Process Optimization System with Machine Learning Model to Optimize Process Execution

A non-transitory computer-readable medium that includes stored thereon computer-executable instructions that when executed by at least a processor of a computing system, wherein the computing system includes one or more computing devices, cause the computing system to:

• train a machine learning model with a plurality of historical logs that comprises execution data from multiple execution cycles of a process type, wherein the process type includes a plurality of tasks that include task parameters comprising at least task execution parameters and task staff assignments;
• wherein the plurality of historical logs comprises at least a first historical log including a first sequence of tasks having a first set of task parameters that were performed during a first execution cycle of the process type, and a second historical log including a second sequence of tasks having a second set of task parameters that were performed during a second execution cycle of the process type;
• wherein the machine learning model is trained to learn patterns that identify delays associated with (i) particular tasks and their assigned parameters, (ii) particular combinations of tasks, and (iii) particular sequences of tasks;
• perform an optimization by inputting a process template to the machine learning model, wherein the process template includes (i) a proposed sequence of tasks for executing the process type and (ii) a proposed set of task parameters including proposed task staff assignments for one or more of the tasks in the proposed sequence;
• evaluate, by the machine learning model, the proposed sequence of tasks including the proposed set of task parameters for executing the process type based on at least the internalized patterns of the machine learning model;
• predict, by the machine learning model, one or more predicted delays based on similar task sequences and combinations of task parameters as in the internalized patterns;
• and
• generate, by the machine learning model, an optimized sequence of tasks for the process template by modifying the proposed sequence of tasks and changing one or more of the proposed set of task parameters based on the historical performance data for the process type that reduces the one or more delays.

A computer-implemented method, the method comprising:

• training a machine learning model based on a plurality of historical logs that comprises execution data from multiple execution cycles of a process type;
• wherein each historical log representing a distinct execution cycle performed at a different time, and containing data showing the process type executed with varying sequences of tasks associated with different performance metrics;
• training the machine learning model to learn and create internalized patterns that identify delays associated with particular tasks and assigned task parameters, and identify delays associated with particular sequences of tasks based on at least the different performance metrics;
• executing an optimization by inputting, into the machine learning model, a process template that includes a proposed sequence of tasks to be executed for completing the process type;
• evaluating, by the machine learning model, the proposed sequence of tasks including assigned task parameters for executing the process type based on at least the internalized patterns;
• predicting, by the machine learning model, one or more delays associated with the proposed sequence of tasks based on similar properties as in the internalized patterns;
• and
• generating, by the machine learning model, an optimized sequence of tasks by changing an order of the proposed sequence of tasks and changing one or more of the assigned task parameters to reduce the one or more delays predicted.