When computing the throughput or processsing time, you can take into account a predefined working schedule. In this way, you can controle for working hours and holidays.
A default work schedule can be created using the function
create_working_schedule
library(edeaR)
create_work_schedule()## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 09:00 17:00
## 2 2 09:00 17:00
## 3 3 09:00 17:00
## 4 4 09:00 17:00
## 5 5 09:00 17:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 2 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
##
## Floating holidays
## # A tibble: 0 × 2
## # … with 2 variables: date <date>, name <chr>
##
## Holiday periods
## # A tibble: 0 × 2
## # … with 2 variables: from <date>, to <date>It will generate a weekly working hours schedule, defaulting to 9 to
5 workdays. You can change the day schedule using the arguments
start_time and/or end_time. The following code
will generate a work schedule with workdays from 8.30am to 4pm.
create_work_schedule(start_time = "08:30:00", end_time = "16:00:00")## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 08:30 16:00
## 2 2 08:30 16:00
## 3 3 08:30 16:00
## 4 4 08:30 16:00
## 5 5 08:30 16:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 2 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
##
## Floating holidays
## # A tibble: 0 × 2
## # … with 2 variables: date <date>, name <chr>
##
## Holiday periods
## # A tibble: 0 × 2
## # … with 2 variables: from <date>, to <date>Once a work schedule is created, you can also change individal days. For example, suppose that on Friday the office closes as 1pm, this can be changed as follows. Note that the days are numbered starting on monday.
create_work_schedule(start_time = "08:30:00", end_time = "16:00:00") %>%
change_day(5, start_time = "08:30:00", end_time = "13:00:00")## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 08:30 16:00
## 2 2 08:30 16:00
## 3 3 08:30 16:00
## 4 4 08:30 16:00
## 5 5 08:30 13:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 2 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
##
## Floating holidays
## # A tibble: 0 × 2
## # … with 2 variables: date <date>, name <chr>
##
## Holiday periods
## # A tibble: 0 × 2
## # … with 2 variables: from <date>, to <date>Next to the working times, the work schedule also contains information on holidays. Three different types should be distinguished:
All holidays are excluded in the calculation of throughput and
processing time. By default, the create_working_schedule
function creates two fixed holidays: Christmas and New Year’s day. Let’s
further add some more holidays.
Another fixed holiday that we can typically add, are national holidays. In Belgium, this would be the 21st of July.
create_work_schedule(start_time = "08:30:00", end_time = "16:00:00") %>%
change_day(5, start_time = "08:30:00", end_time = "13:00:00") %>%
add_fixed_holiday("Belgian National Holiday", 07, 21)## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 08:30 16:00
## 2 2 08:30 16:00
## 3 3 08:30 16:00
## 4 4 08:30 16:00
## 5 5 08:30 13:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 3 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
## 3 7 21 Belgian National Holiday
##
## Floating holidays
## # A tibble: 0 × 2
## # … with 2 variables: date <date>, name <chr>
##
## Holiday periods
## # A tibble: 0 × 2
## # … with 2 variables: from <date>, to <date>A typical floating holiday is Easter. However, as Easter falls on a Sunday, it is already not taking into account. Nonetheless, let us add Easter Monday to the schedule.
For floating holidays, it is important to add all dates relevant for your data, that is, for all the years on which you have data.
Suppose we will be using the patients dataset. This
stretched from 2017 to 2018, so we need to add Easter Monday of both
years, which are 2017-04-17 and 2018-04-02.
library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unioncreate_work_schedule(start_time = "08:30:00", end_time = "16:00:00") %>%
change_day(5, start_time = "08:30:00", end_time = "13:00:00") %>%
add_fixed_holiday("Belgian National Holiday", 07, 21) %>%
add_floating_holiday("Easter Monday", ymd(c(20170417, 20180402)))## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 08:30 16:00
## 2 2 08:30 16:00
## 3 3 08:30 16:00
## 4 4 08:30 16:00
## 5 5 08:30 13:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 3 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
## 3 7 21 Belgian National Holiday
##
## Floating holidays
## # A tibble: 2 × 2
## date name
## <date> <chr>
## 1 2017-04-17 Easter Monday
## 2 2018-04-02 Easter Monday
##
## Holiday periods
## # A tibble: 0 × 2
## # … with 2 variables: from <date>, to <date>Finally, let us assume that we don’t work in the period between Christmas and New year in 2017. We can add a holiday period from December 26th till December 31st.
library(lubridate)
create_work_schedule(start_time = "08:30:00", end_time = "16:00:00") %>%
change_day(5, start_time = "08:30:00", end_time = "13:00:00") %>%
add_fixed_holiday("Belgian National Holiday", month = 07, day = 21) %>%
add_floating_holiday("Easter Monday", dates = ymd(c(20170417, 20180402))) %>%
add_holiday_periods(from = ymd(20171226), to = ymd(20171231))## Week schedule
## # A tibble: 7 × 3
## day start_time end_time
## <int> <time> <time>
## 1 1 08:30 16:00
## 2 2 08:30 16:00
## 3 3 08:30 16:00
## 4 4 08:30 16:00
## 5 5 08:30 13:00
## 6 6 NA NA
## 7 7 NA NA
##
## Fixed holidays
## # A tibble: 3 × 3
## month day name
## <dbl> <dbl> <chr>
## 1 1 1 New Year's Day
## 2 12 25 Christmas
## 3 7 21 Belgian National Holiday
##
## Floating holidays
## # A tibble: 2 × 2
## date name
## <date> <chr>
## 1 2017-04-17 Easter Monday
## 2 2018-04-02 Easter Monday
##
## Holiday periods
## # A tibble: 1 × 2
## from to
## <date> <date>
## 1 2017-12-26 2017-12-31Note that it doesn’t make much sense to use a working schedule for the patients data, as work in a healthcare environment doesn’t obey working hours. But for the sake of illustration, and because the patients data includes start and complete events, let’s continue.
Let’s save our work schedule as ws.
ws <- create_work_schedule(start_time = "08:30:00", end_time = "16:00:00") %>%
change_day(5, start_time = "08:30:00", end_time = "13:00:00") %>%
add_fixed_holiday("Belgian National Holiday", month = 07, day = 21) %>%
add_floating_holiday("Easter Monday", dates = ymd(c(20170417, 20180402))) %>%
add_holiday_periods(from = ymd(20171226), to = ymd(20171231))We can now plug the working schedule in any processing or throughput time calculation.
For example, throughput time would normally be computed as follows.
library(eventdataR)
patients %>% throughput_time()## # A tibble: 1 × 8
## min q1 median mean q3 max st_dev iqr
## <drtn> <drtn> <drtn> <drtn> <drt> <drt> <dbl> <drt>
## 1 1.496088 days 4.313924 days 6.085509 days 6.676308 d… 8.58… 23.1… 3.22 4.27…In order to take into account the working schedule
patients %>% throughput_time(work_schedule = ws)## # A tibble: 1 × 8
## min q1 median mean q3 max st_dev iqr
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 70200 108000 115625. 151200 426600 59688. 81000As another example, consider the processing time by activity.
Without taking into account the working hours:
patients %>%
processing_time(level = "activity")## # A tibble: 7 × 11
## handling min q1 mean median q3 max st_dev iqr total relat…¹
## <fct> <drt> <drt> <drt> <drtn> <drt> <drt> <dbl> <dbl> <drt> <dbl>
## 1 Registration 49.… 124.… 165.… 162.8… 204.… 338… 57.2 79.9 826… 0.184
## 2 Triage and As… 352.… 681.… 786.… 800.4… 901.… 1128… 166. 221. 3931… 0.184
## 3 Discuss Resul… 80.… 138.… 166.… 166.3… 193.… 272… 37.7 54.4 824… 0.182
## 4 Check-out 40.… 96.… 123.… 124.3… 148.… 233… 37.2 51.6 608… 0.181
## 5 X-Ray 137.… 233.… 290.… 287.5… 338.… 490… 76.9 106. 758… 0.0959
## 6 Blood test 185.… 285.… 332.… 328.1… 376.… 488… 63.6 90.7 787… 0.0871
## 7 MRI SCAN 149.… 216.… 248.… 245.4… 281.… 355… 44.1 65.4 587… 0.0867
## # … with abbreviated variable name ¹relative_frequencyWith the working hours:
patients %>%
processing_time(level = "activity", work_schedule = ws)## # A tibble: 7 × 11
## handling min q1 mean median q3 max st_dev iqr total relat…¹
## <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Registrat… 0 0 2810. 0 2262 27000 6162. 2262 1.40e6 0.184
## 2 Triage an… 0 0 16167. 18338. 27000 27000 11621. 27000 8.08e6 0.184
## 3 Discuss R… 0 0 3552. 0 5652. 27000 6557. 5652. 1.76e6 0.182
## 4 Check-out 0 0 2703. 0 3382 27000 5977. 3382 1.33e6 0.181
## 5 X-Ray 0 0 7090. 1072 13736 27000 9274. 13736 1.85e6 0.0959
## 6 Blood test 0 0 7327. 65 16200 27000 9508. 16200 1.74e6 0.0871
## 7 MRI SCAN 0 0 6002. 0 11144 27000 8904. 11144 1.42e6 0.0867
## # … with abbreviated variable name ¹relative_frequencySome caution is required when using the work schedules in your calculations. If a case falls completely in a holiday period, or during a weekend, it will receive a throughput time of zero. The same goes for activities that take place outside of working hours, when computing processing time.
If an activity starts at 7am and is completed at 10am. but your working schedule has 9-to-5 workdays, the activity will have a processing time of only 1 hour. If it was completed anytime before 9am, it will be zero!
As such, using a working schedule will overestimate your performance if a lot of activities doesn’t adhere to the working schedule. At this moment, the performance functions will not notify you if this is the case. Make sure to only use a working schedule if the recorded events fall inside the working schedule most of the time.