Project Overview

For this project, we partnered with a major retail chain to develop an AI-driven system that optimizes inventory levels across their stores. The goal was to reduce stockouts while minimizing excess inventory.

The Challenge

The client was facing significant challenges with inventory management:

• Frequent stockouts leading to lost sales
• Excess inventory tying up capital and warehouse space
• Inefficient manual processes for inventory forecasting

Our Solution

We developed a machine learning model that predicts future demand for each product in each store. The model takes into account various factors, including:

• Historical sales data
• Seasonal trends
• Local events and holidays
• Weather forecasts
• Marketing campaigns

Based on these predictions, the system recommends optimal inventory levels and reorder points for each product.

The Results

After implementing our solution, the client saw significant improvements:

• 30% reduction in stockouts
• 20% decrease in excess inventory
• 15% increase in inventory turnover
• 10% improvement in gross margin

Technologies Used

• Python for data processing and model development
• TensorFlow for deep learning models
• Apache Spark for big data processing
• AWS for cloud infrastructure

This project demonstrates the power of AI and machine learning in solving complex business problems and driving significant improvements in operational efficiency.