Real-time operational metrics so you can act in the moment, not in the debrief. Covers, revenue, and efficiency — per shift, per hour, per station.
Today's snapshot — all shifts combined
Total covers
442
↑ 9% vs last Tuesday
Total revenue
€14,370
↑ 12% vs last Tuesday
Peak shift
Evening
203 covers · €8,120
Avg / cover
€32.51
↑ 3% vs last Tuesday
Covers, revenue, average spend per cover, and revenue per hour — broken down by shift, every day. Four shifts. Four clear pictures. No manual calculation.
If your afternoon is consistently 30% of capacity, that's a staffing and cost opportunity — not a surprise at month-end.
Evening drives 56.5% of daily revenue · Avg cover value 2× morning
Revenue per hour — Tuesday
€14,370 total
Peak
8pm · €1,840/hr
Trough
4pm · €218/hr
Hourly revenue plotted across your full trading day — colour-coded by shift zone. See your true peak window, your dead zone, and everything in between.
If your 4pm hour generates €218 in revenue and you have the same staffing level as your 8pm hour at €1,840 — that's a direct hit to your labour cost percentage. Hourly revenue makes the gap impossible to ignore.
Cross-referencing covers with revenue per hour reveals your operational capacity ceiling — the point where adding more covers starts degrading service quality and average spend.
Dwelitics flags when covers per hour exceed your historical average without a proportional revenue increase — a reliable signal that the kitchen or floor is under strain.
Capacity signal — Friday evening
Covers peaked at 58/hr at 8pm. Revenue per cover dropped to €32 — 20% below your evening average.
This pattern indicates kitchen strain at peak volume. Consider a pre-booking cap or staggered seatings for Friday evenings.
Covers vs revenue/cover — Friday evening
Ticket time by station — this week
Ticket timing data from your POS reveals slow-to-deliver stations automatically. A slow Grill station flags kitchen bandwidth — not a recipe problem.
Slow ticket-to-delivery gaps at a specific station on specific shifts point to staffing issues. Slow gaps across all stations point to a floor problem. The data separates them without you having to guess.
Data source
Derived from POS order timestamps. No additional hardware required. Accuracy improves with full POS integration and per-station ticket printing.
So you can fix it before the shift ends — not explain it in a Monday morning meeting.
Know which shift carries your P&L — before the week is out.
Staff the evening properly. Under-staff the afternoon without guessing.
See your revenue ceiling per hour. Plan around it, not against it.
Fix kitchen bottlenecks before a customer complaint surfaces them.
Book a personalised demo and we'll walk through your operational data setup.
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