F&B Analytics, KDS & Emerging Tech in Singapore

The goal of tech is simple: reduce friction for staff + guests and increase control for owners.

When KDS is worth it

• Ticket volume is high and paper gets chaotic
• You have multiple stations that need timing coordination
• You want reliable timing + error tracking

Setup checklist (minimum viable)

• Stations mapped (hot, cold, grill, fryer, pass/expo)
• Target times set by category
• Colour rules (on time / warning / overdue)
• Backup printer kept ready (failover)

Operational best practices

• Only “bump” when the item is genuinely complete
• Expo owns final check and communicates rush orders
• Review overdue orders weekly and fix bottlenecks

What to measure (KDS analytics)

• On-time completion rate (aim 90%+)
• Avg prep time by station
• Top 10 slowest items (simplify or re-engineer)

Daily (10 minutes)

• Sales by channel (dine-in / takeaway / delivery)
• Voids/discounts/refunds (reasons)
• Top sellers

Weekly (30 minutes)

• Labour % + SPLH
• Waste log review (top 5 items)
• Delivery margin snapshot

Monthly (60–90 minutes)

• Prime cost trend
• Menu engineering refresh
• P&L review (variance vs last month)

• Prime cost % (COGS + labour)
• Food cost % (trend + variance)
• Labour cost % (trend + staffing fit)
• SPLH (sales per labour hour)
• RevPASH (if dine-in heavy)
• Average check (upsell + pricing leverage)

High ROI (often)

• QR ordering (if service model supports it)
• Inventory + recipe costing (start with top 20 SKUs)
• Direct ordering (hybrid with aggregators)

Pilot first

• AI forecasting (only useful if you act on it)
• Service robots (novelty vs labour economics)
• AR menus (marketing-led)

Evaluation framework (non-negotiable)

1. What problem are we solving?
2. What is the monthly cost of that problem today?
3. What is the total cost (hardware + subscription + training + downtime)?
4. Payback period? (months)
5. Exit plan if it fails?