Case Study

Logistics Tracker

A comprehensive fleet management and supply chain visibility solution enabling real-time tracking, route optimization, and delivery predictions.

The Challenge

Understanding the Problem

Logistics companies struggled with visibility into their fleet operations, inefficient routing, and inability to provide accurate ETAs to customers.

Our Approach

We built a real-time tracking platform with GPS integration, ML-powered route optimization, and predictive ETA calculations based on traffic patterns.

Phase 1

Predictive Maintenance

Phase 2

Autonomous Vehicle Support

Phase 3

Blockchain Supply Chain

Phase 4

Carbon Footprint Tracking

The Solution

IoT-enabled tracking devices with real-time dashboard, automated dispatch system, and customer-facing tracking portal with live updates.

25% Fuel Savings
98% On-Time Rate
±5 min ETA Accuracy

The Outcome

Reduced fuel costs by 25%, improved on-time delivery rate to 98%, and increased customer satisfaction scores by 40%.

Impact: 25% Fuel Savings

Technical Deep Dive

Engineering Excellence

A comprehensive look at the technical architecture and implementation details that power this solution.

architecture

IoT data ingestion via AWS IoT Core, real-time processing with Apache Kafka, and ML predictions with SageMaker.

security

SOC 2 compliant with encrypted data transmission. Role-based access for drivers, dispatchers, and managers.

System Architecture

GPS Devices

IoT Trackers

AWS IoT

Data Ingestion

Kafka

Stream Processing

ML Engine

Route Optimization

Dashboard

React Frontend

Telemetry: gps → iot

Stream: iot → kafka

Analyze: kafka → ml

Display: ml → dash

Development Journey

From Concept to Launch

4 weeks

IoT Infrastructure

Deployed GPS trackers, built data ingestion pipeline, and established real-time communication.

8 weeks

Tracking Platform

Created real-time dashboard, driver mobile app, and customer tracking portal.

6 weeks

Route Optimization

Developed ML-powered routing algorithm considering traffic, weather, and delivery windows.

4 weeks

Integration & APIs

Built APIs for ERP integration, automated dispatch, and customer notifications.

4 weeks

Pilot & Rollout

Conducted pilot with 50 vehicles, optimized based on feedback, and rolled out to full fleet.

Measurable Impact

Key Results

Primary Outcome

25% Fuel Savings

Direct business value delivered.

25%

Fuel Savings

Route optimization impact

98%

On-Time Rate

Delivery reliability

±5 min

ETA Accuracy

Prediction precision

Impact Analysis

Fleet Visibility

Before

Hourly updates

After

Real-time

Instant Tracking

Fuel Costs

Before

$45K/mo

After

$34K/mo

25% Savings

Customer Complaints

Before

150/mo

After

12/mo

92% Reduction

Technology Stack

Tools & Frameworks

ReactNode.jsAWS IoTApache KafkaMongoDBRedisTensorFlowGoogle Maps API

Implementation

Real-time Location Stream

WebSocket handler for processing real-time vehicle location updates.

logic.js

javascript

1const processLocation = async (vehicleId, coords) => {
2  const location = { vehicleId, ...coords, timestamp: Date.now() };
3  await kafka.send({ topic: 'vehicle-locations', messages: [{ value: JSON.stringify(location) }] });
4  await redis.setex(`vehicle:${vehicleId}:location`, 60, JSON.stringify(location));
5  io.to(`fleet:${vehicleId}`).emit('location', location);
6};

Performance

Performance Audits

Performance

SEO

Accessibility

Best Practices

"We finally know where every truck is and when deliveries will arrive. Game changer for our operations."

Operations Director, Logistics Company

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