Here is a high-level layout of the PACU simulation model: - Inputs: - Historical daily OR schedule with planned start/end times of surgeries - Distributions of surgery durations - Distributions of PACU length of stay for different surgery types - Process: - Simulate surgeries based on schedule and duration distributions - Patients enter PACU after surgery based on OR schedule - Patients spend time in PACU based on PACU length of stay distributions - Patients discharge from PACU over time - Outputs: - PACU census (number of patients) tracked over time - Staffing requirements calculated to maintain target nurse-to-patient ratios The model simulates patient flows

1. PACU Nursing Staffing:
Optimizing Quality and Cost
2010 World Conference on Quality and Improvement
May 25, 2010
Alexander Kolker, PhD
Outcomes Department
Children’s Hospital and Health System, Milwaukee, WI
Project Team
Brett Norell, MHA/MPH; Mary O’Connor, RN, MSN, MBA
Children’s Hospital of Wisconsin, Surgical Services
1

2. Outline
• The use of Management Engineering methodology for
staffing decision-making.
• Case Study 1 - Quality and Cost: Outpatient Flu Clinic.
• Case Study 2 - Quality and Cost : Optimal PACU Nursing
Staffing.
• Overall Take-Away.
• Summary of Fundamental Management Engineering
Principles.
2

3. Some Definitions
What is Management?
Management is controlling and leveraging available resources (material, financial
and human) aimed at achieving the performance objectives.
Traditional (Intuitive) Management is based on
• Past experience.
• Intuition or educated guess.
• Static pictures or simple linear projections.
Linear projection assumes that the output is directly proportional to the input, i.e.
the more resources (material and human) thrown in, the more output produced
(and vice versa.)
System output
Resource input
3

4. What is Management Engineering?
• Management Engineering (ME) is the discipline of building
and using validated mathematical models of real systems to
study their behavior aimed at making justified business
decisions.
• This field is also known as operations research.
Thus, Management Engineering is the application of mathematical
methods to system analysis and decision-making
4

5. Case Study 1
Quality and Cost:
Outpatient Flu Clinic
5

6. Problem Description
An outpatient flu clinic is supposed to open during a flu season to provide H1N1
flu vaccine shots on a walk-in basis. The clinic stays open from 8:00 a.m. to
6:00 p.m.
It is expected that on an average day patient arrival rate will be:
• from 8:00 a.m. to 10:00 a.m. - about 9 patients per hour
• from 10:00 a.m. to 2:00 p.m. - about 15 patients per hour
• from 2:00 p.m. to 4:00 p.m. - about 9 patients per hour
• from 4:00 p.m. to 6:00 p.m. - about 12 patients per hour
Key point: the average patient arrival rate is highly variable during a typical day.
• Giving a shot will take on average about 8 minutes but could be in the
range from 6 minutes to 10 minutes.
• Flu shot costs a patient $20; the clinic’s cost of one vaccine dose and
supplies is $1. Staffing pay rate is $14/hour.
6

7. The Goal
Clinic’s management should decide:
• How many medical providers are needed to staff the clinic
on a typical day?
• What will the projected net revenue be (on a daily basis)?
7

8. Traditional Management Approach
• Projected total average number of patients for a typical day is 120
(=9*2+15*4+9*2+12*2).
• One provider is going to serve on average 60 minutes/8 minutes = 7.5
patients/hour.
• Hence 120/7.5 =16 hours of staffing time will be needed to serve all patients.
• Thus, two medical providers should be scheduled to staff the clinic.
• One is scheduled to work 8 hours from 8:00 a.m. to 4:00 p.m. and another is
scheduled to work from 10:00 a.m. to 6:00 p.m. (Lunch and a few short
breaks are extra time.) Practically no (or very short) patient waiting time is
expected.
• An average daily revenue is going to be 120*$20 = $2,400.
• Labor cost for two staff team is $14/hour*16 hours = $224.
• The total daily vaccine and supplies average costs is $120.
• Hence, the average clinic’s daily net revenue is expected to be
$2,400 - $224 - $120 = $2,056.
8

9. Management Engineering Approach
• Because of inevitable variability of the daily number of patients
coming for the shot and the time it takes to give a shot, the
actual staffing needs and the actual estimated net revenue will
differ significantly from the average values.
• On top of that, it is observed that some patients will leave
without a shot if their waiting time is longer than 20 minutes.
• In order to develop a realistic evaluation of clinic performance,
the process variability and patients leaving without a shot should
be taken into account.
• It is possible only using simulation analysis of clinic’s operations.
9

10. Management Engineering Approach
Flu clinic simulation model layout
Resources:
RN1, RN2 and part time RN3
10

11. Management Engineering Approach
Scenario 1 - Baseline
One provider works 8 hours from 8:00 a.m. to 4:00 p.m.
Another provider works 8 hours from 10:00 a.m. to 6:00 p.m.
• Predicted Clinic’s Performance Results
95% percent Confidence Interval for the number of served patients: 93 to 94
(much lower than anticipated 120 patients !!!)
Because of waiting longer than 20 minutes, 19 to 20 patients (16% to 17%) will
leave without a shot.
Total daily clinic net revenue is going to be $1,163 (much lower than expected
$2,056 based on averages)
• Next step
Does it make sense to extend staff working hours to increase net revenue and
reduce the number of leaving patients?
Note
From a traditional management standpoint this is not needed because 16
hours of working time on average is enough to meet the average patient
demand for service time. Therefore extended hours would result in staff
under-utilization.
11

12. Management Engineering Approach
Scenario 2
Both providers work 10 hours from 8:00 a.m. to 6:00 p.m. (total 20 hours of work time)
• Predicted Clinic’s Performance Results
The number of served patients: 113.
Because of waiting longer than 20 minutes, only 2 to 3 patients will leave
without a shot.
Total daily clinic net revenue is going to be $1,822 (much better than the
baseline value $1,163 but still lower than the expected based on averages.)
• Take-away
Extended staffing work hours results in additional operational and staffing
costs; however, this costs is well offset by a higher clinic revenue because
more paying patients will be served.
Management Question
Is it possible to improve the clinic’s performance if a third provider is added on a
part-time basis (in addition to two full time extended hours’ staff)?
12

13. Management Engineering Approach
Scenario 3
Two staff work 10 hours from 8:00 a.m. to 6:00 p.m. (total 20 hours of work time)
Additional provider works part-time in the morning from 8:00 a.m. to 1:00 p.m. (0.6 FTE)
• Predicted Clinic’s Performance Results
The number of served patients: 113 to 114.
Because of waiting longer than 20 minutes, only 1 to 2 patients will leave
without a shot.
Total daily clinic’s net revenue is going to be $1,786 which is less than that
in Scenario 2.
• Take-away
Placing third provider in the morning shift does not result in improving
clinic’s performance.
Gain from serving only a few more patients does not offset the cost of
keeping additional staff.
Management Question
How is the performance affected if a third part-time staff is placed in the afternoon
from 1:00 p.m. to 6:00 p.m.?
13

14. Management Engineering Approach
Scenario 4
Two providers work 10 hours from 8:00 a.m. to 6:00 p.m. (total 20 hours of work time.)
Additional provider works part-time in the afternoon shift from 1:00 p.m. to 6:00 p.m.
• Predicted Clinic’s Performance Results
The number of served patients: 117-118.
NO patients will leave because of waiting longer than 20 minutes.
Total daily clinic net revenue is going to be $1,871 which is better
than for all previous scenarios.
• Take-away
The third provider placed in the right shift does help to serve more
patients and increase profitability despite the higher costs of
keeping one more additional individual.
14

15. Summary of the Analyzed Scenarios
Number of Left
# Operations Description served without Daily net NOTE
Scenario patients shot revenue
(95% CI) (95% CI)
1 Baseline 2 providers: 8:00 a.m. – 120 0 $2,056 Projected data are based on
4:00 p.m. shift and 10:00 the average service time and
a.m. to 6:00 p.m. shift the average number of
patients
93 – 94 19 – 20 $1,163 Data are based on the variable
service time and the variable
number of patients
2 Extended 2 providers: 8:00 a.m. to 113 – 114 2–3 $1,822 Additional staffing cost is offset
shift 6:00 p.m. shift for both by revenue from serving more
patients
3 Extended 2 providers: 8:00 a.m. to 113 – 114 1–2 $1,786 Additional staffing cost is NOT
shift with 6:00 p.m. shift for both; offset by revenue from serving
additional 0.6 additional 0.6 FTE from a few more patients
FTE in the 8:00 a.m. to 1:00 p.m.
morning
4 Extended 2 providers: 8:00 a.m. to 117 – 118 0 $1,871 Additional staffing cost is offset
shift with 6:00 p.m. shift for both; by revenue from serving more
additional 0.6 additional 0.6 FTE from patients
FTE in the 1:00 p.m. to 6:00 p.m.
afternoon
15

16. Management Engineering Approach
CONCLUSIONS
• Many other scenarios of staffing shifts and clinic’s operation modes are possible
to analyze using a simulation model.
• This example provides illustration of the flaw of averages in traditional
managerial decision-making.
• Thus, in contrast to traditional ‘pen and paper’ guesswork managerial approach,
management engineering simulation methodology allows predicting process
performance outcomes and, thus, making truly efficient managerial
decisions.
Note
Many other illustrations of fundamental deficiency of managerial decisions based on average
input data without taking into account inevitable process and data variability are provided, for
example, in:
Savage, S., 2009. The Flaw of Averages. John Wiley & Sons, Inc, Hoboken, New Jersey, pp. 392.
Kolker, A., 2009. Queuing Theory and Discrete Events Simulation for Health Care: from basic processes to
complex systems with interdependencies. Chapter 20. In: Handbook of Research on Discrete Event Simulation.
Technologies and Applications. IGI-press Global, pp.443-483.
16

17. Case Study 2
Quality and Cost:
Optimal PACU Nursing Staffing
17

18. Problem Description
• PACU nursing daily workload is highly variable because patient census often
changes fast from 0 to the peak value within an hour or two.
• The required adequate number of nurses to care for the volume of patients
entering the PACU from the OR is not known.
• The anesthesiologist and the OR nurse are required to take care of the
patient until a PACU nurse becomes available. This in turn results in:
Delays in OR because anesthesiologist and OR nurse are not
available.
Frustration among OR staff and surgeons due to delay of cases.
A sense of urgency among PACU staff to ‘hurry’ with the current
patient, so they could take another waiting patient.
This pressure greatly increased the risk of medical errors because
the nurses are rushed.
• Managers should manually reset the staffing (up or down) within a few hours
of staffing periods trying to keep the required nurse-to-patient ratio
(in acute care it is 1:1).
18

19. The Goal
Develop a methodology for calculating an optimal PACU nursing staffing
plan that provides simultaneous maximizing of:
• The percent of patients cared for with the required
nurse-to-patient ratio 1:1 (improving quality of care).
and
• Staff utilization (decreasing the cost of overtime and
the cost of extended shift coverage).
19

20. Traditional Management
• PACU managers typically adjusted nursing staffing
needs manually based on the past historical average
number of patients.
• Because of high variability of the actual number of
patients around the average, the resulting staffing
usually either is not enough to deliver proper quality of
care or it is not cost-effective.
20

21. Typical plot of the PACU daily average number of patients (on annual basis)
16
Staff on this level ?
14
12
10
Patients
8
or Staff on these levels ?
6
4
2
4
0
07 07 08 08 09 09 10 10 11 11 12 12 13 13 14 14 15 15 16 16 17 17 18 18 19 19 20 20 21 21 22 22 23
00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00 30 00
MEAN 0 0.4 1.6 2.9 4.2 5.3 5.7 6 6.2 6.2 6 5.8 5.7 5.5 5.4 5.4 5.3 5.2 6.2 4.4 3.9 3.4 3 2.3 2 1.3 0.9 0.8 0.6 0.5 0.5 0.4 0.2
MIN 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0
MAX 1 3 7 9 10 11 11 15 13 12 12 12 11 13 12 11 13 12 13 11 11 9 11 9 6 5 3 4 4 3 3 3 2
MEDIAN 0 0 1 3 4 5 6 6 6 6 6 6 6 5 5 5 5 5 6 4 4 3 3 2 2 1 1 1 0 0 0 0 0
MODE 0 0 1 3 5 6 6 6 6 7 5 5 6 6 6 5 4 4 7 4 3 3 3 2 2 1 1 0 0 0 0 0 0
Time
21

22. Management Engineering Approach
Step 1
Layout of the simulation model for calculating PACU census at every moment in
which it is changed based on the balance of admissions and discharges.
Census (i) (current period) = census (i-1) (previous period) +
[# admissions (i) – # discharges (i) ]; i = 1, 2, 3, …….
22

23. census PACU
Calculated census for 40 weeks
13
12
11
10
9
8
7
cns
6
5
4
3
2
1
0
0 168 336 504 672 840 1008 1176 1344 1512 1680 1848 2016 2184 2352 2520 2688 2856 3024 3192 3360 3528 3696 3864 4032 4200 4368 4536 4704 4872 5040 5208 5376 5544 5712 5880 6048 6216 6384 6552 6720
3-rd floor PACUtime, hrs week 11
census,
14
Example of week 11 census (from 7:30 am to 11:30 pm)
12
10 Mon Tue Wed Thu Fri
8
cns
6
4
2
0
1856 1858 1860 1862 1864 1866 1868 1870 1872 1874 1876 1878 1880 1882 1884 1886 1888 1890 1892 1894 1896 1898 1900 1902 1904 1906 1908 1910 1912 1914 1916 1918 1920 1922 1924 1926 1928 1930 1932 1934 1936 1938 1940 1942 1944 1946 1948 1950 1952 1954 1956 1958 1960 1962 1964 1966 1968
time, hrs
Time of day 23

24. Step 2
For each staffing time slot (for every hour from 7:30 a.m. to 11:30 p.m.) and for
each possible number of nurses from the nursing pool (from 1 to 14) calculate:
• Percent of covered patients (with the patient-to-nurse ratio 1:1).
and
• Percent of time these nurses are utilized.
• The optimal number of nurses corresponds to the combined maximum.
Note:
National standard (American Society of Perianesthesia Nursing) requires that minimum
two nurses be present at all times when a patient (even only one) is in the PACU.
Therefore if the mathematically optimal number of nurses dropped down to one it should
be kept equal to two to comply with the national standard.
24

25. Example of Hourly PACU Census Variability
Staffing time slot (every hour)
Time of
day
Day of (decimal
week units) 7:30-8:30 8:30-9:30 9:30-10:30 10:30-11:30 11:30-12:30 12:30-13:30 13:30-14:30 14:30-15:30
1 9.06667 1
1 9.31667 2
1 9.41667 3
1 9.85 2
1 10.0333 1
1 10.0667 2
1 10.6 3
1 10.6667 4
1 10.8333 3
1 10.8667 2
1 11.2833 3
1 11.6667 2
1 11.85 1
1 11.9167 0
1 12.0333 1
1 12.3667 2
1 12.4 3
1 12.6167 2
1 13.2 3
1 13.25 2
1 13.8667 1
1 13.8667 2
1 14 1
1 14 2
1 14.5833 1
1 14.5833 2
1 14.75 3
1 14.85 4
1 15 3
1 15.1167 4
1 15.4 3
25

26. Example of Calculated Optimal Staffing (1 hour slot)
% of
daily time optimal # covered
The optimal daily number of nurses for each 1-hour staffing time slot slot of nurses patients
9 7:30-8:30 2 96
8 8:30-9:30 4 100
7 9:30-10:30 5 98
mber of nurses
6 10:30-11:30 5 96
5 11:30-12:30 7 99
4 12:30-13:30 6 93
num
3 13:30-14:30 6 99
2 14:30-15:30 8 99
15:30-16:30 6 95
1
16:30-17:30 5 99
0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 17:30-18:30 4 95
:3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :3 :0
-8 0-9 -10 -11 -12 -13 -14 -15 -16 -17 -18 -19 -20 -21 -22 -23 -24
30 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 18:30-19:30 3 96
7: 8: 9:3 0:3 1:3 2:3 3:3 4:3 5:3 6:3 7:3 8:3 9:3 0:3 1:3 2:3 3:3
1 1 1 1 1 1 1 1 1 1 2 2 2 2 19:30-20:30 4 100
daily time slot
20:30-21:30 3 100
21:30-22:30 2 94
22:30-23:30 2 100
23:30-24:00 2 100
26

27. Comparison of Current and Optimal Staffing (30 minute slot)
Current vs. Optimal Staffing
Current vs. optimal staffing
staffing
14 type
13 Current staffing current
12
Current staffing is too low optimal
is excessive
11
10
mber
9
staffing num
8
7
6
5
4
3
2
1
0
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 0 8 3 9 0 9 3 0 0 0 3 1 0 13 20 23 30 33 40 43 5 0 5 3 6 0 6 3 7 0 7 3 8 0 8 3 9 0 93 00 03 10 13 20 23 3 0
- 0 0 -0 0 -0 0 -0 0 -1 0 -1 0 -1 0 -1 0 -1 0 -1 0-1 0-1 0-1 0-1 0-1 0- 1 0- 1 0 -1 0 -1 0 -1 0 -1 0 -1 0 -1 0 -1 0 -2 0 -2 0-2 0-2 0-2 0-2 0-2
30 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3 0 3
0 7 0 8 0 8 09 09 10 10 11 11 12 12 1 3 1 3 1 4 1 4 1 5 1 5 1 6 1 6 1 7 17 18 18 19 19 20 20 2 1 2 1 2 2 2 2
27

28. Example of a more detailed staffing plan adjusted for
seasonal variability and different days of week (2 hours slot)
Monday Jan-May June-Aug Sep-Dec
8.-10 3 3 3
10.-12 5 7 5
12.-14 6 7 6
14.-16 7 6 5
16-18 4 6 6
18-20 2 3 5
20-22 2 2 2
22-22:30 2 2 2
Tue-Thu
7:30-9:30 5 4 5
9:30-11:30 7 7 6
11:30-13:30 7 7 8
13:30-15:30 11 7 7
15:30-17:30 6 9 7
17:30-19:30 3 4 4
19:30-21:30 2 2 2
21:30-23:30 2 2 2
Friday
7:30-9:30 4 6 7
9:30-11:30 8 8 7
11:30-13:30 7 9 10
13:30-15:30 6 8 7
15:30-17:30 6 6 10
17:30-19:30 3 7 5
19:30-21:30 2 2 2
21:30-23:30 2 2 2 28

29. Optimal Staffing Versus Currently Used
Before implementing PACU optimal staffing plan in 2009 additional
nursing cost was:
Overtime $ 5,430
Extended shift coverage $15,141
Total $20,571
Implementing optimal staffing plan will result in 80% annual nursing
cost saving, i.e. $16,457
29

30. Conclusions
• The optimal staffing nursing plan development based on management
engineering methodology helps managers to take the guesswork out of
their daily decision-making.
• The optimal staffing provides the trade-off between the percent of
covered patients for the required nurse-to-patient ratio (this improves the
quality of care) and nursing staff utilization (this reduces the cost of
doing business.)
• The optimal staffing plan allowed PACU managers making adjustments
to the start times, shift length, and the number of required FTEs.
• This allowed, in turn, placing the correct number of nurses in the PACU
when they are needed, i.e. placing the right amount of resources in the
right place at the right time.
• This methodology and the PACU staffing plans are currently being
implemented for planning surgical services at Children’s Hospital of
Wisconsin.
30

31. Main Take-Away
Management Engineering and System Simulation Modeling is the only
methodology that helps to quantitatively address the following typical hospital
issues:
Given the variable patient volume:
• How many beds are needed for each unit?
• How many procedure rooms are needed for each service?
• How many nurses/physicians should each unit schedule for the particular shift?
• What will patient wait time be and how to reduce it to the acceptable level?
• What will an efficient clinic’s schedule look like?
And so on, and so on…
And the Ultimate Goal
How to manage hospital operations to increase profitability (reduce costs, increase revenue)
while keeping high quality, safety and outcomes standards for patients?
31

32. Summary of Some Fundamental
Management Engineering Principles
• Systems behave differently than a combination of their independent
components.
• All other factors being equal, combined resources are more efficient
than specialized (dedicated) resources with the same total
capacity/workload.
• Scheduling appointments (jobs) in the order of their increased
duration variability (from lower to higher variability) results in a lower
overall cycle time and waiting time.
• Size matters. Large units with the same arrival rate (relative to its
size) always have a significantly lower waiting time. Large units can
also function at a much higher utilization % level than small units with
about the same patient waiting time.
• Work load leveling (smoothing) is an effective strategy to reduce
waiting time and improve patient flow.
32

33. Summary of Some Fundamental Management
Engineering Principles – continued
• Because of the variability of patient arrivals and service time, a
reserved capacity (sometimes up to 30%) is usually needed to
avoid regular operational problems due to unavailable
beds/resources.
• Generally, the higher utilization level of the resource (good for the
organization) the longer is the waiting time to get this resource
(bad for patient). Utilization level higher than 80% to 85% results
in a significant increase in waiting time for random patient arrivals
and random service time.
• In a series of dependent activities only a bottleneck defines the
throughput of the entire system. A bottleneck is a resource (or
activity) whose capacity is less than or equal to demand placed
on it.
33

34. Summary of Some Fundamental Management
Engineering Principles – continued
• An appointment backlog can remain stable even if the
average appointment demand is less than appointment
capacity.
• The time of peak congestion usually lags the time of the
peak arrival rate because it takes time to serve patients
from the previous time periods (service inertia.)
• Reduction of process variability is the key to patient flow
improvement, increasing throughput and reducing delays.
34

35. APPENDIX
35

36. What is a Simulation Model?
A Simulation Model is the computer model that mimics the behavior of a
real complex system as it evolves over the time in order to visualize and
quantitatively analyze its performance in terms of:
• Cycle times.
• Wait times.
• Value added time.
• Throughput capacity.
• Resources utilization.
• Activities utilization.
• Any other custom collected process information.
• The Simulation Model is a tool to perform ‘what-if’ analysis and play
different scenarios of the model behavior as conditions and process
parameters change.
• This allows one to build various experiments on the computer model
and test the effectiveness of various solutions (changes) before
implementing the change.

37. How Does a Typical Simulation Model Work?
A simulation model tracks the move of entities through the system at distinct points of time
(thus, discrete events.) The detailed track is recorded of all processing times and waiting
times. In the end, the system’s statistics for entities and activities is gathered.
Example of Manual Simulation (step by step)
Let’s consider a very simple system that consists of:
• A single patient arrival line.
• A single server.
Suppose that patient inter-arrival time is uniformly (equally likely) distributed between 1
minute and 3 minutes. Service time is exponentially distributed with the average 2.5
minutes. (Of course, any statistical distributions or non-random patterns can be used
instead.)
A few random numbers sampled from these two distributions are, for example:
Inter-arrival time, minutes Service time, minutes
2.6 1.4
2.2 8.8
1.4 9.1
2.4 1.8
…. ….
and so on… and so on….
37

38. We will be tracking any change (or event) that happened in
the system. A summary of what is happening in the system
looks like this:
Event # Time Event that happened in the system
1 2.6 First customer arrives. Service starts that should end at time = 4.
2 4 Service ends. Server waits for patient.
3 4.8 Second patient arrives. Service starts that should end at time = 13.6.
Server idle 0.8 minutes.
4 6.2 Third patient arrives. Joins the queue waiting for service.
5 8.6 Fourth patient arrives. Joins the queue waiting for service.
6 13.6 Second patient (from event 3) service ends. Third patient at the head of
the queue (first in, first out) starts service that should end at time 22.7.
7 22.7 Patient #4 starts service…and so on.
In this particular example, we were tracking events at discrete points in time
t = 2.6, 4.0, 4.8, 6.2, 8.6, 13.6, 22.7
DES models are capable of tracking hundreds of individual entities, each with its own unique set of
attributes, enabling one to simulate the most complex systems with interacting events and component
interdependencies.
38

39. Basic Elements of a Simulation Model
• Flow chart of the process: Diagram that depicts logical flow of a process from
its inception to its completion.
• Entities: Items to be processed (i.e. patients, documents, customers, etc.)
• Activities: Tasks performed on entities (i.e. medical procedures, document
approval, customer checkout, etc.)
• Resources: Agents used to perform activities and move entities (i.e. service
personnel, operators, equipment, nurses, physicians.)
Connections
• Entity arrivals: They define process entry points, time and quantities of the
entities that enter the system to begin processing.
• Entity routings: They define directions and logical condition flows for
entities (i.e. percent routing, conditional routing, routing on demand, etc.)
39

40. Typical Data Inputs Required to Feed the Model
• Entities, their quantities and arrival times
Periodic, random, scheduled, daily pattern, etc.
• Time the entities spend in the activities
This is usually not a fixed time but a statistical distribution. The wider the time
distribution, the higher the variability of the system behavior.
• The capacity of each activity
The maximum number of entities that can be processed concurrently in the activity.
• The size of input and output queues for the activities (if needed.)
• The routing type or the logical conditions for a specific routing.
• Resource Assignments
The number of resources, their availability, and/or resources shift schedule.
40