See which SKUs will sell, stall or stock out, and what to do about each.
Every run, Zyntel compares expected demand with what you hold and what’s on order, scores each product’s risk, and finds out why it’s in trouble before anyone reaches for a discount.
Know the problem before you pick the fix.
SKU health & risk
Every product classified by sell-through, age, days of cover and margin: selling out, matched, overstocked, ageing or not being seen.
ExampleA saree with healthy margin and low views is a visibility problem, not a price problem.
Lifecycle by segment
Fast fashion lives 8 to 12 weeks, premium 20 to 26, luxury a year or more. Each is judged against its own selling life and ladder.
ExampleA week-14 tee is late. A week-14 handbag isn’t.
Expected demand with events
The normal weekly rate, multiplied by what the same event did last year in that region when one is coming.
ExamplePuja demand lands in Kolkata, not Chennai.
Restock recommendations
A quantity and an order-by date for lines selling faster than you hold, worked back from supplier lead time.
ExampleCapped by open-to-buy, storage, shelf life and supplier minimums.
Store transfers
Move stock from stores where it sits to stores where it sells, when the transfer costs less than the markdown it replaces.
ExampleVirtual and physical moves, with a store gap check.
Channel moves
Send stock to or pull it back from marketplace warehouses and dark stores, so it sits where it sells in every channel.
ExampleFlags products you may want in a marketplace sale yourself.
About inventory intelligence
Which data does it need?
Catalogue, sales by store and channel, stock on hand and stock on order. Upload files to start; add daily feeds later.
How often does it run?
Daily for fast categories like grocery and fashion, weekly for slower ones like furniture. You choose per category.
What if our data has gaps?
Each run reports data health and the rows it set aside, with the reason. Automatic decisions pause below the health level you set.
09Walkthrough on your data
Bring one category. See tomorrow’s worklist.
We load a sample of your catalogue, sales and stock, run the engine on it, and walk you through every recommendation, the reason behind it and what it would have earned.
Go live with as few as 100 SKUs and any length of sales history.
- Your catalogue and stock, loaded
- SKU health and risk scores
- Buy, move and markdown calls
- Festival effects for your regions
- Your brand and store rules
- Live cart or recommendations only
- What it would have saved
- A rollout plan by category