AI-Powered Demand Forecasting
Fortune 100 CPG Manufacturer
We developed an advanced demand forecasting system using machine learning, helping a major CPG company improve forecast accuracy by 32% and reduce inventory costs by $12M annually.
Forecast Accuracy Improvement
32%
Annual Inventory Savings
$12M
Challenge
The CPG manufacturer was struggling with inaccurate demand forecasts, leading to excess inventory in some regions and stockouts in others. This inefficiency was costing millions in lost sales, excess storage, and obsolete inventory.
Solution
We implemented a machine learning demand forecasting system that incorporates multiple data sources including historical sales, promotions, seasonality, economic indicators, and social media sentiment to generate highly accurate demand predictions.
Our Approach
- 1
Integrated data from 15+ internal and external sources for comprehensive analysis
- 2
Developed ensemble ML models combining time-series forecasting with deep learning
- 3
Created automated feature engineering pipeline to identify relevant market signals
- 4
Implemented a scenario planning tool for supply chain managers
- 5
Designed a continuous model retraining system to adapt to changing market conditions
Key Outcomes
32% improvement in forecast accuracy across all product categories
$12M annual reduction in inventory holding costs
24% decrease in stockout incidents
18% reduction in obsolete inventory write-offs
Enhanced ability to respond to sudden market changes and disruptions
Technologies Used
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