Techniques are provided for automatically detecting disruptive orders for a supply chain. One method comprises obtaining a quote for an order; extracting features from the quote; and applying the extracted features to a disruptive quote machine learning engine that generates an anomaly score indicating a likelihood that the quote will cause a disruption, based on one or more predefined disruption criteria. The disruptive quote machine learning engine may employ an isolation forest algorithm and/or a multi-dimensional anomaly detection algorithm. The disruptive quote machine learning engine may be trained using historical order information comprising part-level information from historical orders and/or a manufacturing production plan comprising an inventory forecast.