipment such as vibration sensors on rotating assets, temperature probes on motors and bearings, pressure and flow readings for pumps and hydraulics, and energy meters monitoring consumption across lines. Operational logs, historian systems, PLCs and SCADA platforms record valuable runtime data.
However, this data typically sits across heterogeneous vendor ecosystems that dominate UK and European industry like Siemens, Rockwell, Mitsubishi, Emerson and legacy Schneider platforms, each using different protocols, data models and timestamping approaches. As a result, even when the data exists, it is rarely aligned, clean or directly comparable.
The real challenge is engineering coherence: consolidating siloed telemetry, aligning timestamps across asynchronous data streams, and performing sensor fusion so that cross-signal patterns are interpretable. A vibration spike, for example, means something very different if it occurs during a high-load cycle versus at idle, and only becomes meaningful when correlated with temperature, energy draw or PLC-state data. Without this unified, time-aligned view, early signs of degradation often go undetected until equipment fails.
Operational environments introduce further constraints. Many plants operate with limited bandwidth, strict network segmentation or edge-only processing due to security requirements, making real-time data consolidation even more complex. Effective predictive maintenance therefore depends on robust industrial data engineering: building a consolidated, clean and synchronised dataset that reflects true asset behaviour.
With this foundation in place, agentic AI can interpret anomalies with confidence, understanding not just that something has changed, but what it likely means, how severe it is and what action should follow.
Traditional predictive maintenance systems can detect anomalies, but they rarely provide direction on what to do next. They may flag high vibration or elevated temperature, leaving engineers to interpret the signal, assess operational risk and coordinate a response, often resulting in alert fatigue, inconsistent decisions and delayed intervention.
Agentic AI advances this model by adding both interpretation and action. First, it analyses multi-dimensional datasets to infer likely failure modes, correlating vibration, temperature, load, power consumption, PLC states and historical behaviour. Second, it connects these insights to context-aware next steps like mapping asset conditions to production schedules, maintenance capacity, spare-part availability and operational constraints.
This creates a form of closed-loop decisioning within operational guardrails: the system proposes or initiates actions only when they meet predefined safety, compliance and process requirements.
For example, agentic AI may determine that a motor bearing is beginning to fail based on cross-signal patterns. It then evaluates whether a planned maintenance window is upcoming, whether replacement parts are in stock and whether a temporary parameter adjustment is safe. The system can automatically generate a work order, recommend a controlled load reduction or escalate to human review depending on the criticality of the asset.
Crucially, every step is auditable and explainable; an essential requirement in regulated sectors such as pharmaceuticals, chemicals and aerospace. Engineers can trace how the AI reached a conclusion, which data sources were used and why a specific action was recommended, ensuring decisions remain transparent, compliant and operationally trustworthy.
Consider an electric motor driving a conveyor line. Over several weeks, vibration sensors begin showing subtle but consistent changes: rising RMS values, increasing kurtosis, and the early onset of high-frequency impacts detected through envelope analysis. In parallel, temperature probes record intermittent thermal spikes, and power consumption shows a gradual upward drift during steady-state operation.
Individually, these signals might appear insignificant. But once consolidated and time-aligned, their combined pattern becomes a clear early-stage indicator of bearing degradation. Agentic AI analyses this fused dataset against historical baselines and similar failure signatures, reducing false positives by correlating vibration-domain features with thermal behaviour, load conditions and PLC-state data.
After identifying the likely fault, the system evaluates operational constraints: upcoming production windows, safety rules, the availability of replacement bearings and technician capacity. If conditions are favourable, e.g., a low-impact maintenance window is approaching and spares are in stock, the AI can automatically generate a work order within platforms commonly used across European operations, such as SAP PM, IBM Maximo or IFS. It may also recommend a temporary reduction in conveyor speed to reduce mechanical stress until the intervention is carried out.
Human supervisors still approve the decision, but they no longer need to manually review sensor trends, cross-check logs or coordinate planning activities. The AI system has already interpreted the multi-signal evidence, identified the failure mode with explainable logic and orchestrated the safest, most efficient response, preventing unplanned downtime while keeping engineers in control.
Agentic AI can reliably automate a wide range of routine, low-risk tasks in manufacturing environments. These include adjusting non-critical operating parameters, balancing workloads across parallel machines, generating maintenance work orders, logging diagnostics and prioritising assets that require attention. It can also propose alternative operating scenarios such as temporary load redistribution, controlled derating of equipment or short-term parameter adjustments - when these fall within predefined operational guardrails. These automations significantly reduce manual workload, enabling engineers to focus on higher-value and higher-risk interventions.
However, decisions with implications for safety, regulatory compliance or critical production continuity must remain under human supervision. Frameworks such as HAZOP, SIL classifications and internal engineering risk playbooks define clear boundaries for what can be autonomously executed versus what must be reviewed and approved by qualified personnel. Complex failure modes, ambiguous or conflicting sensor signals and interventions that impact core process stability still require engineering judgement, physical inspection and manual sign-off.
In practice, agentic AI operates as a structured, risk-aware decision support system. It accelerates analysis, improves consistency and orchestrates operationally aligned recommendations, while ensuring human oversight remains firmly in place for all actions that carry safety, compliance or high-impact consequences.
Implementing predictive maintenance in European manufacturing environments comes with a distinct set of technical and regulatory challenges. Data privacy is a central consideration: although machine telemetry is often assumed to be anonymous, many datasets include operator-linked elements such as shift logs, badge-based machine access records or operator-tagged production events. Under GDPR, this transforms operational data into personal data, requiring lawful processing, minimisation, retention controls and clear governance. Any AI workflow using such signals must incorporate pseudonymisation, access restrictions and, where appropriate, a Data Protection Impact Assessment (DPIA).
Cybersecurity obligations further shape what is feasible. Many UK and EU plants operate under IEC 62443 principles, relying on segmented networks, tightly controlled firewalls, and strictly whitelisted communication pathways. These architectures often limit cloud connectivity and require AI workloads, especially real-time inference, to run on-prem or at the edge. Data leaving the plant may need to be aggregated, pseudonymised or routed through approved gateways, ensuring full alignment with site-level security policy.
Integration with legacy industrial systems adds another layer of complexity. European factories frequently operate mixed estates of Siemens, Rockwell, Mitsubishi, Schneider and older historian platforms such as OSIsoft PI, AspenTech or bespoke MES layers. These systems use different protocols, sampling rates and timestamp conventions, which complicates data consolidation. Achieving reliable predictive maintenance typically requires tag normalisation, protocol bridging via OPC UA or Modbus, and careful alignment of asynchronous data streams before any advanced modelling can occur.
Long-term data quality is also a significant operational concern. Instrumentation drift, inconsistent sensor mounting practices, ageing hardware and process changes all lead to gradual degradation of signal reliability. Without continuous monitoring for missingness, noise, drift and abnormal distribution shifts, even the best models lose accuracy over time. Effective predictive maintenance programmes therefore embed data-quality telemetry, periodic retraining cycles and sensor-health monitoring as ongoing lifecycle responsibilities rather than one-time setup tasks.
These region-specific constraints, GDPR obligations, IEC 62443 cybersecurity requirements, legacy integration challenges and persistent data-quality drift, define the practical boundary conditions for deploying predictive maintenance in UK and European plants. Addressing them proactively is essential to ensuring stable performance, regulatory compliance and long-term trust in AI-driven maintenance workflows.
Merit stands out by combining engineering expertise with data science to deliver predictive maintenance capabilities that fit into real-world manufacturing environments. Its modular agentic AI components integrate seamlessly with ERP, MES and CMMS systems, enabling manufacturers to adopt predictive maintenance workflows without disrupting operations.
Merit focuses on:
This practical approach ensures manufacturers achieve measurable benefits quickly while maintaining control and oversight.
When predictive maintenance is implemented with disciplined data integration and workflows grounded in real operational conditions, agentic AI can deliver improvements that are both measurable and defensible. While specific gains depend on asset criticality, instrumentation quality and data maturity, manufacturers commonly observe meaningful reductions in unplanned downtime and smoother, more predictable production schedules. As models mature, plants also see improvements in MTBF and fewer manual inspection cycles, since failure patterns become clearer and easier to act on.
The financial impact is similarly tangible. By shifting from rigid maintenance calendars to condition-based interventions, organisations reduce unnecessary part replacements, extend equipment lifespan and improve labour utilisation. AI-supported spare-part planning helps teams move away from overstocking “just-in-case” components, striking a better balance between cost efficiency and operational readiness.
Manufacturers typically report benefits such as:
Importantly, these outcomes do not require a leap to futuristic smart-factory architectures. Most manufacturers achieve meaningful ROI through practical, incremental adoption, leveraging existing infrastructure, improving data fidelity over time and expanding AI’s scope as teams build confidence in its recommendations.
Merit applies a grounded, engineering-first methodology to predictive maintenance, focusing on practical, measurable results rather than futuristic visions. Its teams specialise in unifying diverse industrial data sources like PLC, SCADA, MES, and historian systems, into robust pipelines that make sensor and operational data ready for real-time analysis. This dependable foundation allows agentic AI to interpret asset behaviour accurately and deliver actionable insights from the outset.
Merit’s modular AI components integrate seamlessly with widely adopted ERP, MES and CMMS platforms, following proven reference architectures and common integration patterns. Typical deployments deliver initial predictive insights within 6–10 weeks, enabling manufacturers to realise early value while maintaining operational continuity. Each module performs a specific function, from baseline modelling and anomaly detection to decision recommendation and automated work order creation. This modular approach reduces adoption risk and supports gradual scaling as teams build confidence in AI outputs.
Operational awareness remains central to Merit’s methodology. AI recommendations are designed to align with existing maintenance practices, shift patterns, production windows and safety protocols, ensuring actionable decisions fit seamlessly into day-to-day operations. Traceable, auditable explanations for each AI-driven decision help engineers understand and trust the system, supporting continuous improvement.
Merit also enables multi-site scaling across European operations, standardising workflows while accommodating site-specific constraints such as legacy equipment, regulatory requirements and local operating practices. By combining practicality, reliability and rapid time-to-value, Merit ensures predictive maintenance becomes a sustainable, repeatable operational capability rather than a short-lived experiment.
