A demand-surge prediction model flags store-SKU pairs likely to sell out soon, but the signal is a confidence score, not a certainty. Trigger a restock at the first hint of a possible surge, and shelves stay full through real demand spikes — while many restocks go to SKUs that never actually surge, tying up capital in overstock.
The AI Predictive Retail Lab models 300 store-SKU pairs over a week, each carrying a surge-prediction confidence score. Sliding the restock-trigger confidence trades shelf availability during real surges against markdown risk on inventory that never sold.
What makes this case study interesting is that both failure modes have a real dollar cost attached: an empty shelf during a genuine surge loses sales, and an unnecessary restock ties up capital that a lean policy would have avoided — there's no threshold that eliminates both.
🧪 Try it yourself: the AI Predictive Retail Lab simulation lets you move the restock-trigger confidence and watch the week-long outcome update live.
🧪 Try it yourself: the AI Predictive Retail Lab simulation lets you experiment with everything described above directly in your browser.