Most venues know food waste is a problem in the abstract, but few can say exactly where, in what quantity, or what it's costing. Because without granular tracking, waste is invisible until it's thrown in the bin.
The gap that reveals waste
When expected ingredient usage (calculated from recipes and actual sales) is compared against real stock depletion, the difference is where waste, over-portioning, or shrinkage is happening. That gap is the actual signal.
Turning waste from a guess into a number
Instead of "we probably waste a fair bit of produce," the data can show specifically which ingredient, roughly how much, and whether it's trending up or down, a concrete starting point for fixing it rather than a vague intention.
A gap consistently appearing on a specific prep-heavy ingredient on slow weeknights often points to over-prepping for a forecast that didn't materialise, exactly the kind of thing demand prediction is built to correct.
Where demand prediction connects
Better demand prediction directly reduces one major cause of waste: prepping or ordering for a forecast that turns out wrong. The more accurate the forecast, the less structural waste from simply over-preparing.
An honest caveat
Not all waste is visible through the sales-versus-stock gap, spoilage and prep trim still benefit from being logged directly for a complete picture. The gap analysis catches the largest and least-visible category, not literally everything.