Netstock
Inventory planning for SMBs, connected to the mainstream ERPs.
- Built for smaller teams, not just enterprises
- Plugs into the ERPs SMBs already run
- Clear buying recommendations, not just charts
All guides Data
Buy what will sell. Not what sold last year.
Too much stock ties up cash; too little loses the sale. Most companies split the difference with a spreadsheet and a feeling. AI demand forecasting reads your sales history, seasonality and signals, and turns them into buying decisions. This guide covers how it works, the serious platforms, and when custom wins.
- Less cashsleeping on shelves
- Fewerstockouts on winners
- Seasonalitylearned, not guessed
- Per SKUnot per gut feeling
What AI demand forecasting is
AI demand forecasting learns from your actual sales: per product, per location, per season, with promotions, price changes and trends factored in. It produces forward demand estimates and turns them into recommendations, what to order, when, how much safety stock, that update as reality comes in. The spreadsheet answers what happened; this answers what to do.
01
History gets connected
Sales, stock levels, lead times, promotions, from your ERP, store or spreadsheets.
02
Models learn the patterns
Seasonality, trends, cannibalisation, the lift a discount gives, per SKU rather than on average.
03
Forecasts become orders
Suggested purchase quantities and timing, respecting lead times, budgets and supplier minimums.
04
Reality feeds back in
Every week of actuals sharpens the next forecast, and exceptions get flagged for a human call.
When this solution makes sense
A good fit if
- Cash is tied up in stock that moves slowly while winners run out
- Ordering is one person's spreadsheet plus experience
- Seasonality and promotions regularly surprise your purchasing
- You manage hundreds or thousands of SKUs, possibly across locations
- Supplier lead times force decisions weeks ahead of demand
Probably not yet if
- You sell a handful of products with stable, obvious demand
- Sales history is short, dirty or not recorded per product
- Purchasing decisions are constrained by contracts, not information
- Nobody will act on recommendations they did not make themselves
What changes
Cash released from the shelves
Stock levels sized to demand rather than fear, and the freed money works elsewhere.
Winners in stock when it matters
The products that sell stop being the products that run out mid-season.
Purchasing with reasons
Orders backed by numbers you can inspect, not arguments about whose feeling is older.
Less firefighting
Exceptions surface early, while the good options are still open.
What teams use it for
- Retail and ecommerce replenishment
- Multi-location stock balancing
- Seasonal buying decisions
- Promotion demand planning
- Supplier order optimisation
- Perishables and short shelf life
The established platforms
These platforms lead the category. They are real, established businesses, and for many teams one of them is the right answer.
RELEX Solutions
The heavyweight retail and supply chain planning platform from Europe.
- Category leader in retail forecasting
- Handles fresh, promotions and scale
- European roots and presence
GMDH Streamline
Demand forecasting and inventory planning with broad ERP connections.
- Approachable mid-market planning tool
- Wide ERP and spreadsheet connectivity
- Established name in the space
Inventory Planner by Sage
Forecasting and purchasing for ecommerce, popular with Shopify merchants.
- Ecommerce-native, quick to connect
- Purchasing suggestions merchants really follow
- Part of the Sage family
Off the shelf or custom?
When a platform is enough
If your data lives in a mainstream ERP or store platform and your products behave like retail products, the platforms above are proven and connect fast. Buy, and spend your effort on data hygiene and adoption.
When custom wins
Custom wins when your demand has structure the platforms do not model: project-driven sales, B2B order patterns, weather or events driving your market, data spread across systems that need joining first, or forecasts that must land inside your own tools and workflows. We often build the data foundation and the last mile around a proven forecasting core.
Where Sabemos fits
Custom build
We build forecasting that fits how your demand really works: your data joined and cleaned, models chosen for your patterns, and recommendations delivered inside the systems your buyers already use, in Spanish and English.
Setup and onboarding
Choosing a platform? We connect the data, validate the first forecasts against reality, tune the settings your planner will trust, and stay until the recommendations are being followed.
Frequently asked questions
How accurate can forecasts get?
Depends on your demand's nature: stable staples forecast tightly, fashion and novelty do not. The honest measure is against your current method: the tools usually beat spreadsheets clearly, and by how much shows up in a backtest on your own history.
How much history do we need?
Two seasonal cycles is comfortable; one is workable with care. Less than that, and the project starts with building the data habit rather than the model.
Will it handle promotions and price changes?
The serious platforms model promotion lift explicitly, if you feed them the promotion calendar. That input discipline is a big share of the value.
Do we still need a planner?
Yes, with better work: reviewing exceptions, handling launches, negotiating with suppliers. The role shifts from calculating to deciding.