operations
demand-forecasting
Predicts demand from historical sales data, seasonality patterns, and pipeline data.
Predict product demand from historical sales data, identify seasonal patterns, and project forward from the current order pipeline. Works from CSV or spreadsheet exports — no BI tools, ERP access, or specialist software required.
What it needs
- sales-data — CSV or spreadsheet with at minimum: date column, product/SKU column, quantity sold column
- forecast-horizon — How far to forecast (e.g. 'next 4 weeks', 'next quarter'). Defaults to one period matching the input data granularity.
- method — Forecasting method. 'auto' selects based on data volume and variance. Defaults to auto.
- sales-data — CSV or spreadsheet with date, product/SKU, and quantity columns. Minimum 24 months recommended.
- products — Comma-separated product names or SKUs to analyze. Omit to analyze all products in the file.
- pipeline-data — CSV or spreadsheet with open orders, quotes, or enquiries — including expected close date and quantity.
- conversion-rate — Historical quote-to-order conversion rate as a percentage (e.g. '65%'). If omitted, the skill will ask.
- lead-time-weeks — Production or procurement lead time in weeks. Used to flag urgent gaps.
What you get
- forecast-table (markdown)
- pipeline-forecast (markdown)
- prefer a permanent directory)
- seasonal-index-table (markdown)
Ask it like this
- Forecast demand for next quarter from this sales CSV
- Which of our products have seasonal peaks and when are they?
- Based on our current order book, what do we need to make in the next 8 weeks?
- Set inventory targets for each SKU based on last year's data
- Flag any open quotes that we can't fulfill if they all convert this month