Abstract: Historical information features that each comprise a supply chain performance indicator (such as a case fill rate performance indicator) for each of a plurality of different temporal windows are accessed and then at least some of the historical information features are weighted differently for at least some of the historical information features according to at least a first criterion to provide a training corpus. At least one machine learning model can then be trained using the training corpus to generate a machine learning model (or models) that are configured to predict supply chain performance.