Forecast with Sparse Historical Data
When you are introducing a new product or opening a new store location, you will have little or no historical data with which to forecast. However, you may have similar products or locations you could use as a starting point. Using CCH Tagetik Supply Chain Planning, you can use the existing historical data for the comparable item and over time, the forecast adjusts as you gain information. The level adjusts and CCH Tagetik Supply Chain Planning uses data smoothing techniques to move toward a pattern specific to the new item or location.
The default forecasting method in CCH Tagetik Supply Chain Planning is Bottom-up. However, in cases where you have sparse historical data or where the data is very erratic, you can improve the data quality by applying the Top-down or Comparable forecasting methods.
Top-down models look at the entire market and work down through your data. This model lets you estimate how much of the market can buy your product/service from market and sales trends. A bottom-up forecast begins with your company data and expands it out to include various other data aspects. This model is based on what you need to get your product/service to market (for example, how many employees, how many factories, and how many clients are needed to start projecting accurate sales.) More data is needed to make a Bottom-up forecast work correctly, which is why it may not be a useful model for a brand new company.
See Apply a Top-Down Override to a Group of Records.