Understanding and predicting customer demand allows retailers to plan effectively for peak seasons, promotions, and other events. This helps in optimizing resource allocation, such as staffing and marketing efforts, to meet customer expectations.

Accurate ans systematic forecasting helps retailers determine the right amount of inventory to keep in stock. Overstocking can lead to increased holding costs, markdowns, and potential losses, while understocking can result in lost sales and dissatisfied customers.

You should keep some common factors and best practices in mind. Here are some important considerations:

Prediction engine can handle various scenarios at ease. It is designed to handle outliers and seasonality in most cases. For accurate forecasts it is recommended to have two years worth data at minimum. The quality can also be significantly improved by adding events such as:

Generating predictions will generally be slower than simpler functions. Therefore it is recommended to have fewer items in the live report. For larger number of items it is recommended to use batch processing and writeback. With batch processing it is also guaranteed that the prediction for the same item will not change over time allowing more in-depth analysis of the forecast. 

Forecast can be added to any report so that further calculations can take advantage of predicted values using the following function:

Fn.forecast(values, dates, weeks)

The lists can be easily constructed by using StringConcat aggregation on time and value fields.

The function returns list of tuples where each tuple holds the following values:

Each tuple in the list represents single forecasted value at given date. 

Full list of arguments:

Optional arguments:

Configuration options

Country code - 2-letter ISO country code. If set and no value was passed to the prediction function the holidays will be determined using this value.  If no country code has been set the prediction will not include holiday effect.

Growth cap - If enabled then prediction will limit the forecast grow beyond the throughput capacity.