System and method for resource allocation control with display

1 . A system implemented via a back-end application computer server of an enterprise, comprising:

(a) a task data store containing a plurality of tasks as part of a workflow;

(b) the back-end application computer server, coupled to the task data store, including:

a computer processor;

a computer memory coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to:

train a machine learning model with historical task completion data and workflow data;

receive a workflow;

retrieve a plurality of tasks associated with the received workflow;

retrieve, from an external location-based system, at least one location-based requirement for a first task of the plurality of tasks associated with the received workflow, wherein the location-based requirement is at least one of a release policy and a task requirement;

generate, via the trained machine learning model, a timeline for execution of each task including for at least one task a lead time and a dependent task, wherein the lead time is an overlap between a first task and a second task and the second task starts before the first task finishes and the second task is dependent upon the first task;

receive a user assignment for each task; and

transmit the timeline and the user assignment to a workflow scheduler, the assigned user, and the trained machine learning model as feedback;

retrain the machine learning model with the transmitted timeline and the user assignment to the workflow;

receive a modification to at least one task of the received workflow;

re-generate, via the trained machine learning model, the timeline in response to the modification to the transmitted timeline as determined by the workflow scheduler;

transmit the re-generated timeline to the workflow scheduler;

automatically generate, by the trained machine learning model, an alert and a message including the modification and indicating the re-generated timeline; and

transmit the generated alert and the generated message indicating the re-generated timeline to a plurality of parties via at least one of an email, a text message, and a video message;

and

(c) a communication port coupled to the back-end application computer server to facilitate an exchange of data with a remote device to support interactive user interface displays that provide information about the timeline.

2 . The system of claim 1 , wherein receipt of the task includes receipt of a task type, a task due date, and a task location.

3 . The system of claim 2 , wherein the task location is at least one state in the United States of America.

4 . The system of claim 2 , wherein the lead time is indicative of an amount of time prior to the task due date.

5 . The system of claim 1 , further comprising instructions that cause the back-end application computer server to:

determine upcoming tasks; and

display the determined upcoming tasks in chronological order.

6 . The system of claim 1 , wherein the modification is received from one of the workflow scheduler and a remote user device.

7 . The system of claim 1 , wherein the back-end application computer server is further programmed to support a dashboard display in accordance with at least one dashboard selection.

8 . The system of claim 7 , wherein the dashboard display comprises a task list and for each task at least one of: (i) a lead time, (ii) a dependency, (iii) a responsible party, and (iv) a completed task indicator.

9 . The system of claim 1 , wherein the task is associated with one of an automobile product and a home product.

10 . A method implemented via a back-end application computer server of an enterprise, comprising:

training a machine learning model with historical task completion data and workflow data;

receiving a workflow;

retrieving a plurality of tasks associated with the received workflow;

retrieving, from an external location-based system, at least one location-based requirement for a first task of the plurality of tasks associated with the received workflow, wherein the location-based requirement is at least one of a release policy and a task requirement;

generating, via the trained machine learning model, a timeline for execution of each task, including for at least one task a lead time and a dependent task, wherein the lead time is an overlap between a first task and a second task, and the second task starts before the first task finishes;

receiving a user assignment for each task;

transmitting the timeline and the user assignment to a workflow scheduler, the assigned user, and the trained machine learning model as feedback;

retraining the machine learning model with the transmitted timeline and the user assignment to the workflow;

receiving a modification to at least one task of the received workflow;

re-generating, via the trained machine learning model, the timeline in response to the modification to the transmitted timeline as determined by the workflow scheduler;

transmitting the re-generated timeline to the workflow scheduler;

automatically generating, by the trained machine learning model, an alert and a message including the modification and indicating the re-generated timeline; and

transmitting the generated alert and the generated message indicating the re-generated timeline to a plurality of parties via at least one of an email, a text message, and a video message.

11 . The method of claim 10 wherein receipt of the task includes receipt of a task type, a task due date, and a task location.

12 . The method of claim 10 , wherein the lead time is indicative of an amount of time prior to the task due date.

13 . The method of claim 10 , wherein the modification is received from one of a workflow scheduler and a remote user device.

14 . A non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method implemented via a back-end application computer server of an enterprise, the method comprising:

training a machine learning model with historical task completion data and workflow data;

receiving a workflow;

retrieving a plurality of tasks associated with the received task;

retrieving, from an external location-based system, at least one location-based requirement for a first task of the plurality of tasks associated with the received workflow, wherein the location-based requirement is at least one of a release policy and a task requirement;

generating, via the trained machine learning model, a timeline for execution of each task, including for at least one task a lead time and a dependent task, wherein the lead time is an overlap between a first task and a second task, and the second task starts before the first task finishes;

receiving a user assignment for each task;

transmitting the timeline and the user assignment to a workflow scheduler, the assigned user, and the trained machine learning model as feedback;

retraining the machine learning model with the transmitted timeline and the user assignment to the workflow;

receiving a modification to at least one task of the received workflow;

re-generating, via the trained machine learning model, the timeline in response to the modification to the transmitted timeline as determined by the workflow scheduler;

transmitting the re-generated timeline to the workflow scheduler;

automatically generating, by the trained machine learning model, an alert and a message including the modification and indicating the re-generated timeline; and

transmitting the generated alert and the generated message indicating the re- generated timeline to a plurality of parties via at least one of an email, a text message, and a video message.

15 . The medium of claim 14 , the lead time is indicative of an amount of time prior to the task due date.

16 . The medium of claim 14 , wherein receipt of the task includes receipt of a task type, a task due date, and a task location.

17 . The medium of claim 14 , further comprising:

determining upcoming tasks; and

displaying the determined upcoming tasks in chronological order.