Metrics

Idle Time

The idle time is the time that there is no activity in a case or for a resource. It can only be calculated when there are both start and end timestamps available for activity instances. It can be computed at the levels trace, resource, case and log, and using different time units.

patients %>%
    idle_time("resource", units = "days")

## # A tibble: 7 x 2
##   employee idle_time    
##   <fct>    <drtn>       
## 1 r7       464.4199 days
## 2 r1       450.2124 days
## 3 r4       442.6260 days
## 4 r5       430.1764 days
## 5 r3       429.1064 days
## 6 r6       425.5362 days
## 7 r2       214.7436 days

The output of all metrics in edeaR can be visualized by supplying it to the plot function.

patients %>%
    idle_time("resource", units = "days") %>%
    plot()

Processing Time

The processing time can be computed at the levels log, trace, case, activity and resource-activity. It can only be calculated when there are both start and end timestamps available for activity instances.

patients %>% 
    processing_time("activity") %>%
    plot

Throughput Time

The throughput time is the time form the very first event to the last event of a case. The levels at which it can be computed are log, trace, or case.

patients %>%
    throughput_time("log") %>%
    plot()

Resource Frequency

The resource frequency metric allows the computation of the number/frequency of resources at the levels of log, case, activity, resource, and resource-activity.

patients %>%
    resource_frequency("resource")

## # A tibble: 2,721 x 4
##    employee handling_id absolute relative
##    <fct>    <chr>          <int>    <dbl>
##  1 r1       1                  2 0.000368
##  2 r1       10                 2 0.000368
##  3 r1       100                2 0.000368
##  4 r1       101                2 0.000368
##  5 r1       102                2 0.000368
##  6 r1       103                2 0.000368
##  7 r1       104                2 0.000368
##  8 r1       105                2 0.000368
##  9 r1       106                2 0.000368
## 10 r1       107                2 0.000368
## # ... with 2,711 more rows

Resource Involvement

Resource involvement refers to the notion of the number of cases in which a resource is involved. It can be computed at levels case, resource, and resource-activity.

patients %>%
    resource_involvement("resource") %>% plot

It this example it shows that only r1 and r2 are involved in all cases, r6 and r7 are involved in most of the cases, while the others are only involved in half of the cases, more or less.

Resource Specialization

The resource specalization metric shows whether resources are specialized in certain activities or not. It can be calculated at the levels log, case, resource and activity.

patients %>%
    resource_specialisation("resource")

## # A tibble: 7 x 4
##   employee handling              absolute relative
##   <fct>    <fct>                    <int>    <dbl>
## 1 r1       Registration              1000    143. 
## 2 r2       Triage and Assessment     1000    143. 
## 3 r6       Discuss Results            990    141. 
## 4 r7       Check-out                  984    141. 
## 5 r5       X-Ray                      522     74.6
## 6 r3       Blood test                 474     67.7
## 7 r4       MRI SCAN                   472     67.4

In the simple patients event log, each resource is performing exactly one activity, and is therefore 100% specialized.

Variance

patients %>% activity_presence() %>%
    plot

patients %>%
    activity_frequency("activity")

## # A tibble: 7 x 3
##   handling              absolute relative
##   <fct>                    <int>    <dbl>
## 1 Registration               500   0.184 
## 2 Triage and Assessment      500   0.184 
## 3 Discuss Results            495   0.182 
## 4 Check-out                  492   0.181 
## 5 X-Ray                      261   0.0959
## 6 Blood test                 237   0.0871
## 7 MRI SCAN                   236   0.0867

patients %>%
    start_activities("resource-activity")

## # A tibble: 1 x 5
##   employee handling     absolute relative cum_sum
##   <fct>    <fct>           <int>    <dbl>   <dbl>
## 1 r1       Registration      500        1       1

patients %>%
    end_activities("resource-activity")

## # A tibble: 5 x 5
##   employee handling              absolute relative cum_sum
##   <fct>    <fct>                    <int>    <dbl>   <dbl>
## 1 r7       Check-out                  492    0.984   0.984
## 2 r6       Discuss Results              3    0.006   0.99 
## 3 r2       Triage and Assessment        2    0.004   0.994
## 4 r5       X-Ray                        2    0.004   0.998
## 5 r3       Blood test                   1    0.002   1

patients %>%
    trace_coverage("trace") %>%
    plot()

patients %>%
    trace_length("log") %>%
    plot