Key Features

Optimization Forecast Accelerator brings advanced machine learning to accurately predict future sales, whether you're looking to forecast revenue or quantity for upcoming periods.

With Forecast Accelerator you get precise forecasts for revenue or quantity on a daily, weekly, or monthly basis, helping you make informed decisions. It uses the last price to compute quantity or revenue, providing flexibility in your analysis.

You can access various charts to review outputs, with filtering options for different levels of granularity, giving you a clear and detailed view of your sales forecasts.

 

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Approach

The Optimization Forecast Accelerator follows a structured approach. It starts by mapping the data and incorporating as many features as possible to provide comprehensive context for the forecasting model. This includes additional sources that might influence future values, such as special events.

A preliminary forecasting model is built and tested over a holding period to determine the best model parameters and assess accuracy. The model is trained with the latest available data to capture the most recent trends. It predicts revenue or quantity for the upcoming periods.

The forecasts are exported in a data source format similar to the input data, making them easy to integrate into other processes.

Best Practices

To ensure the Optimization Forecast Accelerator functions optimally, it's important to follow certain best practices. These include using complete and relevant features, ensuring the training period is fully covered, and pre-processing data for new or rarely sold products.

• Attributes/Features: Ensure that all relevant features are included and complete. The accuracy of the forecasts depends on having the right features, so it's crucial to avoid incomplete or irrelevant data.

• Training Periods: Use a complete training period. Partial data, such as half a week, can bias the results, so make sure the training period is fully covered.

• Sparse Data: For new or rarely sold products, aggregations and pre-processing can help improve forecast accuracy. Ensure that past sales data is as comprehensive as possible.

Limitations

While the Optimization Forecast Accelerator is a powerful tool, there are some limitations to be aware of. Incomplete or irrelevant features, partial training periods, and sparse data can affect forecast accuracy. Additionally, the model does not account for long-term contracts or agreements and requires custom code for specific features, which can complicate updates. Understanding these limitations will help you manage expectations and make the most of the tool.

• Attributes/Features: Incomplete or irrelevant features can lead to poor output quality.

• Training Periods: Partial data can bias the results.

• Sparse Data: Forecasts might be less accurate for new or rarely sold products.

• Long-term Contracts/Agreements: The model does not use long-term contracts or agreements stored in the solution.

• No Predefined Extension Points: Custom code is required to add specific features, which can complicate updates.

• Data Requirements: Specific data requirements must be met for optimal performance.