Abstract: Examples provide on-time delivery prediction generation using a machine learning element for orders obtained from an order management component. The obtained order is analyzed to identify a plurality of order attributes. An initial value for each order attribute in the plurality of order attributes is calculated based on sensor data and telemetry data. An initial order-score for the obtained order is determined based on the initial values for each order attribute in the plurality of order attributes. An alternative order-score for the order is calculated after identifying an order attribute variable for the order and calculating updated values for each order attributes in the plurality of order attributes. After outputting the initial order-score and the alternative order-score for the order, with an indication of the order attribute variable associated with the alternative order-score, feedback for the order is received.

Abstract: Examples provide machine language (“ML”)-powered on-time delivery prediction generation for virtual orders obtained from an order management component (“OMC”). Predictions based on order factors deterministic to on-time delivery are output to the OMC. Predictions become increasingly accurate over time based on the accuracy of past delivery predictions and give the probabilistic chance of on-time delivery (the “order-score”) in real-time before an order for goods and/or services is dispatched. The OMC uses the order-score to react preemptively to mitigate delay factors on orders likely to arrive late, increasing the probability of on-time delivery. Customers are given confidence predicted arrival times are accurate enough for scheduling purposes. Providers more easily maintain and grow good reputations and revenue.