Overview:
A rapidly growing e-commerce company was struggling to manage its inventory effectively due to unpredictable fluctuations in demand. The company needed a custom demand forecasting solution using AWS SageMaker to optimize inventory levels, reduce stockouts, and minimize overstocking costs.
Key Components of the Solution:
- Data Preparation: Historical sales data, product information, seasonal trends, and promotional events were collected, cleaned, and preprocessed for model training.
- Feature Engineering: Significant features for predicting demand, such as product category, price, seasonality, and promotional activities, were identified.
- Model Development: Multiple machine learning algorithms were trained and tested on AWS SageMaker, with the best performing model chosen for deployment.
- Model Deployment: The chosen demand forecasting model was deployed on AWS SageMaker, enabling the company to generate demand forecasts regularly and make data-driven inventory decisions.
Results:
The AWS SageMaker-powered demand forecasting solution led to significant improvements in the e-commerce company’s inventory management and overall business performance. Key results included:
- A 25% reduction in stockouts, leading to increased customer satisfaction and reduced lost sales
- A 20% reduction in overstocking, resulting in cost savings and reduced waste
- A 15% improvement in overall inventory management efficiency, contributing to higher revenue and a stronger competitive advantage in the market
- Enhanced data-driven decision-making capabilities, enabling the company to make more informed and strategic inventory decisions