Systems and methods for imputation of shipment milestones
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. Once these docking locations have been identified, a geofence can be established around those locations and used to determine when a shipping vehicle has arrived at the corresponding transportation location and/or docking location.
1 . A computer-implemented method for performing automatic supply chain adjustments in instances of incomplete or inaccurate event data for shipments, the computer-implemented method comprising:
defining, by at least one processor, a set of geofences associated with a set of transportation locations by performing a set of clustering techniques on historical vehicle tracking data;
training, by the at least one processor using a set of training data associated with a set of shipping vehicles, a milestone detection machine learning model, the set of training data comprising vehicle tracking data labeled with a set of milestone events associated with the set of shipping vehicles;
obtaining, by the at least one processor, a set of vehicle tracking data associated with a shipping vehicle, wherein the set of vehicle tracking data is missing a shipment milestone associated with the shipping vehicle;
analyzing, by the at least one processor using the milestone detection machine learning model, the set of vehicle tracking data to determine, for the shipment milestone that is missing from the set of vehicle tracking data, that there has been a change in presence of the shipping vehicle at a transportation location, of the set of transportation locations, associated with a geofence of the set of geofences;
in response to determining that there has been the change in presence of the shipping vehicle at the transportation location associated with the geofence, processing, by a supply chain adjustment machine learning model, data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to produce at least one automatic supply chain adjustment; and
inserting a data entry into a data record associated with the shipping vehicle that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence, to reflect the shipment milestone that is missing, and to enable predictive visibility into shipment status and proactive supply chain management action.
2 . The computer-implemented method of claim 1 , further comprising:
generating, by the at least one processor, an alert that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence; and
transmitting, by the at least one processor, the alert to an entity associated with the shipping vehicle.
3 . The computer-implemented method of claim 1 , wherein processing the data to produce the at least one automatic supply chain adjustment comprises:
processing, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a requested delivery date.
4 . The computer-implemented method of claim 1 , wherein processing the data to produce the at least one automatic supply chain adjustment comprises:
processing, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a labor fulfillment date.
5 . The computer-implemented method of claim 1 , wherein analyzing the set of vehicle tracking data comprises:
analyzing, by the at least one processor using the milestone detection machine learning model, the set of vehicle tracking data to determine that either (i) the shipping vehicle has arrived at the transportation location associated with the geofence, or (ii) the shipping vehicle has departed the transportation location associated with the geofence.
6 . The computer-implemented method of claim 1 , further comprising:
filtering, by the at least one processor, the set of vehicle tracking data to produce a set of filtered vehicle tracking data indicating a set of instances in which the shipping vehicle is stopped.
7 . A shipping management computing system for performing automatic supply chain adjustments in instances of incomplete or inaccurate event data for shipments, comprising:
a memory storing (i) a milestone detection machine learning model, (ii) a supply chain adjustment machine learning model, and (iii) a set of computer executable instructions; and
at least one processor coupled to the memory and configured to execute the set of computer executable instructions to cause the at least one processor to:
define a set of geofences associated with a set of transportation locations by performing a set of clustering techniques on historical vehicle tracking data,
train, using a set of training data associated with a set of shipping vehicles, the milestone detection machine learning model, the set of training data comprising vehicle tracking data labeled with a set of milestone events associated with the set of shipping vehicles,
obtain a set of vehicle tracking data associated with a shipping vehicle, wherein the set of vehicle tracking data is missing a shipment milestone associated with the shipping vehicle,
analyze, using the milestone detection machine learning model, the set of vehicle tracking data to determine, for the shipment milestone that is missing from the set of vehicle tracking data, that there has been a change in presence of the shipping vehicle at a transportation location, of the set of transportation locations, associated with a geofence of the set of geofences,
in response to determining that there has been the change in presence of the shipping vehicle at the transportation location associated with the geofence, process, by a supply chain adjustment machine learning model, data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to produce at least one automatic supply chain adjustment, and
insert a data entry into a data record associated with the shipping vehicle that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence, to reflect the shipment milestone that is missing, and to enable predictive visibility into shipment status and proactive supply chain management action.
8 . The shipping management computing system of claim 7 , wherein the at least one processor is configured to execute the set of computer executable instructions to further cause the at least one processor to:
generate an alert that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence, and
transmit the alert to an entity associated with the shipping vehicle.
9 . The shipping management computing system of claim 7 , wherein to process the data to produce the at least one automatic supply chain adjustment, the at least one processor is configured to:
process, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a requested delivery date.
10 . The shipping management computing system of claim 7 , wherein to process the data to produce the at least one automatic supply chain adjustment, the at least one processor is configured to:
process, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a labor fulfillment date.
11 . The shipping management computing system of claim 7 , wherein to analyze the set of vehicle tracking data, the at least one processor is configured to:
analyze, using the milestone detection machine learning model, the set of vehicle tracking data to determine that either (i) the shipping vehicle has arrived at the transportation location associated with the geofence, or (ii) the shipping vehicle has departed the transportation location associated with the geofence.
12 . The shipping management computing system of claim 7 , wherein the at least one processor is configured to execute the set of computer executable instructions to further cause the at least one processor to:
filter the set of vehicle tracking data to produce a set of filtered vehicle tracking data indicating a set of instances in which the shipping vehicle is stopped.
13 . A computer program product for performing automatic supply chain adjustments in instances of incomplete or inaccurate event data for shipments, the computer program product comprising a non-transitory computer readable medium storing a set of computer instructions executable by at least one processor to:
define a set of geofences associated with a set of transportation locations by performing a set of clustering techniques on historical vehicle tracking data;
train, using a set of training data associated with a set of shipping vehicles, a milestone detection machine learning model, the set of training data comprising vehicle tracking data labeled with a set of milestone events associated with the set of shipping vehicles;
obtain a set of vehicle tracking data associated with a shipping vehicle, wherein the set of vehicle tracking data is missing a shipment milestone associated with the shipping vehicle;
analyze, using the milestone detection machine learning model, the set of vehicle tracking data to determine, for the shipment milestone that is missing from the set of vehicle tracking data, that there has been a change in presence of the shipping vehicle at a transportation location, of the set of transportation locations, associated with a geofence of the set of geofences;
in response to determining that there has been the change in presence of the shipping vehicle at the transportation location associated with the geofence, process, by a supply chain adjustment machine learning model, data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to produce at least one automatic supply chain adjustment; and
insert a data entry into a data record associated with the shipping vehicle that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence, to reflect the shipment milestone that is missing, and to enable predictive visibility into shipment status and proactive supply chain management action.
14 . The computer program product of claim 13 , wherein the set of computer instructions are executable by the at least one processor further to:
generate an alert that indicates the change in presence of the shipping vehicle at the transportation location associated with the geofence; and
transmit the alert to an entity associated with the shipping vehicle.
15 . The computer program product of claim 13 , wherein to process the data to produce the at least one automatic supply chain adjustment, the at least one processor executes the set of computer instructions to:
process, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a requested delivery date.
16 . The computer program product of claim 13 , wherein to process the data to produce the at least one automatic supply chain adjustment, the at least one processor executes the set of computer instructions to:
process, by the supply chain adjustment machine learning model, the data that describes (i) the set of vehicle tracking data and (ii) the change in presence of the shipping vehicle that was determined, to modify a labor fulfillment date.
17 . The computer program product of claim 13 , wherein to analyze the set of vehicle tracking data, the at least one processor executes the set of computer instructions to:
analyze, using the milestone detection machine learning model, the set of vehicle tracking data to determine that either (i) the shipping vehicle has arrived at the transportation location associated with the geofence, or (ii) the shipping vehicle has departed the transportation location associated with the geofence.