M.GEMI
Luxury Footwear Retailer
Before Optimization
M.Gemi, an online luxury footwear retailer, faced challenges in managing inventory for its high-end, handcrafted shoes. Due to the nature of their products and the need to maintain exclusivity, it was crucial to manage inventory tightly to avoid both stockouts and excess inventory.
AI Implementation
M.Gemi implemented an AI-driven inventory management system that uses machine learning algorithms to predict customer demand and optimize inventory levels. The system analyzes sales data, customer preferences, and seasonal trends to ensure the right products are available in the right quantities.
After Optimization
The AI system helped M.Gemi reduce stockouts, ensuring popular sizes and styles were always available, which improved ustomer satisfaction and sales. Additionally, by optimizing inventory levels, the company was able to minimize excess stock, reducing the need for discounting and maintaining the brand’s premium image.
AI-driven inventory management software specifically designed for small to medium-sized
businesses. It offers a comprehensive set of tools to manage inventory, orders, and customer
relationships, leveraging AI to optimize stock levels and streamline operations.
Key Features
Demand Forecasting
AI to analyze historical sales data and predict future demand, helping businesses maintain optimal inventory levels.
Automated Reordering
The system automatically generates purchase orders based on forecasted demand and current stock levels, reducing the risk of stockouts and overstocking.
Multi-Channel Management
Integrates with various sales channels (e.g., Shopify, Amazon) to synchronize inventory across platforms, ensuring accurate stock levels in real time.
Insights and Reporting
The software provides AI-driven insights and reports, helping businesses identify trends, optimize pricing strategies, and improve overall inventory management.
Scenario: M.Gemi's Inventory Management
- Product Type: High-end handcrafted shoes.
- Initial Situation:
- Average Monthly Demand: 5,000 pairs of shoes.
- Average Stockouts per Month: 500 pairs (10% of demand).
- Average Excess Inventory per Month: 1,000 pairs.
- Cost per Pair of Shoes: $200.
- Selling Price per Pair: $400.
- Storage Cost per Pair per Month: $5.
- Lost Profit per Stockout (including potential loss of future sales): $600 per pair.
Before AI-Driven Optimization
- Revenue:
- Units Sold: 4,500 pairs (due to 500 stockouts).
- Revenue: 4,500 pairs * $400/pair = $1,800,000.
- Stockout Losses:
- Stockouts: 500 pairs.
- Lost Revenue from Stockouts: 500 pairs * $600/pair = $300,000.
- Excess Inventory Costs:
- Excess Inventory: 1,000 pairs.
- Storage Costs: 1,000 pairs * $5/pair = $5,000.
- Total Costs and Losses:
- Total Costs (Excess Inventory + Lost Revenue from Stockouts): $5,000 + $300,000 = $305,000.
After AI-Driven Optimization
Let’s assume the AI system reduces stockouts by 80% and excess inventory by 60%.
- Stockouts After Optimization:
- Reduced Stockouts: 500 pairs * 20% = 100 pairs.
- Lost Revenue from Stockouts: 100 pairs * $600/pair = $60,000.
- Excess Inventory After Optimization:
- Reduced Excess Inventory: 1,000 pairs * 40% = 400 pairs.
- Storage Costs: 400 pairs * $5/pair = $2,000.
- New Revenue:
- Units Sold: 4,900 pairs (since stockouts reduced to 100 pairs).
- Revenue: 4,900 pairs * $400/pair = $1,960,000.
- Total Costs and Losses After Optimization:
- Total Costs (Excess Inventory + Lost Revenue from Stockouts): $2,000 + $60,000 = $62,000.
Before vs. After Comparison
- Revenue:
- Before Optimization: $1,800,000.
- After Optimization: $1,960,000.
- Revenue Increase: $1,960,000 - $1,800,000 = $160,000.
- Costs and Losses:
- Before Optimization: $305,000.
- After Optimization: $62,000.
- Cost Reduction: $305,000 - $62,000 = $243,000.
Net Impact
Total Improvement (Revenue Increase + Cost Reduction):
$160,000 (Revenue Increase) + $243,000 (Cost Reduction) = $403,000 improvement per month.
Explanation
Revenue Increase
The reduction in stockouts allowed M.Gemi to sell 400 more pairs of shoes, increasing monthly revenue by $160,000.
Cost Reduction
By reducing excess inventory and stockouts, the company saved $243,000 in costs related to storage and lost sales.
This example demonstrates the significant financial impact that AI-driven inventory optimization
can have on a luxury retailer like M.Gemi. By better aligning inventory levels with customer demand, M.Gemi can not only boost sales but also reduce unnecessary costs, leading to a more profitable and efficient operation.