Why One-Click Tech Transfer Matters Now
Tech transfer is still measured in months because knowledge remains fragmented, manual, and difficult to reuse.
Despite increasing digitalization, critical transfer knowledge remains scattered across documents, systems, and expert teams. One Click Tech Transfer transforms this fragmented knowledge into a structured, reviewable, and compliant foundation for faster decision-making and transfer execution.
Manual effort
Time-to-transfer
First-time-right quality
Knowledge reuse
Merck already has strong foundations — the challenge is connecting them into reusable tech-transfer decisions.
Merck has invested significantly in process knowledge, semantic foundations, and manufacturing expertise. Yet critical transfer knowledge remains spread across documents, systems, and expert teams, creating manual effort and limiting reuse across transfers.
Process knowledge exists across documents, systems, and people but lacks a unified structure for reuse
CURRENT STATEManual comparison
Fragmented knowledge
Experts still compare process parameters and receiving-site capabilities manually
PCS effort
Process Control Strategies, work instructions, and manufacturing scenarios require significant manual drafting effort
Semantic↔Document Gap
Valuable semantic models exist but are not yet directly connected to the document-centric transfer workflow
Manual downstream prep
DESIRED STATEConnected knowledge
Automated assessment
AI-assisted generation
Structured outputs
From fragmented inputs to an SME-endorsed Process Control Strategy through a governed six-step knowledge flow.
Capgemini transforms documents, process data, equipment specifications and expert knowledge into a governed AI-supported transfer workflow.
1. Ingest sending-unit transfer knowledge
Capture PDFs, Word, Excel/CSV, diagrams and equipment specifications.
4. Build and select production scenarios
Define target production target based on different scenario model based on previous knowledge.
2. Build the digital process map
Structure sending units: roles, constraints, ranges and equivalence.
5. Generate receiving-unit strategies
Propose scenarios using equipment fit, constraints, compliance and robustness.
3. Translate into scale-independent logic
Convert process knowledge into descriptors such as P/V, kla and mixing time.
6. Draft the process control strategy
Create reviewable PCS parameters, controls, ranges and justifications.
A Knowledge Twin explains not only what is happening, but why transfer decisions should be made.
Data
What it provides Raw measurements, documents,
records, and process outputs.
Typical questions answered What data exists? What happened?
What was recorded?
OutputStructured Information
Digital Twin
What it provides A representation of process state, equipment status, and operational behavior
Typical questions answered What is happening? Where is it happening? How is the process performing?
OutputProcess visibility & state awareness
Digital Twin
What it provides Context, decision logic, scientific rationale, governance rules, and institutional memory.
Typical questions answered Why is this decision recommended? What should happen next? How can the same decision be applied consistently elsewhere?
OutputExplainable and reusable decisions
The solution extends Merck's existing technology direction by combining semantic foundations, agent orchestration, user-centric workflows, and embedded governance into a single operating model for technology transfer.
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Purpose: Provide structured and contextualized process knowledge.
Output: Structured knowledge model.
Key Enablers: Altair Graph Studio, semantic models. -
Purpose: Coordinate reasoning and workflow execution.
Output: Transfer recommendations and generated artifacts.
Key Enablers: AWS AgentCore. -
Purpose: Enable review, collaboration, and approval.
Output: Reviewable work products.
Key Enablers: Mendix. -
Purpose: Ensure explainability, traceability, and human oversight.
Output: GxP-defensible decisions.
Key Enablers: Human-in-the-loop review, auditability, change control. -
Purpose: Connect technology, transfer methodology, and delivery expertise.
Output: End-to-end operational solution.
Key Enablers: TT expertise, AI expertise, semantic architecture, implementation experience.
Built on Merck’s target architecture, not alongside it.
The solution extends Merck's existing technology direction by combining semantic foundations, agent orchestration, user-centric workflows, and embedded governance into a single operating model for technology transfer.
Technology succeeds when the right experts work together.
Meet the experts behind the solution.