REAL-TIME GLOBAL LOGISTICS TRACKING

Our client is a global logistics provider. The company operates a fleet of over 2,000 mixed-asset vehicles across Europe.

IoT fleet management and logistics monitoring platform

PROJECT DESCRIPTION

At one point, it faced systemic data fragmentation. The company’s operations relied on separate systems that included the following: third-party GPS trackers; standalone temperature logs for refrigerated units; manual driver vehicle inspection reports; legacy fleet management software.

This patchwork created critical blind spots in maintenance scheduling and cargo integrity assurance, as well as route optimization. Consequently, the absence of a single source of truth for vehicle and cargo data led to costly unplanned downtime and compliance risks for sensitive pharmaceutical and food shipments.

To address these interconnected challenges, K-Softex initiated a discovery phase to map the complete data ecosystem, which revealed over 15 distinct data sources requiring synthesis.

Objective

The project's aim was to construct a real-time IoT platform that could ingest and process high-volume telemetry from in-vehicle sensors, as well as correlate it with external operational data. In addition, the client wanted to switch from reactive monitoring to predictive operational intelligence. The platform required a design for horizontal scalability to accommodate future expansion into new regions and asset types.

Backend and data pipeline

PHP 8 / Laravel Lumen / Symfony components / Python / Node.js

Data storage

InfluxDB / MySQL 8 / Redis / AWS S3

Infrastructure and Frontend

AWS IoT Core / AWS EC2 / Vue.js / Mapbox GL JS

Development

The K-Softex engineering team architected a hybrid solution. We selected PHP’s Lumen framework for its efficiency in building the high-throughput API gateway required to manage persistent connections from hundreds of moving endpoints. This gateway validates and routes incoming MQTT payloads to appropriate processing channels.

For complex time-series analysis and predictive calculations, we implemented discrete Python services. These services consume batched telemetry data and run algorithms against historical patterns to flag potential vehicle subsystem failures or unplanned route deviations.

A dedicated data aggregation layer collates information from the telemetry pipeline with external data sources, such as GPS coordinates, traffic feeds, and warehouse loading schedules. This unified data model feeds the Vue.js dashboard, which presents fleet managers with a single operational view.

We designed the alerting engine with a rules builder, allowing managers to configure thresholds for parameters like fuel consumption variance or cargo temperature without developer intervention.

Fleet tracking and predictive telematics dashboard

THE RESULT

K-Softex delivered a consolidated monitoring platform that correlates real-time sensor data with business logistics data. The system’s alerting functions reduced critical refrigeration unit failures by 60% in the first quarter post-deployment. Fleet managers can now access a unified dashboard that displays vehicle location and operational health. This eliminates the need to cross-reference multiple applications.