AI Sales & Demand Forecasting

We build a proper Frappe app (its own module, not a pile of loose customizations) that reads your existing ERPNext transaction history and produces forecasts you can act on. New DocTypes such as Demand Forecast, Forecast Line (child table) and Reorder Suggestion hold the model outputs, so every prediction is a first-class, permissioned, reportable record rather than a throwaway chart. A Scheduler Event (daily/weekly, configurable) refills forecasts in the background via the Frappe job queue, pulling from Sales Invoice, Sales Order, Delivery Note and the Stock Ledger Entry table. The forecasting logic runs as server-side Python (time-series decomposition for trend and seasonality, per item/territory), and results are surfaced through whitelisted methods on the Frappe REST API so your own dashboards or a Number Card on the ERPNext workspace can consume them.

Technically, the app hangs off well-defined extension points so upgrades stay clean. hooks.py doc events (for example on_submit of Sales Invoice) flag affected items for incremental re-forecast instead of recomputing everything. Reorder-point and safety-stock suggestions are computed from forecasted demand variability and your configured lead times, and can optionally write back to the Item/Item Reorder records under a controlled, role-gated action rather than silently. Cash-flow projection walks open receivables and payables plus the order book to produce a forward liquidity curve. A What-If Scenario DocType lets a planner clone a baseline forecast and flex assumptions — growth rate, a promo uplift, a lead-time change — to see the downstream effect on stock and cash before committing. Client Scripts add the in-form buttons and inline charts; a dedicated Forecast Planner role profile (with permission rules) keeps write access to suggestions separate from read-only viewers in finance.

Because this is build-to-order, nothing ships until we have agreed your scope. We start with a short scoping call, confirm which items/territories/warehouses matter, which history depth is meaningful, and how aggressively you want write-backs to reorder levels. We then build against a staging copy of your ERPNext (v15 or v16), tune the models on your real data, run UAT with your planners, and hand over installable source plus a git repository. You get the working app installed on your instance, documentation, training, and a post-go-live support window — not a black box, and not a generic marketplace download you have to force-fit.