Novobi Demand Forecasting

Data-driven forecasting helps you anticipate and maintain stock for thousands of items by automatically applying demand trends to your inventory operations.

Demand Forecasting: where your purchase planners meet your sales team.

Armed with intelligence, ensure you're ready to meet your supply needs.

Reduce the Unpredictability of Seasonality

Ride the wave of your seasonal demand with confidence. Optimize your supply decisions by layering intelligence onto your historical data.

Organize Your Warehouse Around Demand

Keep your inventory operations nimble. Automatically assign demand classification to products based on historical performance.

Don't Second Guess Your Forecasting 

Reduce costly forecast errors. With improved demand type classification and model recommendation, you can rely on greater forecasting accuracy.

More accurate forecasting with the power of machine learning right at your fingertips. Demand Forecasting from Novobi features:

Variable Characteristic Models

Different inventory types based on demand

Analyze different inventory types with variable characteristics (such as seasonality or irregular demand) using a variety of forecasting methods.

Demand Type Classification

Historical data informs demand types

Classify products using "demand type" as a unique attribute to help create historical data sets for future analysis. 

Model Recommendation

The right forecasting for the right product

Let the system do the work for you! Using demand type classifications and variable characteristics, the engine will determine the right forecasting model.

Are you ready for Demand Forecasting?

Learn more about how intelligence and automation can streamline your procurement workflows, bridging the gap between your sales and your purchasing teams.

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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

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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.