Abstract: Techniques are disclosed for training a machine learning model to select a route for performing tasks in a target set of inventory tasks. The machine learning model may be trained by obtaining training data sets that include characteristics of previously performed tasks by one or more task performers. Example characteristics may include locations associated with the previously performed tasks, a duration of time taken to perform the previous tasks, a route taken to perform the tasks, a sequence in which tasks a set of tasks were performed, and attributes of the task performers themselves. The machine learning model may be trained using these training data sets and the applied to a received set of target tasks. The trained machine learning model may then generate a route and/or sequence in which the tasks of the target set of tasks may be performed.

Abstract: A system for analyzing supplier communications regarding deviations from a supply plan is described. The system may determine the severity of the deviation and determine an impact to a supply chain or inventory level caused by the deviation. A remedial action may be identified in supplier communications and the system may determine whether the remedial action is acceptable for addressing the deviation. Analyses of supply plan deviations, the severity of deviations, the acceptability of remedial actions, and/or other factors may be used to generate a supplier score.

Abstract: A target quantity of a target product is needed. However, the target quantity of the target product is not available from a set of sources. A fulfillment plan is generated. The fulfillment plan includes obtaining quantities of more than one product in order to fulfill the need for the target quantity of the target product. The fulfillment plan is executed. Executing the fulfillment plan includes distributing tasks, orders, notifications to various entities to fulfill the target quantity of the target product.

Abstract: Techniques for applying a machine learning model to historical shipping data to generate an interactive graphical user interface of a transport route are disclosed. A machine learning model is trained to compute route attributes for at least one transport provider for transporting items along a route between a source location and a destination location. The system is further configured to display the actual or estimated location of an item along the route based on a timeline. The position of the item along the route is updated as the user drags a time marker along a timeline. The system identifies and displays the attributes of the transportation segment that includes the currently displayed position of the item along the route.

Abstract: A technique is described for updating a datastore using a value from an inbound transmission that corresponds to a prior outbound transmission. The outbound transmission and the inbound transmission may be associated with one another so that a system may extract a particular value from one or more of the transmissions. The extracted value may be used to update a datastore. A system may extract the particular value from an editable data object in the inbound transmission. A system may also compare a current value of a data object in a data store with an updated value and generate a notification or other transmission prior to extracting the value and updating the data store.