Machine learning uses data to build models that estimate patterns or outcomes rather than specify every decision through handwritten rules. Its usefulness depends on whether the learned relationships support the task on new, relevant cases.
Define the prediction target and how the result will be used. Check whether the available labels represent the intended outcome and whether important information would be available at the time of prediction. Avoid including future information that makes the evaluation look artificially strong.
Compare the model with a simple baseline using appropriate held-out data. Examine different kinds of error and their consequences, not just one average score. A demand model that performs well on stable products may still fail on new or intermittent items.
Monitor performance as inputs and behavior change. Record the training and evaluation data versions and decide when retraining or review is needed. A trained model is a maintained component of a process rather than a permanently correct rule.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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Reading adjacent decision areas keeps the topic from becoming an isolated note.