Hub for Demand Forecasting

Demand forecasting stalls on two things at once. One model is applied across a portfolio whose SKUs behave nothing like each other, because running and comparing several is more work than a planning team has time for. And the forecast is delivered as a number rather than as a position that can be interrogated — so when a planner wants to know what happens if the cold snap holds or if price moves ten percent, the answer requires a modelling request, a queue, and a week. By then the decision has been made on instinct.

• One model is stretched across SKUs with entirely different demand behaviour.
• External drivers such as weather are known to matter but are not in the forecast.
• What-if questions require a data science request, so they are asked rarely and answered late.
• Forecasts arrive without their drivers, which is why planners do not trust or use them.

Hub generates forecasts across the entire SKU base, running multiple models in parallel and comparing them so the strategy chosen for each SKU is the one that actually fits its behaviour. Inputs span internal data — historical sales, inventory — and external data such as weather patterns, so drivers outside the four walls are part of the model rather than an explanation offered afterwards. Beyond the forward projection, the relevant inputs to each forecast are exposed through what-if simulation: a planner states the scenario in plain language, and the platform orchestrates the parameter changes needed to run it. Model outputs including correlation coefficients and confidence intervals feed a simulation cockpit built for executives and planners rather than for modellers, and low-code tooling lets business experts iterate and validate models against real data on their own judgment. Cadence is configurable — daily, weekly, monthly or yearly — across customers, SKUs and product lines.

The forecasting itself is statistical, and deliberately so; the model's contribution is at the interface and in the orchestration. Translating “what if the weather is colder than expected next month?” into the specific parameter changes that scenario implies is the step that has historically required a data scientist, and it is exactly the step a language model removes. That matters because the value of simulation is a function of how many scenarios actually get run, and every friction between the question and the answer cuts that number. Confidence intervals travel with every output, so the cockpit shows uncertainty rather than a single confident line.

COVERAGE

Entire SKU base; multiple models run in parallel and compared

INPUTS

Internal sales and inventory plus external signal such as weather

SIMULATION

Plain-language what-if, orchestrated into parameter changes

OUTPUTS

Correlation coefficients and confidence intervals, surfaced in the cockpit

ITERATION

Low-code model iteration and validation by business experts

CADENCE

Daily, weekly, monthly or yearly, by customer, SKU or product line

1. 01Multiple forecasting models run in parallel per SKU and compared to pick the right strategy.
2. 02External data such as weather patterns modelled alongside internal sales and inventory.
3. 03What-if scenarios stated in plain language and executed as parameter changes.
4. 04Simulation cockpit built for planners and executives, showing correlation and confidence.
5. 05Low-code iteration so business experts validate models against real data themselves.
6. 06Configurable cadence across customers, SKUs and product lines.

Per SKU

Model selection, against one model portfolio-widedesign intent

Plain language

Scenario entry, against a modelling requestdesign intent

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