Optimize Your
Odoo Inventory with Novobi’s Demand Forecasting Solution

Data Science That Delivers Accurate Forecasts for Thousands of Products

Artificial Intelligence
For Your Business

Novobi’s Demand Forecasting Engine streamlines the time-consuming tasks of data preprocessing and statistical analysis, delivering continuous improvement and adaptation to changing customer demand.

Harness the potential of data science effortlessly for your business! Our demand prediction system assists you in optimizing your inventory choices through a user-friendly platform.

Variable Characteristic

Examine a variety of inventory types that exhibit diverse traits, including characteristics like seasonality or irregular demand patterns.

Employ a range of forecasting techniques to analyze and predict future inventory needs accurately, enabling informed decision-making and optimized inventory management strategies.

Learn More

Demand Type

Categorize products based on their “demand type,” a distinct attribute that aids in generating historical data sets for subsequent analysis.

This classification enables more informed insights and strategic decision-making for future inventory management.

Learn More


Experience effortless automation with our system! Through demand type classifications and varied attributes, the engine automatically selects the optimal forecasting model.

This ensures accurate predictions, streamlining decision-making and enhancing inventory management efficiency.

Learn More

Variable Characteristic Models

Handle different inventory types with variable characteristics (seasonal or irregular demand) using a variety of forecasting methods:

  • Linear approximation
  • Linear regression
  • Triple exponential smoothing
  • ARIMA with seasonality
  • Custom Croston
  • Machine learning

Demand Type Classification

Products in each warehouse are classified based on their demand types and characteristics of their historical demand.

Demand is classified into one of four unique types:

  • Intermittent
  • Lumpy
  • Smooth
  • Erratic

Model Recommendation

Given the precomputed demand type of each product, the forecast engine selects the appropriate forecasting models. This is done automatically to ensure you don’t need to manually analyze the best forecasting method to optimize your results.  

Unlock the Future of Precision Planning & Elevate Your Inventory Management Today