71% better demand forecasting for new product launches
A global market leader in construction tools wanted to improve demand forecasting for new products. With our ML models we achieved 71% improved accuracy and €778K annual savings.
0%
Improved accuracy
€0K
Annual savings
0%
Forecast accuracy
0,247
Products analyzed
- Client
- Global Construction Leader
- Sector
- Supply Chain
- Result
- 71%Improved accuracy
Background
The challenge
This global leader in professional construction tools faced a complex challenge when launching new products. Traditional forecast methods did not work for products without historical data. This led to either excess inventory costs or missed sales opportunities due to stockouts.
Key problems
- No historical data available for new products
- High inventory costs due to demand overestimation
- Missed revenue from underestimation and stockouts
- Manual forecasts were time-consuming and inconsistent
Methodologie
Our approach
We developed a machine learning model that combines product characteristics, market data, and comparable product launches to make accurate predictions for new products.
01
Data Integration
Consolidation of product data, market trends, seasonal patterns, and historical launch data into one unified dataset.
02
Feature Engineering
Development of predictive features such as product category similarity scores, market penetration indices, and cannibalization effects.
03
Model Development
Training of ensemble ML models (XGBoost + Neural Networks) with cross-validation on historical product launches.
04
Production Deployment
Implementation in planning systems with automated retraining and real-time forecast updates.
Impact
Results
The new forecasting solution delivered results within 6 months of implementation.
- 0%
Improved forecast accuracy
From 40% to 68% forecast accuracy for new product launches
- €0K
Annual cost savings
Through optimal inventory levels and fewer rush deliveries
- 0%
Reduction in manual work
Forecasters now focus on strategy instead of data entry
- 0 weeks
Faster time-to-market
Through better supply chain and distribution planning
“Stratalytic helped us implement a data-driven approach that directly impacted our bottom line. The team understood our business challenges and delivered a solution that truly works.”
Head of Supply Chain Planning
Global Construction Leader
Technologies
Tools & platforms used
- Python
- XGBoost
- TensorFlow
- Azure ML
- Power BI
- SAP Integration
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