Forecast based on demand drivers
To enhance forecast accuracy while configuring your forecast, you can use demand drivers. Demand drivers are related time series inputs that capture product trends and seasons. Instead of depending on historical demand, you can use demand drivers to influence the supply chain based on various factors. For example, promotions, price changes, and marketing campaigns. Demand Planning supports both historical and future demand drivers.
Prequisites to use demand drivers
Before ingesting data for demand drivers, make sure that the data meets the following conditions:
-
Make sure to ingest the demand drivers data in the supplementary_time_series data entity. You can provide both historical and future demand driver information. For information about the data entities that Demand Planning requires, see Demand Planning.
If you cannot locate the supplementary_time_series data entity, your instance might be using an earlier data model version. You can contact AWS Support to upgrade your data model version or create a new data connection.
-
Make sure that the following columns are populated in the supplementary_time_series data entity.
-
id – This column is the unique record identifier and is required for a successful data ingestion.
-
order_date – This column indicates the timestamp of the demand driver. It can be both past and future dated.
-
time_series_name – This column is the identifier for each demand driver. The value of this column must start with a letter, should be 2–56 characters long, and may contain letters, numbers, and underscores. Other special characters are not valid.
-
time_series_value – This column provides the data point measurement of a particular demand driver at a specific point in time. Only numerical values are supported.
-
-
Select a minimum of 1 and a maximum of 13 demand drivers. Make sure that the aggregation and filling methods are configured. For more information on filling methods, see Demand drivers data filling method. You can modify the settings at any time. Demand Planning will apply the changes in the next forecast cycle.
The following example illustrates how a Demand Plan is generated when the required demand driver columns are ingested in the supplementary_time_series data entity. Demand Planning recommends providing both historical and future demand driver data (if available). This data helps the learning model to learn and apply the pattern to the forecast.
The following example illustrates how you can set up some common demand drivers in your dataset.
When you provide leading indicators, Demand Planning highly recommends that you adjust the time series date. For example, say that a particular metric serves as a 20-day leading indicator with a 70% conversion rate. In this case, consider shifting the date in the time series by 20 days and then applying the appropriate conversion factor. While the learning model can learn patterns without such adjustments, aligning leading indicator data with corresponding outcome is more effective in pattern recognition. The magnitude of the value plays a significant role in this process, enhancing the model's ability to learn and interpret patterns accurately.
