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Predictive Inventory System

Developed an AI-powered predictive inventory system that forecasts demand, optimizes stock levels, and reduces carrying costs by 35% while preventing stockouts.

Project Overview

The Challenge

A retail chain with 50+ locations was struggling with inventory management:

  • Frequent stockouts of popular items
  • Excess inventory leading to high carrying costs
  • Manual demand forecasting was inaccurate
  • No visibility across multiple locations
  • Seasonal variations not properly accounted for
Our Solution

What We Delivered

🔮

Demand Forecasting

  • ML models for demand prediction
  • Seasonal pattern recognition
  • Trend analysis and anomaly detection
  • Multi-location forecasting
📊

Inventory Optimization

  • Automated reorder point calculation
  • Safety stock optimization
  • ABC analysis automation
  • Dead stock identification
🔗

System Integration

  • POS system integration
  • Supplier integration
  • ERP synchronization
  • Real-time data feeds
📈

Analytics Dashboard

  • Real-time inventory metrics
  • Forecast accuracy tracking
  • Cost analysis and savings
  • Alert and notification system
Impact

Results Achieved

💰

35% Cost Reduction

Lower carrying costs through optimized stock levels

📉

85% Fewer Stockouts

Predictive ordering prevented inventory gaps

🎯

92% Forecast Accuracy

ML models achieved high prediction accuracy

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50% Faster Ordering

Automated purchasing decisions

📊

Real-Time Visibility

Complete inventory transparency across all locations

🚀

15% Sales Increase

Better availability led to higher sales

Technology Stack

Tools & Technologies Used

ML/AI

Python, Scikit-learn

Analytics

Pandas, NumPy

Backend

Python, Django

Database

PostgreSQL, TimescaleDB

Visualization

React, D3.js

Timeline

5 Months

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