What is Operational AI in Stok.ly?
Operational AI is the tier that runs the daily business.
It answers forty questions every night, for every SKU at every location, in your catalogue.
Four questions that make a difference to your purchasing and replenishment are:
how many units will this product sell,
how much buffer does it need,
how long will current stock last,
how likely is it to run out.
This data is ready to be reviewed and interrogated every morning when you walk into work.
Once prompted, the Ai Assistant will create the purchase order, manufacturing order or transfer ready for review and approval.
The distinction that matters is between static and active statistical replenishment.
A min/max system uses static thresholds set at the item level.
Active statistical replenishment derives the threshold from what demand is actually doing, and updates it daily.
A product whose sale velocity is increasing is flagged for your attention.
A product that becomes spiky (sales velocity is volatile) may require more of a buffer to manage peak sales days.
A product that sees slowing velocity requires less buffer stock and possibly a pricing review.
Every day in the background, Stok.ly is re-calculating 40+ variables on every SKU in your catalogue ready for your review and interrogation with the goal to put you in Operational Control.
The Problem: Buying by Memory and Static Thresholds
Manual replenishment means someone deciding, from a sales report or a gut feeling, what to reorder — and a min/max threshold set once, then left untouched until it is obviously wrong.
Neither min or max responds to what demand is actually doing.
A product that suddenly accelerates it’s sales velocity blows past a stale minimum before anyone notices.
A product that slows down keeps getting reordered at the old rate, tying up cash on a shelf.
The recalibration exercise is the tell.
Any business running min/max at scale eventually finds itself manually reviewing thresholds across every location, because the thresholds have drifted away from reality and nobody has time to keep them honest.
We are massively overstocked and we are having to manually calculate minimums and maximums on spreadsheets every month – it wastes so much time that could be used researching new products or negotiating better deals with suppliers.
— Director, Hardware Suppliers
That is 16 stores of thresholds being adjusted by hand, every month.
Trust your stock. Transfer with confidence. Control orders. Fulfill every day like clockwork.
How Stok.ly Forecasts Demand
Stok.ly forecasts every SKU at every location using two models together.
A 30-day moving average provides the baseline for stable, non-seasonal products.
A trend-adjusted version detects when demand is accelerating or decelerating across 30, 60 and 90 days and scales the baseline accordingly, so a product ramping up is caught early rather than after it has already stocked out.
Why stockout days are excluded from sell-through
This is the single most common error in demand analysis, and correcting it changes purchasing decisions immediately.
Days on which a product had zero stock are excluded from its sell-through rate.
Buy 100 units of two different products. One sells out in 10 days. The other takes 100 days to clear.
A simple bestseller list shows both as identical: 100 units sold.
In reality, they are nothing alike, and every downstream calculation — forecast, safety stock, reorder point, GMROII — inherits the error if the correction is not made at source.
How the AI knows how confident to be
Every recommendation carries its evidence with it: literally, how many days of demand history exist for that product at that location. Five days of data still produces a forecast, but the AI says so and hedges accordingly.
A full year of history produces a more confident recommendation, because there is a full year of evidence behind it.
Confidence is clearly stated by the Ai, not implied.
One consistent demand signal, however you sell
Demand is measured at the moment stock actually needs to leave a location, not when a sale is logged.
For a retail till in a store, that is the moment someone pays for an item and walks away with it.
For a wholesale order taken today and fulfilled in ten weeks, sale and fulfilment are not on the same date — and the forecast is built around the fulfilment date, because that is what determines whether stock was there when it was needed.
Run retail, wholesale and ecommerce together and this holds consistently across all three.
G2 Customer Reviews
What do you like best about Stok.ly – Inventory-Centric Cloud ERP?
“we have been a user for the last 4 years and we cannot think of life without it for our retail business. it makes listing to shopify and other marketplaces easy, keeps inventory accurate online and the POS is easy to use. accurate inventory across all sales channels is the big win for us.”
Order management and inventory ERP software for growing retail, wholesale and distribution businesses.
Four Numbers, Recalculated Every Day
The forecasting engine outputs four fields per SKU per location. Together they replace the weekly export, the memory and the stale minimum.
| Signal | What it tells you | How it is calculated | How the buying team uses it |
|---|---|---|---|
| Reorder point | The exact threshold at which to place a purchase order today. | (Trend-adjusted forecast daily demand × lead time in days) + safety stock | When available stock falls below this number, order now. |
| Safety stock | The buffer needed to absorb demand variability at a 95% service level. | 90-day demand standard deviation × √(lead time in days) × 1.645 | Set and forget. It recalculates daily as variability changes. |
| Days of cover | How long available stock will last at the current rate of demand. | Available stock ÷ trend-adjusted daily demand | Sort ascending to see imminent stockouts. |
| Stockout risk | A single urgency score from 0 to 1. | Composite of current availability, forecast demand and demand variability | Sort descending for today’s purchasing priority list. |
On the 1.645. That is the z-score for a 95% service level — the standard for a business that intends to be in stock on 19 of every 20 demand days. It is a choice, not a law of nature, and it is stated here because a buyer should be able to interrogate it. Where lead time has not been configured on the product record, safety stock returns null and the AI flags the product for setup rather than quietly guessing.
Statistical Replenishment vs Min/Max: What Actually Changes
| Min/max replenishment | Stok.ly statistical replenishment | |
|---|---|---|
| Where the threshold comes from | A person, once | Actual demand variability, daily |
| How it responds to a demand spike | It does not, until someone notices | Safety stock increases automatically |
| How it responds to a slowdown | Keeps ordering at the old rate | Safety stock reduces, releasing capital |
| Treatment of stockout days | Counted as low demand | Excluded from sell-through |
| Service level | Implicit and unknown | Explicit: 95% |
| Maintenance overhead | Periodic manual recalibration | None |
| Lead time handling | Usually baked into the minimum | A separate configured input |
Stok.ly still supports location-specific min/max rules, cascading replenishment logic, shortage allocation priority and weighted distribution for businesses that want rules-based control. The statistical layer is not a replacement for operational governance; it is a replacement for guessing the numbers that feed it.
Cross-Location Stock Balancing
Stock sitting overstocked in one location while another runs short is one of the most common and least visible sources of lost margin in a multi-location business. The cash is already spent; the stock is in the wrong building.
Our stock transfer process was manual, error-prone and time-consuming. Between the three of us, we’ve found about 1 day a week extra because of the Ai Assistant and the speed at which it calculates and creates transfers.
Stok.ly’s AI classifies every product as fast- or slow-moving per location, compares stockout risk and days of cover across locations simultaneously, identifies exactly where the imbalance exists, and can recommend or directly create the stock transfer to fix it.
The question it answers is not “which location is lowest” but “where is this stock most likely to sell, and where is it currently doing nothing”.
The sequencing matters commercially: rebalancing existing stock before buying more is the difference between moving cash and spending it.
Ask a competitor’s AI to check for a transfer before it recommends a purchase order — it is a fair test of whether the intelligence is joined up.
Ask a Question in Plain English. Get an Answer. Let It Act.
Stok.ly’s AI assistant does not stop at giving you the answer.
Where the next step is clear, it can take it — creating a purchase order, raising a stock transfer, starting a manufacturing run, or updating pricing and attributes.
Analyse, recommend and action – the cornerstones of Stok.ly Ai.
Example questions the AI Assistant can answer in seconds
- Which items do I need to order today? (Sorted by stockout risk, descending.)
- Which products will run out first? (Sorted by days of cover, ascending.)
- What is the reorder point for this SKU at this location, and how was it calculated?
- What SKUs and on what channels is demand accelerating fastest this month?
- Which products have had no demand for more than 30 days, and at which locations?
- Which SKUs have manually set minimums significantly above or below their statistically correct safety stock?
- Where am I overstocked relative to demand, and where should that stock go instead?
It will push back on a bad purchase order
Select a draft purchase order and ask the AI to review it — margin, stockout-corrected sell-through, what is already in stock, how fast it is actually moving.
These items are slow-moving and low-margin.
Are you sure about these quantities.
Have you considered these other lines instead, where demand is accelerating and cover is already tight.
This is the behaviour that distinguishes an assistant from an autocomplete.
Running continuously in the background
In the background, the AI business intelligence layer analyses inventory, suppliers, customers, pricing, purchasing, returns and warehousing continuously.
Always on, always analysing, the Ai Assistant is designed to deliver Operational Control for you and your team.
How This Compares to Cin7, Brightpearl, Orderwise and Linnworks
Positions below reflect vendor-documented capability as at 27 July 2026.
- Cin7. ForesightAI forecasts demand up to 24 months ahead, calculates reorder points, recommends safety stock and generates purchase orders, using ABC segmentation, and recommends at least six months of history for good accuracy. It is a paid add-on for Core and Omni, priced on gross merchandise value. Cin7 does not document a pre-computed intelligence layer, a defined statistical service level, or exclusion of stockout days from sell-through — and Ask Me Anything, its conversational tool, is documented as an insight and guidance tool rather than one that commits transactions.
- Brightpearl. Forecasting and replenishment lives in Inventory Planner, a connected Sage for Retail product, which forecasts from sales history, seasonality, stockouts and trends and can push approved purchase orders into Brightpearl. Sage Copilot adds a prioritised insight panel — priority replenishments, purchase-order value review, overdue stock orders, missing cost prices — but it is a Premium-only beta, several insights require ABC analysis enabled, and it depends on synchronisation between two products rather than reading one live record.
- Orderwise. Data-driven forecasting using historical sales, seasonal trends, min/max levels and lead times, with automated purchase planning. Its public material does not identify this as AI or machine learning. MyForterro AI is invitation-only and proof-of-concept.
- Linnworks. No demand forecasting capability of this kind; the product’s centre of gravity is multichannel listing and order routing.
None of the four recalculate reorder point, statistical safety stock, days of cover and stockout risk automatically overnight for every SKU at every location, and none combine that with an assistant that can act on the finding directly.
That is a researched claim, and it is the one worth testing in a demo as you research options in the ERP space.
Frequently Asked Questions
Does Stok.ly calculate safety stock automatically?
Yes. Safety stock is recalculated daily for every SKU at every location from actual demand variability, using the 90-day demand standard deviation multiplied by the square root of lead time in days multiplied by 1.645, the z-score for a 95% service level. It is not set manually once and left to go stale. A product that becomes spiky receives more buffer automatically; a product that stabilises receives less, freeing capital.
How does the AI decide when to reorder?
Through a reorder point that combines three things: trend-adjusted forecast demand, your configured lead time, and statistical safety stock. The calculation is (trend-adjusted forecast daily demand multiplied by lead time in days) plus safety stock. When available stock drops below that number, today is the day to place the order for stock to arrive before a stockout.
What is the difference between min/max replenishment and statistical demand forecasting?
Min/max uses thresholds a person set once and must periodically recalibrate by hand. Statistical forecasting derives the threshold from what demand is actually doing and updates it daily. Min/max cannot respond to a demand spike until someone notices; statistical safety stock increases automatically. Min/max has an implicit and unknown service level; Stok.ly’s is explicitly 95%. Stok.ly still supports min/max rules for businesses that want them, including location-specific thresholds and cascading replenishment actions.
Does the AI account for stockout days?
Yes. Days on which a product had zero stock are excluded from its sell-through rate. This corrects the most common error in demand analysis: a product that sold out in ten days and then sat at zero for weeks is not treated as a slow mover. Because the correction is made at source, every downstream figure — forecast, safety stock, reorder point, GMROII — inherits the corrected number.
What is the stockout risk score and how do I use it?
It is a composite score between 0 and 1 combining current availability, forecast demand and demand variability, recalculated daily per SKU per location. Sorting descending gives the buying team the day’s purchasing priority list in one action. Sorting days of cover ascending gives the same list framed by urgency in days rather than risk.
Can the AI actually create purchase orders and transfers, or does it just tell me what to do?
Both. Ask a question and get an answer, or ask it to act — creating the purchase order, the stock transfer or the manufacturing run directly, with your approval. It shows the full workings before it asks, and nothing is committed until you confirm.
Can the AI move stock between locations automatically?
Yes. It classifies every product as fast- or slow-moving per location, identifies where stock is overstocked relative to demand and where it is short, and can recommend or directly create the rebalancing transfer. It will also check whether a transfer solves the problem before recommending a purchase, which is the difference between moving cash and spending it.
Will the AI tell me if a purchase order I have drafted is a bad idea?
Yes. Shown a draft purchase order, it reviews each line against margin, stockout-corrected sell-through, current stock and demand trend, and will say if items are slow-moving and low-margin, question quantities, and suggest alternative lines where demand is accelerating and cover is already tight.
Can we train the AI on our own purchasing and forecasting rules?
Yes, through a Model Context Protocol knowledge base built specifically for teaching Stok.ly’s AI business-specific rules. You write training articles describing your purchasing criteria, forecasting adjustments, range integrity definitions and operational processes, and the AI applies them when answering questions and executing tasks. Competing ERP AI features are configured rather than taught.
What happens if lead time is not configured for a product?
Safety stock returns null for that product and the AI flags it for setup rather than substituting a guess. This is deliberate: a silently assumed lead time produces a confident-looking reorder point that is wrong, which is worse than no number at all.
Can the buying team get these numbers outside the AI assistant?
Yes. All replenishment signals are available conversationally through the AI assistant and via API endpoints, including for Power BI, so a buying team can have a morning dashboard as well as a conversation. The figures are identical because they come from the same pre-computed fields.
How does Operational AI handle wholesale orders fulfilled weeks after they are placed?
Demand is measured at the point stock needs to leave the location, not when the order is logged. For a wholesale order taken today and fulfilled in ten weeks, the forecast is built around the fulfilment date, because that is what determines whether stock was there when it was needed. Businesses running retail, wholesale and ecommerce together therefore get one consistent signal rather than three that disagree.
Contact Information
All our sales, support and development team are located in Hereford and Cheltenham in the U.K. Please submit the contact form and we will contact you within the same business day.