See demand forming before it costs you service or cash.
AI demand forecasting predicts what your customers will buy, by SKU, location, and channel, from the signals already in your data. Mined XAI catches shifts early, so you position inventory before demand arrives and protect service levels without tying up cash in the wrong products and places.
A forecast nobody can defend gets overridden anyway.
Most AI hands you a number and asks you to trust it. Ours shows the drivers and a confidence range behind every forecast — the same signals a senior analyst would check, surfaced automatically, so your team can defend the number in the room rather than apologizing for it later.
While the market races toward autonomous agents, we think the next big unlock in enterprise forecasting is a model your planners can actually use — one that earns override decisions instead of triggering them by default.
What your planners say when the model returns a new number.
These are the four questions that kill a planning cycle. Not because they don't believe the data — because the data arrived without context.
A forecast your team can't explain is a forecast your team won't trust. And they're right not to.
The miss you can’t afford lands at the horizon, not next week.
Forecast accuracy that holds
through the buying window
Forecast accuracy by prediction time-period
A number nobody can question gets overridden anyway.
Read the shift, position for it.
The change you need to catch shows up first as a demand signal, a shift in who’s ordering, how often, and through which channel — long before it reaches your top-line. Mined XAI reads it at the lowest level a decision gets made — the individual SKU, location, and customer, so your next call is clear.
Forecast where the commitment gets made
By SKU, location, customer, and channel, not a top-line that hides the volatility underneath.
Show the drivers, with confidence
Every shift arrives with the signals behind it and a range. A dashboard says demand moved. We say what moved it and how sure we are.
Turn the forecast into the buy
Size the purchase, divide stock across branches, and cut excess and stockouts at once, without a blanket safety-stock increase.
Act on propensity lists
Because we forecast at the customer level, the same model tells sales who’s likely to buy which products and when. That’s the on-ramp to the commercial track below.
The same forecast, pointed at growth.
The model that positions your inventory also predicts your revenue. At the customer, product, and channel levels, it shows where growth is forming, which accounts warrant attention, and what’s driving the opportunity, so commercial leaders plan against evidence instead of instinct.
Account propensity for your sellers
Ranked lists of who's likely to buy what and when, fed straight to your sales tools. With one client, more than 70% of the named accounts converted.
Revenue predictability the CFO trusts
A demand plan that reconciles to the financial plan, with the drivers to explain any gap.
Promotions and rebates before the spend goes out
Forecast promo lift and volume-incentive rebates at the account level, so you tell genuine demand from a customer buying early to clear a tier. For one client, that earned $3.4M in quarterly rebates.
Where teams use it.
Mined XAI works across distribution, manufacturing, and CPG. The model is the same; the granularity and the lead buyer change.
Wholesale distribution
At the SKU-branch-customer level: right-size inventory branch by branch, protect fill rate, and clear the excess.
Manufacturing
Forecast far enough out to commit capacity and long-lead materials, and reconcile the demand plan to production and finance through S&OP.
CPG
Anticipate demand by product, customer, and channel, then translate it into pricing, promotion, and assortment. Consumer volatility and promotions drive the number, not last year's shipments.
One picture, and every team is working from it.
Same shifts, same confidence, same drivers — open to question by anyone in the room.
Start small. Prove it in 30 to 60 days.
You don't need a full enterprise rollout to see that this works. Show quick value and gain trust before you scale.
Request an analysisPick the decision
A category, a region, a customer segment. We connect your data and set the baseline.
First forecast, with the reasoning
You see it and its drivers against that baseline.
Judge it on results
Measured accuracy and a clear read on whether to expand.
What is AI demand forecasting?
AI demand forecasting is the use of machine learning to predict future customer demand from the signals already in your data: order patterns, seasonality, pricing, promotions, and outside factors like weather. Unlike traditional statistical forecasting, which projects the past forward, AI models catch shifts in behavior early and forecast at a granular level, down to individual SKUs, customers, locations, and channels. Explainable AI demand forecasting goes further: it shows why the number changed, so your team can judge the forecast instead of just accepting it.
Demand forecasting FAQs.
Direct answers on forecast horizon, pilot data requirements, and how this fits alongside your existing planning tools.
STILL HAVE QUESTIONS? TALK TO USWhat is SKU-level demand forecasting?
How accurate is AI demand forecasting, and how far out?
How is explainable AI forecasting different from a black-box model?
Can the forecast drive sales, not just inventory?
Can you forecast promotions and volume-incentive rebates?
Can you use external data or only our internal data?
How is Mined XAI different from a demand planning module in our ERP?
If you’ve got a forecast nobody can explain, show us.
Send twelve months of history and one decision you’re trying to make, and Mined XAI will show you what we find and why.
Send us your data