Stockout forecasting
A stockout forecast is an estimate, not a prophecy.
Stockout forecasting answers a practical question: if consumption continues roughly like this, when will the available quantity become a problem? Invumi builds that estimate from inventory movements and keeps the threshold, trend and projected date visible together.
01
Start with consumption, not calendar magic
Outgoing movements provide the consumption signal. Recent history is weighted, seasonal comparisons can adjust the projection, and the current quantity is projected forward until it crosses the configured threshold. The result is a date tied to the data the team can inspect.
Receiving stock, correcting an inventory and normal consumption are different events. Keeping them as movements makes it possible to reason about the history instead of treating every quantity change as the same thing.
02
Insufficient data is a valid result
A new product may not have enough movement history for a meaningful trend. In that situation, Invumi does not need to manufacture a precise-looking date. The interface can state that the data is insufficient and use a configured monthly consumption fallback when one exists.
This is less impressive in a demo and more useful in a real order. False precision is still false, even when it has a gradient behind it.
03
Threshold and stockout are different dates
The alert threshold is the quantity at which attention is required. Stockout is the later point where projected stock reaches zero. A team with a long supplier lead time may care much more about the threshold date than the final empty-shelf date.
Target quantity adds the other side of the decision: not only when to react, but roughly how much stock the team wants after replenishment.
04
Forecasts support ordering decisions
The forecast should be read beside supplier context, delivery timing and known future changes. It is not a purchase order generator and it does not guarantee future consumption. Invumi surfaces the estimate so a human can make the operational decision earlier.
The useful outcome is simple: fewer emergency orders and fewer boxes bought merely because nobody remembers whether the last count was current.