AgribusinessKS-002

Kijanicart Analytics

Stock expires on shelves and restocking is guesswork.

70%
The problem

Agribusiness margins die quietly: produce expires on the shelf while the same SKU runs out next week because restocking is done on gut feel. Nobody can say what will sell, when to reorder, or which market is moving. The data exists — it just isn't intelligence yet.

The approach

This is a data intelligence product, not an inventory system. Stock records are the raw material; the product is the answers — what to restock, when, and how much.

FEFO (first-expired-first-out) batch tracking gives every unit an expiry clock. That stream feeds the forecasting layer, so expiry risk and demand are read together instead of in separate spreadsheets.

Demand forecasting turns sales history into restock timing: reorder points calculated per product, not one rule for the whole shop.

Market intelligence sits on top — price and demand signals across markets, so buying decisions stop being local guesses.

What we built
  • FEFO batch tracking with expiry-risk flags
  • Demand forecasting per product from sales history
  • Restock timing — when to reorder and how much
  • Market intelligence: price and demand signals
  • Dashboards built for daily decisions, not admin
Kijanicart AnalyticsScreenshot coming
PLACEHOLDER — add 2–3 screenshots (forecast view, restock recommendations, expiry-risk board).
The result
70%

Reduction in expired stock — PLACEHOLDER: real pilot figure

[X] days

Earlier restock signal vs. gut feel — PLACEHOLDER

PLACEHOLDER — replace with real pilot results and the pilot partner's name.

Stack
Django RESTReactTypeScriptPostgreSQLPandasCelery

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