Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.

Abstract: Embodiments are disclosed for autonomously clustering shipping units. An example method includes accessing clustering information units from a clustering data management tool. The example method further includes extracting features from clustering information units, wherein the features are representative of one or more of the shipper behavior data and the package information. Exemplary shipping units are shippers, buildings handling packages, package delivery drivers, and package handlers. The example method further includes generating, using a shipping unit clustering learning model and the features, an output comprising cluster of shipping units. Corresponding apparatuses and non-transitory computer readable storage media are also provided.

Abstract: Embodiments are disclosed for autonomously generating volume forecasts. An example method includes accessing volume information units from a volume forecast data management tool. The example method further includes extracting features from volume information units, wherein the features are representative of one or more of a package received time, or package information. The features can be categorized by different hierarchical level information. The example method further includes generating, using a volume forecast learning model and the features, an output comprising a volume forecast for a particular hierarchical level. Corresponding apparatuses and non-transitory computer readable storage media are also provided.

Abstract: Embodiments are disclosed for autonomously predicting shipper behavior. An example method includes the following operations. One or more learning models are generated. Shipper behavior data for at least one shipper is extracted. The shipper behavior data includes a plurality of features associated with the at least one shipper scheduled to ship one or more parcels. It is predicted whether one or more shipments will be sent or arrive at a particular time based at least in part on running the plurality of features of the at least one shipper through the one or more learning models.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for determining delivery or pick-up windows. In one embodiment a method is provided comprising determining whether sufficient historical information/data to determine an estimated pick-up/delivery time is received for each weekday when deliveries are made and in response to determining that the sufficient historical information/data is available for a first weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the first weekday based on the sufficient historical information/data for the first serviceable point and for the first weekday. Similarly, in response to determining that the sufficient historical information/data is not available for a second weekday, determining an estimated pick-up/delivery time for the first serviceable point and for the second weekday based on the first historical information/data.