Provides summary statistics concerning the throughput times of cases.
throughput_time(eventlog, level, append, append_column, units, ...)# S3 method for eventlog
throughput_time(eventlog, level = c("log", "trace",
"case"), append = FALSE, append_column = NULL, units = c("days",
"hours", "mins", "secs", "week"), ...)
# S3 method for grouped_eventlog
throughput_time(eventlog, level = c("log", "trace",
"case"), append = FALSE, append_column = NULL, units = c("days",
"hours", "mins", "weeks"), ...)
The dataset to be used. Should be a (grouped) eventlog object.
eventlog.
Level of granularity for the analysis: log, case, activity, resource or resource-activity.
For more information, see vignette("metrics", "edeaR")
Logical, indicating whether to append results to original event log. Ignored when level is log or trace.
Which of the output columns to append to log, if append = T. Default column depends on chosen level.
Time units to be used
Deprecated arguments
eventlog: Throughput time for eventlog
grouped_eventlog: Throughput time for grouped eventlog
The throughput time of a case is the total duration of the case, or the difference between the timestamp of the end event and the timestamp of the start event of the case. Possible idle time is also included in this calculation.
On log level, the summary statistics of these throughput to describe the throughput time of cases in an aggregated fashion.
Instead of looking at all cases in the log, it can be interesting to analyse the different process variants or traces in the log
Swennen, M. (2018). Using Event Log Knowledge to Support Operational Exellence Techniques (Doctoral dissertation). Hasselt University.