How MAHs Reduce EU FMD Alerts (Root Causes, Prevention, and Continuous Improvement)
Reducing alerts is often framed as an execution problem, how quickly can teams investigate and close them? But in practice, the real issue is different. It is not the volume of alerts, but the quality of those alerts.
Across the EU FMD landscape, most alerts are not indicators of falsification risk. They are byproducts of process gaps, data inconsistencies, and timing misalignments across a highly fragmented digital ecosystem. NMVO consistently highlights that alerts may originate from end-user handling, system behavior, or missing or delayed MAH uploads and not necessarily from product integrity concerns.
Teams spend time investigating events that add little value, while the same root causes continue to generate repeat alerts, resulting in what many organizations experience as alert fatigue.
Organizations that move ahead recognize this and shift their focus. Instead of optimizing how alerts are handled, they ask a more fundamental question: why do alerts happen in the first place?
Alert Reduction as a Quality Discipline
A more effective model mirrors a CAPA-driven quality system. Alerts are categorized, trends are reviewed regularly, and corrective actions are tracked until the underlying cause is removed.
Over time, this changes the dynamic, shifting the focus from reacting to individual alerts toward preventing recurrence, reducing noise at the source rather than managing it downstream.
This is where alert management begins to intersect with broader digital supply chain transformation. The objective is no longer just compliance, but stability, predictability, and control across the serialization landscape.
The 6 Most Common Alert Root Cause Categories
Although alerts are designed as safeguards against falsified medicines, operational experience shows that most originate from a small number of recurring drivers:
Upload completeness and timing: One of the most common drivers. When batch or pack data is not uploaded fully -or not uploaded in time relative to physical product movement- end users encounter “not found” scenarios:
- Typical indicators: A2 and A3 spikes after release windows or system changes (in practice, this is less about system failure and more about synchronization gaps between release, upload, and distribution)
- Recommended preventive control: daily upload reconciliation report per market.
Master data mismatch: Differences between packaging order data and serialization payloads create inconsistencies that surface at the point of verification:
- Especially around batch numbers or expiry dates
- Recommended preventive control: stronger validation at release (but often persist due to fragmented ownership between systems and teams)
Packaging execution: Here, the issue is not in the data itself but in how it is physically applied. These issues tend to be localized but can generate significant alert volumes when they occur.
- Line clearance issues, incorrect setup, or print degradation resulting in 2D codes that do not match the intended data or expiry dates
- Recommended preventive control: automated print verification and camera checks aligned to release master data
End-user handling: Double scanning, workflow deviations, or incorrect execution at the pharmacy level can trigger alerts such as “already decommissioned.” While outside direct MAH control, these are widely recognized across NMVOs as a frequent contributor.
- Repeated alerts like A7 or A24 tied to specific markets or dispensing environments
- Recommended preventive control: track and classify end-user-related alerts and align with NMVO guidance before initiating deep investigations; support training where patterns persist
System and integration behaviors: Even when data is correct, delays in transmission, temporary hub unavailability, or interface instability can create the appearance of missing data. These cases are particularly challenging because they often present as widespread spikes across multiple products or markets. frequent contributor.
- Sudden, cross-market or multi-SKU alert spikes without a clear operational change
- Recommended preventive control: monitor interfaces closely, validate transmissions, and have automatic retries and confirmations in place
Market-specific differences: Variations in national procedures, expectations, and SOPs mean that the same underlying issue can manifest differently across countries. Without a clear understanding of these nuances, organizations risk misclassifying or over-investigating alerts.
- Inconsistent alert patterns across markets for the same product or process
- Recommended preventive control: Manage this through market-specific playbooks and by factoring local procedures into investigations.
Mini-Case Example: When “Not Found” Is Not a Mystery
Consider the scenario where A2 alerts spike in a market after a release cycle. It may look like a data issue, but often the cause is simpler: batches were released and moved forward, while the upload was incomplete or delayed.
Without strong reconciliation, teams end up reacting, chasing alerts and reconstructing what happened after the fact. This can be addressed through simple but effective digital controls, such as daily reconciliation between released batches and uploaded data, including confirmation of successful uploads.
With that visibility in place, gaps are caught early, and what was once a reactive issue becomes a predictable, controlled process.
A Structured Path to Reducing Alerts
Reducing alerts in a sustainable way typically follows a progression.
Phase 1: Stabilize (Weeks 1–4)
Goal: Create visibility and consistency
- Standardize how alerts are recorded and investigated
- Ensure quick access to initial evidence
- Build basic dashboards by market and alert type
- Identify early patterns and recurring issues
Phase 2: Diagnose (Weeks 5–8)
Goal: Understand root causes with data
- Analyze trends by SKU, CMO, site, and timing
- Link alert spikes to changes (systems, packaging, release cycles)
- Run cross-functional reviews (serialization, quality, packaging, IT)
- Move from isolated cases to pattern recognition
Phase 3: Prevent (Weeks 9–12)
Goal: Eliminate repeat drivers
- Implement release gates (upload completeness, data validation)
- Strengthen packaging controls with CMOs (alignment)
- Ensure printed data matches master data
- Remove recurring causes and stabilize alert patterns
Measuring What Actually Matters
The most useful metrics are not total alert counts, but patterns. Alert rates normalized by volume, repeat alerts tied to specific SKUs or CMOs, and time to gather initial evidence all provide a clearer view of where the system is breaking down.
Together, these indicators help shift the focus from volume to behavior, highlighting where issues repeat and where intervention is needed.
A Practical KPI Table for Alert Reduction
KPI | Why it matters | Typical target direction |
Alert rate per 10,000 scans | Normalized view of alert volume across markets | Sustained decrease with stable, predictable patterns |
Repeat alerts by SKU | Identifies product-level issues not resolved at source | Eliminate recurring SKUs over time |
Repeat alerts by CMO / site | Highlights execution gaps across manufacturing and packaging sites | Improve underperforming sites; reduce variability |
Time to first evidence | Measures speed of initial investigation and validation | Faster, consistent response with minimal manual effort |
% alerts from end-user handling | Distinguishes external causes from MAH-driven issues | Stable, understood, not over-investigated |
Alert recurrence rate (same cause) | Tracks effectiveness of root cause elimination efforts | Continuous reduction of repeat root causes |
Upload reconciliation rate | Ensures alignment between released and uploaded data | Near-complete alignment; discrepancies resolved same day |
What Enables Scale
As organizations grow, manually managing alerts quickly becomes limiting. To drive sustainable improvement, companies need digital support that shifts effort from data gathering to analysis.
This starts with consolidating alert data across markets into a single, unified view, enabling consistent trending and comparison. Rules-based routing reduces triage effort, while automated evidence collection shortens investigation cycles by pulling data directly from source systems.
Extending this visibility to CMO performance -through structured scorecards and regular reviews- helps create shared accountability and more consistent execution.
Reducing EU FMD alerts is not about lowering numbers for reporting purposes. It’s about building a system where alerts are meaningful, explainable, and increasingly limited to true exceptions.
Organizations that succeed move beyond reactive investigation. They prevent common issues, eliminate repeat causes, and use digital capabilities to align data, processes, and teams around a shared understanding of how the system should operate.
The result is not just fewer alerts, but a more stable and resilient, digitally enabled supply chain, one where attention is focused on what truly matters.
For more information about SCW Consultancy Services;
For additional detail and help, please contact:
Mia Van Allen – Managing Partner – mia.vanallen@supplychainwizard.com
