MACHINE LEARNING TECHNOLOGIES FOR PREDICTING ORDER FULFILLMENT

1 . A computer-implemented method of using machine learning to predict order fulfillment, the method comprising:

training, by one or more computer processors, a machine learning model using a training dataset comprising: (i) a training set of order milestones, (ii) a training set of shipment statuses, and (iii) a training set of differences between planned shipment amounts and actual shipment amounts;

storing the machine learning model in a memory;

accessing, by the one or more computer processors, order data associated with a shipping agreement, the order data comprising (i) a set of order milestones for the shipping agreement, (ii) a planned inventory level specifying an amount of products planned to be transported as part of the shipping agreement, and (iii) an actual inventory level specifying an amount of products actually being transported by a set of vehicles;

retrieving, from a set of electronic logging devices integrated within a set of vehicles, real-time tracking data associated with the set of vehicles transporting products;

determining, by the one or more computer processors, a difference between the amount of products planned to be transported and the amount of products actually being transported;

analyzing, by the one or more computer processors using the machine learning model, the order data associated the shipping agreement and the difference between the amount of products planned to be transported and the amount of products actually being transported;

based on the analyzing, outputting, by the machine learning model, a probability that the shipping agreement will be successfully fulfilled, wherein the probability is less than a threshold percentage;

updating, by the one or more computer processors, the machine learning model with information indicating the order data associated with the shipping agreement and the probability that the shipping agreement will be successfully fulfilled; and

in response to outputting the probability that the shipping agreement will be successfully fulfilled:

automatically generating an electronic communication indicating the probability that the shipping agreement will be successfully fulfilled, and

automatically sending the electronic communication to a computing device of an entity associated with the shipping agreement.

2 . The computer-implemented method of claim 1 , wherein training the machine learning model comprises:

training, by the one or more computer processors, the machine learning model using the training dataset that further comprises a training set of economic trends.

3 . The computer-implemented method of claim 2 , wherein accessing the order data associated with the shipping agreement comprises:

accessing, by the one or more computer processors, the order data associated with the shipping agreement, the order data further comprising a set of economic trends.

4 . (canceled)

5 . The computer-implemented method of claim 1 , wherein the computing device is associated with a shipper entity, and wherein automatically sending the electronic communication comprises:

updating, by the one or more computer processors, the electronic communication to reflect at least one additional shipping agreement associated with the shipper entity; and

automatically sending the electronic communication that was updated to the computing device via a network connection.

6 . (canceled)

7 . The computer-implemented method of claim 1 , wherein accessing the order data associated with the shipping agreement comprises:

accessing, by the one or more processors, (i) a first portion of the order data from a set of parameters associated with the shipping agreement, and (ii) a second portion of the order data from a set of data sources while the amount of products is being transported by the set of vehicles.

8 . A system for using machine learning for transportation assignment, comprising:

a memory storing a set of computer-readable instructions and data associated with a machine learning model; and

one or more processors interfaced with the memory, and configured to execute the set of computer-readable instructions to cause the one or more processors to:

train the machine learning model using a training dataset comprising: (i) a training set of order milestones, (ii) a training set of shipment statuses, and (iii) a training set of differences between planned shipment amounts and actual shipment amounts,

access order data associated with a shipping agreement, the order data comprising (i) a set of order milestones for the shipping agreement, (ii) shipping a planned inventory level specifying an amount of products planned to be transported as part of the shipping agreement, and (iii) an actual inventory level specifying an amount of products actually being transported by a set of vehicles,

retrieve, from a set of electronic logging devices integrated within a set of vehicles, real-time tracking data associated with the set of vehicles transporting products;

determine a difference between the amount of products planned to be transported and the amount of products actually being transported,

analyze, using the machine learning model, the order data associated the shipping agreement and the difference between the amount of products planned to be transported and the amount of products actually being transported,

based on the analyzing, output, by the machine learning model, a probability that the shipping agreement will be successfully fulfilled, wherein the probability is less than a threshold percentage,

update the machine learning model with information indicating the order data associated with the shipping agreement and the probability that the shipping agreement will be successfully fulfilled, and

in response to outputting the probability that the shipping agreement will be successfully fulfilled:

automatically generate an electronic communication indicating the probability that the shipping agreement will be successfully fulfilled, and

automatically send the electronic communication to a computing device of an entity associated with the shipping agreement.

9 . The system of claim 8 , wherein the training dataset further comprises a training set of economic trends.

10 . The system of claim 9 , wherein the order data further comprises a set of economic trends.

11 . (canceled)

12 . The system of claim 8 , wherein the computing device is associated with a shipper entity, and wherein to automatically send the communication, the one or more processors is configured to:

update the electronic communication to reflect at least one additional shipping agreement associated with the shipper entity, and

automatically send the electronic communication that was updated to the computing device via a network connection.

13 . (canceled)

14 . The system of claim 8 , wherein the one or more processors accesses (i) a first portion of the order data from a set of parameters associated with the shipping agreement, and (ii) a second portion of the order data from a set of data sources while the amount of products is being transported by the set of vehicle.

15 . A non-transitory computer-readable storage medium configured to store instructions executable by a computer processor, the instructions comprising:

instructions for training a machine learning model using a training dataset comprising: (i) a training set of order milestones, (ii) a training set of shipment statuses, and (iii) a training set of differences between planned shipment amounts and actual shipment amounts;

instructions for storing the machine learning model in a memory;

instructions for accessing order data associated with a shipping agreement, the order data comprising (i) a set of order milestones for the shipping agreement, (ii) shipping a planned inventory level specifying an amount of products planned to be transported as part of the shipping agreement, and (iii) an actual inventory level specifying an amount of products actually being transported by a set of vehicles,

instructions for retrieving, from a set of electronic logging devices integrated within a set of vehicles, real-time tracking data associated with the set of vehicles transporting products;

instructions for determining a difference between the amount of products planned to be transported and the amount of products actually being transported;

instructions for analyzing, using the machine learning model, the order data associated the shipping agreement and the difference between the amount of products planned to be transported and the amount of products actually being transported;

instructions for, based on the analyzing, outputting, by the machine learning model, a probability that the shipping agreement will be successfully fulfilled, wherein the probability is less than a threshold percentage;

instructions for updating, by the one or more computer processors, the machine learning model with information indicating the order data associated with the shipping agreement and the probability that the shipping agreement will be successfully fulfilled; and

instructions for, in response to outputting the probability that the shipping agreement will be successfully fulfilled:

automatically generating an electronic communication indicating the probability that the shipping agreement will be successfully fulfilled, and

automatically sending the electronic communication to a computing device of an entity associated with the shipping agreement.

16 . The computer-implemented method of claim 1 , wherein the instructions for training the machine learning model comprise:

instructions for training the machine learning model using the training dataset that further comprises a training set of economic trends.

17 . The computer-implemented method of claim 2 , wherein the instructions for accessing the order data associated with the shipping agreement comprise:

instructions for accessing the order data associated with the shipping agreement, the order data further comprising a set of economic trends.

18 - 19 . (canceled)

20 . The computer-implemented method of claim 1 , wherein the instructions for accessing the order data associated with the shipping agreement comprise:

instructions for accessing (i) a first portion of the order data from a set of parameters associated with the shipping agreement, and (ii) a second portion of the order data from a set of data sources while the amount of products is being transported by the set of vehicles.