Abstract: A method and system are provided that apply a combination of machine learning and graph techniques to classify and transform sequential event data. In some embodiments, the method and system are applied to generate raw data in the shipping industry to automatically classify a sequence of status codes extracted from EDI data files corresponding to a series of physical events experienced by a shipping container into a sequence of meaningful milestones to provide improved visibility regarding the actual status of the shipping container. The method and system can be applied to classify and transform sequential event data for use in the shipping industry and in other applications.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: A method and system are provided that apply a combination of machine learning and graph techniques to classify and transform sequential event data. In some embodiments, the method and system are applied to generate raw data in the shipping industry to automatically classify a sequence of status codes extracted from EDI data files corresponding to a series of physical events experienced by a shipping container into a sequence of meaningful milestones to provide improved visibility regarding the actual status of the shipping container. The method and system can be applied to classify and transform sequential event data for use in the shipping industry and in other applications.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: A method and system are provided that apply a combination of machine learning and graph techniques to classify and transform sequential event data. In some embodiments, the method and system are applied to generate raw data in the shipping industry to automatically classify a sequence of status codes extracted from EDI data files corresponding to a series of physical events experienced by a shipping container into a sequence of meaningful milestones to provide improved visibility regarding the actual status of the shipping container. The method and system can be applied to classify and transform sequential event data for use in the shipping industry and in other applications.

Abstract: The present disclosure provides systems and methods that impute missing shipment milestones using vehicle tracking data (e.g., global positioning system (GPS data, automatic identification system (AIS) data, and/or the like). In particular, the present disclosure provides improved techniques to impute when a shipping vehicle (and the cargo loaded thereon) has arrived at a transportation location (e.g., port). In some implementations, the milestone imputation process first includes collecting a historical sample of the vehicle tracking data. Next, density-based clustering methods can be applied to identify vessel stops. This historical set of vessel stops can then be further clustered in order to identify individual docking or loading/unloading locations for all the transportation locations around the world.

Abstract: The present disclosure provides systems and methods that impute planned transshipment locations for an itinerary associated with an item of cargo. In particular, according to one aspect of the present disclosure, a supply chain management computing system can obtain itinerary data that describes a planned shipment of an item of cargo from an origin location to a destination location. For example, the itinerary data can identify at least a shipping vehicle planned to transport the item of cargo. The supply chain management computing system can access vehicle location data associated with at least the shipping vehicle and can predict, based at least in part on the itinerary data and the vehicle location data, a transshipment location at which the item of cargo is transferred from the shipping vehicle to a different shipping vehicle.

Abstract: A method and system are provided that apply a combination of machine learning and graph techniques to classify and transform sequential event data. In some embodiments, the method and system are applied to generate raw data in the shipping industry to automatically classify a sequence of status codes extracted from EDI data files corresponding to a series of physical events experienced by a shipping container into a sequence of meaningful milestones to provide improved visibility regarding the actual status of the shipping container. The method and system can be applied to classify and transform sequential event data for use in the shipping industry and in other applications.