Restaurant Chain POS Integration
35% faster operations, unified reporting
Custom POS integration connecting 15 restaurant locations with centralized inventory, reporting, and staff management.
- Client
- FoodChain Group
- Sector
- Custom software
- Engagement
- Phased rollout, 15 sites
- Duration
- 6 months, in four waves
- Team
- 4 — lead, two engineers, rollout lead
- Stack
- Node.js, React, MongoDB, Socket.IO, Docker

IMPACT
- 35%
- faster operations
- 15
- locations in one report
- 4 hrs
- saved per manager each week
THE WORK
01The challenge:
Fifteen restaurants, two POS systems and a Sunday-night spreadsheet
FoodChain Group had grown to 15 restaurants by buying a four-store group that ran a different POS. Each general manager counted stock on paper, typed it into a spreadsheet on Sunday night and emailed it in. Head office merged the files by hand, so the owner saw last week's numbers on Wednesday.
Ordering was done from memory. One store ran out of chicken on a Friday while another three miles away threw some out on Monday, and nobody knew until the invoices arrived. Managers spent about twelve hours a week on counts, close-out and reports instead of on the floor.
02What we built:
Every sale deducts stock, and every store reports to one screen
A Node.js connector reads both POS systems ticket by ticket and maps each menu item to its recipe, so selling a bowl deducts rice, protein and packaging from that store's MongoDB inventory. Each location runs the connector in a Docker container on a small in-store box that queues sales when the internet drops and replays them later.
Socket.IO pushes every change to a React dashboard at head office and a tablet in each kitchen. Par levels per store produce a suggested order each afternoon, and a transfer button moves surplus between nearby sites. Shifts and clock-ins come from the POS, so labor runs against sales by the hour.
03What changed:
Back-office work runs 35% faster, and one report covers all 15 stores
Close-out, counts and ordering run 35% faster, taking each manager from about twelve hours a week to just under eight. Reporting is unified: sales, food cost and labor for all 15 locations sit in a single view that updates as tickets close, broken out by store, daypart and item.
Transfers between neighboring stores replaced most emergency orders by the second month. FoodChain's two-person IT team took over after the fourth wave with the Docker images, a written runbook and four weeks of shadowed support, and they now add menu items and recipes without calling us.
WHAT WE DID
Strategy
- Menu-to-recipe mapping
- Par levels by store
- Rollout wave plan
Build
- Dual-POS connector
- MongoDB inventory model
- Socket.IO live dashboard
- In-store Docker boxes
Handover
- Runbook and diagrams
- IT team training
- Four weeks of shadowing
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