Overstock
Cash frozen on the shelf
Buffer every series to feel safe and capital sits as inventory you have already paid for, then gets marked down or written off.
Use case · Retail demand planning
Plan retail demandwith ranges.
sktime models demand across products, stores, and regions with probabilistic forecasts, so teams can see risk and keep hierarchy totals coherent.
Retail demand forecast · next week, units
Food preparation2,2202,180
Hobs1,240
Ovens980
Food preservation2,2302,310
Fridges1,510
Freezers720
Home appliances · total4,4504,500
Every level adds up. Bottom-up reconciliation keeps the parts and the whole coherent.
Forecast separately, the subtotals and total don't reconcile, 1,240 + 980 ≠ 2,180.
Illustrative figures.
- Open source
- Free to run, fully auditable, and no per-seat or per-SKU license.
- Proven methods
- The same approaches used on the M5, Walmart, and Rossmann forecasting problems.
- Built by the maintainers
- You talk to the people who actually build sktime, not a reseller.
What the status quo costs
Forecasts that don't agree leak money at every level.
Stockouts
Demand you never observe
Run lean and empty shelves censor demand: observed sales understate what shoppers wanted, and the lost basket never appears in the data.
Manual work
Planner days lost to reconciling
Teams burn hours forcing bottom-up and top-down numbers to meet in spreadsheets, and they still rarely agree.
Where the money comes from
Uncertainty-aware demand forecasts pay for themselves.
When you know the full demand distribution, you size inventory to real risk instead of a single point estimate. Most of the savings land in stock decisions, the rest in the hours and licenses you stop spending.
Less safety stock
Probabilistic forecasts size buffers to real risk, not gut feel.
Fewer markdowns & waste
Order closer to true demand, so less stock ends up discounted or dumped.
Recovered stockout demand
Catch the demand you used to miss when shelves ran empty.
Forecasting license fees
sktime is open source. No per-seat or per-SKU vendor bill.
Manual reconciliation hours
Hierarchy-aware workflows keep totals coherent when planning needs them.
Directional benefits only. Your real numbers come from a short scoping call on your own data.
What you can build on
Built for real retail planning.
Full probabilistic outputs
Intervals, quantiles, variance, and predictive distributions for risk-aware planning.
Global & panel models
One model trained across many related series, instead of one model each.
Real-world drivers
Add holidays, promotions, prices, store IDs, and SNAP purchase-day flags from M5/Walmart: dates when SNAP purchases are allowed in CA/TX/WI stores.
Hierarchy when you need it
Bottom-up, top-down, and optimal reconciliation keep product, store, and regional totals coherent.
How we start
Three steps from your data to a forecast you trust.
- 01
Scoping call
We map your series, your hierarchy, and where the numbers hurt most today.
- 02
Pilot on your data
We model a real slice of your demand data, quantify uncertainty, and keep hierarchy totals coherent where needed.
- 03
Run in your planning
Roll it into your S&OP cadence, with the maintainers a message away.
Before you ask
The three things planning leads check first.
- Do we need a data-science team?
- No. We help stand it up on your data, and your planners work with the forecasts, not the code.
- Is open source safe for enterprise?
- sktime is permissively licensed, auditable, and widely used in production. Nothing is locked behind a vendor.
- How fast can we see value?
- A short pilot on a slice of your demand data shows the lift before you commit to anything.
Open source meets enterprise
Bring uncertainty-aware forecasts into your planning.
sktime is free and open-source. When you want this running on your own demand data, the people behind it can help.