Use case

Demand planning & forecasting for CPG brands

Demand planning is how a consumer packaged goods (CPG) brand decides what it will sell, so it can decide what to make, buy, and ship. Corvera runs demand planning on your unified data: orders, inventory, sales, retailer sell-through, and supplier lead times connected into one governed context layer, legible to any AI tool through MCP (Model Context Protocol).

Book a walkthrough. The team maps your demand data live on the call.

Why demand planning breaks in most CPG brands

The forecast is only as good as the context it starts from, and in most brands that context is scattered.

The data lives in silos

Sell-in sits in the ERP, sell-out in retailer portals, inventory in the 3PL's system, promotions in a spreadsheet. No single view of true demand exists.

The spreadsheet is the system

Exports get stitched together by hand every week. By the time the forecast is assembled, the inputs are already stale.

Signals arrive too late

A velocity change at a retailer shows up weeks later in a portal export - after the purchase order was already sized.

How demand planning runs on Corvera

The sequence, in order:

1
Connect your demand sources
Orders, inventory, sales, retailer sell-through, and supplier data plug in from the systems you already run. No rip-and-replace.
2
Unify into one governed context
The Corvera team maps your data with you: products, channels, and locations reconciled into a single dictionary of your business.
3
Forecast with AI on complete context
Any MCP-compatible AI tool reads the unified picture - so forecasts, safety-stock questions, and S&OP prep start from all of the signal, not one export.

What your team can do

Each capability rests on a named part of the platform, not on a promise.

See true demand across channels

Sell-in and sell-out in one view, by product, channel, and location, instead of one number per SKU per month.How: Unified mappings across ERP, retailer, and 3PL sources

Ask forecasting questions in plain English

Which SKUs are trending ahead of forecast? What does velocity look like after the promotion? Answered from live context, not last week's export.How: MCP access for any AI assistant your team already uses

Size safety stock from real variability

Lead times and demand variability come from your actual supplier and sales history, so buffers reflect how your supply chain behaves.How: Supplier and order history unified in the context layer

Walk into S&OP with one version of the truth

Sales, ops, and finance argue about the plan, not about whose numbers are right.How: One governed source of truth with a full audit trail

Go deeper on demand forecasting

The operator's guides behind this page.

The Corvera platform

What the context layer is and how the pieces fit.

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