Logistics Ai
AI-Driven Fleet Management Platform for LogiTrack
Buraq engineered a real-time AI fleet management platform that reduced fuel costs by 42% and improved on-time delivery rates to 99.2% for a multinational logistics provider operating across 12 countries.
LogiTrack International
6 months
Primary KPI
42% Cost Reduction
Key measurable outcome
Tech Stack
Next.jsAzure IoT HubAzure MLAzure Data ExplorerPythonTypeScriptSAP Integration
Tags
Fleet ManagementAI/MLIoTAzureReal-Time AnalyticsPredictive Maintenance
The Challenge
Problem Statement
LogiTrack International managed a fleet of 3,500+ vehicles across 12 countries with outdated GPS tracking and manual dispatch processes. Route planning relied on static spreadsheets, leading to excessive fuel consumption, missed delivery windows, and zero visibility into driver behaviour. Fleet maintenance was reactive rather than predictive, resulting in $2.4 M in unplanned downtime annually.
The operations team had no centralised dashboard — dispatchers in different regions used incompatible tools, making cross-border shipment coordination error-prone and slow. Customer complaints about late deliveries had increased 35% year-over-year, threatening key enterprise contracts worth over $18 M.
The Solution
Our Approach
Buraq designed and deployed a cloud-native fleet intelligence platform built on Microsoft Azure, combining IoT telemetry ingestion, machine-learning route optimisation, and a unified command-centre dashboard.
Key engineering decisions:
• Real-Time Telemetry Pipeline — We connected 3,500 vehicle OBD-II sensors to Azure IoT Hub, streaming location, speed, fuel-level, and engine-health data at 5-second intervals into Azure Data Explorer for sub-second queries.
• ML Route Optimisation — A custom reinforcement-learning model trained on 18 months of historical trip data generates dynamic route plans every 15 minutes, factoring in live traffic, weather, toll costs, and delivery-window constraints. The model runs on Azure Machine Learning with auto-scaling inference endpoints.
• Predictive Maintenance Engine — Anomaly-detection models analyse engine telemetry patterns and flag vehicles likely to need service within the next 500 km, enabling proactive scheduling instead of roadside breakdowns.
• Unified Command Centre — A Next.js dashboard with real-time map overlays, driver scorecards, and automated alert workflows replaced six legacy tools. Role-based access lets regional managers see their fleet while HQ gets the global picture.
• Integration Layer — RESTful APIs and Azure Service Bus connect the platform to LogiTrack's existing SAP ERP for automated fuel-cost reconciliation and invoice generation.
Key Highlights
Fleet Management
AI/ML
IoT
Azure
Real-Time Analytics
Predictive Maintenance
The Outcome
Results & Impact
Within six months of full rollout:
• 42% reduction in fuel costs — dynamic routing eliminated 1.8 M unnecessary kilometres per quarter.
• 99.2% on-time delivery rate — up from 78%, securing renewal of all at-risk enterprise contracts.
• $1.6 M saved in Year 1 through predictive maintenance — unplanned downtime dropped by 68%.
• 27% improvement in driver safety scores — real-time coaching alerts reduced harsh-braking events.
• Single pane of glass for 12-country operations — dispatcher efficiency improved 3×, reducing headcount needs in the NOC by 40%.
LogiTrack's CTO described the platform as "the single biggest operational upgrade in the company's 20-year history."
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