Analysis reportJuly 1, 2026
Warehouse KPI Dashboard
- Source
- sample-data.csv · 6 rows
Picking accuracy
99.4%
+0.2ppUnits per hour
142
+7Dock-to-stock
5.1h
-0.8hInventory accuracy
98.7%
+0.3ppUnits picked per hour by shift
Sample data — this week
Warehouse KPI dashboard
Warehouse dashboards work when every number maps to a shift-level behavior: pick accuracy to error cost, units per hour to staffing, dock-to-stock to receiving discipline. Abstract ratios don't move floors; these do.
Live render with sample data — upload your own export and this structure regenerates from your numbers, with the computation attached to every figure.
The warehouse KPIs that matter, defined
- Picking accuracy
- Correct picks divided by total picks. Each miss costs a return, a re-ship, and trust — small percentage moves are big money.
- Correct picks ÷ Total picks × 100
- Units per hour (UPH)
- Units processed per labor hour, by function and shift. The staffing-model input, not a whip — pair it with accuracy.
- Dock-to-stock time
- Trailer arrival to sellable location. Long dock-to-stock quietly starves fill rate upstream.
- Inventory accuracy
- System count matching physical count at cycle counts. Under ~98%, everything downstream (picking, planning) degrades.
- Accurate locations ÷ Counted locations × 100
- Order cycle time
- Order drop to shipped, at the median, split by service level.
Frequently Asked Questions
Everything you need to know about using AnalyzeData.
Picking accuracy, UPH by shift, dock-to-stock time, inventory accuracy, and order cycle time — each one maps directly to a floor behavior a supervisor can coach today.
Export pick logs and receipts as CSV, upload, and ask for the shift review. UPH and accuracy compute from raw scan rows, with the computation visible.
Because UPH read alone becomes a whip. Units per hour is a staffing-model input, not a productivity target to crank; push it without watching picking accuracy and pickers simply make more mistakes, and each miss costs a return, a re-ship, and customer trust. Track the two together so throughput gains are real, not just errors moved downstream. Correct picks divided by total picks keeps the speed honest.
Aim to stay above roughly 98%. Inventory accuracy is system count matching physical count at cycle counts, and once it slips under about 98% everything downstream degrades: picks fail because the stock is not where the system says, and planning works off numbers it cannot trust. It is a foundation metric, and fixing accuracy first is usually what makes picking and fill-rate improvements actually stick.
Build your warehouse KPI dashboard
Upload the export you already have — the dashboard computes itself, verifiably.
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