Project Overview
Our client, a leading logistics enterprise, faced significant challenges in resource allocation and data visibility across their global operations. Legacy systems were creating data silos, leading to a 25% inefficiency rate in supply chain management.
GryphalCode was tasked with re-engineering their core ERM platform. We architectural a microservices-based solution integrated with predictive AI models to forecast demand and automate inventory routing.
The Challenge
- Disconnected legacy databases slowing down decision-making.
- Lack of real-time visibility into inventory levels.
- High operational costs due to manual scheduling.
The Solution
We implemented a React-based frontend for intuitive user interaction, backed by a Node.js and Python (Django) backend handling heavy data processing. The core innovation was a custom Machine Learning model deployed via Docker containers that analyzed historical data to predict resource needs.
Key Results
Post-deployment, the client observed a 40% reduction in operational overhead and a 15% increase in order fulfillment speed within the first quarter. The system now processes over 10TB of data monthly with 99.99% uptime.