Athena makes merchandise demand forecasting straightforward by utilizing AI-first Athena Notebooks to assess historical sales data. Eliminate the need to code and use visualize sales trends and identify which products are in high demand, enabling effective inventory management and strategic planning.

For the Formula 1 Red Bull Racing team, forecasting the demand for car parts is essential. By analyzing past sales data of critical components such as tires and engines, the team can predict future needs and adjust their inventory accordingly. This proactive approach minimizes downtime during races and ensures the team is prepared for each event, contributing to their competitive edge.

Merchandise Demand Forecasting using Athena

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Step-by-step Instructions

1

Create a Notebook

Navigate to Spaces. Click on the ’+’ icon -> Notebook on top of the left sidebar to create a new notebook.

2

Upload your dataset

In the Notebook you just created, upload the required sales dataset. Also upload it as an Athena asset by clicking on + icon -> Upload.

3

Load the dataset

In Chat, enter the following prompt and press Enter.

Prompt

Load @your-dataset-name into @your-notebook-name.

Replace your-dataset-name & your-notebook-name by selecting the dataset and notebook from the Athena Assets dropdown.
4

Analyse sales trends and forecast demand

Enter the following prompt and press Enter.

Prompt

Generate code to visualize sales trends over time and identify seasonality patterns using line charts. Use this to generate a predictive model for forecasting demand of various car parts in the future. Provide insights on your findings based on the data.

5

Visualize product demand

Enter the following prompt and press Enter.

Prompt

Provide insights on demands of various car parts.

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