Introduction

Modern medical-device manufacturers face growing complexity across the product lifecycle. Bills of materials, supplier certifications, test reports, and regulatory filings often live in disparate systems (PLM, ERP, QMS, MES). These information silos hinder the ability to trace changes and identify root causes.

Research on digital threads shows that seamless traceability across design, manufacturing, and maintenance is essential for high-quality manufacturing and knowledge reuse. The digital-thread vision links heterogeneous systems dynamically, without one-to-one data mappings, reducing the cost of integration and enabling data-driven decision-making.

This use case proposes a graph-based solution that complements existing PLM platforms. By modeling relationships among parts, processes, documents, and stakeholders, companies gain real-time visibility, enhance compliance, and unlock analytics that were previously impractical.

Challenges Faced by Medical-Device Manufacturers

1. 1. Supply-Chain Quality & Root-Cause Analysis

   • Complex devices built across multi-tier supply chains.
   • Engineering teams rely on spreadsheets → inconsistent data and slow root-cause identification.
   • Faulty component searches are slow; ad-hoc analytics cannot prevent quality issues.
2. 2. Flexible Workflows for Customized Devices

   • Engineer-to-order devices demand tailored workflows.
   • Manual sub-processes reduce visibility and delay orders.
   • Rigid schemas make it difficult to adjust workflows for new specs or regulations.
3. 3. Regulatory & Clinical Evidence Management

   • Integrating pre-clinical, clinical, and manufacturing data is slow and error-prone.
   • Manual terminology capture leads to compliance risks.
   • Graphs ensure automation, consistency, and reuse across evidence packages.
4. 4. Risk & Fraud Detection

   • Fraudulent claims and counterfeit detection require recognizing multi-party patterns.
   • Traditional rule-based approaches fail with complex networks.
   • Unified graph analytics reveal hidden connections in claims, sales, and geography.
5. 5. Digital Thread & Traceability

   • Without a connected foundation, requirements cannot be traced through design, test, and field ops.
   • Linked data methods provide persistent IDs and cross-lifecycle visibility.

Proposed Solution: Enterprise Product & Quality Knowledge Graph

Core Model

• Nodes: parts, assemblies, processes, documents, suppliers, tests, regulations, events.
• Edges: relationships among these entities (e.g., part-of, tested-by, supplied-by, cites).
• Pipelines: ingest structured/unstructured data (CAD, sensors, docs) into a unified lakehouse → graph model.

Solution Modules

1. 1. Supply-Chain Quality & Root-Cause Module

   • Graph modeling: represent parts, products, and quality issues as nodes; trace defects across assemblies.
   • Graph analytics: proximity and centrality rank likely root causes.
   • Business impact: faster investigations, reduced scrap/rework, higher yields.
2. 2. Dynamic Workflow & Manufacturing Module

   • Graph workflows: tasks, resources, and dependencies as connected nodes.
   • Polyglot persistence: documents for workflow metadata + graph for execution.
   • Business impact: adapt quickly to changes, ensure compliance, improve efficiency.
3. 3. Regulatory & Clinical Evidence Graph

   • Integrated evidence: trial data, CAPA records, and regulatory guidelines ingested into the graph.
   • Relationship tracing: requirements ↔ design outputs ↔ test protocols ↔ submissions.
   • Generative support: RAG for submission drafts with built-in citations.
4. 4. Risk & Fraud Analytics Module

   • Unified datasets: warranty claims, assistance programs, service records, geodata.
   • Entity resolution: similarity functions merge fragmented identifiers.
   • Graph detection: community detection and centrality algorithms surface anomalies.
5. 5. Digital Thread & Traceability Backbone

   • Data federation: connectors for PLM, ERP, MES, QMS, and external sources.
   • Traceability queries: instant impact analysis for requirement changes.
   • Role-based access: insights surfaced through existing PLM UIs.

Business Value Estimation

To estimate value, organizations should define baselines:

• Average cost per defect
• Time to resolve issues
• Cost of fraud events
• Number of custom orders
• Cost per day of regulatory delay

Key Outcomes

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Conclusion: By adopting a knowledge-graph approach, medical-device manufacturers can rapidly detect and resolve quality issues, deliver customized devices with efficient workflows, accelerate regulatory approvals through automated evidence assembly, detect fraud and risks earlier, and enable a true digital thread for seamless traceability and knowledge reuse. Ultimately, the knowledge graph transforms PLM from a passive repository into an intelligent engine for continuous improvement, compliance, and innovation.