Cross-System Visibility in Pharmaceutical Quality Management
A strategic framework for connecting quality systems to improve compliance posture, accelerate investigations, and enable data-driven decisions.
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Executive Summary
Pharmaceutical quality teams operate across an average of 5-7 disconnected systems daily. This fragmentation creates compliance risk, slows investigations, and prevents data-driven decision making. This whitepaper examines the root causes of system fragmentation and presents a practical framework for achieving cross-system visibility without replacing existing investments.
Key Findings
of quality teams spend more than half their time searching for information across systems
average time to complete impact assessments for supplier changes
higher audit finding rate when data is manually reconciled between systems
reduction in investigation time with unified visibility
The Problem: System Fragmentation
Typical pharmaceutical quality operations involve multiple specialized systems that evolved independently over decades. While each system excels at its core function, critical relationships between data points are lost in the gaps between systems.
Common System Landscape
Document Management
- Veeva Vault
- Documentum
- MasterControl
Quality Events
- TrackWise
- Veeva QMS
- SAP QM
ERP & Supply Chain
- SAP S/4HANA
- Oracle ERP
- JD Edwards
Laboratory
- LIMS (LabWare, STARLIMS)
- Empower Chromatography
- Stability Management
Impact on Operations
- 1 Delayed Investigations
Investigators spend days gathering data from multiple systems before analysis can begin.
- 2 Audit Vulnerability
Inspectors find inconsistencies when data is reconciled on-the-fly during audits.
- 3 Reactive Quality
Without cross-system visibility, quality teams can't anticipate issues or see patterns.
- + Additional impacts covered in full whitepaper
Including regulatory submission delays, supplier management gaps, and recall response time
The Solution Framework
Cross-system visibility doesn't require replacing existing systems. Instead, it requires an orchestration layer that understands relationships between entities across systems and can traverse those relationships in real-time.
Architecture Principles
Semantic Data Model
Define entities (Product, Supplier, Batch, Document) and their relationships in a unified ontology.
Bi-Directional Connectors
Read from and write to source systems while maintaining data lineage and audit trails.
Real-Time Relationship Traversal
Query across systems in milliseconds to answer "what's affected?" questions instantly.
Additional architecture patterns in full whitepaper
Including event-driven workflows, AI-powered analysis, and compliance automation
Use Case Examples
Supplier Change Impact
A supplier notifies you of a manufacturing process change. Traditional approach: 3-5 days of manual investigation.
Deviation Investigation
A batch fails testing. Traditional approach: Manually search 4+ systems to build investigation timeline.
Additional use cases in full whitepaper
Including audit preparation, regulatory submission support, and recall readiness
Implementation Roadmap
Cross-system visibility is achieved incrementally, starting with high-value connections and expanding as value is demonstrated.
Phased Approach
Connect document management + ERP for supplier-material-document relationships.
Add quality event system for deviation-batch-product traceability.
Including laboratory integration, regulatory systems, and AI-powered workflows
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The full whitepaper includes detailed architecture diagrams, ROI calculation models, vendor evaluation criteria, and implementation playbooks.