Real-time digital intelligence for a global automotive group
A 14-country automotive group running on fragmented systems and 48-hour reporting cycles. We unified inventory, AI-powered demand forecasting, and order tracking on Laravel + React + AWS — eliminating 80% of manual workflows and cutting reporting latency from 48 hours to under 5 minutes.
The challenge
A leading global automotive group operated across 14 countries on fragmented systems with no unified view of inventory, sales, or operations. Country managers ran on local spreadsheets and disparate ERPs. Group reporting was a manual 48-hour exercise. Inventory reconciliation happened by email. Demand forecasting was reactive at best.
The cost wasn’t only operational drag — it was invisible risk. By the time the parent group saw an issue in any one country, the window to act had closed.
What we built
A unified enterprise web platform that became the single source of truth for the group’s global operations:
- Real-time inventory management across all 14 country operations
- AI-powered demand forecasting using historical sales, dealer signals, and macro indicators
- Seamless order tracking end-to-end, from manufacturer to dealer to customer
- Live executive dashboards with drill-down per country, brand, and product line
- Automated reconciliation replacing manual spreadsheet exchange between regions
The forecasting model alone reduced overstock by 34% in the first quarter of operation.
Architecture
We built on a Laravel + React.js stack on AWS, designed for auto-scaling and a 99.9% uptime SLA from day one:
- Backend: Laravel API services with horizontal scaling on AWS ECS
- Frontend: React.js SPA with WebSocket subscriptions for live data
- AI/ML: Python + TensorFlow forecasting service deployed as a managed model endpoint
- Data layer: PostgreSQL for transactional data, Redis for hot caches, event bus for cross-region propagation
- Real-time: WebSockets for the dashboards; sub-second propagation of inventory changes globally
The architecture eliminated 80% of manual workflows on day one and cut group reporting latency from 48 hours to under 5 minutes — a 600× improvement.
Outcomes
- 50% cost reduction across reporting and reconciliation operations
- 70% faster day-to-day operations across the country teams
- 14 country operations integrated onto one platform
- 48h → under 5min reporting latency
- 99.9% uptime delivered
- 34% overstock reduction in Q1 from AI forecasting
Why it worked
Three deliberate calls made the difference:
- Real-time over batch. We refused the easier “nightly sync” architecture. Group leadership needed live signals; everything else followed from that constraint.
- AI as a feature, not a bolt-on. Forecasting was modeled, evaluated, and shipped as a first-class product surface — not a script wedged into an existing report.
- One platform, 14 contexts. The system ships per-country localizations (currency, tax, regulatory) without forking the codebase. Every country gets the same release on the same day.
The engagement started as a 6-month build and extended into ongoing infrastructure partnership. The platform has been running in production for over two years.