Do you have any question about this SAP term?
Component: SCM-IBP-DM
Component Name: Demand
Description: A calculation method used by the auto-ARIMA algorithm to optimize automatic forecasting.
Key Concepts: The Akaike information criterion (AIC) corrected is a statistical measure used to evaluate the quality of a model. It is based on the Akaike information criterion (AIC), which is a measure of the relative quality of a statistical model for a given set of data. The AIC corrected is used in SAP's Demand Management component of its Supply Chain Management-Integrated Business Planning (SCM-IBP-DM) to evaluate the accuracy of demand forecasts. How to use it: The AIC corrected is used to compare different models and determine which one best fits the data. It takes into account both the accuracy of the model and its complexity, so that simpler models are not penalized for being less accurate than more complex ones. The AIC corrected can be used to compare different forecasting methods, such as exponential smoothing or ARIMA, and determine which one produces the most accurate forecasts. Tips & Tricks: When using the AIC corrected, it is important to remember that it only evaluates the accuracy of a model, not its usefulness. Therefore, it is important to consider other factors such as ease of use and cost when selecting a forecasting method. Additionally, it is important to remember that the AIC corrected does not take into account any external factors that may affect demand, such as seasonality or economic conditions. Related Information: The Akaike information criterion (AIC) was developed by Hirotugu Akaike in 1974 and has since become a widely used measure for evaluating statistical models. It has been used in many fields, including economics, engineering, and biology. Additionally, there are several variations of the AIC, such as the Bayesian information criterion (BIC) and the Hannan-Quinn information criterion (HQC).