Solutions by Industry
Nutraceuticals/Cosmetics
Moving judgments made by eye onto data
Questions this page answers
- How do you build a smart factory for cosmetics manufacturing?
- Can the cause of a color defect be analyzed from data?
- How do you reduce quality variance in nutraceutical manufacturing?
Requirements in this industry
- HACCP
- GMP
- Color difference (ΔE)
- Formulation conditions
- Sensory evaluation
- Lot traceability
Processes that rely on an experienced eye
In cosmetics and nutraceutical manufacturing, quality judgment has long rested on an experienced operator’s senses. Noticing that a shade is slightly off, or that the texture is not quite usual, is something only accumulated experience can do.
The trouble is that the standard differs between people, and when that person is away the standard moves with them. Explaining why a judgment was made is equally difficult.
Moving it onto data
Color defect root cause analysis — Analyzes formulation conditions, raw material lots and process variables alongside final color data to narrow the candidate causes. It is a matter of confirming numerically what was already known by feel.
Process optimization — Quality prediction models identify which conditions produce stable results.
Appearance and foreign matter inspection — Deep learning vision inspection detects fine defects and contaminants automatically.
HACCP operations system — Critical control point measurements are logged automatically through the food and cosmetics MES core, together with raw material traceback and shelf-life management. Breaching a limit raises an immediate alarm, which cuts the preparation time hygiene inspections used to need.
Smart factory construction — Collection infrastructure, monitoring and AI deployment built in stages.
Regulatory fit
Systems are designed within the hygiene and quality requirements of the sector, including HACCP and GMP. Structuring the data so that lot traceability is preserved is the starting point.
Solutions applied
- SMART FACTORYManufacturing execution systems built to each industry's processes and regulations
- A²LABAn integrated solution for building a sustainable AI manufacturing environment
- AI OrchestrationA language model manages the agents; those agents control the equipment
- B²LABA big data platform that optimizes manufacturing equipment as the base for AI
- Data StandardizationManufacturing data standardization that builds an interoperable ecosystem