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Real-Time Inventory Analytics Platform with Unified Data Hub

tridhyatech.com

Business services

Information technology

Logistics

Operational Inefficiencies in Inventory Management

The client faces challenges with fragmented data sources, delayed analytics capabilities, and lack of real-time visibility across their inventory ecosystem. Legacy systems struggle with processing high-velocity data from multiple ERP platforms, leading to delayed decision-making and increased operational costs.

About the Client

Enterprise software solutions provider specializing in supply chain optimization

Strategic Modernization Goals

Establish unified data repository for multi-source inventory data
Implement real-time analytics capabilities for proactive inventory control
Create intuitive visualization layer for actionable business insights
Build scalable architecture for future AI/ML integration

Core System Capabilities

Multi-source data ingestion pipeline (ERP, IoT sensors, POS systems)
Delta change data capture with sub-minute latency
Interactive dashboard with customizable KPIs
Predictive analytics engine for stock optimization
Role-based access control with audit trails

Technology Stack Requirements

Hadoop ecosystem (HDFS, YARN)

Apache Sqoop for data transfer

Apache Oozie for workflow scheduling

Apache Hive for SQL-like queries

Apache Kafka for real-time streaming

React.js for dashboard UI

System Integration Needs

Oracle E-Business Suite
Teradata Data Warehouse
MySQL inventory databases
IoT device telemetry APIs
Third-party logistics APIs

Operational Requirements

99.99% system availability with failover mechanisms
Support for 1M+ transactions per minute
End-to-end data encryption with GDPR compliance
Horizontal scalability to petabyte-scale data
Response time <2 seconds for 95% of dashboard queries

Business Transformation Outcomes

The implementation will reduce inventory carrying costs by 30% through predictive analytics, enable real-time stock visibility across global warehouses, and accelerate reporting processes by 5x compared to legacy systems. The architecture will support future AI-driven demand forecasting and automated replenishment workflows.