E-commerce Platform
Logistics SaaS2024 · 10 min read · 10 months project
LogiTrack Pro: AI-Powered Route Optimization That Saved Clients 30% on Logistics Costs
Engineering a real-time fleet management platform with WebSocket tracking, Mapbox-powered AI route optimization, and an operations dashboard — resulting in 30% cost reduction for logistics companies across Java.
Client
LogiTrack Pro
Industry
Logistics & Supply Chain
Duration
10 months
Year
2024
500+
Vehicles Tracked
1,200+
Routes Optimized/Day
30%
Logistics Cost Saved
94%
Delivery ETA Accuracy
The Challenge
Logistics operators across Java were running routes on spreadsheets and WhatsApp groups, leading to double-bookings, missed ETAs, and wasted fuel. LogiTrack needed a platform that could ingest real-time GPS from hundreds of vehicles and output optimized routes in under a second.
Our Solution
We built a WebSocket hub on GCP Cloud Run that handles 500+ concurrent vehicle streams. A Python ML microservice runs a modified Clarke-Wright savings algorithm with real-time traffic data from Mapbox Traffic API — generating optimized routes in ~800ms.
// deep dive
The Spreadsheet-Driven Logistics Problem
Indonesia's logistics sector is growing at 8% YoY, but operations at most mid-market companies are still manual. Double-dispatching, uncollected proof-of-delivery, and driver downtime were costing companies 25–35% operational overhead.
Real-Time WebSocket Architecture at Scale
Each vehicle's GPS pings every 10 seconds. At 500 vehicles, that's 3,000 events/minute. We built a GCP Pub/Sub → Cloud Run consumer pipeline that normalizes, deduplicates, and fans out location updates to relevant operations dashboards in < 200ms end-to-end.
ML Route Optimization: Clarke-Wright + Live Traffic
Pure algorithmic routing falls apart in Jakarta traffic. We trained a lightweight neural network on historical traffic patterns layered over the Clarke-Wright VRP solver — giving us routes that adapt to real-world conditions, not just map distances.
30% Cost Reduction — Measured, Not Estimated
We instrumented every route deviation, idle period, and fuel log. Clients could see in their dashboards exactly how much each optimization saved. Transparent metrics made the ROI undeniable and led to three contract renewals before the pilot even ended.
// outcome
The Result
Three enterprise logistics clients saved an average of 30% on monthly fuel and driver labor costs within 60 days of deployment. ETA accuracy reached 94%, dramatically improving client SLA compliance.
ReactWebSocketGCPMapboxPythonTensorFlow
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