Last week's sales. Last week's instock. Last week's units. Table, chart, red arrow next to the bad numbers. I've seen this report a hundred times from a hundred different suppliers. It always looks the same.
And it's always late.
That DC that stopped ordering your #2 SKU? That was 6 days ago. The instock drop across the Southeast? Those stores already lost the sales. You're looking at a crime scene, not a live feed.
Most supplier teams burn their entire analytics effort on one question: what happened last week? But that question can't help you anymore. It already happened.
The question that actually protects your business: what's about to happen next week?
The wild part is you already have the data to answer it. It's sitting in your Scintilla files right now. Nobody's connecting the dots.
Take demand forecast vs. DC orders. Demand forecast tells you what Walmart expects stores to sell. Order forecast tells you what DCs are actually planning to ship. Gap between those two? That's an instock problem you can spot 5-7 days before a single shelf goes empty. But most suppliers look at each file on its own. They never put them next to each other. When you do, the gaps are obvious.
Or future valid store counts. Your item is valid in 3,200 stores today. Future valid file shows it dropping to 2,900 next week. That's 300 stores about to disappear. Could be a mod reset, could be a trait change — doesn't matter. You need to know now, not after the sales dip hits and everyone's scrambling.
Same thing with sell-through vs. replenishment. Stores moving product faster than DCs are shipping it? On-hand is bleeding down. Might take 2-3 weeks to actually show up as an instock problem, but the math is right there if you bother to check POS units against DC ship quantities.
None of this requires a forecasting model or a data science team. It's about asking better questions of data you're already downloading.
Instead of "what was our instock?" try "which items are trending toward out-of-stock based on sell-through vs. replenishment?" Instead of "what were sales?" try "are DCs ordering enough to keep up with this velocity?" Instead of "how many stores carry us?" try "are we gaining or losing distribution over the next 2-4 weeks?"
Real scenario: DC 6022 hasn't ordered your top 3 SKUs in 10 days. Demand forecast shows 4,200 units needed next week across the stores it serves. Right now that's a fixable problem — call replenishment, file an SSO, escalate. Two weeks from now when those stores are empty? That's a $50K+ hole you can't fill. Day 2 vs. day 14. Quick fix vs. quarterly miss.
And it's everywhere, not just DC gaps. Sell-through spiking with no bump in DC orders means you're about to stock out. Future valid count dropping on a strong seller means someone changed a trait — find out before you lose 300 stores. Forecast under-projecting on a seasonal item means replenishment will be short for weeks unless you catch it now.
Three comparisons. That's all it takes.
Demand forecast + order forecast = ordering gap detection. POS velocity + DC shipments = replenishment health. Current valid stores + future valid stores = distribution trajectory.
Do those every week. You'll catch problems 1-2 weeks before they hit your topline numbers.