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Overview
Perishable distribution, hands on, and the published research on why demand models break the moment conditions shift. Drag the conditions yourself and watch a forecast fall past useless.
News
The bouquets got better and sales fell
Shelf life went from half a week to 14-21 days and sales fell anyway. Customers could see the size of a bouquet. They could not see the freshness.
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The metric was measuring my model, not the problem
A transferability score held at 0.54 across two independent budgets, then collapsed to 0.14 when I changed the model class. It was tracking the algorithm.
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The training set moves the error 390x. Nothing predicts it.
Across 231 training-set choices the worst case moved by a factor of 390. I tested nine cheap ways to pick a good one. None of them held up.
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A scratched label still scans, and that is not luck
A check digit tells you a barcode scan was wrong. Reed-Solomon parity tells you what it should have been. That difference decides what a damaged label costs.
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You cannot count the sale you did not make
Sales data records what you sold, not what people wanted. The two differ exactly on the days they matter most, and the gap teaches a forecast to shrink.
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