Business process reengineering at the hospitals: A case study at Singapore hospital
ABSTRACT K KE EY YW WO OR RD DS S B Bu us si in ne es ss s p pr ro oc ce es ss s r re ee en ng gi in ne ee er ri in ng g, , H He ea al lt th hc ca ar re e. . A AB BS ST TR RA AC CT T As health care costs increase, there is a need for healthcare service providers to look for ways to contain costs and to achieve a higher efficiency at their operating facilities without sacrificing quality. This paper studies a case in employing business process reengineering techniques on one aspect of a health care service – surgical work. The system is simulated focusing on the processes that contribute to the effective functioning of an operating theatre. I IN NT TR RO OD DU UC CT TI IO ON N Business process reengineering (BPR) has become increasingly important in recent years. Customers now have the choice of different product and service providers, to provide them with the same core product or service that they want. Over the last fifteen years, companies have been forced to reengineering their business processes to stay competitive because customers are demanding better products and services. Improving and redesigning business processes is paramount for businesses to stay competitive. With the escalating health care costs, healthcare service providers in Singapore are also continuously seeking ways to stay competitive and provide quality service to the customers. Little research has been done on the employment of BPR in healthcare systems. Healthcare industry has traditionally emphasized on breakthroughs in operating procedures and technology in the bid to stay competitive. Healthcare service providers are beginning to understand that BPR initiatives could be a better solution to achieving competitive advantage.
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ABSTRACT: Purpose – The purpose of this paper is to present a case study of a hospital nursing unit that has evaluated and approved a two-bin “e-kanban” replenishment system based on passive high frequency radio-frequency identification (RFID) technology. Design/methodology/approach – The case study analysis is based on both qualitative and quantitative data that were collected using semi-structured interviews, on-site observations and experience from previous implementations. The data and simulation analysis presented in this paper were validated by key respondents thereby increasing their reliability. Findings – Results indicate that implementing the e-kanban RFID solution in conjunction with the redesign of the ward floor and of the roles and functions can substantially improve business and operational performance. The most important benefits for the hospital are derived from the time saved from non-value-added activities that can be transferred to patient care activities and the significant reduction of on-hand inventory at distributed storage locations. The solution is considered an alternative that requires less initial investment than RFID-enabled cabinets used in the replenishment of consignment and high-value supplies in operating rooms and cardiac catheterization laboratories. Research limitations/implications – There is a need to conduct further research on RFID supply chain management (SCM) applications in the healthcare sector as this area holds a great potential for performance improvements. Additionally, there is a need to conduct more in-depth research into the isolated impact of RFID technology in comparison to the change management and process redesign that it generates. One key limitation of this research is the case study approach based on a single case. This paper, therefore provides direction for practitioners on how to assess RFID's potential impact in the healthcare supply chain. Originality/value – While most of the research on RFID in healthcare sector focuses on active RFID technology for asset management, this research presents a novel RFID application and contributes to our understanding of RFID's potential in intra-organizational SCM processes.Business Process Management Journal 11/2010; 16(6):991-1013.
Conference Paper: Analysis of Target Inventory Via Discrete-Event Simulation[Show abstract] [Hide abstract]
ABSTRACT: Discrete-event process simulation, originally the benefactor of the manufacturing sector of the economy, has expanded aggressively into the service sector of the economy, much to the benefit and gratitude of its new cadre of industrial engineers and management strategists. The study documented in this paper originated within a large health-care insurance provider seeking optimal strategies relative to target inventories of pending inquiries concerning insurance policy coverage and concomitant staffing levels of policy analysts. Since several clients of this insurance provider were large companies within the automotive industry, the provider dedicated significant staffing segments to the service of these accounts (hence to the employees of those automotive companies who thereby held insurance coverage). The simulation study worked within this constraint to provide management valuable strategic recommendations. Most specifically, the insurance provider wished to develop a model capable of predicting service levels (average time required to answer specific questions submitted on behalf of two major clients and average inventory level of these questions pending) as a function of number of full-time equivalent analysts assigned to each of those clients.Proceedings of the 20th European Conference on Modelling and Simulation, Bonn, Germany; 05/2006
Conference Paper: Simulation Improves Patient Flow and Productivity at a Dental Clinic[Show abstract] [Hide abstract]
ABSTRACT: The health care industry in the United States, and in many other countries as well, is undergoing unremitting pressures to improve standards of patient care and service, reduce costs, and increase efficiency. These pressures stem from higher expectations by health-care consumers, increased demands stemming from changing demographics (particularly the “graying” of populations), and more rigorous auditing of expenditures by both private insurers and government. In response, health-care industry practitioners, managers, and administrators are increasingly availing themselves of the analytical techniques, including simulation, provided by the discipline of industrial engineering. In this paper, we document a simulation study undertaken to improve patient service at a dental clinic. The simulation analysis validated innovative ways to improve patient throughput and decrease patient waiting times with zero incremental cost.Proceedings of the 21st European Conference on Modelling and Simulation, Prague, Czech Republic; 06/2007
BUSINESS PROCESS REENGINEERING AT THE HOSPITALS:
A CASE STUDY AT SINGAPORE HOSPITAL
Arun Kumar and Linet Ozdamar
School of Mechanical & Production Engineering
Nanyang Technological University
50 Nanyang Avenue, SINGAPORE-639798
K KE EY YW
B Bu us si in ne es ss s p pr ro oc ce es ss s r re ee en ng gi in ne ee er ri in ng g, , H He ea al lt th hc ca ar re e. .
WO OR RD DS S
A AB BS ST TR RA AC CT T
As health care costs increase, there is a need for
healthcare service providers to look for ways to
contain costs and to achieve a higher efficiency at
their operating facilities without sacrificing
quality. This paper studies a case in employing
business process reengineering techniques on one
aspect of a health care service – surgical work.
The system is simulated focusing on the processes
that contribute to the effective functioning of an
I IN NT TR RO OD DU UC CT TI IO ON N
Business process reengineering (BPR) has
become increasingly important in recent years.
Customers now have the choice of different
product and service providers, to provide them
with the same core product or service that they
want. Over the last fifteen years, companies have
been forced to reengineering their business
processes to stay competitive because customers
are demanding better products and services.
Improving and redesigning business processes is
paramount for businesses to stay competitive.
With the escalating health care costs, healthcare
service providers in
continuously seeking ways to stay competitive
and provide quality service to the customers.
Little research has been done on the employment
of BPR in healthcare systems. Healthcare industry
has traditionally emphasized on breakthroughs in
operating procedures and technology in the bid to
stay competitive. Healthcare service providers are
beginning to understand that BPR initiatives
could be a better solution to achieving
Singapore are also
The operating theatre suite is a critically
important segment of any healthcare organization
that delivers surgical care to patients. It can
consume multitudes of resources, but at the same
time can generate significant revenue if managed
properly. The conflict between the national goal
of healthcare and the high cost of surgical
operations is a powerful incentive to improve the
quality of management of the surgical suite. For
this reason, many hospitals are reengineering their
operating theatre processes in an effort to
establish, restore or boost profitability while
retaining quality (Harris and Zitzmann 1998;
Gabel et al. 1999). Reengineering techniques
enable healthcare service providers to take a
careful look at the processes involved within the
inefficiency that can be removed from the system.
This research employs the concept of BPR to
improve the efficiency and effectiveness of
certain processes involved in surgical operations.
This paper intends to explore the possibilities of
cost containment/reduction in a particular aspect
of the healthcare industry with the application of
BPR. A simulation model has been formulated to
reduce any inefficiencies or bottlenecks inherent
in the system under study. The scope of this
research is limited to an operating theatre suite
within a hospital.
The aggregate per capita healthcare expenditure
in Singapore has risen consistently for the last
three decades from about S$150 in the 1960s to
S$800 in the 1997 (Tan and Chew 1997). The
healthcare industry in Singapore, like its global
counterparts, has been facing tremendous
pressures since the turn of the last century. The
challenges faced by the industry in the near future
are as follows.
The accelerated population ageing will have
serious implications to the provisions of health
care for the elderly population who will occupy
Proceedings 18th European Simulation Multiconference?
Graham Horton (c) SCS Europe, 2004?
ISBN 3-936150-35-4 (book) / ISBN 3-936150-36-2 (CD)
most of the hospital beds with a low turnover rate.
Moreover, the entry of more private-sector
hospitals and medical service will lead to more
professionals (Zhang 2001).
to health care
There is a lack of health care professionals in
Singapore. The local doctor-to-patient ratio was
140 doctors for every 100,000 of the population
for the year 2000. According to OECD data, the
average ratios for the decade of the '90s for
Australia and New Zealand were 240 and 218
respectively (Wee 2002).
Business Process Reengineering in Healthcare
Managers use process reengineering methods to
discover the best processes for performing work,
and that these processes be reengineered to
optimize productivity (Weicher et al. 1995).
Hammer and Champy (1993) state that BPR
refers to the fundamental rethinking and radical
redesign of business processes to achieve
dramatic improvements in critical, contemporary
measures of performance, such as cost, quality
and speed. Business processes are sequences and
combinations of activities that deliver value to a
customer (Coulson-Thomas 1996). A core
business process usually creates value by the
capabilities it gives
competitiveness. A limited number of such core
business processes can be identified in any
company, and enhancing those processes can lead
to business improvement.
the company for
Over the last few years, the reengineering concept
has evolved from a "radical change" to account
for the contextual realism (Caron et al. 1994, Earl
1995). Davenport and Short (1990) prescribe a
five-step approach to BPR. They argue that
process reengineering requires taking a broader
view of both IT and business activity, and of the
relationships between them. The rhetoric of BPR
also encourages fundamental step, or frame-
breaking change (Coulson-Thomas 1996). BPR is
organizational change characterized by strategic
transformation of interrelated organizational sub-
systems producing varied levels of impact. This
organizational change perspective recognizes that
business process reengineering is not a monolithic
concept but rather a continuum of approaches to
process change (Kettinger et al. 1997). The faster
the speed of change the more difficult and
stressful it is to manage (Edwards and Walton
as a form of
With 80 percent of the expenses tied to patient
care activities, hospitals and healthcare systems
can garner substantial savings and improve
clinical practices by better managing their labor,
supplies, equipment, and facilities. The benefits
of reinventing hospitals hold the tangible and
realistic promise of radically reducing cost while
dramatically increasing the quality of care
provided (Harmon 1996).
A case study at Karolinska Hospital in Sweden by
Jacob (1995), and Hout and Stalk (1993) reveals
that rising costs and a weakened economy in
1990s were forcing the government to reassess
and reduce health care expenditures. Karolinska
followed Boston Consulting Group’s (BCG)
Time-Based Management methods to reengineer
the way work was done. BCG reorganized work
at the hospital around patient flow by creating a
new position of "nurse coordinator" in most
departments. By redesigning operating procedures
and staffing patterns, Karolinska was able to cut
the time required for preoperative testing from
months to days, close 2 of 15 operating rooms
and still increase the number of operations per
day by 30 percent.
Operating theatre management often involves
human resources, information systems, finance,
physical plant design and utilization, capital
equipment, clinical quality and efficiency and
Furthermore, surgical cases are conventionally
classified into elective and emergency. An
elective case is one whereby the patient can wait
at least three days without sustaining morbidity or
mortality. A surgical group comprises of several
surgeons who share allocated operating theatre
time. The term block time is the time allocated to
each surgical group into which only the surgeons
belonging to that surgical group can schedule
Managing operating theatre suites is a difficult
task, because individual theatres and the entire
suite are highly complex and tense environments.
Many personnel working in the suite are not
under the direct control of the operating theatre
manager. The operating theatre schedule sets the
stage for the daily flow of patients and staff. Once
the day starts, however, deviations from this
schedule are frequent and expected. Emergency
cases must be accommodated, cases may be
longer or (rarely) shorter than scheduled, patients
may be late or fail to arrive at all, and personnel
may call in sick or become ill during the course of
the day (Gabel et al. 1999).
Modern operating theatre management requires
an information system that includes an effective
scheduling system. Such a system has two basic
but critical functions: performing the actual
scheduling of cases, which involves finding out
the time available on the schedule, whether that
time occurs in a surgeon’s specific block time,
and to facilitate intelligent management of
resources. It must provide data on how resources
are being used in relation to their availability
(Harris et al. 1998). Block scheduling assigns a
surgeon (or a surgical group) a block of time that
is exclusively for his cases.
The anaesthesia service is often a separate
department; in some hospitals it is a division
under surgery department. In contrast with
surgical sub-specialties, anaesthetists specializing
in specific clinical areas such as pediatric
anaesthesia, obstetric anaesthesia and cardiac
anaesthesia are not typically organized into
distinct departments. The anaesthesia department
must be organized in such a way as to ensure
availability of a sufficient number of anaesthesia
providers for elective and emergency cases,
which requires 24-hour-a-day coverage (Gabel et
Simulation in the Health Care Industry
The health care industry is a dynamic system with
complex interactions, in which the simulation
technique would play an indirect but vital role to
achieve the optimal result (Zhang 2001). Kelton
et al. (1998) state that the real power of the
simulation technique is fully realized when it is
used to study a complex system. Numerous
healthcare service providers such as D. R.
Hospital in North Carolina, and St John Hospital
in Detroit, U.S.A. have successfully employed the
understanding their processes and to optimize
them (ProModel Corporation 2002).
to help them in
The Department of Surgery at the Singapore
Hospital oversees the operations of the surgical
theatres. The main operating theatre complex at
Block 3 of the hospital grounds is where surgical
operations of different specialties take place. The
local demand for surgery services has increased
over the last two decades. The capacity of the
operating theatres at the complex has reached
high levels of utilization, and action is necessary
to ensure that the department is able to cope with
increasing patient load. Due to the increasing
demand by patients on the services provided by
this operating theatre complex and the acute
shortage of manpower in the local health care
industry, the Department of Surgery has to
employ reengineering practices to achieve more
efficient and effective utilization with its existing
There are a total of 21 operating theatres at the
main OT complex at Block 3 of the hospital. In
the year 2000, the number of surgical operations
conducted at the hospital was 59,377, of which
about 45% were outpatient (day) surgeries. The
daily average was 162. Out of the 21 theatres, 19
are allocated for elective surgery and operate 8
hours a day (from 8:30 to 17:30), and the
remaining 2 are employed as emergency
operating theatres and operate 24 hours a day.
Historical data was extracted from the hospital’s
scheduling database for the period January to
September 2001. The data includes the percentage
utilization of all the operating theatres, and the
surgeons’ log of all the surgical operations
conducted within the same period.
Every day, each operating theatre is reserved for a
specific clinical discipline to carry out surgical
operations. Some of the operating theatres are
exclusively reserved for a particular discipline,
whereas others may be used by different
disciplines for each day of the week.
MODELING OF THE OPERATING
MedModel is a simulation-based powerful
software tool for evaluating, planning or re-
designing hospitals and other healthcare systems.
It provides a basis for the comprehensive
evaluation of large and complex health care
systems. MedModel is also equipped with an
constructs. Before a model for the operating
theatre complex can be developed, a flow chart of
the operating theatre process is provided in Figure
1 to illustrate the entities, resources and locations
involved. Figure 2 shows the layout of the
completed simulation model. The proportion of
elective surgical operations for each clinical
discipline varies greatly.
To keep simulation as simple as possible, this
model deals with only 8 operating theatres, each
Figure 1: Flow Chart of the Operating Theatre Process
set up when
OT by gurney
Figure 2: Layout of Completed Simulation Model
Table 1: Patient Types and Distribution
Patient Type Clinical code Percentage(%) Surgery required
1 CLR 12 Colorectal surgery
2 CTS 6 Cardiothoracic surgery
3 ENT 9 Ear, nose and Throat surgery
4 GES 26 General surgery
5 GYN 11 Obstetrics and gynaecology
6 OTHERS 9 Other surgery
7 OTO 22 Orthopaedic surgery
8 PLS 5 Plastic surgery
reserved for a different category of surgery. As
such, the number of entities and resources used in
this model will be scaled down from the real-life
numbers obtained. There are 2 entities in this
simulation model, namely Patient and Setup. In
accordance with the 8 categories of surgical cases,
the patient is classified into 8 different types
using the attribute aPt_Type and the user-defined
distribution dPt_Type. The patient types and
distribution are listed in Table 1. It should be
noted that the number convention assigned to
each type of surgery (such as “1” for CLR, “2”
for CTS) is the same throughout the simulation.
Before the entity Patient is routed into the
operating theatre, the entity Setup is first routed
into the operating theatre, together with the
resource Anaesthetist. This is to model the pre-
operation procedures required to get the operating
theatre ready for surgery on the incoming patient.
These pre-operation procedures take 0.5 hours or
30 minutes. As such, there is no need to classify
this entity into 8 different types as for the entity
Patient. The entity Setup stays in the operating
theatre for 30 minutes with the resource
Anaesthetist before the entity Patient is
summoned into the operating theatre to join them.
Locations represent fixed places in the system
where entities are routed for processing. This
model has 5 locations. Moreover, entrance is the
point of entry for the entity Patient. The number
of entries (or the number of arrivals of this entity)
at this location is determined by an arrival cycle.
The entity Patient is next routed to the pre-
operation area (Pre-op), where it waits for 30
minutes before it is called to the next location on
the process logic, which can be any of the 8
operating theatres. Should this next location be
full, the entity remains at this location until the
next location becomes available. The location
Pre-op is a multi-capacity location; its capacity is
20 patients. The location Recovery has a capacity
of 16. The model assumes that each patient
spends 0.25 hours or 15 minutes in the recovery
A resource is a person or piece of equipment used
for one or more of the following functions:
treating or moving patients, assisting in
performing tasks for entities at locations,
performing maintenance on or for locations or
other resources. In this model, there are 10 groups
of resources. Of the ten, eight types represent 8
groups of surgeons from the eight different
surgical specialties, Surgeon1 to Surgeon8. The
other two groups are Anaesthetist and Gurney.
Reengineering the Operating Theatre Complex
It has been noted that the level of utilization for
the operating theatres at the complex is rather
high. The next step is to improve the efficiency of
the system, such that it can achieve greater output
with utilization of the same amount of resources.
Currently, the elective operating theatres at the
hospital operate eight hours a day, from 08:30 to
17:30. Despite this, surgical operations often end
beyond 17:30, due to delays occurring in the
individual operating theatre throughout the course
of the day. Sometimes it could simply be due to
the complexity of the surgery.
In an effort to improve the efficiency of the
complex, operating theatre personnel have
suggested the possibility of implementing a shift
system in place of the current system. By making
changes to the variables used, the operating
theatre process is reengineered to incorporate the
shift system and investigated using the simulation
model developed above. In simulating the shift
system, 2 changes are made to the original model.
??Arrival Cycle: Instead of patients arriving
between 08:00 and 18:00 over a 24-hour
period starting at 08:00, patients now arrive
between 08:00 and 04:00 over the same
period and with the same distribution. This
represents 2 shifts with 10 hours to each shift.
??Number of resource units: Since the new 2-
shift model utilizes the same amount of
resources, as before, the pool of resources has
to be shared between the two shifts. This
results in less number of surgeons and
anaesthetists on duty at any one time. This is
incorporated in the new model by halving the
number of resource units available.
In implementing a shift system, the system might
not have sufficient resources to cope with the
increased workload. In an extreme scenario, twice
the amount of resources is needed to maintain the
level of effectiveness of the system. This is
simulated in a third model, by maintaining the
number of resource units with the implementation
of the shift system.
When two specialties are allocated the use of an
operating theatre on the same day, one uses the
theatre in the morning and the other in the
afternoon. In the extreme scenario, declassifying
all the operating theatres means that no surgical
specialty has the exclusive right to any operating
theatre. This facilitates the allocation of surgical
on a first-come-first-served basis. To model the
new system with no classification of operating
theatres, the attribute aPt_Room and its
assignments are removed from the model.
Removing it would allow the entity Patient to go
to any operating theatre location regardless of the
R RE ES SU UL LT TS S A AN ND D D DI IS SC CU US SS SI IO ON N
The simulation model was run for 168 hours (7
days), with a warm-up period of 48 hours, with 20
replications. Table 2 gives a summary of the
utilization of the locations. It can be seen that
high utilization occurs at OT (OTO), which is the
OT reserved for orthopaedic surgery. The pre-
operation area is also highly utilized due to the
number of patients waiting for orthopaedic
surgery. This creates a bottleneck at the pre-
operating area, and leads to patient arrival
failures. This important issue suggested the
possibilities for reengineering.
Figure 3 shows the utilization of resources for the
simulation. As the crucial resources in our model
are the surgeons and the anaesthetists, it was
assumed that gurneys are always available when
needed in developing the model. It can be seen
that of all the resources available, the group of
anaesthetists within the system is the most highly
utilized at 18.25%. On top of this, anaesthetists
also have teaching and research responsibilities.
Thus, the actual utilization hours for the resources
used in this simulation model is higher than
reflected in Figure 3.
The three suggested models for reengineering
were similarly run for 168 hours with 20
replications. The location utilization of the
reengineered models is compared with the
original model in Table 3. We will refer to the
shift system model as Model 1, the shift system
with increased staff model as Model 2 and the
declassified operating theatres model as Model 3.
It should be noted that for Model 3, the operating
theatres have been renamed to OT1-OT8. The
resource utilization of the reengineered models is
compared with the original model in Figure 4.
Table 4 summarizes the relevant entity states and
efficiency for the 4 models.
Based on the results of simulation, the most
efficient model is Model 3, which declassifies the
operating theatres and allows any surgical
specialty to conduct surgical operations in any
operating theatre. This method reduces the
utilization of the pre-operating area from over
90% to 69%, which indicates alleviation of the
bottleneck seen previously at this location. The
efficiency of this proposed system is found to be
64.8%, an improvement from 45.6% of the
An in-depth study of the operating complex at the
Singapore Hospital has been conducted with the
use of simulation software, MedModel. The
utilization of the operating theatre complex and
its two main resources, the surgeons and
anaesthetists, were analyzed in detail. The
software modeled the complex operating theatre
system accurately and with confidence in results.
Due to the comprehensive nature of the
simulation software tool, assumptions and
shortcuts that have routinely characterized health
care and hospital simulations were no longer
necessary. The software has allowed modeling the
gamut of operating theatre activities quickly and
efficiently, from patient admission to disposition.
Several possibilities for process reengineering
were proposed to reduce the utilization of the
operating theatres within the complex. These
possibilities were implemented on the simulation
model. The results of the simulation have
indicated that operating theatres servicing certain
surgical specialties within the operating theatre
complex are highly utilized. The surgeons
belonging to those specialties are also in high
demand. The results also indicate that the
anaesthetists serving the complex are highly
utilized, possibly due to their anaesthetic
responsibilities outside the operation theatre and
the pre-operative and post-operative work they
conduct for surgical cases.
Thus, in order to maximize the productivity of the
operating theatre complex without increasing the
workload of the surgeons and anaesthetists, the
management needs to look for a way to redesign
the operating theatre process. It is also
recommended that data collection with regards to
operating theatre utilization
periodically for accuracy and transparency in the
data collection process. This is crucial in order to
obtain a true representation of the utilization
states of the operating theatres, and in turn an
accurate productivity index can be derived.
Table 2: Location Utilization
Surgeon5 Surgeon6 Surgeon7
Figure 3: Resource Utilization
Location Capacity Total Entries Avg minutes per entry Utilization (%)
Entrance 1 45.65 80.69 33.26
Pre Op 20 63.65 3012.29 92.43
Recovery 16 50.85 31.49 0.99
Exit 1 50.85 0.00 0.00
OT (CLR) 1 5.20 199.75 10.24
OT (CTS) 1 2.75 286.62 8.18
OT (ENT) 1 4.90 286.56 14.26
OT (GES) 1 18.45 379.87 69.09
OT (GYN) 1 6.10 194.34 11.58
OT (Others) 1 6.60 1478.34 88.87
OT (OTO) 1 6.10 1790.40 100.00
OT (PLS) 1 2.25 350.80 8.56
Table 3: Location Utilization – Comparing the 4 Models
Figure 4: Resource Utilization – Comparing the 4 Models
Model 1 Model 2 Model 3
Entrance 33.26 59.16 55.76 23.00
Pre Op 92.43 96.40 96.06 69.36
Recovery 0.99 1.05 0.97 1.90
Exit 0.00 0.00 0.00 0.00
OT(CLR)/OT1 10.24 9.23 10.29 92.44
OT(CTS)/OT2 8.18 10.34 9.18 91.13
OT(ENT)/OT3 14.26 13.05 15.85 92.49
OT(GES)/OT4 69.09 78.37 69.21 93.94
OT(GYN)/OT5 11.58 10.46 9.42 92.77
OT(Others)/OT6 88.87 91.04 83.37 94.27
OT(OTO)/OT7 100.00 100.00 100.00 94.15
OT(PLS)/OT8 8.56 7.34 7.45 94.12
Table 4: Entity States and Efficiency – Comparing the 4 Models
Model 1 Model 2 Model 3
Average time in system (mins) 1237.80 1207.46 1291.56 1314.72
Average time in blocked state (mins) 874.34 852.37 920.46 864.70
Total number of exits 99.40 100.10 96.55 186.95
Total remaining in system 16.90 17.50 17.30 1.90
Total number of failed arrivals 138.95 141.85 141.90 102.5
Efficiency (%) 45.6 45.3 44.5 64.8
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Care Industry. Nanyang Technological
ARUN KUMAR is an Associate Professor of
System & Engineering Management at Nanyang
Technological University (NTU), Singapore. He
received his Ph.D. degree in Operations
Research from Virginia Tech. Prior to joining
NTU, he taught at State University of New
Jersey, USA for thirteen years.