A product goes viral on social media, creating high demand. Distribution channels are facing supply chain issues because the testing and training models that are used for sales forecasting have not encountered similar demand. Which of the following best describes this situation?

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The situation illustrates data drift. The model was trained and tested on historical, typical demand patterns, but a viral event causes demand to explode in ways the training data didn’t cover. When the data feeding the forecasts changes in distribution, the model’s learned relationships no longer match reality, leading to inaccurate predictions and supply chain problems. This isn’t about bias in the model or incorrect sizing by itself; it’s about the underlying data characteristics shifting after the model was built. To address it, you’d refresh the training data with recent demand patterns, monitor for drift, and adapt forecasting approaches to accommodate rapid changes (for example, using more frequent retraining or adaptive/allocation strategies).