Forecast setup
To determine if forecasting has already been set up in your tenant, go to the Item List page. In the lower-right corner of the page, you should find the Forecast FactBox. If the following message appears in the Forecast FactBox, then forecasting hasn't been set up.
To set up the Sales and Inventory Forecast, follow these steps:
Select the Search for page icon, which opens the Tell Me feature. Enter Sales and Inventory Forecast Setup and then select the related link.
Select Update Forecast.
A message displays explaining that the sales forecasts are being updated in the background. The process might take a few minutes.
After a few minutes, refresh the screen. The Last Run Completed and Used Processing Time fields should contain values.
Open the Item List page and select an item in the list. The Forecast FactBox should contain data now.
To change the forecast settings, you can use the menu in the Forecast FactBox.
When you select Forecast Settings, the Sales and Inventory Forecast Setup page opens.
To make predictions about future sales, the web service requires quantitative data about past sales. That data comes from the Posting Date, Item No, and Quantity fields on the Item Ledger Entries page, where:
The entry type is Sale.
The posting date is between the work date and the date that is calculated based on the values in the Historical Periods and Period Type fields on the Sales and Inventory Forecast Setup page.
Before using the web service, Business Central compresses transactions by Item No. and Posting Date based on the value in the Period Type field on the Sales and Inventory Forecast Setup page.
In Business Central, the connection to Microsoft Azure AI is already set up for you. However, you can configure the forecast to use a different type of period to report by, such as changing from forecasting by month to forecasting by quarter. You can also choose the number of periods to calculate the forecast by, depending on how granular you want the forecast to be. We recommend that you forecast by month and with a 12-month horizon for the forecast.
Consider the period length that the service will use in its calculations. The more data that you provide, the more accurate the predictions will be. Also, watch for large variances in periods, which will also impact predictions. If Azure AI doesn't find enough data, or the data varies significantly, the service won't make a prediction.