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American Journal of Industrial and Business Management, 2016, 6, 1001-1017
http://www.scirp.org/journal/ajibm
ISSN Online: 2164-5175
ISSN Print: 2164-5167
DOI: 10.4236/ajibm.2016.610096 October 19, 2016
Optimization of Operations by Simulation—A Case
Study at the Red Cross Flanders
Karen Moons, Helena Berglund, Valerie De Langhe, Katrien Kimpe, Liliane Pintelon,
Geert Waeyenbergh
Research Group Sustainable Engineering, KU Leuven, Leuven, Belgium
Abstract
The Blood Service at the Belgian Red Cross-Flanders is responsible for blood colle
c-
tion in Flanders (Belgium). One of their missions is guaranteeing a constant and su
f-
ficient supply of safe blood products. This is a critical public health need, since the
blood products can save lives of victims from traffic accidents or in the event of m
a-
jor blood losses in hospitalized patients. The main objective of this project is opt
i-
mizing the operations flow in donor centers, in such a way that the waiting time for
donors is minimized and that the donor center occupation or productivity is ma
x-
imized. In this case study, the flow o
f three types of donations is investigated. Blood
and plasma are donated in all donor centers (
i.e
. 11 donor centers in Flanders), while
blood platelets are collected in only six donor centers. Based on data collected from
the 11 donor centers in Flanders, a simplified simulation model was developed,
which
can be used to optimize the operations flow based on the expected number of d
o-
nors and their moment of arrival at the donor center. The simulation model
is
built in Enterprise Dynamics 9.0 simulation software. The input data in the mo
-
del are data that have been collected in collaboration with the Belgian Red Cross
-
Flanders. Different scenarios will be analyzed to gain insight in the impact of small
changes in the input parameters on the performance of
the flow. In this paper, a gap
analysis is conducted to identify extra data needs. With these additional data, a more
detailed model can be constructed to test the scenarios, and a dynamic planning tool
will be developed to rely on when setting up the capa
city of the donor center in order
to find a scenario with the most optimized flow.
Keywords
Optimization, Simulation, Operations Flow, Blood Supply Chain
How to cite this paper:
Moons, K., Berg
-
lund, H
., De Langhe, V., Kimpe, K., Pin
-
telon, L
. and Waeyenbergh, G. (2016) Opti
-
mization of Operations by Simulation
—
A
Case Study at the Red Cross Flanders
.
Ame-
rican Journal of Industrial and Business
Ma-
nagement
,
6
, 1001-1017.
http://dx.doi.org/10.4236/ajibm.2016.610096
Received:
September 12, 2016
Accepted:
October 16, 2016
Published:
October 19, 2016
Copyright © 201
6 by authors and
Scientific
Research Publishing Inc.
This work is licensed under the Creative
Commons Attribution International
License (CC BY
4.0).
http://creativecommons.org/licenses/by/4.0/
Open Access
K. Moons et al.
1002
1. Introduction
1.1. Problem Formulation and Objectives
The Belgian Red Cross-Flanders, part of the international Red Cross and Red Crescent
Movement, is an independent volunteer organization with a threefold mission which
consists of stimulating self-reliance, providing assistance in the event of emergencies
and excelling in blood supply. The department Blood Service is responsible for ensur-
ing a continuous and sufficient supply of safe blood products that will be distributed to
the majority of hospitals in Flanders [1]. Their activities are based on two principles:
voluntary unpaid donations and national self-sufficiency in blood products [2]. In
Flanders, the Blood Service is operating in 11 fixed donor centers and about 770 mobile
sites to make donating more convenient for donors.
The availability of sufficient quantities of safe, high-quality blood is a critical public
health need as it is helping doctors to save lives. Matching supply and demand for
blood, however, is not straightforward because of several external factors. Seasonality,
regional trends and other unforeseen circumstances, like flu epidemics or crises, may
impact the availability and need for blood. This matching process is further compli-
cated by the limited shelf life of blood products. As a consequence, a constant supply of
blood is needed as well as a good inventory policy in order to reduce blood shortages,
which may cause increased mortality rates and hence high costs for society [3]. As
Kendall has noted, “Blood is essential for surgical procedures and medical therapy. It is
critical that blood be available to everyone who needs it, and yet it is also important that
little of this valuable and limited resource is wasted” [4]. Several components (
i.e
.
blood, plasma, blood platelets, etc.) can be extracted from the whole blood, and each
component performs a specific function in the human body. Different blood products
will be used in different situations when treating patients. Red cells are used for surgical
patients or other patients in the event of major blood loss, for anemic patients and for
premature infants; platelets are used for cancer patients as well as for surgical patients
and patients in the event of major blood loss; and plasma is used for surgical patients,
patients in the event of major blood loss and for treatment of liver diseases and burn
injuries [2].
In Belgium, 70% of the Flemish people will need a blood transfusion during his/her
lifetime, while only 3% are donating. Each year 350,000 bags of blood are donated in
Flanders. In the production laboratory, these blood bags are transformed into 600,000
deliverable high-quality blood products which are transferred to the majority of hospit-
als in Flanders [5].
The Blood Service at the Red Cross-Flanders aims to improve the efficiency of their
donor flow. External factors and variability complicate the organization of donors and
resources at donor centers. As a consequence, it is difficult to ensure an optimal utiliza-
tion of the resources, which may result in queues (
i.e
. waiting lines) for donors and un-
der- or overutilization of the resources at certain moments of the day. The Blood Ser-
vice wants to improve their operations with focus on the comfort and satisfaction of
donors.
K. Moons et al.
1003
To gain insights in the performance (e.g. queueing problems) of the donor centers,
an operations analysis was conducted about the current situation. This in order to build
a simulation model. Simulation enables to model the donor flow and test several scena-
rios based on changing the input parameters, without experimenting or spending any
money on the work floor. In this paper, we present the efforts that were done to devel-
op a first simulation model that visualized the donor flow in a reference donor center.
The outcome of the operations analysis indicated that various parameters are in-
fluencing the performance of the donor flow. Seasonality in the arrival pattern of do-
nors, the location of the donor center, launching a campaign, the capacity of the re-
sources, the ratio of new and experienced donors, etc. are used as input parameters in a
simulation model. The simulation software Enterprise Dynamics 9.0 was used to build a
first, simplified model that represents the current donor flow. As reference donor cen-
ters for the model, the data from donor center Ghent and Geel were used as input pa-
rameters. Based on this model, a gap analysis was conducted to identify additional data
needs to build a more detailed simulation model. The final objective of our study is to
develop a dynamic planning tool on which the Blood Service can rely on when setting
up the capacity of the donor center in terms of both personnel and beds. In the ideal
case it would be possible to have a dashboard that can show the optimal organization of
resources related to the parameters of the expected situation.
Building the simulation model under the strategy of lean thinking can help to elimi-
nate the waiting times, have a more efficient utilization of the resources, and an effi-
cient use of space. Healthcare quality and costs depend on delivery processes, which of-
ten include unnecessary or inappropriate steps that do not contribute to the value of
patient care [6]. Lean management, commonly associated with the Toyota Production
System (TPS) in manufacturing, is a relatively new concept to healthcare. In 2005, the
American Institute for Healthcare Improvement [7] advocated using lean management
as it began to show promising results (
i.e
. maximizing value and eliminating waste) in
healthcare. Lean thinking is based upon two concepts: the reduction of costs through
the elimination of waste (
i.e
. activities that do not add value to the product) and the full
utilization of workers’ capabilities [8]. D’Andreamatteo, Ianni, Lega and Sargiacomo
[9] review the diffusion of lean in healthcare. Various journals (especially medical and
nursing journals) are interested in lean thinking in order to improve operational effi-
ciency, clinical outcomes of care processes and well-being at work. In a healthcare ser-
vice environment, the patients constitute the primary flow. It is therefore necessary to
incorporate a sociotechnical approach to the healthcare system and the design of
healthcare facilities. The design can have a large impact on efficiency and outcomes
[10], because multiple stakeholders, many outcomes and flows (patients, staff, family
and friends, medications, supplies, equipment and information) are interacting. The
Virginia Mason Hospital in Seattle used the Production Preparation Process (3P) me-
thod, which is part of the lean design process, together with the seven flows of medicine
for optimizing their facilities design to encourage collaboration between the stakehold-
ers early in the design process [11] [12]. However, Nicholas [6] noted that “lean is no
K. Moons et al.
1004
panacea, but it can significantly reduce waste”. In order to implement the lean prin-
ciples, an organizational culture should be created that is receptive to lean thinking.
When applied rigorously and throughout the entire organization, lean management
strategies can help improve processes, reduce costs and increase satisfaction among pa-
tients and employees.
The remainder of this paper is organized as follows. In the next section, the donor
flow at the Red Cross-Flanders is described. Section 2 focuses on the use of simulation
as a tool to optimize the donor flow. An operations analysis was done by the Blood Ser-
vice, since they were observing queueing problems in the flow. Section 3 provides the
process of building the first simplified simulation model and implementing the availa-
ble data. It is crucial to check for model validation and verification since simulation
models are simplifications of reality. The results are provided in Section 4. This section
also conducts a gap analysis to identify the missing data and provides recommenda-
tions in order to build the dashboard and test different scenarios in the second phase of
the project. The conclusion and suggestions for future work are presented in Section 6.
1.2. Operations Flow
The operations or donor flow is divided into five steps. The sequence of these steps is
similar in the 11 donor centers of the Red Cross-Flanders, but they differ in the types of
donations that can be made. In the donor centers, mainly three types of donations are
collected. Blood and plasma can be donated in all donor centers, whereas blood plate-
lets in only six donor centers. In this case study, a simplified simulation model is built
by analyzing two reference donor centers. In Ghent, the three types of donations are
collected, while the donor flow in Geel is not equipped to donate blood platelets.
Figure 1 shows a schematic representation of the five steps that are performed dur-
ing a visit at the donor center, regardless of the type of donation. The main objective is
to dynamically optimize this donor flow by improving the customer (or donor) service
and by using the resources more efficiently during the day, week and year. The flow
starts when a donor enters the donor center and ends with the donation collected in
step 4. This blood bag will be further processed into end products in the production la-
boratory, from where it will be distributed to the hospitals in order to help patients in
need of these products. The Blood Service is continuously improving their customer
service in every step of the donation process to offer qualitative blood products to pa-
tients and to deliver optimal quality to hospitals [13].
Figure 1. Donor flow at Red Cross Flanders.
1. Registration 2. Medical
questionnaire 3. Medical
examination 4. Actual
donation 5. Donor corner
K. Moons et al.
1005
In this paragraph, the different steps of the donor flow, displayed in Figure 1, will be
explained, which constituted the main building blocks in the simulation model. In be-
tween the steps in the donor flow, a queue can occur depending on the number of do-
nors in the flow and the capacity of the resources. The donor flow starts when the do-
nor arrives at the donor center. The donor details are recorded at the registration desk
by scanning the electronic identity card. The person at the registration desk prints a
medical questionnaire and the donor gets a post-donation card with a unique number
for each donor and donation. In the second step, the donor chooses one of the available
spots where he/she can quietly answer the medical questionnaire. The questionnaire is
designed to elicit information on the state of health and lifestyle of the donor, as well as
to identify any medical procedures or potential risks. In step 3, the doctor is examining
the donor by recording the weight of the donor and by taking blood pressure and pulse.
The hemoglobin level is measured for new donors and for donors whose hemoglobin
level at the previous donation was below the acceptable limit. Based on this medical
checkup, the answers to the questionnaire and any additional oral questions, the doctor
decides if the donor is allowed to donate or not. The first three steps are the same in the
donor flow in each donor center, independent of the type of donation. In the fourth
step, the donor is assigned to a bed for the actual donation. From this step, the donor
flow is split up in three separate flows: blood, plasma and platelets collection. At the be-
ginning or during the first minutes of the actual donation, the donor is registered in the
blood information system by scanning the post-donation card and the materials and
products used for the donation. After the donation, a compression bandage is put on
the arm to cover the small puncture wound. The last step in the donor flow gives the
donor the opportunity to relax and have a drink in the assigned refreshment area (
i.e
.
donor corner).
2. Simulation
The healthcare sector is becoming more competitive. Hospitals are aiming at delivering
efficient and effective healthcare which requires high quality medical care. “In health-
care, efficiency means a better allocation of scarce resources which will result in a high-
er overall quality of healthcare” [2]. Simulation is a useful tool to make operations more
efficient, and it is defined as “the process of designing a model of a real system and
conducting experiments with this model for the purpose of understanding the behavior
of the system and/or evaluating various strategies for the operation of the system” [14].
In this paper, Enterprise Dynamics 9.0 simulation software is used to apply dis-
crete-event simulation to build the donor flow and to investigate the impact of opera-
tional changes at the Red Cross-Flanders [15]. It is an object-oriented simulation plat-
form that allows to model, visualize and monitor dynamic flow process activities. The
software enables you to create insights in the donor flow, evaluate potential resource
investments, answer what-if questions by modeling several scenarios, and to estimate
the impact of external factors and variable process times on the performances of the
flow [16].
K. Moons et al.
1006
The main objective of this project is improving the operational flow in a donor cen-
ter by using simulation in order to find the optimal flow in different situations. An effi-
cient donor flow can be obtained by optimizing the utilization rate of the resources and
decreasing the waiting times for donors which will result in the collection of safe, qua-
litative blood products for saving patient lives. However, the results of the operations
analysis (see Section 1.1) showed that various parameters are influencing the perfor-
mance of the donor flow. A simulation model enables the Blood Service to change the
input parameters and answer what-if questions to find the optimal flow in each situa-
tion. Moreover, the visualization of the donor flow, which can also be displayed in 3D,
is a strong advantage when using simulation to convince medical experts of the bene-
fits.
At first, simplified simulation model, corresponding to the current donor flow, is
built in cooperation with employees and medical experts at the Blood Service. Involve-
ment of the personnel in the healthcare sector is vital so that the model under devel-
opment can be validated based on their understanding of the operations flow [2].
Moreover, monitoring the current flow and identifying potential improvements by us-
ing realistic visualizations (3D viewer) is important to convince the medical experts of
the advantages of the project for both donors and the resource utilization. The model
allows the user to analyze how the output is affected by (changing) the input parame-
ters and to identify the bottlenecks in the donor flow. Answering what-if questions by
analyzing different scenarios (without spending any money on utilizing more personnel
for example) in order to find the optimal flow in different situations, makes simulation
an attractive tool to investigate the problem from different perspectives and to analyze
trade-offs in healthcare systems [17].
Keep in mind that simulation models are simplifications and that sometimes it might
be difficult to guarantee their validity. It is crucial that the model reflects the behavior
of the real donor flow. Model validation and verification (see Section 3.4) will check for
this. In order to build a valid simulation model, data should be implemented in the
model. The Blood Service is collecting data in a blood information system (CTS Serveur
software package by Haemonetics). By applying goodness-of-fit tests, probability dis-
tributions are selected that best fit the data. However, lack of data is a well-known
problem in healthcare. Additional data should be collected to obtain accurate simula-
tion results in order to develop the dynamic planning tool.
3. Modelling
3.1. Operations Description
The core of the model to build is to be found in the operations layout (summarized in
Figure 1). The donor flow was observed in collaboration with experts of Blood Service
at the Red Cross-Flanders in order to create knowledge on the donation process in a
donor center. Each donor goes through the same five steps in the donor flow. Depend-
ing on the type of blood product they are donating, the duration of the actual donation
(step 4) will take more or less time.
K. Moons et al.
1007
3.2. Data Collection
The Blood Service is recording data about the donor flow in the blood information sys-
tem (
i.e
. CTS Serveur software package). These data are converted to useful informa-
tion reports using a business intelligence database (cognos database), in which the
number of arriving donors per hour and per day can be retrieved, as well as the time
the donors are spending in the donor flow (
i.e
. end-to-end donation time). However,
only two time registrations are shown in this database: (1) the moment at which the
medical questionnaire is printed at the registration desk, which is assumed to be the
start of the donor flow (step 1), and (2) the start of the actual donation (step 4), which
is recorded at the moment of scanning the post-donation card in the blood information
system during the first minutes of the donation. This second registration moment is not
pre-defined and hence not accurate. For the donation of blood, this registration should
happen prior to the start of the actual donation, while plasma and platelets donations
already start and the donation is registered during its first minutes. The remaining du-
ration of the actual donation is estimated by medical experts to be 6, 40 and 75 minutes
for the donation of blood, plasma and platelets respectively. Furthermore, the blood
information system provides important data on the busiest times (
i.e
. peak hours) and
least congested times (
i.e
. trough hours) of the day. At these times, the arrival pattern of
donors will be different. Changing this parameter in the simulation model will affect
the performance of the donor flow. The objective of this project is to find the most op-
timal flow, such that the Blood Service is capable of adapting the input parameters in
real-time to the arrivals of donors by optimizing the organization (utilization rate of the
resources) of the donor center. As a result, the waiting times should decrease.
As mentioned in paragraph 1.1, Ghent and Geel are serving as the two reference do-
nor centers in this study. Data from available manual data collections in the 11 donor
centers can be used to be implemented in the simulation model. Some data, however,
are center-specific, such as arrival times of the donors. In the first, simplified simula-
tion model, the exact arrival times were implemented in order to mimic the behavior of
the real donor flow. Later on, probability distributions will be selected that best fit the
arrival data. Ghent is one of the largest cities in Flanders. The donor center in Ghent is
equipped to collect the three types of donations. In 2015, 24,304 donations were col-
lected, which is higher than in an average Flemish donor center. They collected 18.6%
of the blood donations, 13.8% of the plasma donations and 22.5% of the platelets dona-
tions across all donor centers in Flanders. The donor center in Ghent was also selected
because it serves as a reference center in a pilot study, approaching this project. The
donor center in Geel is representative for all donor centers that are not collecting plate-
lets donations. In 2015, a total of 7,804 donations are collected, which can be split up to
4.5% of the blood donations and 6.6% of the plasma donations. It is assumed that the
donor flow works similarly in the 11 donor centers in Flanders, except for the type of
donations they are collecting.
The available data at the Red Cross-Flanders, collected either by the blood informa-
tion system or manually by the employees, are implemented in a first, simplified simu-
K. Moons et al.
1008
lation model. These data represent times of the day and should be converted to times
expressed in minutes. For example, 9:05 is equivalent to 5 minutes, because we only
take into account the opening hours (
i.e
. 9 am till 7.30 pm) of the donor center. Anoth-
er example, 15:45 can be expressed by 405 minutes (15 * 60 + 45 − 9 * 60). To capture
variability in the data, AutoFit is used to find the probability distributions that best fit
the observed data. AutoFit is a goodness-of-fit test integrated in Enterprise Dynamics,
which determines whether a certain data set can be represented by a certain distribu-
tion based on shape, mean and standard deviation. By default, the Anderson-Darling
test is used [16]. Table 1 summarizes the collected data and distributions for each step
in the donor flow.
3.3. Building the Simulation Model
The simulation model, as shown in Figure 2, represents a good visualization of the
current flow at the donor centers at the Red Cross-Flanders. The structure is equivalent
to the donor flow displayed in Figure 1. In the simulation software Enterprise Dynam-
ics 9.0, atoms (
i.e
. building blocks) are representing each step of the donor flow.
Table 1. Probability distributions of the donor flow.
Donor flow Datapoints Distribution (minutes)
Registration desk 53 Erlang (2.55, 4.00)
Medical questionnaire + waiting 26 Weibull (5.06, 1.80)
Doctor 574 Weibull (3.91, 2.50)
(Queue before plasma) 22 Erlang (3.79, 2.00)
(Queue before platelets) 14 Erlang (6.31, 1.00)
Actual donation blood 0 Normal (6.00,1.00) (assumption)
Actual donation plasma 42 Logistic (42.82, 9.30)
Actual donation platelets 28 Normal (77.91, 66.59)
Figure 2. First simulation model in enterprise dynamics 9.0.
K. Moons et al.
1009
The basic atoms used in this model are Source, Queue, (Multi-) Server and Sink atoms.
The simulation model can easily be adapted to a flow without platelets donations, as
these are only collected in six donor centers.
The donors arrive at the donor center according to a random arrival pattern. Data
regarding arrivals at the donor center in Ghent were used and are converted to data
lines in the ArrivalList atom. This atom creates donors based on the pre-defined list,
which contains data on the arrival time, a name, the arrival quantity and a channel
through which the incoming donors are sent by using three labels. As such, a distinc-
tion can be made between the three types of donations. When entering the donor cen-
ter, the donor may have to wait in a queue before being served by the person at the reg-
istration desk (
i.e
. entering queue). At the registration desk, which is represented by a
Server atom, one employee is occupied with registering all arriving donors. On average,
this activity takes 2.55 minutes and AutoFit suggests an Erlang distribution (see Table
1). After the registration, the donor gets time for completing the medical questionnaire.
At the reference donor center, five spots are arranged for filling in this questionnaire
quietly. These spots are depicted as a Multi-Service atom, because the donors can do
this activity simultaneously with four other donors. In the simplified model, the time
for completing the questionnaire (Weibull distribution with an average of 5.06 mi-
nutes) also includes the waiting time for entering the doctor’s office. Hence, no Queue
atom is built in front of step 3. Based on the medical examination, the answers to the
questionnaire and any additional oral questions, the doctor will decide whether or not
the donor can donate. This activity takes on average 3.91 minutes and is Weibull dis-
tributed. Donors who are not allowed to donate (
i.e
. postponed donors) leave the flow
and do not continue to the actual donation step. Donors who are allowed to donate are
assigned to a bed, if any bed is available. Otherwise the donor waits in a queue. From
now on, the flow is split up in three separate flows for the three types of donations.
Hence, it is important to send the donors by the right label as these are indicating the
type of donation. For example, donations of blood are labeled in the ArrivalList atom to
go through the first channel, while plasma and platelets donors go through the second
and third channel respectively. Once the donor is installed on the bed, the actual dona-
tion can start. Multi-Service atoms represent the beds for donation. In this case, there
are three beds for blood donations, seven beds for plasma donations and four beds for
platelets donations. The duration of the actual donation is assumed to be 6, 40 and 75
minutes for blood, plasma and platelets respectively. However, the collected data for
plasma and platelets donations follow a logistic and normal distribution with an aver-
age of 42.82 and 77.91 minutes, respectively. For the actual donation of blood, there are
no data available, so a normal distribution with an average of 6 minutes is assumed.
When the donation ends, a compression bandage is put on the small puncture wound
and the donor leaves the bed. In the donor corner (Sink atom), he/she can have a drink
and relax before going home. This step does not have any impact on the efficiency of
the donor flow, but it serves to satisfy the donors.
In the first model, Queue atoms are used to model waiting lines. However, the Red
K. Moons et al.
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Cross-Flanders collected data on the time that donors are spending in a queue before
being assigned to a bed in step 4. Therefore a second simulation model has been devel-
oped in which the queues are replaced by Multi-service atoms, representing the ob-
served waiting times. By comparing the two simulation models and the observed data,
one can check whether the first simulation model is valid (see paragraph 3.4). The ob-
served data suggest an Erlang distribution with an average waiting time of 3.79 minutes
for plasma and 6.31 minutes for platelets. For blood donations, there are no data avail-
able on queueing. The first simulation model suggests that this queue is always empty.
Hence no Multi-service atom has been included in the flow of donors in the second si-
mulation model (see Figure 3).
3.4. Validation and Verification of the Simulation Model
When developing a simulation model, it is crucial to do a validation and verification.
The Red Cross-Flanders will make important decisions based on the results of the
model, which will affect the employees and the donors. Model verification is often de-
fined as “ensuring that the model and the implementation are correct”, while model va-
lidation determines whether “the model within its domain of applicability possesses a
satisfactory range of accuracy consistent with the intended application of the model”
[18]. Unfortunately, there is no set of specific tests that can easily be applied to deter-
mine the correctness of the model [19].
The simulation model is verified by investigating whether the model is built in the
right way. The models in Figure 2 and Figure 3 are built to specifications by imple-
menting the observed data in the different steps of the donor flow. After running the
model for one day (
i.e
. opening hours between 9 am and 7.30 pm), no errors occurred
and all the donors who arrived at the donor center also left the donor corner. As such,
we can secure the right operation of the simulation model in regard to the functional
processes.
Model validation is important to ensure that the right model is built to meet the in-
tended purpose of the project. Three tests were executed to check for model validation.
Figure 3. Second simulation model in enterprise dynamics 9.0.
K. Moons et al.
1011
In the first test, the ArrivalList atom was observed to make sure that the donors arrive
at the donor center according to the observed data. The second validation method
monitors the Sink atoms according to the principle “what comes in, goes out”. The 112
incoming donors also leave the model, labeled by the three types of donation. By using
the ArrivalList atom, label values were assigned to the incoming donors referring to the
type of donation. A third test is performed to check whether the model corresponds to
reality by comparing the average end-to-end donation time (
i.e
. the time a donor
spends in the donor flow). Table 2 shows the average end-to-end donation time in the
two simulation models and in the observed data. The calculated end-to-end donation
time based on the observed data is not yet very accurate because the blood information
system only collects data at two time registrations (
i.e
. printing of medical question-
naire and start of actual donation). The estimated durations for the actual donations are
added to these times. In Table 2, column 2 should approximate column 4, because the
second simulation model represents the waiting times as observed in the data. Due to
lack of data, the end-to-end donation times differ slightly. The two simulation models
in column 3 and 4 also indicate different end-to-end donation times. The time differ-
ence between the first and second model is 5 minutes for plasma and 7 minutes for
platelets, which is due to the pre-defined waiting time in front of the actual donation in
the second model. For blood donations, however, there is no difference between the
models because there is no queue. The first simulation model indicates that the current
end-to-end donation time could be reduced.
4. Results and Recommendations
4.1. Results
This section presents the results obtained by running the first simulation model. Based
on Table 2, the first model is selected to further investigate because the end-to-end do-
nation time is lower which may point at a potential optimization of the current donor
flow. However, it should be noted that the human resource team is not yet accurately
modeled. In Enterprise Dynamics 9.0 simulation software, four techniques are available
to present the results. The results have been obtained by doing ten times a simulation
run of 12 hours (
i.e
. donors can arrive to the center between 9 am and 7.30 pm).
A first method for interpreting the results is tracking the information on the atoms
while the model is running in order to check if the model is working logically (
i.e
.
Table 2. End-to-end donation times of the observed data and the two models.
Type of
donation Reality (observed data) First model (queue atom
before actual donation)
Second model (multi-service
atom before actual donation)
Blood 17 minutes
(estimation of 6 minutes) 21.2 minutes 21.3 minutes
Plasma 57.7 minutes 60.7 minutes 65.3 minutes
Platelets 95 minutes 91.6 minutes 98.5 minutes
K. Moons et al.
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model verification). The ArrivalList atom represents the number of arriving donors and
the Sink atoms mention the number of donors, separated by the type of donation, who
leave the flow and have a drink in the donor corner. Furthermore, the Server atoms in-
dicate the utilization rate of the servers, which is the ratio of the busy time and the total
time. For example, the doctor is examining donors for 60.87% on average of his/her
time during a simulation run of 12 hours (see Figure 4). While running the simulation
model, the utilization rates are increasing during peak times and decreasing during the
less congested hours. The Queue and Multi-Service atoms are also changing during the
run, showing the number of donors that are waiting or being served in the correspond-
ing step.
Results can also be obtained by connecting monitors (e.g. bar graphs, status pies,
etc.) to atoms. As shown in Figure 4, the status pie ‘Status Registration’ indicates that
the person at the registration desk is busy 40% of the total time. The monitor ‘Queue
Registration’, connected to the entering queue, shows that the arriving donors are
waiting on average 52 seconds before being served by the person at the registration
desk. After being registered, donors are using on average 1.3 out of five spots to answer
the medical questionnaire. Furthermore, donors are waiting on average 1.7 minutes in
order to get assigned to a plasma bed, while there is no average waiting time for dona-
tion of blood and platelets. The monitoring of the occupation of the beds reveals an
underutilization as only 0.3, 3.8 and 1.8 beds are utilized out of 3, 7 and 4 beds respec-
tively for blood, plasma and platelets.
Thirdly, results can be interpreted by retrieving a summary report (see Figure 5),
which represents an overview of the donor flow’s status and provides feedback on the
impact of changing the input parameters. The average content in Figure 5 indicates
possibilities for optimizing the resource utilization. For example, on average 1.276 spots
(out of 5 spots) are used to complete the medical questionnaire. This result is similar to
the monitors in Figure 4. The average content in a queue determines how many donors
are waiting. Notably, the queues are not so large. On average, donors are waiting one
minute before a bed becomes available for donating plasma, while there are no waiting
Figure 4. Results after a 12-hour run of the first simulation model.
K. Moons et al.
1013
Figure 5. Output of the summary report for the first model.
times for donating blood or platelets. In the observed data, however, the waiting times
were longer, as measured in the second simulation model.
The three above mentioned methods are particularly useful to display results directly
during the simulation run, but these are less appropriate when making decisions based
on the results of the model. These techniques are primarily utilized for building and
testing the model (
i.e
. model verification and validation in section 4.4), while the fourth
measuring technique, experimentation, is used later on in the process when the model
is more reliable. In this case, an experiment will be conducted in which performance
measures (e.g. maximum waiting time in each step, maximum utilization of beds, etc.)
can be defined. The experiment was set to execute ten separate runs of 12 hours.
Table 3 presents the results from the experiment, which are comparable to the re-
sults obtained above. The results will be discussed now, and the next section provides
some recommendations.
A donor is spending on average 52 seconds in the entering queue. In peak hours,
however, the waiting time can increase to 6 minutes on average for the ten observa-
tions. In the donor centers, five spots are arranged for completing the medical ques-
tionnaire, while only 1.3 of the spots are used on average. When observing the queues
before being assigned to a bed, donors are spending on average 1.3 minutes in the
waiting line for donating plasma, while they do not have to wait to donate blood or
platelets. During peak hours, donors are waiting maximally 13.3 minutes for plasma
donations. In the second simulation model the donors are forced to wait for some time
(see Table 2 with probability distributions), even if beds are available. The experiment
wizard of the second model reports waiting times of on average 6.3 minutes for platelets
and 5.1 minutes for plasma, which is much more than the 1.3 minutes for plasma in the
first model. These time differentials between the two models could be explained by the
K. Moons et al.
1014
Table 3. Output of experiment wizard of the first model.
Atom Average/Maximum St. Deviation Lower Bound (95%) Upper Bound (95%) Minimum Maximum
Queue Registration (s/min) 52 s/6.2min 2 s/23s 51 s/5.9min 53 s/6.5min 48 s/5.6min 54 s/6.9min
MQ + Waiting (spots) 1.3/5 0.01 1.3 1.3 1.3 1.3
Queue Blood (min) 0.00 0.00 0.00 0.00 0.00 0.00
Donation Blood (beds) 0.3/2.1 0.01 0.3 0.3 0.3 0.3/3
Queue Plasma (min) 1.3 min/13.3min 36 s/4.1min 51 s/10.4min 1.7 min/16.2min 24 s/6.5min 2.4 min/19.6min
Donation Plasma (beds) 3.8/7 0.11 3.7 3.8 3.6 3.9/7
Queue Platelets (min) 0.00 0.00 0.00 0.00 0.00 0.00
Donation Platelets (beds) 1.9/4 0.05 1.8 1.9 1.8 1.9/4
personnel occupation (
i.e
. human resource team). Finally, the actual donation time dif-
fers between the three types of donations, as well as the number of beds. The blood do-
nation atom has a capacity of three beds and an average content of 0.28 beds utilized.
Plasma donation has a capacity of seven beds in the donor center, while the average
content is only 3.8 beds utilized. For the donation of platelets, four beds are available in
the donor center from which 1.9 beds are utilized. This result illustrates that, on aver-
age, there is underutilization of the beds. Although, in peak hours, the beds are max-
imally utilized.
4.2. Recommendations
The results of the experiments show that simulation is useful for a better understanding
of the current donor flow. Moreover, the visualization of the flow can be a useful tool to
convince the medical experts of the improvements that can be made by adapting the
capacity of the donor center to the expected number of arriving donors, and hence de-
creasing the waiting time. By analyzing the results obtained in the two simplified mod-
els, recommendations can be made. The purpose of these recommendations is identi-
fying the gaps of information: in order to build a more detailed simulation model for
testing different scenarios and optimizing the donor flow, more data will be needed. In
this gap analysis, it will become clear which additional data should be collected or ex-
tracted from available reports within the Red Cross-Flanders. When more data are
available, suggestions for improvements can be made in several realistic scenarios by
changing the input parameters.
Donors arriving at the donor center wait in the entering queue. However, some of
them have an appointment for their donation and do not have to wait in this queue.
When donating plasma or platelets, it is recommended to make an appointment be-
cause these donations have a longer duration. To implement two queues (
i.e
. for donors
with/without appointment) in the model, data on the percentage of donors that have an
appointment should be collected. This information will also be very useful for the sche-
K. Moons et al.
1015
duling of personnel as it creates a better knowledge on the inflow of donors to the do-
nor centers. However, as donating blood is a voluntary activity which can be done
when it suits the donor best, one should be careful to instigate donors to make an ap-
pointment. Another recommendation could be distinguishing between new and expe-
rienced donors. A new donor is spending more time at the registration desk than an
experienced donor, because he/she receives more information and asks questions about
the donor flow. Analyzing the end-to-end donation time of these two types of donors
will indicate whether it is advantageous to make separate queues. It is important to col-
lect new data for the time spent at the registration desk in order to build the more de-
tailed model, because the currently observed data are not very reliable as they represent
the time that the medical questionnaire is printed, which is often at the end of the reg-
istration step.
A third recommendation to the simplified model is splitting the
Multi-Service
atom
“MQ + waiting”, which consists of answering the medical questionnaire and the wait-
ing time in front of the medical examination at the doctor’s office. In the current mod-
el, these two activities are merged together because the available data include the time
at which the donor starts filling in the questionnaire and the time at which the donor
enters the doctor’s office. This creates a bias in the waiting time in our model. The
Mul-
ti-Service
atom for completing the questionnaire should therefore be followed by a
Queue
atom, and data should be collected on the duration for completing the ques-
tionnaire. In this way, the end-to-end donation time could be reduced and a potential
bottleneck could be identified.
The number of personnel varies during the day because of lunch breaks, phone calls,
administrative tasks, bathroom breaks, etc. As a consequence, the employees are not
available to serve the donors for 100% of their time. The utilization rate of the resources
in the first model is not accurate because the available staff has been ignored. In the
more detailed model, the time that employees are not working with donors directly (
i.e
.
down time) should be included in the model. In Enterprise Dynamics, additional staff
(
Human Resource Team
) can be added to analyze the impact of changing the capacity
of the personnel. In the actual donation step, the nurses and assistants should be as-
signed to the right beds and donors as this might impact the queue in front of the actual
donation. For example, when a donation bed is available, the donor goes to this bed in
the model. In reality, however, a nurse is needed to assist the donor to the bed and to
prepare the bed for the next donation. Furthermore, it is recommended to collect addi-
tional data in the actual donation step. The durations for blood, plasma and platelets
donations are ending when the donation set is removed from the donor. Hence, no data
are available on the times that the donor leaves the bed (and the bed becomes available
for preparing it for the next donation), which is needed to maximize the utilization of
the beds.
At the doctor’s office, the donor selection includes an eligibility assessment involving
a medical questionnaire, an interview and physical examination (pulse, weight and
blood pressure). Recently, a hemoglobin screening was implemented for new donors
K. Moons et al.
1016
and donors who had a low hemoglobin level the last time they donated. The
Server
atom at the doctor’s office in the simplified model contains data in which the hemoglo-
bin screening is included versus not included. In order to obtain a reliable model, the
actual percentage of hemoglobin screenings should be compared with the ratio of
screenings in the observed data. Another recommendation when building the medical
examination atom is to make a connection to the postponed donors
Sink
atom. These
donors do not fulfill the health requirements and are refused to continue the donation
(
i.e
. they leave the flow after step 3). It is important to identify the postponed donors,
because they increase the end-to-end donation time of all donors as they also utilize the
donor flow until the medical examination. To be able to dispose the right amount of
donors, data should be collected to indicate the disposed percentage in the atom.
By collecting the additional data and implementing them in the model, the model
will become more reliable and potential bottlenecks may be identified. Different scena-
rios, such as building two queues (appointments versus no appointments), changing
the capacity of the resources, etc. can be tested and compared. In the end, a dynamic
planning tool will be developed based on which the Blood Service can adapt the capaci-
ty of the donor center to the expected number of donors in order to get the optimized
donor flow which decreases the waiting times for donors and increases the productivity
of the resources.
5. Conclusions
The overall aim of the research presented is to optimize the operations flow at the do-
nor centers of the Red Cross-Flanders in order to improve customer (donor) service
and the donor center productivity or utilization. Since this project is still ongoing, this
paper only presents a first simplified simulation model, which visualizes the current
flow at the donor center. A gap analysis is conducted in which the missing data are
identified that are needed to develop a more detailed simulation model. Several rec-
ommendations are made which will be tested in different scenarios in order to find the
optimized flow at the Red Cross-Flanders.
Decision makers can make better and less risky decisions regarding changes in use of
human and material resources on the knowledge created by simulation experiments.
There are some limitations in our approach. Models are simplifications and sometimes
it is difficult to guarantee that they will be valid. Simulation calls for special expertise
and a detailed knowledge of the system being depicted is necessary. In the future, a
dashboard will be developed, based on the scenarios tested in the detailed simulation
model, which the Red Cross-Flanders can rely on when setting up the capacity of the
donor center in terms of both personnel and beds.
References
[1] Vlaanderen, R.K. What Do We Do? http://www.rodekruis.be/en/what-do-we-do/
[2] Rytilä, J.S. and Spens, K.M. (2006) Using Simulation to Increase Efficiency in Blood Supply
Chains.
Management Research News
, 29, 801-819.
K. Moons et al.
1017
http://dx.doi.org/10.1108/01409170610717826
[3] Beliën, J. and Forcé, H. (2012) Supply Chain Management of Blood Products: A Literature
Review.
European Journal of Operational Research
, 217, 1-16.
http://dx.doi.org/10.1016/j.ejor.2011.05.026
[4] Kendall, K.E. (1984) Planning for Innovation in Health Care: A Breakthrough in Blood
Banking.
Long Range Planning
, 17, 127-131.
http://dx.doi.org/10.1016/0024-6301(84)90225-5
[5] Vlaanderen, R.K. Kerncijfers Bloedinstelling.
http://www.rodekruis.be/jaarverslag/2014/kerncijfers/#horizontalTab2
[6] Nicholas, J.M. (2012) An Integrated Lean-Methods Approach to Hospital Facilities Design.
Hospital Topics
, 90, 47-55. http://dx.doi.org/10.1080/00185868.2012.679911
[7] Womack, J. and Miller, D. (2005) Going Lean in Health Care. Institute for Healthcare Im-
provement.
[8] Sugimori, Y., Kusunoki, K., Cho, F. and Uchikawa, S. (1977) Toyota Production System and
Kanban System: Materialization of Just-in-Time and Respect for Human Systems.
Interna-
tional Journal of Production Research
, 15, 553-564.
http://dx.doi.org/10.1080/00207547708943149
[9] D’Andreamatteo, A., Ianni, L., Lega, F. and Sargiacomo, M. (2015) Lean in Healthcare: A
Comprehensive Review.
Health Policy
, 119, 1197-1209.
http://dx.doi.org/10.1016/j.healthpol.2015.02.002
[10] Hicks, C., McGovern, T., Prior, G. and Smith, I. (2015) Applying Lean Principles to the De-
sign of Healthcare Facilities.
International Journal of Production Economics
, 170, 677-686.
http://dx.doi.org/10.1016/j.ijpe.2015.05.029
[11] Bohmer, R.M.J. and Ferlins, E.M. (2005) Virginia Mason Medical Center. Harvard Business
School Publishing, Boston.
[12] Coletta, A.R. (2012) The Lean 3P Advantage: A Practitioner’s Guide to the Production Pre-
paration Process. CRC Press, Boca Raton. http://dx.doi.org/10.1201/b11811
[13] Vlaanderen, R.K. Activiteiten Dienst Voor Het Bloed.
http://www.dienstvoorhetbloed.be/DVB-Channel/sites-Dienst-voor-Bloed-Nederlands/Ove
r-Dienst-voor-het-Bloed/Activiteiten-Dienst-voor-het-Bloed.html
[14] Shannon, R.E. (1998) Introduction to the Art and Science of Simulation.
Proceedings of the
1998
Winter Simulation Conference
, 7-14. http://dx.doi.org/10.1109/wsc.1998.744892
[15] Chemweno, P., Thijs, V., Pintelon, L. and Van Horenbeek, A. (2014) Discrete Event Simu-
lation Case Study: Diagnostic Path for Stroke Patients in a Stroke Unit.
Simulation Model-
ling Practice and Theory
, 48, 45-57. http://dx.doi.org/10.1016/j.simpat.2014.07.006
[16] In Control Simulation Solutions. Enterprise Dynamics.
http://www.incontrolsim.com/en/enterprise-dynamics/enterprise-dynamics-overview.html
[17] Roberts, D.S. (2011) Tutorial on the Simulation of Healthcare Systems. North Carolina
State University Edward P. Fitts, Department of Industrial and Systems Engineering.
[18] Schlesinger, S.,
et al
. (1979) Terminology for Model Credibility.
Simulation
, 32, 103-104.
http://dx.doi.org/10.1177/003754977903200304
[19] Sargent, R.G. (2007) Verification and Validation of Simulation Models.
Proceedings of the
2007
Winter Simulation Conference
, 124-137. http://dx.doi.org/10.1109/wsc.2007.4419595
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