How can restaurants test POS changes before full rollout?
Testing POS changes is difficult because the POS touches real-world chaos: peak volume, staff turnover, network variability, and edge cases like split tenders and offline payments. The answer is not “test more.” It’s test differently—in a way that mirrors live service.
Layer 1: Contract testing for integrations
Most enterprise failures come from integration breaks. Contract tests validate that:
payloads contain required fields
data types and enumerations are consistent
timing assumptions still hold (e.g., refund events, tender events)
idempotency rules prevent duplicate orders from creating duplicate downstream entries
If you have an orchestration layer, you can test against the standardized contract rather than against every downstream system each time. That’s a major scale advantage.
Layer 2: Scenario testing for frontline workflows
Build a scenario library that reflects reality:
rush hour: rapid-fire orders, modifiers, holds
comps/discounts with manager override
split payments, gift cards, partial refunds
online-to-in-store pickup with changes
cash drawer reconciliation edge cases
kitchen routing: re-fire tickets, item voids, course timing
The point is to ensure the update doesn’t just “work”—it works at speed.
Layer 3: “Degraded mode” testing
The most overlooked question: what happens when things go wrong?
Test:
ISP outage: offline mode and data reconciliation
payment gateway slowness: timeouts and fallback logic
peripheral failure: printer down, KDS disconnected
downstream system outage: does checkout continue?
If your architecture allows checkout to proceed while non-critical systems degrade gracefully, you’ve built real resilience.
Layer 4: Performance and concurrency testing
Many POS changes pass functional tests but fail under load:
UI becomes sluggish
ticket send latency increases
authorization queues back up
Simulate peak order volume and concurrency across terminals. If you can’t simulate perfectly, use pilot stores as performance probes and watch latency metrics closely.
Layer 5: “Test in production” safely with feature flags
For certain changes, feature flags allow you to:
deploy code broadly
enable functionality only in pilot stores
roll forward/back quickly without reinstalling
This is especially effective when paired with standardized integration layers, because the “shape” of data sent to downstream systems can remain stable while you toggle upstream behavior.
Bottom line: pre-rollout testing needs to cover contracts, scenarios, degraded modes, and performance. If you only test “happy path ordering,” you’re not testing the business.