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AI DEMAND FORECASTING

Stop guessing what to buy.Forecast it from your own data.

Klovio learns from your sales and movement history — not someone else's benchmark — and projects what each SKU will sell, by location, with seasonality and trend built in. Buy the right amount, cut stockouts and dead stock at the same time.

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app.klovio.app/forecast
FORECAST ACC.
91.6%
HORIZON
13 wk
REORDER NOW
7
Projected demand · Sea Salt 500g · next 13 weeks
300 200 100 0 FORECAST →
History (52 wk) Projection 90% confidence
91.6%
Median forecast accuracy
Across active SKUs with a full season of history. Backtested every week against what actually sold.
100%
Of your catalog forecast
Every SKU, every location — not just your top sellers. Slow movers get a model too.
26wk
Forecast horizon
Project demand out to a full half-year, so purchasing and budgeting see the same numbers.
31%
Avg. overstock reduction
Less cash tied up in shelves and slow movers — without trading it for more stockouts.
Why forecasting matters

Guessing demand costs you twice.

Most teams set order quantities from a gut feel, a rolling average, or last year's number scribbled on a PO. The trouble is that the same guess produces both failures at once: too much of the wrong SKUs and not enough of the right ones. You pay for the overstock in cash and the stockouts in lost sales.

Cash frozen on the shelf

Over-order one season and you're sitting on stock that won't move for months. That's working capital you can't spend — and eventually a markdown or a write-off.

The sale you couldn't make

Under-order and the shelf goes empty right when demand peaks. The customer buys a substitute, and you eat the lost margin plus the cost of the rush reorder.

Seasonality you keep forgetting

The spring rush, the holiday spike, the summer lull — your business has a rhythm. A flat average smooths it away and leaves you wrong in exactly the weeks that matter most.

Before vs. after

Gut-feel ordering vs. data-driven forecasting.

Same catalog, same suppliers, same lead times. The only difference is whether the number on the PO comes from a guess or from your own history.

❌ Ordering on gut feel
  • "Order what we did last time." A single number, copied forward, ignoring how demand has shifted.
  • Seasonality gets flattened. A 12-week average is wrong every spring and every holiday.
  • Overstock and stockouts together. Too much of the slow SKUs, not enough of the fast ones.
  • One number for every location. The downtown branch and the suburb warehouse get the same order.
  • No way to test a scenario. "What if we run the promo?" gets answered with a shrug.
  • The model never improves. Same guess next quarter, regardless of what actually sold.
✅ With Klovio forecasting
  • A projection per SKU. Built from your real sales and movement history, not a benchmark.
  • Seasonality and trend, modeled. The spike and the lull are in the forecast, not smoothed out.
  • Both failures shrink at once. Right quantity for the slow SKUs and the fast ones alike.
  • Per-location demand. Each warehouse gets its own curve, so each order fits its own pattern.
  • Scenario views. Model a promo, a price change, or a new location before you commit cash.
  • It sharpens every week. Each closed sales week feeds back in — accuracy climbs as data accrues.
How it works

Three steps from history to a purchase order.

No data-science team, no spreadsheet models. Klovio reads the data you already have and turns it into a number you can act on.

STEP 01

It reads your own history

Every sale, pick, and movement Klovio already tracks becomes training data — by SKU, by location, week over week. No exports, no manual prep.

Your data
STEP 02

It projects demand forward

Statistical models separate the trend, the seasonality, and the noise, then project what each SKU will sell — with a confidence band, not a single false-precise number.

Per SKU · per location
STEP 03

It suggests what to order

The forecast becomes a reorder point and an order quantity that accounts for lead time and the safety stock you actually need — ready to push to a PO.

Reorder + qty
Per-SKU, per-location

A forecast for every SKU — and every place you stock it.

A single warehouse-wide number hides the truth: the same SKU sells on a completely different rhythm in each location. Klovio builds an independent projection for every SKU at every site, so the order that lands at each dock fits that dock's real demand.

  • Separate trend and seasonality per SKU, per location
  • Confidence band, so you can size safety stock honestly
  • Slow movers get an intermittent-demand model, not a flat zero
  • New SKUs borrow from similar items until they have history
See Reorder Points →
Projected demand · next 4 weeks · by location
Sea Salt 500g · Houston DC
Proj. 188 units · trend ↑ 6% · 92% acc.
Sea Salt 500g · Austin DC
Proj. 74 units · trend flat · 90% acc.
Olive Oil 1L · Houston DC
Proj. 312 units · seasonal ↑ peak · 89% acc.
Honey 250g · Austin DC
Proj. 41 units · intermittent demand · 86% acc.
Scenario · holiday promo on Olive Oil 1L
B
Baseline: 312
next 4 weeks
→ +
P
Promo lift: +38%
−15% price
↓ Re-run forecast with scenario ↓
Suggested buy
312431
Order sized for the promo — before you commit the cash.
Scenario views

Ask "what if" before you spend the money.

A promotion, a price change, a new location, a supplier with a longer lead time — every one of these moves demand. Guessing the impact is how overstock and stockouts get baked into a PO before it's even sent.

Klovio lets you layer a scenario on top of the baseline forecast and watch the suggested order quantity update. You see the demand and the buy side by side — and decide with numbers, not nerves.

  • Model promos, price changes, and new locations
  • Compare scenario vs. baseline order quantity instantly
  • Adjust lead time and safety stock and see the buy move
Under the hood

How a forecast gets built.

From the sales and movement data you already have, to a projection you can put on a purchase order. Data-driven, season by season — and sharper every week.

HISTORY → ORDER How a forecast gets built From your own data — no benchmarks, no guesses. 52 WEEKS STEP 1 · YOUR DATA Sales & movement history Every sale, pick, and transfer Klovio already tracks — by SKU, by location, week over week. DECOMPOSE STEP 2 · THE MODEL Separate trend, season, and noise Statistical models pull apart the long-term trend, the repeating seasonality, and the random noise. OUTPUT A Demand projection With a 90% confidence band. QTY 431 OUTPUT B Suggested order Reorder point + quantity. Every closed week feeds back in. The model retrains on what actually sold — so accuracy climbs as your data accrues. Accuracy figures are backtested weekly against actual sales. New SKUs improve as history builds.
The specifics

What "forecasting" actually means in Klovio.

Related modules

Pairs well with.

Stop guessing. Start buying the right amount.

20 minutes is all it takes to see Klovio forecast demand on your own catalog and history.

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