System and method for improving human-centric processes

Abstract

A system and method of generating a plurality of actionable insights is disclosed herein. A computing system retrieves data corresponding to a work procedure. Each work procedure includes a plurality of steps. The computing system generates a predictive model for each actionable insight using a plurality of machine learning models by generating an input training based on the retrieved work procedure data and learning, by the plurality machine learning models, a metric corresponding to each actionable insight based on each respective input training set. The input data set for each actionable insight includes actionable insight specific information. The computing system receives a request to generate a plurality of actionable insights for a current work procedure. The computing system generates, via the predictive models, a plurality of metrics for a plurality of actionable insights based on data corresponding to the current work procedure.

1 - 20 . (canceled) 
     
     
         21 . A method of generating an actionable insight, comprising:
 retrieving, by a computing system, data corresponding to a work procedure, wherein the data corresponding the work procedure comprising:
 a first set of data corresponding to an author of instructions corresponding to the work procedure, the instructions comprising a sequence of steps for carrying out the work procedure, the first set of data comprising:
 a type of instruction corresponding to each step in the sequence of steps; and 
 a target time for completing each step in the sequence of steps; and 
 
 a second set of data corresponding to a plurality of workers performing the instructions for carrying out the work procedure, the second set of data comprising: an order in which the worker performed the sequence of steps; and an actual time for completing each step in the sequence of steps; and 
   generating, by the computing system, an amount of improvement of time to complete the work procedure by comparing the first data set with the second data set.   
     
     
         22 . The method of  claim 21 , further comprising determining an action related to the work procedure to capture the amount of improvement. 
     
     
         23 . The method of  claim 21 , further comprising calculating an ideal target time for carrying out the work procedure based on the first and second data sets. 
     
     
         24 . The method of  claim 21 , wherein the data corresponding to the work procedure comprises one or more of version information, changes made to the sequence of steps over time, a number of steps in each work procedure, cards corresponding to each step in each work procedure, style of the steps presented to a user in each work procedure, execution data, cycle time of each step, or patterns of up and down times. 
     
     
         25 . The method of  claim 21 , further comprising generating a hierarchy of steps with the most capturable productivity gains based on the data corresponding to the work procedure. 
     
     
         26 . The method of  claim 21 , further comprising generating an actionable insight to a worker index that quantifies learning needs and performance index of a worker of the plurality of workers. 
     
     
         27 . The method of  claim 26 , wherein the actionable insight is generated through a machine learning model trained with the first and second sets of data. 
     
     
         28 . The method of  claim 21 , wherein the generating the improvement includes learning, by a machine learning model, to generate a worker metric quantifying the performance of the plurality of workers and learning needs of each of the plurality of workers. 
     
     
         29 . The method of  claim 21 , wherein the data is collected from the plurality of workers via monitoring a remote device of a worker executing the work procedure. 
     
     
         30 . A system, comprising:
 a processor; and   a memory having programming instructions stored thereon, which, when executed by the processor, performs one or more operations comprising:
 retrieving data corresponding to a work procedure, wherein the data corresponding the work procedure comprising:
 a first set of data corresponding to an author of instructions corresponding to the work procedure, the instructions comprising a sequence of steps for carrying out the work procedure, the first set of data comprising:
 a type of instruction corresponding to each step in the sequence of steps; and 
 a target time for completing each step in the sequence of steps; and 
 
 a second set of data corresponding to a plurality of workers performing the instructions for carrying out the work procedure, the second set of data comprising: an order in which the worker performed the sequence of steps; and an actual time for completing each step in the sequence of steps; and 
 
   generating an amount of improvement of time to complete the work procedure by comparing the first data set with the second data set.   
     
     
         31 . The system of  claim 30 , wherein the operations further comprise determining an action related to the work procedure to capture the amount of improvement. 
     
     
         32 . The system of  claim 30 , wherein the operations further comprise calculating an ideal target time for carrying out the work procedure based on the first and second data sets. 
     
     
         33 . The system of  claim 30 , wherein the data corresponding to the work procedure comprises one or more of version information, changes made to the sequence of steps over time, a number of steps in each work procedure, cards corresponding to each step in each work procedure, style of the steps presented to a user in each work procedure, execution data, cycle time of each step, or patterns of up and down times. 
     
     
         34 . The system of  claim 30 , wherein the operations further comprise generating a hierarchy of steps with the most capturable productivity gains based on the data corresponding to the work procedure. 
     
     
         35 . The system of  claim 30 , wherein the operations further comprise generating an actionable insight to a worker index that quantifies learning needs and performance index of a worker of the plurality of workers. 
     
     
         36 . The system of  claim 35 , wherein the actionable insight is generated through a machine learning model trained with the first and second sets of data. 
     
     
         37 . The system of  claim 30 , wherein the generating the improvement includes learning, by a machine learning model, to generate a worker metric quantifying the performance of the plurality of workers and learning needs of each of the plurality of workers. 
     
     
         38 . The system of  claim 30 , wherein the data is collected from the plurality of workers via monitoring a remote device of a worker executing the work procedure. 
     
     
         39 . A non-transitory computer readable medium comprising one or more sequences of instructions, which, when executed by one or more processors, cause the one or more processors to perform operations, comprising:
 retrieving data corresponding to a work procedure, wherein the data corresponding the work procedure comprising:
 a first set of data corresponding to an author of instructions corresponding to the work procedure, the instructions comprising a sequence of steps for carrying out the work procedure, the first set of data comprising:
 a type of instruction corresponding to each step in the sequence of steps; and 
 a target time for completing each step in the sequence of steps; and 
 
 a second set of data corresponding to a plurality of workers performing the instructions for carrying out the work procedure, the second set of data comprising: an order in which the worker performed the sequence of steps; and an actual time for completing each step in the sequence of steps; and 
   generating an amount of improvement of time to complete the work procedure by comparing the first data set with the second data set.