The AI kit for Demand Planners

Builds baseline forecasts from history and trends, plus inventory and contingency plans; e.g. next quarter's plan for 300 SKUs.

Folds market, economic and real-time signals into forecasts; e.g. revising next month's plan after a competitor price cut.

Covers segmentation, seasonality and price impact; e.g. showing how a 10% price rise shifts demand by region.

Cleans demand data and models forecasts; e.g. removing a stockout week so it stops dragging next year's baseline down.

Like "Plan Dataset Cleaning Approach"

Like "Draft Baseline Forecast Assumptions"

Like "Estimate Promotion Lift"

Like "Calculate Safety Stock Levels"

Forecasts that improve over time because every method is tested on the same history

Prepare the right amount of each item for each service, with a midday correction.

Keep enough feed on hand without overbuying, with rations matched to feed quality.

A promotion plan limited to items that have stock and keep margin

Lets your AI check stockout risks, forecasts, and purchase orders from your ReplenishRadar inventory data.

Ask your AI to run forecasts and predictions using Chronulus AI agents, right inside your chat.

Lets your AI read, write, and format your Google Sheets for you, right from the chat.

Let your AI assistant open and work inside real Microsoft Excel on your Windows PC.

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Pane is an AI spreadsheet that edits cells, writes real formulas, extracts tables from PDFs, cleans messy data and builds charts-so every change is visible, edi

Rebuild your plan each cycle with versioned assumptions and honest error tracking, so you stop arguing about numbers and start acting.

Model real observed lead-time spread instead of one flat number, so safety stock matches what suppliers actually deliver.

Plan promotions against local stock, so you stop pushing offers your stores cannot actually ship.

Transparent event assumptions and error tracking for support-specific planning.

Forecast demand, set safety stock, and plan replenishment for multi-location retail.

Sizes or audits safety stock using the z*sigma*sqrt(LT) formula with empirical stress tests per variability class.

Evaluate demand-forecast quality with WMAPE, bias, and Forecast Value Added vs. naive.

Segment SKUs by value and demand variability, assign planning policies, and reallocate planner attention.