Bottleneck detection for processes

Patent metadata

Field	Value
Publication number	US-2021286658-A1
Application number	US-202016816184-A
Country	US
Kind code	A1
Filing date	Mar 11, 2020
Priority date	Mar 11, 2020
Publication date	Sep 16, 2021
Grant date	—

Abstract

Systems and methods for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck are provided. An event log for a plurality of instances of execution of a process is received and segments executed during one or more of the plurality of instances of execution are identified from the event log. The segments represent a pair of activities of the process. For each particular segment of the identified segments, a measure of performance is calculated for each of the one or more instances of execution of the particular segment based on the event log, each of the one or more instances of execution of the particular segment is classified based on the calculated measures of performance, and one or more metrics are computed for the particular segment based on the classified one or more instances of execution of the particular segment. The identified segments are compared with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck.

First claim

What is claimed is:

1 . A computer-implemented method for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck, comprising:
identifying segments executed during one or more of the plurality of instances of execution from the event log, each of the segments representing a pair of activities of the process; for each particular segment of the identified segments:
calculating a measure of performance for each of the one or more instances of execution of the particular segment based on the event log,
classifying each of the one or more instances of execution of the particular segment based on the calculated measures of performance, and
computing one or more metrics for the particular segment based on the classified one or more instances of execution of the particular segment; and
comparing the identified segments with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck.

2 . The computer-implemented method of claim 1 , wherein the pair of activities comprises a source activity and a destination activity, where execution of the destination activity directly follows execution of the source activity in the event log.

3 . The computer-implemented method of claim 1 , wherein the measure of performance is a time interval between the pair of activities.

4 . The computer-implemented method of claim 1 , wherein classifying each of the one or more instances of execution of the particular segment based on the calculated measures of performance comprises:
classifying each of the one or more instances of execution of the particular segment into one of a plurality of classes using a Jenks Natural Breaks Optimization algorithm.

5 . The computer-implemented method of claim 1 , wherein computing one or more metrics for the particular segment based on the classified one or more instances of execution of the particular segment comprises:
computing 1) an effect size metric for the particular segment representing an average difference between classes of the particular segment, 2) a lost time metric for the particular segment representing a time difference between a best performing class and other classes of the particular segment, and 3) a weighted impact metric representing contribution of the particular segment to an overall performance.

6 . The computer-implemented method of claim 5 , wherein comparing the identified segments with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck comprises:
ranking the identified segments based on the one or more metrics.

7 . The computer-implemented method of claim 6 , wherein ranking the identified segments based on the one or more metrics comprises:
determining an individual ranking of the identified segments for each of the effect size metric, the lost time metric, and the weighted impact metric; and determining an aggregate ranking of the identified segments based on the individual rankings.

8 . The computer-implemented method of claim 7 , further comprising:
identifying the one of the identified segments that is most likely to have the bottleneck based on the aggregate ranking.

9 . The computer-implemented method of claim 1 , further comprising:
causing to display results of the comparing to identify the bottleneck.

10 . The computer-implemented method of claim 1 , wherein the process is a robotic process automation process.

11 . An apparatus comprising:
a memory storing computer instructions for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck; and at least one processor configured to execute the computer instructions, the computer instructions configured to cause the at least one processor to perform operations of: identifying segments executed during one or more of the plurality of instances of execution from the event log, each of the segments representing a pair of activities of the process; for each particular segment of the identified segments:
calculating a measure of performance for each of the one or more instances of execution of the particular segment based on the event log,
classifying each of the one or more instances of execution of the particular segment based on the calculated measures of performance, and
computing one or more metrics for the particular segment based on the classified one or more instances of execution of the particular segment; and
comparing the identified segments with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck.

12 . The apparatus of claim 11 , wherein the pair of activities comprises a source activity and a destination activity, where execution of the destination activity directly follows execution of the source activity in the event log.

13 . The apparatus of claim 11 , wherein the measure of performance is a time interval between the pair of activities.

14 . The apparatus of claim 11 , wherein classifying each of the one or more instances of execution of the particular segment based on the calculated measures of performance comprises:
classifying each of the one or more instances of execution of the particular segment into one of a plurality of classes using a Jenks Natural Breaks Optimization algorithm.

15 . A computer program embodied on a non-transitory computer-readable medium for analyzing an event log for a plurality of instances of execution of a process to identify a bottleneck, the computer program configured to cause at least one processor to perform operations comprising:
identifying segments executed during one or more of the plurality of instances of execution from the event log, each of the segments representing a pair of activities of the process; for each particular segment of the identified segments:
calculating a measure of performance for each of the one or more instances of execution of the particular segment based on the event log,
classifying each of the one or more instances of execution of the particular segment based on the calculated measures of performance, and
computing one or more metrics for the particular segment based on the classified one or more instances of execution of the particular segment; and
comparing the identified segments with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck.

16 . The computer program of claim 15 , wherein computing one or more metrics for the particular segment based on the classified one or more instances of execution of the particular segment comprises:
computing 1) an effect size metric for the particular segment representing an average difference between classes of the particular segment, 2) a lost time metric for the particular segment representing a time difference between a best performing class and other classes of the particular segment, and 3) a weighted impact metric representing contribution of the particular segment to an overall performance.

17 . The computer program of claim 16 , wherein comparing the identified segments with each other based on the one or more metrics to identify one of the identified segments that is most likely to have a bottleneck comprises:
ranking the identified segments based on the one or more metrics.

18 . The computer program of claim 17 , wherein ranking the identified segments based on the one or more metr

Assignees

• Uipath Inc

Inventors

• Copier Martijn
• Scheepens Roeland Johannus

Classifications

• G06Q20/405

  Establishing or using transaction specific rules · CPC title

• G06Q20/326

  Payment applications installed on the mobile devices · CPC title

• G06Q20/02 Primary

  involving a neutral party, e.g. certification authority, notary or trusted third party [TTP] · CPC title

• G06Q10/0633

  Workflow analysis · CPC title

• G06F11/3409 Primary

  for performance assessment · CPC title