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Insights › Solution stories

Solution stories

Paints and Coatings: Shade Formulations and Batch Correction

Turning tinters’ experience into shared, approved correction suggestions.

Trikaan Research & Engineering·10 Nov 2026·4 min read
Paints and Coatings: Shade Formulations and Batch Correction

KEY TAKEAWAYS

  • Batch corrections were the hidden cost of shade variation.
  • Formulation history linked to raw-material lots explained corrections.
  • AI suggested starting corrections from past batches.
  • Approved changes became new formulation versions.
About this story: This is an illustrative solution story. It is a composite built from situations we commonly see in this industry, not an account of a single named customer. Figures shown as [X] are placeholders to be replaced with verified results.

A paints and coatings manufacturer produced many shades and spent significant time correcting batches to match standards.

This story shows how the products combined.

The situation

Formulations were in spreadsheets. Corrections were made by experienced tinters and recorded on paper. Pigment lot variation was not linked to corrections.

What they had already tried

  • Relying on senior tinters.
  • Tighter pigment specifications.
  • Extra QC checks.

How we diagnosed the real problem

We analysed correction records for top shades.

  • Corrections correlated with specific pigment lots.
  • The same corrections were repeated across batches.
  • Knowledge sat with two people.

The solution, product by product

No single product solved this. The value came from the products working on the same data.

ProductRole in this solution
ASTR ProductionVersioned shade formulations, batch corrections recorded and linked to lots
ASTR AISuggested correction additions based on past batches and lot properties
AnvayaPigment and resin lots, stock, tinting orders, dispatch
ASTR ReportingCorrections by shade, pigment lot and supplier

How it rolled out

  • Formulation migration.
  • Correction recording.
  • AI correction suggestions with approval.
  • Supplier lot reviews.

What changed

Correction knowledge became shared and fewer rounds were needed.

  • Correction rounds per batch reduced by [X]%.
  • Batch cycle time reduced by [X]%.
  • Pigment supplier issues identified with data.
In practice: The best tinter’s experience was turned into a starting suggestion everyone could use.

What we would tell a similar company

  • Record every correction.
  • Link corrections to lots.
  • Use AI suggestions as starting points, approved by experts.
  • Version formulations after approved improvements.

Frequently asked questions

Can AI match shades?

It suggests corrections from history; experts approve and measure.

Does it connect to spectrophotometers?

Instrument integration is possible. [Confirm devices.]

Is this a real customer?

It is an illustrative composite.

  • #paint manufacturing software case study
  • #shade matching formulation
  • #coatings batch correction
  • #paint plant ERP
  • #colour formulation management

Trikaan Research & Engineering

Trikaan

Written by the researchers and engineers who build Anvaya, ASTR and Seed Track, with input from Trikaan's delivery and engineering teams.

ON THIS PAGE

  • The situation
  • What they had already tried
  • How we diagnosed the real problem
  • The solution, product by product
  • How it rolled out
  • What changed
  • What we would tell a similar company
  • FAQ

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