Amethyst Technologies
Led development of AI-powered forecasting pipeline that improved prediction accuracy by ~40%
Role
Product Manager
Timeframe
6 months
Team Size
8 engineers, 2 designers
Tools & Technologies
Key Impact
Improved forecasting accuracy by ~40%
Context
Amethyst Technologies needed to improve their demand forecasting capabilities to reduce inventory costs and improve customer satisfaction. The existing manual forecasting process was time-consuming and prone to human error.
Problem
The company was experiencing significant inventory management issues due to inaccurate demand predictions, leading to both stockouts and overstock situations that impacted profitability.
Approach
Conducted stakeholder interviews to understand current forecasting pain points
Analyzed historical sales data to identify patterns and seasonality trends
Collaborated with data science team to design machine learning pipeline
Implemented A/B testing framework to validate model performance
Created dashboard for real-time forecast monitoring and adjustments
Outcome
Achieved ~40% improvement in forecasting accuracy across all product categories
Reduced inventory holding costs by 25% through better demand prediction
Decreased stockout incidents by 60% leading to improved customer satisfaction
Established automated forecasting pipeline reducing manual effort by 80%
Next Steps
Expand forecasting model to include external market factors
Implement real-time inventory optimization recommendations
Develop mobile dashboard for field sales team access