Able to forecast down to an individual unit basis with other dimensions like Channel or Region

Description

This granularity offers businesses the opportunity to precisely project their expected sales, profits, and other financial metrics. It allows a deep, segmented analysis of various operational factors across channels and regions, enabling more informed decision making.

Example Use Case

Scenario: A global cosmetics company uses CPM software for their financial planning. They want to forecast their sales not just on a general level, but also down to each individual product line in various sales channels (e.g., online, retail, wholesale) across different regions (e.g., North America, Europe, Asia-Pacific).

Solution: The CPM software allows for granular forecasting down to an individual unit basis with the added context of sales channels and regions. This means they can project their lipstick sales separately for online channel in North America, Retail in Europe, and Wholesale in Asia-Pacific. This enables the company to identify key growth areas, potential risks and optimize their operations and marketing tactics accordingly.

Considerations

While forecasting at such a granular level offers advantages, it does increase the complexity of the forecasting process and may require additional resources. When forecasting using historical data, you will need that data to be broken down to the same granularity used in the forecast for best results. Make sure you have that information before proceeding with an implementation.

Confirm as well that, should you not have that data, a forecast can still be created at a higher level, and modified in the future when you do have the appropriate historical data.

Questions to Ask a Vendor

• Capability: How does your system enable forecasting down to an individual unit basis considering other dimensions, like channel and region?
• Exports: Are we able to export the forecast information at the granularity we require for import into our operations system?
• Dimensions: Does your system accommodate all the dimensions we need to accomplish this granular forecast?
• Attributes: We have an extensive amount of matrix items. Are we able to factor in attributes like color and size along with other dimensions we need?