Cosmetic R&D maturity model: from individual files to connected product development
Digitalization is not binary. Cosmetic R&D usually evolves from person-dependent files through shared standards to connected product data and data-driven decisions. The VYVA maturity model provides a simple framework.
The five maturity levels
Level 1: individual and file-based. Level 2: standardized templates. Level 3: central data and explicit versions. Level 4: connected workflows across R&D, regulatory, QA and procurement. Level 5: reusable development knowledge and data-driven assistance. Not every company needs to jump to level 5.
Key takeaways
- Assess process maturity separately from tool maturity
- Prioritize data quality before AI
- Choose the next level, not a five-year vision only
- Measure team adoption
- Include regulatory and QA in the assessment
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Levels 1–2: files and standards
Early maturity relies on individual spreadsheets, folders and personal knowledge. Standard templates improve consistency but do not solve distributed data or version conflict.
Level 3: central structured data
Materials, formulas, projects and tests become explicit records. Versions and ownership are visible. This creates the foundation for reliable digital collaboration.
Level 4: connected workflows
R&D, regulatory, QA and procurement work on the same product data. Changes trigger reviews, tests connect to versions and approvals are documented systematically.
Level 5: knowledge and assistance
With structured history, advanced analysis becomes useful: finding similar formulas, reviewing stability patterns or preparing decision support. AI should build on reliable data rather than hiding missing structure.
R&D maturity model
| 1 – Individual | Local files, personal knowledge |
| 2 – Standardized | Templates, shared storage |
| 3 – Structured | Central records, versions |
| 4 – Connected | Cross-functional workflows |
| 5 – Knowledge-driven | History, analytics, assistance |
Frequently asked questions
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