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Inventory Analytics Platform

Comprehensive inventory management system processing 100M+ transactions, reducing stockouts by 40% and aging inventory by 60% through predictive analytics and automated alert systems.

10M+ Transactions
140+ Retail Stores
55% Stockout Reduction

Project Overview

Developed a comprehensive inventory analytics platform that consolidates stock data, sales, purchases, and forecast models across Western International Group's retail network. The system provides automated alert-based reporting for auto-replenishment and risk flagging, resulting in significant capital optimization and reduced stockouts.

🎯 Key Objectives

  • Minimize stockouts and excess inventory across 140+ retail stores
  • Reduce aging inventory and optimize capital allocation
  • Provide inventory insights and automated alerts
  • Enable data-driven procurement and replenishment decisions
  • Integrate with existing ERP and POS systems

Technical Implementation

10M+ Transactions Processed
140+ Retail Stores
55% Stockout Reduction
75% Aging Inventory Reduction

Technology Stack

PySpark Power BI Forecasting Models Real-time Analytics Automation SQL Python ETL Pipelines

Platform Features

📊

Real-Time Monitoring

Live inventory tracking across all stores with instant alerts for low stock, overstock, and aging inventory scenarios.

🤖

Automated Alerts

Smart notification system for auto-replenishment triggers, risk flagging, and critical inventory thresholds.

📈

Predictive Analytics

Advanced forecasting models using historical data to predict demand patterns and optimize inventory levels.

🔄

Data Integration

Seamless integration with ERP systems, POS data, and supplier information for comprehensive inventory management.

📱

Mobile Dashboard

Responsive Power BI dashboards accessible on mobile devices for on-the-go inventory management.

Performance Optimization

High-performance data processing using PySpark for handling large-scale inventory datasets efficiently.

Implementation Process

1

Data Architecture Design

Designed scalable data architecture to handle inventory data from 140+ stores, including real-time data ingestion and processing pipelines.

2

ETL Pipeline Development

Built automated ETL processes using PySpark to consolidate inventory data from multiple sources and systems.

3

Forecasting Model Development

Implemented demand forecasting models using historical sales data to predict inventory requirements and optimize stock levels.

4

Alert System Implementation

Created automated alert mechanisms for low stock, overstock, and aging inventory, enabling proactive inventory management.

5

Dashboard Development

Built comprehensive Power BI dashboards with real-time KPIs, interactive filters, and drill-down capabilities for detailed analysis.

6

System Integration

Integrated the platform with existing ERP and POS systems, ensuring seamless data flow and operational continuity.

Business Impact & Results

📈 Key Achievements

📦

55% Stockout Reduction

Significantly reduced out-of-stock scenarios across all stores

💰

75% Aging Inventory Reduction

Optimized inventory turnover and reduced capital tied up in slow-moving items

Real-Time Visibility

Instant access to inventory status across all 140+ retail locations

🎯

Automated Alerts

Proactive notifications for inventory issues and replenishment needs

Technical Challenges & Solutions

🔧 Challenges Overcome

  • Data Scale: Processed 10M+ transactions using PySpark for distributed computing
  • Real-time Processing: Implemented streaming data pipelines for live inventory updates
  • System Integration: Connected multiple legacy systems through standardized APIs
  • Performance Optimization: Optimized query performance and dashboard refresh rates
  • User Adoption: Created intuitive interfaces for non-technical inventory managers