44 Chapter 2 REVIEW OF LITERATURE, NEED, SCOPE, OBJECTIVES AND RESEARCH METHODOLOGY This chapter deals with the review of the existing literature that was relevant for the subject matter of the thesis. The first step in the research involved the collection of secondary data from all the possible sources that directly or indirectly focused on the main theme of the research study. Accordingly literature scan was undertaken. Efforts were made to prepare the list of relevant material and procure it so that conceptual clarity could be achieved. Secondary data was collected from various publications, journals, magazines, books, newspapers, institutional reports and internet etc. Leading on from that, the literature review seeks to lay a foundation for the current research. The gaps in previous studies were identified to uphold the need of this research. This chapter also deals with the scope, objectives, the methodology followed and limitations of the research. 2.1 REVIEW OF LITERATURE A lot has been written about simulation. A review of literature indicates three important phases of thinking and writing about the subject. The first phase, the time soon after the simulation concept formulation saw accumulation of literature on the concept crystallization and elaboration. Here the basic issue that attracted attention of scholars was to clarify what simulation is and what it is not. Once the consciousness began to emerge over the meaning and concept, the attention of thinkers and scholars shifted to its role in business processes, consequently the second phase of Business Process Simulation arrived. As the idea of Business Process Reengineering (BPR)
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Chapter 2
REVIEW OF LITERATURE, NEED, SCOPE, OBJECTIVES AND RESEARCH METHODOLOGY
This chapter deals with the review of the existing literature that was relevant for the
subject matter of the thesis. The first step in the research involved the collection of
secondary data from all the possible sources that directly or indirectly focused on the
main theme of the research study. Accordingly literature scan was undertaken. Efforts
were made to prepare the list of relevant material and procure it so that conceptual
clarity could be achieved. Secondary data was collected from various publications,
journals, magazines, books, newspapers, institutional reports and internet etc.
Leading on from that, the literature review seeks to lay a foundation for the current
research. The gaps in previous studies were identified to uphold the need of this
research. This chapter also deals with the scope, objectives, the methodology followed
and limitations of the research.
2.1 REVIEW OF LITERATURE
A lot has been written about simulation. A review of literature indicates three
important phases of thinking and writing about the subject. The first phase, the time
soon after the simulation concept formulation saw accumulation of literature on the
concept crystallization and elaboration. Here the basic issue that attracted attention of
scholars was to clarify what simulation is and what it is not. Once the consciousness
began to emerge over the meaning and concept, the attention of thinkers and scholars
shifted to its role in business processes, consequently the second phase of Business
Process Simulation arrived. As the idea of Business Process Reengineering (BPR)
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won appreciation of both business managers and scholars, the concern shifted to the
question as to how it can be adopted and implemented in business, what type of tools
have to be built so as to minimize the risk involved in full scale production. This era also
saw proliferation of simulation tools and thus the user got confused about which tool should be used
and why. Thus in the recent past, scholarly attitude has shifted to evaluation and selection of simulation
tools.
The three main components in the study are - business process simulation, business
process simulation tools and the evaluation & selection of business process simulation
tools, thus the following literature review has been divided into three sub-sections so
that the gap in each subsection may be clearly identified.
2.1.1 Business Process Simulation
The survey of the literature in this domain provides a list of reasons for the
introduction of simulation modelling into business process modelling.
Wild and Otis (1987) described the case of a company that considered the
implementation of 77 new machine tools, for a new production line. However, when
the operation was simulated, they found that 4 machines were actually needed,
presenting a total saving of $750 000. Such savings are not unusual when simulation
is used.
Curtis et al. (1992) suggested that a business process model must be capable of
providing various information elements to its users. Such elements include what
activities comprise the process, who is performing these activities, when and where
are these activities performed, how and why are they executed, and what data
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elements they manipulate. Simulation modelling techniques differ in the extent to
which their constructs highlight the information that answers these questions. To
provide this information, a modelling technique should be capable of representing one
or more of the following modelling perspectives:
§ Functional perspective: Represents what process elements (activities) are
being performed.
§ Behavioural perspective: Represents when activities are performed as well as
aspects of how they are performed through feedback loops, iteration, decision-
making conditions, entry and exit criteria, and so on.
§ Organizational perspective: Represents where and by whom activities are
performed, the physical communication mechanisms used for transfer of
entities, and the physical media and locations used for storing entities.
§ Informational perspective: Represents the informational entities (data)
produced or manipulated by a process and their relationships.
According to Busby and Williams (1993), business process models identify the
structure of the current operations and provide valued information on instituting a self
adjustment mechanism for process improvement. They also indicated that process
models permit process owners and managers to identify inadequate connections
between activities and information systems, duplications of activities, and the creation
of a macro model about cross functional interconnections.
Klein (1994) described the six categories of BPS:
§ Project management e.g. Microsoft Project.
§ Co-ordination, e.g. Microsoft Excel, Lotus Notes, and e-mail.
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§ Modelling, e.g. computer aided software engineering (CASE) tools such as
Popkin System Architect.
§ Analysis, e.g. the same tools as used for modelling.
§ Human resource allocation and design, e.g. assessment, teambuilding and
organization charting (CorelDRAW).
§ Systems development, e.g. Visual C++, and Borland Delphi.
Huckvale and Ould (1995) identified several objectives for which business process
simulation could be employed in a business process reengineering project. They
suggested that business process modelling could provide a means for discussing,
communicating, and analyzing existing processes, an avenue for designing new
processes, a baseline for continuing improvement, and a software program for
controlling processes. They argued that different purposes or objectives of using
processing modelling would require different modelling methods in terms of their
modelling properties and characteristics.
Levas et al. (1995) discussed some of business process modelling issues (such as
problem definition, data collection, socio-political issues, hierarchical and modular
modelling, granularity, integration and multi-perspective issues) needing attention in
BPR projects.
Swain (1995) stressed that simulation can be considered as a vital component in the
enterprise-wide modelling, in which processes once treated as separate functions (e.g.,
manufacturing, sales and design) can be modeled as a group and optimized as a
system.
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Harrel and Field (1996) argued that much of the process definition used in a
simulation model is contained in a process map, yet insufficient data are provided in a
process map for running simulation. Therefore additional information has to be
manually added on the simulation side.
Giaglis and Paul (1996) gave some modelling requirements specific to simulation-
assisted business renovation modelling which are as follows:
§ Processes need to be formally modelled and documented.
§ Modelling should take stochastic nature of business processes into account,
especially the way in which they are triggered by external factors.
§ There is a need to quantitatively evaluate the value of proposed
alternatives.
§ The evaluation is highly dependent on the objectives of the particular study.
§ Modelling tools should be easy to use to allow users of the processes to be
involved in the modelling process.
Greasley and Barlow (1998) identified several areas in business process simulation
projects where simulation modelling can be useful viz. identification of processes for
change, identification of change possibilities, identification of process vision,
understanding of current processes, design and prototyping of new processes.
Fathee et al. (1998) postulated that simulation is the only suitable technique for BPR
as business processes are too complex and confusing because of following reasons:
§ Many business processes are undetermined and include random variables.
§ Activities and resources that are main business process elements have
interactions.
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§ Business processes of organizations affect each other and are changed by
agents outside the organization.
Ackermann et al. (1999) demonstrated that discrete-event simulation can support
five most popular change management approaches. It could be claimed that modelling
provides an important means of discovering the essential aspects of the organizational
system where improvements will make a real difference in performance as well as
providing a sound basis for managing the consequences of the agreed actions.
Muthu et al. (1999) gave a structured approach to business process modelling. They
introduced a consolidated, systematic approach to the redesign of a business
enterprise. The methodology included the five activities i.e. Prepare for reengineering,
Map and analyze As-Is process, Design To-be process, Implement reengineered
process and Improve continuously.
Becker et al. (2000) stressed six principles that are important for business process
modelling: correctness, relevance, economic efficiency, clarity, comparability, and
systematic design. The principle of clarity is extremely subjective and postulates that
the model is understood by the model user. Clarity of models is especially important
when the objective of business process modelling is to facilitate human understanding
and communication or to support process improvement.
According to Irani et al. (2000) the reasons for the introduction of simulation
modelling into process modelling are: simulation allows for the modelling of process
dynamics, the influence of random variables on process development can be
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investigated, re-engineering effects can be anticipated in a quantitative way, process
visualization and animation are provided, and simulation models facilitate
communication between clients and an analyst. The final reason for using simulation
modelling is the fact that it can be increasingly used by those who have little or no
simulation background or experience.
McLean and Leong (2001) pointed that manufacturing simulation will have a major
impact on the way products are manufactured. Due to the high costs of acquisition,
integration, maintenance, limited interoperability, functionality, and performance —
simulation technology is not for everyone yet. Standard interfaces will increase the
functionality and reduce the costs of implementing this new technology. Their
research identified several sets of simulation interfaces that need to be standardized.
Yassine et al. (2001) presented a subjective assessment procedure for rework
probabilities used in project and process management simulation models, in general,
and in the Design Structure Matrix (DSM) simulation model. The assessment
proceeds in three phases: subjective evaluation of task variability and sensitivity,
mapping and calibration, and validation. The application example shows that the
probabilities required for simulating a DSM can be evaluated subjectively.
Furthermore, this assessment method can also be used to shed some light on
evaluating process improvement and reengineering efforts by defining two new
terminologies: reliability and robustness. They said that the goal of restructuring an
iterative process is not to break all iterative loops. Robustness obtained from iterative
task structures can be more valuable than the reliability obtained in a sequential
process structure.
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Lin et al. (2002) suggested that simulation is essential for business process change
projects, enabling two important functions: (1) to capture existing processes by
structurally representing their activities and related elements; and (2) to represent new
processes in order to evaluate their performance. In addition to these functions, a
business process modelling method should enable process evaluation and selection of
alternatives. They stressed that discrete event simulation seems to be an appropriate
method, and it offers a great potential in analyzing business processes.
Hlupic and Vuksic (2004) conducted a case study by engaging business process
modelling techniques. Based on the data presented from the modelling already
undertaken, a reengineered business process was proposed and refined. The effects of
reengineered model were created by performing "what if" analysis. In this phase of
the research a "prototype" of the “TO-BE” model was developed. The improvements
made in the process were evaluated presenting the simulation results to the managers
and end-users. The model was well accepted by both of them and management was
impressed enough to plan to make simulation modelling an integral part of its
business renovation plans.
Mahmudi and Tavakkoli (2004) discussed that there are two kinds of Business
Process Models: analytical (such as flowchart, spreadsheet, data flow diagram,
activity – role diagram, Integrated Computer-Aided Manufacturing and so on) and
simulation. They believed that the core reason for non-success in this area is the use
of analytical models (especially flowcharts and spreadsheets) for redesigning business
processes. To date, many researchers have identified this fact, but there is only a small
body of research to illustrate “why and how “simulation gives better results than
analytical models. They demonstrated by use of a case study that when you use more
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than two criteria for measuring and optimizing the business processes, the results of
analytical model might be non-optimal and even paradoxical.
Further they discussed that analytical tools don’t help analysts in the following areas:
§ Time variable properties of many processes
§ Time based processes (changing the state of system by time)
§ Non-linear relations between elements of processes
§ Randomness property of real processes
§ Unwanted events and occurrence in business environment
Hlupic et al. (2006) discussed the use of simulation for business process modelling
application to business process analysis and re-engineering projects. They argued that
simulation modelling tools are well suited for this purpose and that business
organizations can significantly benefit from a wider application of this approach.
They also argued that design of experiments or suitable search techniques can be
paired with simulation to maximize these benefits and overcome the inherent
limitations of simulation tools. Considering the important role of simulation in
business process modelling, it is critical to promote a wider diffusion of these tools
within the business community.
Popovic et al. (2006) said that process maps have all elements required for business
process simulation and that they have some other benefits, very important for business
process renovation. Business process models built by using process maps technique
can serve as a base for identifying information requirements and planning of
information system development projects. They are also very suitable for the
introduction of a workflow management system. They said that process maps can
serve as a foundation for IS modelling.
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With a simulation tool, we can take a dynamic picture of models. Some researchers
postulate that simulation is the only suitable technique for BPR because business
processes are too complex and confusing:
§ Many business processes are undetermined and include random variables.
§ Activities and resources that are main business process elements have
interactions.
§ Business processes of organizations affect each other and are changed by
agents outside the organization.
The various studies conducted by various researchers at different times on business
process simulation illustrated the advantage of business process simulation in various
areas. Some researchers suggested the capabilities that a business process model
should have. A few studies described the categories of business process simulation
and identified the various objectives for which business process simulation could be
employed. These studies also specified the principles that are important for business
process modelling.
2.1.2 Business Process Simulation Tools
Currently the market offers a variety of discrete-event simulation software packages.
Some are less expensive than others. Some are generic and can be used in a wide
variety of application areas while others are more specific. The following studies
focus on evolution, types and categories of simulation tools in the marketplace.
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Suri and Tomsicek (1990) described rapid modelling tools such as
ManuPlan/SimStarter, marketed by Network Dynamics Inc. The purpose of these
tools is to gain an idea about such measures of performance as throughput and
bottlenecks. The system is modelled in very general terms, omitting many of the
details in order to get an idea about the performance measures. In many instances, this
level of output is sufficient as it answers the questions that are being asked in a timely
manner.
Bhaskar et al. (1994) proposed a set of requirements that should be met by tools used
for modelling and simulation of business processes. These requirements can be
divided into five groups: process documentation, process redesign, performance
measurement, communication, and institutional learning. They have developed
BPMAT software package that can be used throughout a business process
reengineering effort. A major benefit that BPMAT affords through its ease of use is
that re-engineering practitioners can simulate the business process under investigation
and obtain quantitative measurements of key business processes (cost, cycle time,
resource utilization, etc.). This allows the evaluation of alternative process designs in
terms of quantitative criteria and the selection of the most promising ones for
implementation.
Greasley (1994) evaluated a number of tools for the redesign of processes through the
use of two case studies. There is a particular emphasis on the use of Business Process
Simulation in conjunction with Activity Based Costing and Activity Based Budgeting
within the context of a Business Process Reengineering approach. The use of a
balanced scorecard and marking guide can be used to identify suitable processes for
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redesign. A process map enables a study of the relationship between the activities that
form the process. The process map relates to the conceptual map in a simulation
study.
Aalst and Hee (1995) proposed high-level Petri nets as a tool for the modelling and
analysis of business processes. Petri nets have proved to be useful in the context of
logistics and production control. However, the application of these Petri nets is not
restricted to logistics and manufacturing, they can also be used to support business
process reengineering efforts. High-level Petri nets have a formal semantics. A Petri
net model of a business process is a precise and unambiguous description of the
behaviour of the modelled process. The precise nature and the firm mathematical
foundation of Petri nets have resulted in an abundance of analysis methods and tools.
Tumay (1995) gave an overview of business process simulation, described the
modelling and analysis considerations, and listed typical model input, simulation and
output requirements. The study provided a classification of simulation software
products to aid the user in understanding the business process simulation tools and
categorized the business process simulation tools into three categories: flow
diagramming based simulation tools, system dynamics based simulation tools and
discrete event based simulation tools. Further this study presented a simulation
exercise to illustrate the power and suitability of simulation for analyzing a business
process.
Wright and Burns (1996) said that many tools are available to stimulate, simulate
and support the business process modelling approaches, based on different
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methodologies and frameworks. They tended to describe the business process analysis
and modelling market as having three groups:
§ drawing packages with templates;
§ drawing packages with templates and some spreadsheet-like capabilities; and
§ the heavyweight business process analysis tools proper, akin to discrete event
simulators.
Bing and David (1997) examined the wide range of Business Process
Analysis/Modelling (BPA/M) tools available, and compared the features of 12
specific tools. They presented two case studies with examples of software tool
analysis results. The discussion addressed whether these tools met the needs of
managers of change and Business Process Re-engineering (BPR) initiatives, and
offered suggestions for future tool evolution. The focus is to highlight the differences
between the often lofty claims of tool vendors, and both the real needs of BPR
analysts and implementers, and the actual capabilities of the BPR tools.
Kettinger et al. (1997) conducted a survey of existing methodologies, tools, and
techniques for business process change and developed a framework to facilitate
referencing of tools and techniques that help in reengineering strategy, people,
management, and technology dimensions of business processes. Simulation is
mentioned as one of the modelling methods in this survey, and the authors identified a
need for more user-friendly multimedia process capture and simulation software
packages that could allow easy visualization of business processes and enable team
members to actively participate in modelling efforts.
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Law and McComas (1997) classified simulation tools into Languages and
Simulators. Thus, a Simulation Language is a software package that is general in
nature (in terms of the applications it can address) and where model development is
done by programming. The major advantage of a good simulation language is
modelling flexibility, whereas the major disadvantage is that programming expertise
is required. A manufacturing-oriented simulation language is one where the modelling
constructs are specifically oriented toward manufacturing or material handling. The
Manufacturing-oriented Simulator is a simulation package that is designed to model a
manufacturing system in a specific class of systems. This type of software has two
main characteristics: its orientation is toward manufacturing, and little or no
programming is required to build a model (relative to simulation languages).
Aguilar et al. (1999) stated that Business Process Simulation is a powerful tool
supporting analysis and design of business processes. The added value of simulation
is based on four factors:
§ Process performance analysis is improved by simulation-triggered
measurements.
§ Simulation (especially animation) is an effective tool to communicate process thinking and
process analysis results.
§ Simulation enables the migration towards dynamic models for business
processes.
§ Simulation provides essential decision support by anticipating changes.
Stemberger et al. (2003) discussed the level of information system modelling and
simulation modelling methods and tools integration in the conditions of dynamic e-
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business environment. They stressed the necessity for integrating simulation
modelling and information system modelling. The integrated BPM tools combine
formerly diverse areas of business process, IT, resource and financial modelling,
enabling the companies to form a complete view of their operations and providing a
framework for efficient development of robust and complete enterprise architecture.
Vreede et al. (2003) considered the suitability of Arena to simulate business
processes. They stated that a weak point in simulating business processes is the time
consuming and complicated process to create simulation models. They took
advantage of the possibility to develop their own template with predefined building
blocks, which they considered to be successful in several simulation studies they
carried out.
There are large numbers of simulation software available in the market and
researchers have made an attempt to describe many of these business process
modelling tools. Some researches proposed requirements that should be met by
business process simulation tools and based upon this they have divided them into
groups with specific characteristics.
2.1.3 Evaluation & Selection of Business Process Simulation Tools
Evaluation of simulation packages is not new. Many researchers have carried out
surveys on available packages for different purposes. However, there are only a
limited number of papers that describe methods to perform an evaluation and
selection of simulation packages. Some of the studies are:
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One of the best known early simulation software evaluation and comparison was
carried out by Tocher (1965). The simulation languages analyzed were: GPSS,
SIMPAC, SIMSCRIPT, SIMULA, CSL, ESP, GSP, MONTECODE and SIMON.
These languages are examined on the basis of the following groups of criteria: the
organization of time and activities in a simulation programming system, naming and
structure of entities and generalized activity specification, testing of conditions in
activities, test formation facilities, statistics collection procedures. It is estimated how
well the languages under consideration satisfy the criteria within each group.
Subsequently, each language is briefly described with an emphasis on its main
qualities and weaknesses. The languages evaluated have not been ranked nor were
particular ones recommended for use.
A comprehensive evaluation of fifteen simulation languages is provided by Cellier
(1983). Languages examined are ACSL, DARE-P, SIMNON, DYMOLA, SYSMOD,
FORSIM-IV, SIMULA 67, PROSM, SIMSCRIPT-II, GPSS_FORTRAN-II,
GPSS_FORTRAN-III, SLAM-II, GASP-V, GASP-VI and COSY. The evaluation
criteria are classified in six groups regarding expressiveness of the language,
numerical behaviour, structural features, status of implementation, portability, and
documentation. Features within each group are assessed according to their availability
and quality.
Haider and Banks (1986) addressed the issues relating to the choice of simulation
software products for the analysis of manufacturing systems and established the
following desirable features for simulation software: input flexibility; structural
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modularity; modelling flexibility; macro capability and hierarchical modelling;
materials handling modules; standard statistics generation; data analysis; animation;
interactive model debugging; micro/mainframe compatibility; the support provided by
the supplier; and the cost of simulation software.
Grant and Weiner (1986) analysed simulation software products such as BEAM,
Cinema, PCModel, SEE WHY and SIMFACTORY II.5, Modelmaster, RTCS with
the main emphasis on their graphical and animation features. The analysis was done
on the basis of the information provided by the vendors. The features examined are
grouped in three main groups. The first group i.e. simulation model building system
group includes the main orientation of the software and flexibility. The second group
which includes animation graphics related features determines the type of graphics
and animation. The last group i.e. criteria within the operational considerations
include the cost of the software, platforms on which software can be run and
determination of need for a specialized VDU. The authors did not comment on the
provided features of software tools. They concluded with a specification of general
trends regarding simulation software tools such as the implementation of software on
microcomputers, manufacturing oriented preprocessors, lower priced systems and
interactivity both for model building and model animation.
Deaver (1987) identified a need to thoroughly analyze system requirements before
selecting simulation software, as simulation packages vary widely in capability. Some
of the factors that should be considered prior to the selection of simulation software
include: identification of potential simulation users, consideration of future training
for employees, determination of types of systems to be simulated, analyzing the
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resources currently available and consideration of the amount of time that is to be
dedicated to simulation. In addition, several criteria are presented that can be used for
software evaluation. These criteria include features such as graphics, interaction,
statistical data gathering and analysis, flexibility, support provided by vendor, ability
for discrete-event and continuous-processes modelling.
Szymankiewicz et al. (1988) provided a list of features which manufacturing
simulators should possess. Some of these features include an effective user interface;
an implemented set of algorithms for sequencing production orders; interactivity; an
interface to external data sources; a mechanism to store all input and output data in a
database; and fast execution of simulation; coded algorithms; standard and user-coded
performance reports; storage of data used for model design in an external file; and
orientation to the design issues including randomness.
Bovone et al. (1989) proposed a simple three step method for the selection of
simulation software. The purpose of using this method is to obtain the weights which
can express the importance of software evaluation criteria with regard to the
simulation objectives. The applicability of this method is illustrated using the
following criteria: flexibility, learning and use, modelling speed, running speed, report
features, debugging, stochastic capacity, ease of transport, service and reliability.
Separate evaluation tables are constructed both for the conceptual design and for the
detailed design which emphasizes the importance of flexibility. On the basis of
evaluation of several simulation packages using this method, the authors concluded
that no product is superior to the others with regard to both software purposes.
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Ekere and Hannam (1989) presented an evaluation of the event, activity and
process-based approaches for modelling as well as an evaluation of three software
tools for simulation. They evaluated the simulation language SLAM, the program
generator CAPS/ECS, and the data driven simulation package HOCUS. The criteria
specified for the evaluation of software features are classified into four categories.
The first group relates to model characterization and programming, the second group
relates to model development features, third group relates to experimental and
reporting features and the fourth group relates to commercial and technical features.
Law and Haider (1989) provided a simulation software survey and evaluation on the
basis of information provided by vendors. Both simulation languages and simulators
such as FACTOR, MAST, WITNESS, XCELL + and SIMFACTORY 11.5 are
included in this study. Instead of commenting on the information presented about the
software, the authors concluded that there is no simulation package which is
completely convenient and appropriate for all manufacturing applications.
Kochhar and Ma (1989) addressed the essential and desirable features of simulation
software for its effective use in manufacturing environments, provided criteria which
should be used for the selection of manufacturing simulation software tools. These
criteria relate to: modelling assistance; interactivity; graphics; a data handling
capability; the time scale for model development; the learning curve and the required
skills for the use of software; ease of model editing; portability; simulation speed; and
interfacing the simulation package with external systems.
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Kochhar (1989) presented criteria for the assessment of manufacturing simulators.
These criteria include: the world views adopted by the simulator; modelling assistance
provided; interactive capability; animation facilities; a data handling capability;
learning curve; ease of use; portability of simulation software; simulation speed;
reliability and service; flexibility and facilities for data recording and output results.
Pidd (1989) provided some general advice regarding selection of discrete simulation
software. Concerning the assessment of vendors’ claims, the author warned of several
facts that the potential users should be sceptical about. For example, one should not
believe any vendor who claims that his product is better than everyone else’s for any
application or that the software can run on any computer under any operating system.
In addition, when asked about the support they can provide in case of problems
caused by bugs, the majority of vendors would probably deny the possibility of the
existence of bugs. Furthermore, the author claimed that the type of simulation
software to be chosen depends on the intended application. Finally, general advice for
simulation software selection is provided which includes: development of a
preliminary model of application, consideration of available resources and future
applications, examination of the available software and asking the vendors for
assistance if possible.
Breedam et al. (1990) conducted a survey in order to evaluate several simulation
software tools. They distributed a questionnaire to experienced users of simulation,
who were asked to rate a sample of simulation packages on the proposed criteria.
These criteria include flexibility, learning time, run-time observation, run-time
alterations, statistics, data input/output facilities, on-line analysis, animation, customer
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support, literature and price. On the basis of received answers, they classified the
evaluated software into clusters according to their main features, because there is no
package which sores highly on all the criteria. They proposed the use of these clusters
for the segmentation of the simulation software market.
Holder (1990) proposed a structured approach to selection of simulation software.
This approach suggests that software selection should commence with a consideration
of the available resources within the organization, and a determination of the
simulation objectives. Subsequently, the essential features of the software are to be
determined in order to eliminate software products that would certainly not be
suitable. This should result in a short-list of products that are to be evaluated using the
evaluation table provided. The table comprises evaluation features categorized in six
groups: technical features, user needs (system development), user needs (end user),
future development, functionality and commercial features. No weighing of the
proposed criteria is established. These criteria are to be used to determine whether the
products have the features required, and on the basis of this, a recommendation as to
which software seems to be most suitable is to be derived.
Bright and Johnson (1991) discussed that there are many Visual Interactive
Modelling (VIM) users and there is rapid widening in the range of VIM application
areas. They said that market pressures raise some questions in the developer’s mind
like what is the intrinsic nature of a problem etc. They discussed the intrinsic nature of
visual interactive modelling software. Three main features of this type of software
have been addressed: speed and adaptability; width of application; and ease of use.
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Banks (1991) made a classification of simulation modelling tools and discussed a
collection of features of simulation software. They provided guidance for selecting a
simulation modelling tool. They also described a technique to reduce the vast number
of simulation modelling tools to a manageable few. The selection of simulation
software depends on the problems to be solved as much as the characteristics of the
various modelling tools.
Banks et al. (1991) evaluated SIMFACTORY II.5, XCELL +, WITNESS and
ProModelPC by modelling two manufacturing systems. The criteria for the evaluation
are classified in five groups. The first group relates to the basic features such as
routes, schedules, capacities, downtimes or transporters. The robust features (within
the second group) include programming, conditional routing, part attributes, global
variables and interface to other software. The main results of the evaluation revealed
that SIMFACTORY II.5, WITNESS and ProModelPC are similar in their basic
features, whilst XCELL + does not model downtimes and requires the user to
construct transporters and conveyors from available elements. Those simulators that
were found to be similar differed in their operational procedures. Whilst in
SIMFACTORY II.5 and ProModelPC, the complete route is specified directly on the
screen, in WITNESS the user builds the route one step at a time when specifying
other characteristics. SIMFACTORY II.5 and XCELL + do not have robust features
whilst WITNESS and ProModelPC have most or all of them. Such conclusions were
obtained on the basis of twenty two criteria.
Williams and Trauth (1991) ranked 30 manufacturing software packages. Each
criterion was weighted from 0.1 to 1 and the features of each package were scored a
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number from 1 to 10. Finally Analytic Hierarchy Process (AHP) was used to find the
best of the top three packages found from previous evaluation. AHP showed that their
previous evaluation technique has been consistent with the top three packages.
Law and Kelton (1991) evaluated AutoMod II, ProModel, SIMFACTORY II.5,
WITNESS and XCELL +. The main strength of Automod II is considered to be its
three dimensional animation capability and a comprehensive set of material-handling
modules. On the other hand, this package has very limited statistical capabilities.
ProModel is regarded as one of the most flexible simulators currently available, due to
its programming-like constructs and its ability to call C or Pascal routines to model
complex decision logic. The greatest advantages of SIMFACTORY II.5 are its ease of
use and good statistical capabilities and the main shortcoming is its inadequate
modelling flexibility for certain manufacturing applications. WITNESS is regarded as
a very flexible manufacturing simulator due to its programming-like input/output
rules and actions but its main shortcoming is the lack of an easy mechanism for
making multiple replications of simulation. XCELL + is easy to learn and use with
menus being employed to place and connect predefined graphical representations of
system components but its statistical facilities are poor.
Pidd (1992) identified general principles for selecting discrete simulation software by
dividing these principles into three main groups. The first one is focused on computer
programming, covering the field of logical machines, machine code, assembly
languages, compilers and interpreters. The second group of principles analyses
different simulation executive approaches, model logic, distribution sampling, random
number generation and report generation. The last group of principles examines a
range of factors which should be considered when appraising DES software, such as:
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the type of application, the expectation for end-use, knowledge, computing policy and
user support.
Davis and Williams (1993) illustrated the evaluation and selection of simulation
software using the analytic hierarchy process method. They evaluated five simulation
software systems using this method in order to recommend suitable simulation
software for a U.K. company. The chosen criteria include: cost, comprehensiveness of
the system, integration with other systems, documentation, training, ease of use,
hardware and installation, and confidence related issues. An illustration of the main
phases of software evaluation and comparison using the analytic hierarchy process
method is provided. In the first stage, the criteria are ranked according to their relative
importance when selecting a simulation package. Several other steps follow, finally
producing an overall ranking for each package being evaluated. It is emphasized that
it is not possible to derive absolute measures of how well any package performs
against a given criterion. Only its relative performance compared to the other
packages can be obtained.
Mackulak and Savory (1994) carried out a questionnaire survey on the most
important simulation software features. The most important features identified
include: a consistent and user friendly user interface; database storage capabilities for
input data; an interactive debugger for error checking; interaction via mouse; a
troubleshooting section in the documentation; storage capabilities for simulation
models and results; a library of reusable modules of simulation code; and a graphical
display of input and output.
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Bradley et al. (1995) defined seven different categories to evaluate business process
simulation tools. The seven categories are as follows:
1. Tool capabilities, including a rough indication of modelling, simulation and
analysis capabilities.
2. Tool hardware and software, including, e.g., the type of platform, languages,
external links and system performance.
3. Tool documentation, covering the availability of several guides, online-help and
information about the learning curve of the tool.
4. User features: amongst others user friendliness, level of expertise required, and
existence of a graphical user interface.
5. Modelling capabilities, such as identification of different roles, model integrity
analysis, model flexibility and level of detail.
6. Simulation capabilities, summarizing the nature of simulation (discrete vs.
continuous), handling of time and cost aspects and statistical distributions.
7. Output analysis capabilities such as output analysis and BPR expertise.
Hlupic and Paul (1995) provided an evaluation of several manufacturing simulators.
During the evaluation not every single criteria within each group was examined,
because the aim was to generally perceive basic features of each simulator. Specific
features are probably going to change and be added to with new releases of the
simulators under consideration. A comparison of the evaluated simulators is provided.
The general quality of each group of criteria was ranked for each simulator. This
revealed that although all simulators belong to the same type of simulation software,
there is a variety of differences between them. In addition, none of the simulators
satisfies all criteria, and none is equally good for all purposes. Although some
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simulators are more comprehensive and flexible than others, a simulator that can suit
any manufacturing problem does not exist. At the same time those simulators that are
more robust and adaptable are usually more expensive and difficult to learn and use
properly. The fact that the selection of a piece of simulation software is a matter of
compromise between many factors is substantiated by this research. One of the most
important factors that determined which software is more suitable than others is its
intended purpose. Other factors to consider are financial constraints and subjective
factors such as individual preferences and previous experience in using simulation
software.
Aalst (1996) suggested three good reasons for using a Petrinet based workflow
management system which appears to be critical in large BPS projects. These reasons
are: (1) the existence of formal semantics despite the graphical nature, (2) the state
based diagrams instead of event based diagrams (as can be encountered in many
workflow products) and (3) the abundance of analysis techniques.
Banks and Gibson (1997) suggested some considerations to be made while
purchasing the simulation software like accuracy and detail, powerful capabilities,
fastest speed, demo-solution of problem, opinions of companies with similar
applications, attending the user group meetings.
According to Oakshot (1997), a range of features desired from a simulation tool are
modelling flexibility, ease of use, animation, general simulation functions (e.g. warm-
up period, multiple runs), statistical functions, interface with other software, product
help and support, price and expandability.
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Kalnins et al. (1998) presented the comparison of main Business Process
Reengineering (BPR) tools from the point of view of modelling languages supported
by them. One of the tools considered was the GRADE tool developed by IMCS LU.
The proposed comparison criteria are language support for the selected basic
modelling activities common to most BPR methodologies. The main emphasis was on
the semantic properties of modelling languages.
Nikoukaran et al. (1998) presented a comprehensive list of criteria structured in a
hierarchical framework for evaluating and selecting simulation software. Issues
related to criteria for simulation software evaluation and selections are categorized
into seven main groups and several sub-groups. The hierarchy can be used for
obtaining a better view of the features of simulation software and as a guide to test
and analyze simulation modelling packages. With the help of a suitable evaluation
technique, such as the Analytic Hierarchy Process (AHP), the hierarchy could be used
to evaluate simulation software. The software, the vendor and the user are the
important elements which form the elements of the highest level of the hierarchy.
Considering the process of modelling a problem using a simulation package, they
defined model and input, execution, animation, testing and efficiency and output.
Cheung and Bal (1998) said that process analysis tools are needed for business
improvement projects such as business process re-engineering. A variety of such tools
exists and software enabled ones are better than paper based ones, especially when
modelling processes are in dynamic situations where changes often occur. General
considerations for selecting a process analysis tool fall into six broad categories i.e.
hardware and software features, user features, modelling capabilities, simulation
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capabilities, analysis capabilities and integration capabilities. An emerging IT
management practice where the system is determined before business analysis
prevents integration problems but this may be restrictive where innovation is involved
as in the product introduction process.
Hlupic and Paul (1999) presented criteria for the evaluation of simulation packages
in the manufacturing domain together with their levels of importance for the particular
purpose of use. They suggested general guidelines for software selection. They
pointed that to expect a particular package to satisfy all criteria. However, it is
indicated which criteria are more important than others, according to the purpose of
software use. These guidelines can be used both by users that are looking for a
suitable simulator to buy, and by developers of such simulators who wish to improve
existing versions of simulators, or who wish to try to develop a new and better
manufacturing simulator.
Nikoukaran et al. (1999) created a framework of criteria to be considered when
evaluating discrete-event simulation software. This framework is structured, and pays
attention to a rich set of criteria on which simulation packages can be compared. It is,
however, difficult to base a decision for a large multinational company on these
criteria, as it is only a comparison, without weighing and without a method to
determine the relative weights, and the weight differences between parts of the
simulation team.
Hommes and Reijswoud (2000) developed a framework for the evaluation and
selection of business process modelling tools. They proposed eight evaluation criteria,
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which can be divided into two groups: one related to the conceptual modelling in
general and another group related to the business process modelling in particular.
They refered to the quality of the way of modelling and the way of working of a
modelling tool respectively. These criteria are:
§ Expressiveness - the degree to which a given modelling tool is capable of
denoting the models of any number and kinds of application domains;
§ Arbitrariness - the degree of freedom one has when modelling one and the
same domain;
§ Suitability - the degree to which a given modelling technique is specifically
tailored for a specific kind of application domain.
§ Comprehensibility - the ease with which the way of working and way of
modelling are understood by the participants;
§ Coherence - the degree to which the individual sub-models of a way of
modelling constitute a whole;
§ Completeness - the degree to which all necessary concepts of the application
domain are represented in the way of modelling;
§ Efficiency - the degree to which the modelling process utilizes resources such
as time and people;
§ Effectiveness - the degree to which the modelling process achieves its goal.
Law and Kelton (2000) described desirable software features for the selection of
general purpose simulation software. They identified the following groups of features:
§ General capabilities, including modelling flexibility and ease of use
§ Hardware and software considerations
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§ Animation, including default animation, library of standard icons, controllable
speed of animation, and zoom in and out
§ Statistical capabilities, including random number generator, probability period,
and specification of performance measures
§ Customer support and documentation
§ Output reports and plots, including standard reports for the estimated
performance measures, customization of reports, presentation of average,
minimum and maximum values and standard deviation, storage and export of
the results, and a variety of (static) graphics like histograms, time plots, and
pie charts
Perera and Liyanage (2001) stressed that the ability to develop and deploy
simulation models quickly and effectively is far more important than ever before. As
process modelling is very much a business rather than technical role, a modelling tool
must be simple to use by a non-technical business user. However, a number of factors
such as inefficient data collection, lengthy model documentation and poorly planned
experimentation prevent frequent deployment of simulation models.
Tewoldeberhan et al. (2002) proposed a two-phase evaluation and selection
methodology for simulation software selection. Phase one quickly reduces the long-
list to a short-list of packages. Phase two matches the requirements of the company
with the features of the simulation package in detail. Different methods are used for a
detailed evaluation of each package. Simulation software vendors participate in both
phases.
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Becker et al. (2003), in order to manage the increasing complexity of business
processes, have formulated six main quality criteria for business process models.
These criteria are:
§ Correctness: the model needs to be syntactically and semantically correct.
§ Relevance: the model should not contain irrelevant details.
§ Economic efficiency: the model should serve a particular purpose that
outweighs the cost of modelling.
§ Clarity: the model should be (intuitively) understandable by the reader.
§ Comparability: the models should be based on the same modelling
conventions within and between models.
§ Systematic design: the model should have well-defined interfaces to other
types of models such as organizational charts and data models.
Melao and Pidd (2003) conducted a survey of practitioners asking how and why BPS
is used in practice. The survey revealed that users want tools that are not only easy-to-
use, but also flexible enough to tackle different application areas and complex human
behaviour. Survey revealed a low usage of simulation in the design, modification and
improvement of business processes. It confirms that BPS projects are typically short,
relatively non-technical, and rely on good project management for their success. Most
BPS users employ general purpose simulation software rather than purpose-designed
business process simulators. There is no evidence of a skills gap, rather a feeling that
there is no net gain from employing simulation methods when simpler methods will
suffice. The results of the survey have implications for four groups of people i.e.
Practitioners, Educators, Researchers and Software developers.
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Seila et al. (2003) presented a framework for choosing simulation software for
discrete event simulation. By evaluating about 20 software tools, the proposed
framework first tried to identify the project objective, since a common understanding
of the objective will help frame discussions with internal company resources as well
as vendors and service providers. It is also prudent to define long-term expectations.
Other important questions dealt with model dissemination across the organization for
others to use, model builders and model users, type of process (assembly lines,
counter operations, material handling) the models will be focused, range of systems
represented by the models etc.
Popovic et al. (2005) developed criteria that can help experts in a flexible selection of
business process management tools. They classified the simulation tools selection
criteria in seven categories: model development, simulation, animation, integration
with other tools, analysis of results, optimization, and testing and efficiency. The
importance of individual criteria (its weight) is influenced by the goal of simulation
project and its members (i.e., simulation model developers and model users).
Pidd and Carvalho (2006) suggested that typical simulation package should provide
the following:
§ Modelling tools: a graphical modelling environment, built-in simulation
objects with defined properties and behaviour, sampling routines, property
sheets and visual controls
§ Tools to execute the simulation: a simulation executive to run a model,
animated graphic, virtual reality representation and user interaction with the
simulation as it runs
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§ Tools to support experimentation: tools to define run lengths and parameters,
analysis tools to enable optimization, results interpretation and presentation
§ Links to other software (links to spreadsheets, databases, ERP systems).
Vullers and Netjes (2006) in a study, discussed a number of simulation tools that are
relevant for the Business Process Management (BPM) field, and evaluated their
applicability for Business Process Simulation (BPS). They evaluated the tools on their
modelling capabilities, simulation capabilities and possibilities for output analysis.
Many researchers have carried out surveys on available packages for different
objectives. Many researchers have compared simulation languages and classified
them into groups and have compared them depending on criteria chosen. List of
features which simulation software should possess have been provided and have
proposed different methods for the selection of simulation software.
Borrowing from past literature, it can be summarized that though there is a rapidly
growing body of literature on how to implement Business Process Simulation (BPS)
projects (Grover et al., 1994, 1995; Hammer and Champy, 1993; Tumay, 1995) and
how to manage the radical changes brought about by BPS (Davenport, 1993; Stoddard
and Jarvenpaa, 1995) but relatively few studies are there that have examined how to
effectively conduct evaluation and selection process of simulation software (Mayer et
al., 1995; Hlupic and Vuksic, 2004). Many simulation software tools are available in
the market (Wright and Burns, 1996) thus the need for having an efficient selection
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method for simulation packages is increasing as the simulation application domain
broadens.
A recapitulation of the findings drawn from the background research material
analysis, that supports the objectives of research of this thesis, is as follows:
1. In software evaluation, many factors are to be assessed, and their significance
weighted in order to evaluate, compare and select adequate software.
2. Although several studies provide criteria for the evaluation of simulation
packages, these criteria are not as comprehensive asthose are provided in this
thesis.
3. An analysis of the studies that provide evaluation criteria or desirable features of
simulation software reveals that these features mainly relate to graphics and
animation, interactivity, modelling flexibility, ease of use, ease of learning,
modelling assistance, portability, execution speed and price.
4. Several evaluation studies are based on information provided by vendors, and are
lacking criticism.
5. Although some of the evaluation studies consider WITNESS, SIMFACTORY
II.5, XCELL+ and ProModelPC, none of these evaluations and comparisons is
comprehensive.
6. None of the research studies have found that a particular simulation package is
superior to other packages for any purpose of simulation and for any application.
7. Many studies have recognized the importance of software selection. For example,
one of the studies (Haider and Banks, 1986) claims that “selection of an
appropriate simulation software product can make a significant difference in how
well simulation analyses support managerial decision making”.
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8. Although the majority of the survey studies investigate issues related to simulation
software, none of them examine users’ opinions about possible ways to improve
software.
9. As the existing simulation packages are continuously being revised, and new ones
are being released in the market, the significance of some of the earlier studies is
diminished.
10. A final conclusion has been drawn from the literature review, which justifies the
objectives of this research. Due to the intensive use of simulation in
manufacturing environment, and the costs involved in purchasing simulation
software, hardware and training, further research in software evaluation,
comparison and selection seems to be needed and applicable.
2.2 NEED AND SCOPE OF THE STUDY
An increase in the use of simulation as a modelling and analysis tool has resulted in a
growing number of simulation software products in the market. This has been caused
by the greater complexity of automated systems, reduced computing costs brought
about by microcomputers and engineering workstations, improvements in simulation
software which have reduced model development time, and the availability of
graphical animation which has resulted in greater understanding and use of simulation
by engineering managers. Increased interest in simulation has, in turn, led to an
explosion in the number of simulation packages. As a result, a person trying to select
simulation software for his organization is now faced with a bewildering variety of
choices in terms of technical capabilities, ease of use and cost. A person new to the
field of simulation modelling could literally spend three or more months carefully
evaluating software for a particular simulation project.
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As time is money, by reducing the time taken to evaluate packages we can reduce the
cost of evaluation. Also the repeated advocation of need to evaluate simulation tools
in literature is not backed up by any significant empirical work.
Some other specific needs of the study:
§ To address the previously noted gaps in our knowledge regarding body of
simulation and simulation tools.
§ Some studies have come up with useful implications. However such
implications cannot be generalized to automobile industry.
§ The need to undertake manufacturing industry specific study is to remove the
shortcomings like intra group heterogeneity with other sectors like services etc.
§ Inadequate research evidence in India.
The scope of the study has been limited to Automobile Manufacturers in North India.
The study will help automobile manufacturers in best selection of simulation
software. Also, the study will be beneficial for simulation software developers
because the study will provide a comparison of simulation software and the developer
companies can evaluate their software and can take necessary step to improve the
software at lacking points.
2.3 OBJECTIVES OF THE STUDY
The aim of this research is to define the main simulation features of business process modelling tools.
The results of the theoretical findings from the review of literature will be used to develop the
criteria/guidelines that can help managers and IT experts in making a flexible and customized selection
of business process modelling tools depending on their simulation features. Further through an
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empirical research the present simulation tools used by the automobile industry will be evaluated
against the developed criteria. An open source software will be developed for the purpose of providing
support for users when selecting simulation software.
To fulfill the above-mentioned need, the specific objectives of the study are:
1. To study the features of the commonly used simulation tools used by
automobile manufacturers.
2. To develop a framework for evaluation of simulation tools used by automobile
industry in India.
3. Define a set of criteria, for comparing different simulation tools.
4. To develop guidelines for the selection of simulation tools.
5. To verify empirically the state of simulation tools used currently in Indian
Automobile Industry in comparison to the developed framework.
6. To develop an open source software for the purpose of providing support for
users when selecting simulation software.
2.4 LIMITATIONS OF THE STUDY Like most other studies, this study also does not offer limitationless explanation of the
issues under study. This study has been carried out under indispensable constraints of
time and other resources. Through extensive literature review, an effort to integrate all
available literature was made yet understanding and findings may have been
constrained by the vision of the researcher.
There are so many simulation tools available in the market but it is not possible to
study them all. The simulation tools used by automobile industry in North India have
been studied. The other limitation of the study is with respect to the generalization of
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the results. Although the manufacturing units included in sample are operating all
over North India, yet, the results cannot be generalized to the whole universe of
manufacturing organizations because the sample consists of only automobile
manufacturers.
2.5 RESEARCH DESIGN AND METHODOLOGY Research design is a set of advance decisions that make up the master plan specifying
the methods and procedures for collecting and analyzing the needed information
(Malhotra, 2004). The research design has been defined as a blue print of the research
work that indicates the draft for the methodology for data collection, the instrument of
the research, the method of sampling and analysis etc (Ramaswamy & Namakumari,
2002). In the words of James Findley, Executive Vice President, Testing Services Info
Resources Inc. “The essence of research design is quite simple to know the business
issues; determine what you need to learn; select the right tools; and establish clear
action standards”.
For the present study descriptive research design was selected. Descriptive research
design implies natural observation of the characteristics of the research subject
without any deliberate manipulation of the variables or control over the research
settings. Regarding the choice between a longitudinal design and a cross-sectional
design the latter was considered the best option considering time and cost constraints.
A cross-sectional design is a type of research design involving the collection of
information from a given sample of population elements only once (Malhotra, 2007)
whereas longitudinal design involves a fixed sample of population elements that is
measured repeatedly on the same variables (Malhotra, 2007a). The present study is
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descriptive in nature and the cross sectional research design was used, which involved
the following steps:
2.5.1 Survey of Secondary Sources
The researcher makes use of both secondary as well as primary data. In order to get a
complete understanding of the simulation concept, simulation software tools and their
applications, the secondary data is essential. Therefore the foremost step in the
research involved the collection of secondary data from all possible sources that
directly or indirectly focus on the main theme of the research study. Accordingly a
literature scanning was undertaken. Efforts were made to prepare a comprehensive list
of relevant material and procure them so that conceptual clarity could be achieved.
Secondary data was collected from following listed publications, journals, magazines,
books and statistical reports etc.
§ Journal of Integrated Manufacturing Systems
§ Journal of Simulation
§ Journal of Industrial Engineer
§ Journal of Business Process Management
§ Journal of Simulation Practice and Theory
§ Journal of Manufacturing Systems
§ ACM Journal of Transactions on Modelling and Computer Simulation
§ International Journal of Simulation Systems, Science & Technology
§ International Journal of Manufacturing System Design
§ International Journal of Advanced Manufacturing Technology
§ International Journal of Flexible Manufacturing Systems
§ International Journal of Management and Systems
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§ International Journal of Modelling and Simulation
§ International Journal of Intelligent Games and Simulation
§ MIS Quarterly
§ IEEE Software
§ CIM Review
§ OR/MS Today
§ Simulation Technology Magazine
Secondary data helped in identification of all relevant features of simulation software
and in the design of the instrument, which was administered on the sample companies
to collect relevant primary data for the research.
2.5.2 Collection of Primary Data
Primary data, which is of immense importance and backbone to the study, has been
obtained from the respondents with the help of widely used and well known method –
sample survey, utilizing fully structured questionnaire. The survey method of
obtaining information is based on the questioning of respondents. The respondents for
the study will be middle level managers in Computer Aided Engineering (CAE),
Testing, R & D, Designing departments and Product designers etc.
2.5.3 The Study Population
The aim of the study was to carry out the objectives on automobile industry in North
India. The automobile industry was chosen for the study so that the existing body of
knowledge on functioning of automobile firms in an era of globalization, particularly
in developing economy can be expanded. The scope of this research has to be
narrowed to one industry: automobile industry and one part of country: North India,
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so that the simulation software used and factors relating to successful use of
simulation software in automobile firms operating in India comes into sharp focus.
2.5.4 Sample Size and Response
In this study, survey method was used to collect primary data from the respondents. In
survey method, a structured questionnaire is given to a sample of a population and
designed to elicit specific information from respondents. A sample is a subgroup of
the population selected for participation in the study (Malhotra, 2007b). The unit in
the study is the automobile manufacturer who is registered as members with Society
of Indian Automobile Manufacturers (SIAM). SIAM is the apex body representing all
the major automobile manufacturers in India, thus the sampling frame for the sample
is the SIAM membership roster. The judgment used for selection of companies
registered with SIAM was necessitated by the need that the data on performance and
other relevant information of each company required for research was available with
SIAM.
In order to reach out to respondents, a combination of personal, mail and online
method was employed. One questionnaire was mailed to each organization. The
questionnaire was uploaded on www.simvehic.com. The survey was completed within
six months. Later e-mail method along with telephonic reminders was used in order to
improve the response rate. Also, few of the organizations were approached in person.
In total, 20 companies were contacted for the purpose of the study. Finally, a total of
40 filled questionnaires were obtained from 18 companies, constituting an overall
response rate of approximately 90 percent. So the present study is based on the
findings of these 18 companies.
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2.5.5 The Research Instrument
Primary data in the form of the response of automobile companies, which was of
immense importance and backbone of the study, was obtained from respondents with
the help of widely used and well known method, survey, utilizing fully structured
questionnaire.
2.5.5.1 Development of the Instrument
The instrument used for this study was prepared after a thorough review of literature
and accordingly the procedure recommended by many researchers for development of
better evaluation criteria for simulation software construct was followed. The
procedure involved steps shown in the Figure 2.1.
The first step was to develop a clear understanding of what exactly falls within the
construct domain and what does not. To draw the boundaries of what is to be included
and what is to be omitted. A review of all major conceptual literature on evaluation
and selection of simulation software was undertaken.
Second step in scale development process was to tap and develop a large pool of
items pertaining to each variable falling within the construct. This was accomplished
through literature survey and opinion survey. At this stage in order to establish face
validity of the construct these items were presented to the academicians in field of
simulation, simulation software developers and simulation software users in
automobile industry so that one can be sure of that what we are measuring is what we
think we are measuring. On their suggestions some items, which scored lower in
value in terms of consistency, were deleted while some additional items were added.
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After finalizing the items, these items were scaled. Likert scale was employed to the
items used in questionnaire utilized for the study.
Source: Verma (2007)
Figure 2.1: Steps in Instrument Development Process The next step was pre-testing of preliminary questionnaire. For clarity and ease of
response a pilot study was conducted in which the instrument was administered on the
15% of the respondents. Respondents were asked to complete the questionnaire so
that potential difficulties and ambiguities, still remaining could be eliminated. On
receiving their suggestions, the draft was further improved in light of presentation of
matter, and the appropriateness of the language of questions. This is how the final
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instrument was developed for the use in this study.
2.5.5.2 Validity of the Instrument
Validity of an instrument is defined as the characteristic of an instrument wherein it
truly measures what it was intended to measure and the difference in observed scores
using the instrument reflects the true difference on the characteristic that is being
measured. There are many facets and dimensions of the concept of the validity
(Cronchback, 1971).
Face Validity implies that the instrument appears to be valid. This is the apparent
validity of the instrument by which the instrument appears seemingly right to the
reader. Since the validity of the instrument goes much beyond the appearance or the
face, the concept of face validity is now integrated with the content validity. Content
validity implies that the items in the instrument pertain to and are true representative
of the subject about which the opinion is being sought (Lacity and Jansen, 1994).
Content validity sometimes called face validity, is a subjective but systematic
evaluation of how well the content of a scale represents the measurement task at hand
(Malhotra, 2007c).
In the present study multiple items for the questionnaire were developed that
characterized the features of simulation software. The face and content validity was
first done by extensive review of the relevant literature. Subsequently these items
were submitted to various experts of simulation in industry for evaluation. They rated
each item for its consistency and also recommended additional items for the inclusion.
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2.5.5.3 Administration of the Instrument
After assessing the validity, the final instrument was uploaded on
www.simvehic.com. However, in spite of necessary telephonic follow-ups, the
response rate was very low. In order to improve the response rate, questionnaire was
e-mailed along with repeated telephonic reminders. Further, for the convenience of
the respondents and to save their time, a personal visit to respondent’s site was
arranged and responses were collected personally. The filled in questionnaire were
then checked for completeness and were then analysed.
REFERENCES
Aalst, W. M. P. V. (1996). Three Good Reasons for Using a Petri-net-based
Workflow Management System. In S. Navathe, & T. Wakayama (Eds.),
Proceedings of the International Working Conference on Information and Process
Integration in Enterprises (pp. 179-201). Camebridge, Massachusetts: Kluwer
Academic Publishers.
Aalst, W. M. P. V., & Hee, K. M. V. (1995). Framework for Business Process
Redesign. In J. R. Callahan (Ed.), Proceedings of the Fourth Workshop on
Enabling Technologies: Infrastructure for Collaborative Enterprises (pp. 36-47).
Berkeley Springs: IEEE Computer Society Press.
Ackermann, F., Walls, L., Meer, R., & Borman, M. (1999). Taking a Strategic View
of BPR to Develop a Multidisciplinary Framework. Journal of the Operational
Research Society, 50: 195-204.
Aguilar, M., Rautert, T., & Pater, A. J. G. (1999). Business Process Simulation: A
Fundamental Step Supporting Process Centered Management. In P. A. Farrington,
H. B. Nembhard, D. T. Sturrock, & G. W. Evans (Eds.), Proceedings of the 1999
Winter Simulation Conference (pp. 1383-1392). Phoenix: IEEE Computer Society
Press.
Banks, J. (1991). Selecting Simulation Software. In B. L. Nelson, W. D. Kelton, & G.
M. Clark (Eds.), Proceedings of the 1991 Winter Simulation Conference (pp. 15-
20). Washington, DC: IEEE Computer Society.
89
Banks, J., Aviles, E., McLaughlin, J. R., & Yuan, R. C. (1991). The Simulator: New
Member of the Simulation Family. Interfaces, 21(2): 76-86.
Banks, J., & Gibson, R. R. (1997). Selecting Simulation Software. IIE Solutions,
29(5): 30-32.
Becker, J., Kugeler, M., & Rosemann, M. (2003). Process Management - A Guide for
the Design of Business Processes. Berlin: Springer-Verlag, p. 37.
Becker, J., Rosemann, M., & Uthmann, C. (2000). Guidelines of Business Process
Modelling. In W. V. Aalst, J. Desel, & A. Oberweis (Eds.), Business Process
Management, Models, Techniques, and Empirical Studies (pp. 241-262). London:
Springer-Verlag.
Beri, G. C. 2008. Marketing Research (4th Ed.). New Delhi: Tata McGraw-Hill, p.
561.
Bhaskar, R., Lee, H. S., Levas, A., Petrakian, R., Tsai, F., & Tulskie, B. (1994).
Analyzing and Reengineering Business Processes using Simulation. In M. S.
Manivannan, & J. D. Tew (Eds.), Proceedings of the 26th Winter Simulation
Conference (pp. 1206-1213). San Diego: Society for Computer Simulation
International.
Bing, Y., & David, T. W. (1997). Software Tools Supporting Business Process
Analysis and Modelling. Journal of Business Process Management, 3(2): 133-150.
Bovone, M., Ferrari, D. V. and Manuelli, R. (1989). How to Choose an Useful
Simulation Software. In D. M. Smith, J. Stephenson, & R. N. Zobel (Eds.),
Proceedings of the 1989 European Simulation Multiconference (pp. 39-43). SCS,
San Diego: Society for Computer simulation International.
Bradley, P., Browne, J., Jackson, S., & Jagdev, H. (1995). Business Process
Reengineering (BPR)– A Study of the Software Tools Currently Available.
Computers in Industry, 25(3): 309-330.
Breedam, A. V., Raes, J., & Velde, K. V. D. (1990). Segmenting the Simulation
Software Market. OR Insight, 3(2): 9-12.
90
Bright, J. G., & Johnson, K. J. (1991). Whether VIM? – A Developer’s View.
European Journal of Operational Research, 54: 357–362.
Busby, J. S., & Williams, G. M. (1993). The Value and Limitations of Using Process
Models to Describe the Manufacturing Organizations. International Journal of
Production Research, 31(9): 2179-2194.
Cellier, F. E. (1983). Simulation Software: Today and Tomorrow. In J. Burger, & Y.
Jarny (Eds.), Proceedings of the IMACS International Symposium (pp. 3-19),
Amsterdam: Elsevier Science Publishers.
Cheung, Y., & Bal. J. (1998). Process Analysis Techniques and Tools for Business
Improvements. Journal of Business Process Management, 4(4): 274-290.
Cronchback, L. J. (1971). Test Validation. In R.L. Thorndike (Ed.), Educational
Measurement (p. 20). Washington DC: American Council of Education.
Curtis, W., Kellner, M. I., & Over, J. (1992). Process Modelling. Communications of
the ACM, 35(9): 75-90.
Davenport, T. H. (1993). Process Innovation: Reengineering Work Through
Information Technology. Boston: Harvard Business School Press, p.114.
Davis, L., & Williams, G. (1993). Evaluating and Selecting Simulation Software
using the Analytic Hierarchy Process. Integrated Manufacturing Systems, 5(1):
23-32.
Deaver, R. A. (1987). Selecting a Manufacturing Simulation System. CIM Review,
3(3) : 6-8.
Ekere, N. N., & Hannam, R. G. (1989). An Evaluation of Approaches to Modelling
and Simulating Manufacturing Systems. International Journal of Production
Research, 27(4): 599-611.
91
Fathee, M. M., Redd, R., Gorgas, D., & Modarres, B. (1998). The Effects of
Complexity on Business Processes Reengineering: Values and Limitation of
Modelling and Simulation Technologies. In D.J. Medeiros, E. Watson, S.
Manivannan, & J. Carson (Eds.), Proceedings of 1998 Winter Simulation
Conference (pp. 1339-45). Washington, D.C.: IEEE Computer Society Press.
Giaglis, G. M., & Paul, R. J. (1996). It’s Time to Engineer Re-Engineering:
Investigating the Potential of Simulation Modelling in Business Process Redesign.
In S. Reiter, & E. Stickel (Eds.), Business Process Modelling (pp. 313-32). Berlin:
Springer-Verlag.
Grant, J. W., & Weiner, S. A. (1986). Factors to Consider in Choosing a Graphical
Animated Simulation System. Industrial Engineer, 31: 65-68.
Greasley, A. (1994). Effective uses of Business Process Simulation. In J. A. Joines
(Ed.), Proceedings of 2000 Winter Simulation Conference (pp. 2004-09). New
York: Association for Computing Machinery.
Greasley, A., & Barlow, S. (1998). Using Simulation Modelling for BPR: Resource
Allocation in a Police Custody Process. International Journal of Operations &
Production Management, 18(9): 978-988.
Grover, V., Fiedler, K.D. and Teng, J.T.C. (1994). Exploring the Success of
Information Technology Enabled Business Process Reengineering. IEEE
Transactions on Engineering Management, 41(3): 276-84.
Haider, S. W., & Banks, J. (1986). Simulation Software Products for Analyzing
Manufacturing System. Industrial Engineer, 12: 98–103.
Hammer, M., & Champy J. (1993). Reengineering the Corporation: A Manifesto for
Business Evolution. New York: Harper Business, p. 50.
92
Harrel, C. R., & Field, K. C. (1996). Integrating Process Mapping and Simulation. In
J. Charnes, D. Morrice, J. J. Swain, & D. Brunner (Eds.), Proceedings of the 1996
Winter Simulation Conference (pp. 1292-96). Los Alamitos: IEEE Computer
Society.
Hlupic, V., & Paul, R. J. (1995). A Critical Evaluation of Four Manufacturing
Simulators. International Journal of Production Research, 33(10): 2757 – 2766.
Hlupic, V., & Paul, R. J. (1999). Guidelines for Selection of Manufacturing
Simulation Software. IIE Transactions, 31(1): 21-29.
Hlupic, V., Vreede, G. J., & Orsoni, A. (2006). Modelling and Simulation Techniques
for Business Process Analysis and Reengineering. International Journal of
Simulation, 7(4): 1-8.
Hlupic, V., & Vuksic, V. B. (2004). Business Process Modelling using Simul8. In G.
Lipovszki, & I. Molnar (Eds.), Proceedings of 16th European Simulation
Symposium (pp. 127-138). Hungry: The Society for Modelling and Simulation
International.
Holder, K. (1990). Selecting Simulation Software. OR Insight, 3(4): 19-24.
Hommes, B., & Reijswoud, V. (2000). Assessing the Quality of Business Process
Modelling Techniques. In R. H. Spraque (Ed.), Proceedings of the 33rd Hawaii
International Conference on System Sciences (pp. 1-10). Washington, D.C.: IEEE
Computer Society.
Huckvale, T., & Ould, M. (1995). Process Modelling - Who, What and How: Role
Activity Diagramming. In V. Grover, & W. J. Kettinger (Eds.), Business Process
Change: Reengineering Concepts, Methods and Technologies (pp. 330-349).
London: Idea Group Publishing.
93
Irani, Z., Hlupic, V., Baldwin, L. P., & Love, P. E. D. (2000). Re-engineering
Manufacturing Processes through Simulation Modelling. Logistics Information
Management, 13(1): 7-13.
Kalnins, A., Kalnina, D., & Kalis, A. (1998). Comparison of Tools and Languages for
Business Process Reengineering. In D. H. Withers, & R. N. Zobel (Eds.),
Proceedings of the Third International Baltic Workshop on Databases and
Information Systems (pp. 24-38). Washington, D.C.: IEEE Computer Society.
Kettinger, W. J., Teng, J. T. C., & Guha, S. (1997). Business Process Change: A
Study of Methodologies, Techniques, and Tools. MIS Quarterly, 21(1): 55-80.
Klein, M. M. (1994). Reengineering Methodologies and Tools. Information System
Management, 8(2): 30-35.
Kochhar, A. K. (1989). Computer Simulation of Manufacturing Systems – 3 Decades
of Progress. In J. Stephenson, D. J. M. Smith, & R. N. Zobel (Eds.), Proceedings
of the 3rd European Simulation Congress (pp. 3-9). San Diego: Society for
Computer Simulation International.
Kochhar, A. K., & Ma, X. (1989). Discrete Event Simulation Software Tools for the
Simulation of Advanced Manufacturing Systems. In G. Lazeolla, A. Lehmann, &
H. J. D. Herik (Eds.), Proceedings of the 1989 European Simulation Conference
(pp. 13–18). San Diego: Society for Computer Simulation International.
Lacity, M., & Jansen, M. A. (1994). Understanding Qualitative Data: A Framework of
Text Analysis Methods. Journal of Management Information System, 11(2): 137-
155.
Law, A. M., & Kelton, W. D. (2000). Simulation Modelling and Analysis. New York:
McGraw-Hill, p. 87.
94
Law, A. M., & Kelton, W. D. (1991). Simulation Modelling and Analysis. Singapore:
McGraw-Hill, p. 95.
Law, A. M., & McComas, M. G. (1997). Simulations of Manufacturing Systems. In
S. Andradhttir, K. J. Healy, D. H. Withers, & B. L. Nelson (Eds.), Proceedings of
the 1997 Winter Simulation Conference (pp. 86-89). Norwell, MA: Kluwer
Academic Publishers.
Law, A. M., & Haider, S. W. (1989). Selecting Simulation Software for
Manufacturing Applications: Practical Guidelines and Software Survey. Journal of
Industrial Engineering, 21(5): 33-46.
Levas, A., Jain, P., Boyd, S., & Tulskie, W. (1995). Panel Discussion on the Role of
Modelling and Simulation in Business Process Reengineering. In C. Alexopoulos,
K. Kang, D. Goldsman, & W. Lilegdon (Eds.), Proceedings of the 1995 Winter
Simulation Conference (pp. 1341-46). Piscataway, N.J.: Institute of Electrical and
Electronics Engineers.
Lin, F., Yang, M., & Pai, Y. (2002). A Generic Structure for Business Process
Modelling. Journal of Business Process Management, 8(1): 19-41.
Mackulak, G. T., & Savory, P. A. (1994). Ascertaining Important Features for
Industrial Simulation Environments. Simulation, 63(4): 211–221.
Mahmudi, J., & Tavakkoli, V. (2004). Simulation: The Best Solution for BPR. In A.
Zerger, & R. M. Argent (Eds.), International Congress on Modelling and
Simulation (pp. 2810-17). Australia: Modelling and Simulation Society.
Malhotra, N. K. (2004). Marketing Research (4th Ed.). Singapore: Pearson Education,
p. 74.
Malhotra, N. K. (2007). Marketing Research: An Applied Orientation (5th Ed.). New
Delhi: Prentice Hall. p. 84.
95
Malhotra, N. K. (2007a). Marketing Research: An Applied Orientation (5th Ed.). New
Delhi: Prentice Hall. p. 86.
Malhotra, N. K. (2007b). Marketing Research: An Applied Orientation (5th Ed.). New
Delhi: Prentice Hall. p. 335.
Malhotra, N. K. (2007c). Marketing Research: An Applied Orientation (5th Ed.). New
Delhi: Prentice Hall. p. 286.
Malhotra, N. K. (2007d). Marketing Research: An Applied Orientation (5th Ed.). New
Delhi: Prentice Hall. p. 284.
Mayer, R. J., Benjamin, P. C., Caraway, B. E., & Painter, M. K. (1995). A Framework
and a Suite of Methods for Business Process Reengineering. In V. Grover, & W.
J. Kettinger (Eds), Business Process Change: Reengineering Concepts, Methods,
and Technologies (pp. 245-290). London: Idea Group Publishing.
McLean, C., & Leong, S. (2001). The Expanding Role of Simulation in Future
Manufacturing. In B. A. Peters, J. S. Smith, D. J. Medeiros, & M. W. Rohrer
(Eds.), Proceedings of the 2001 Winter Simulation Conference (pp. 1478-86).
Netherland: Elsevier Science Publishers.
Melao, N., & Pidd, M. (2003). Use of Business Process Simulation: A Survey of
Practitioners. Journal of the Operations Research Society, 54(1): 2-10.
Muthu, S., Whitman, L., & Cheraghi, S. H. (1999). Business Process Reengineering:
A Consolidated Methodology. In C. Alexopoulos, K. Kang, D. Goldsman, & W.
Lilegdon (Eds.), Proceedings of the 4th Annual International Conference on
Industrial Engineering Theory, Applications and Practice (pp. 81-91). Piscataway,
N.J.: Institute of Electrical and Electronics Engineers.
Nikoukaran, J., Hlupic, V., & Paul, R. J. (1998). Criteria for Simulation Software
Evaluation. In D. J. Medeiros, E. F. Watson, J. S. Carson, & M. S. Manivnnan
96
(Eds.), Proceedings of the 30th Conference on Winter Simulation (pp. 399-406).
Washington, D.C.: IEEE Computer Society.
Nikoukaran, J., Hlupic, V., & Paul, R. J. (1999). A Hierarchical Framework for
Evaluating Simulation Software. Simulation Practice and Theory, 7(3): 219-231.
Oakshott, L. (1997). Business Modelling and Simulation. London: Pitman Publishing,
p. 80.
Perera, T., & Liyanage, K. (2001). IDEF Based Methodology for Rapid Data
Collection. Integrated Manufacturing Systems, 12 (3): 187-194.
Pidd, M. (1989). Choosing Discrete Simulation Software. OR Insight, 2(3): 22-23.
Pidd, M. (1992). Computer Simulation in Management Science. New York: John
Wiley & Sons, p. 306.
Pidd, M., & Carvalho, A. (2006). Simulation Software: Not the same Yesterday,
Today or Forever. Journal of Simulation, 1(1): 7-20.
Popovic, A., Jaklic, J., & Vuksic, V. B. (2005). Business Process Change and
Simulation Modelling. System Integration Journal, 13(2): 29-37.
Popovic, A., Stemberger, M. I., & Jaklič, J. (2006). Applicability of Process Maps for
Simulation Modelling in Business Process Change Projects. Interdisciplinary
Journal of Information, Knowledge, and Management, 1: 109-122.
Ramaswamy, V. S., & Namakumari, S. (2002). Marketing Management, 3rd ed., New
Delhi: Macmillan India Ltd, pp. 399.
Seila, A. F., Ceric, V., & Tadikamalla, P. (2003). Applied Simulation Modelling.
Australia: Thomson Learning, p. 74.
Stemberger, M. I., Popovic, A., & Vuksic, V. B. (2003). Simulation and Information
Systems Modelling: A Framework for Business Process Change. In A. Verbraeck,
97
& V. Hlupic (Eds.), Proceedings of 15th European Simulation Symposium and
Exhibition (pp. 75-81). North Holland: Elsevier Science Publishers.
Stoddard, D. B., & Jarvenpaa, S. L. (1995). Business Process Redesign: Tactics For
Managing Radical Changes. Journal of Management Information Systems, 12(1):
81-107.
Suri, R. D., & Tomsicek, M. (1990). Modelling Manufacturing Systems Using
ManuPlan and SimStarter-A Tutorial. In O. Balci, R. P. Sadowski, & R. E. Nance
(Eds.), Proceedings of the 1990 Winter Simulation Conference (pp. 168-176).
New Orleans: IEEE.
Swain, J. J. (1995). Simulation Survey: Tools for Process Understanding and
Improvement. Journal of Computing, 15(2): 64-79.
Szymankiewicz, J., McDonald, J., & Turner, K. (1988). Solving Business Problems
by Simulation. London: McGraw Hill, p. 124.
Tewoldeberhan, T. W., Verbraeck, A., Valentin E., & Bardonnet, G. (2002). An
Evaluation and Selection Methodology for Discrete-Event Simulation Software.
In E. Ycesan, J. L. Snowdon, J. M. Charnes, & J. Wayne (Eds.), Proceedings of
the 2002 Winter Simulation Conference (pp. 67-75). Boston: Kluwer Academic
Publisher.
Tocher, K. D. (1965). Review of Simulation Languages. Operational Research
Quarterly, 16(2): 189-217.
Tumay, K. (1995). Business Process Simulation. In D. Goldsman, C. Alexopoulos, &
K. Kang (Eds.), Proceedings of 1995 Winter Simulation Conference (pp. 55-60).
Piscataway, N.J.: Institute of Electrical and Electronics Engineers.
Verma, R. (2007). Impact of Market Orientation on Corporate Success in Indian
Automobile Industry. Ph.D. Thesis, IMS, H.P. University, Shimla.
98
Vreede, G. J. D., Verbraeck, A., & Eijck, D. T. T. V. (2003). Integrating the
Conceptualization and Simulation of Business Processes: A Modelling Method
and an Arena Template. Simulation, 79(1): 43–55.
Vullers, M. H. J., & Netjes, M. (2006). Business Process Simulation - A Tool Survey.
In K. Jensen (Ed.), Proceedings of Seventh Workshop and Tutorial on the
Practical Use of Coloured Petri Nets and the CPN Tools (pp. 77-96). Denmark:
University of Aarhus.
Wild, W. G., & Otis, P. (1987). This Video Game is Saving Manufacturing Millions,
Business Week, 82–84.
Williams, G. B., & Trauth, J. (1991). Evaluation and Selection of Manufacturing
Software for a Small Business. In G. W. Owen, & A. R. Mileham (Eds.),
Proceedings of the Seventh National Conference on Production Research (pp.
102-106). London: Pinter Publishers.
Wright, D. T., & Burns, N. D. (1996). Guide to using the WWW to Survey BPR
Research, Practitioners and Tools. IEE Engineering Management Journal, 6(5):
211-216.
Yammarino, F. J., Skinner, S. J., & Childers, T. L. (1991). Understanding Mail
Survey Response Behaviour. Public Opinion Quarterly, 55: 613.
Yassine, A. A., Whitney, D. E., & Zambito, T. (2001). Assessment of Rework
Probabilities for Simulating Product Development Processes using the Design
Structure Matrix (DSM). In N. Shimizu (Ed.), Proceedings of DETC ’01 ASME
2001 International Design Engineering Technical Conferences on Computers and
Information in Engineering (pp. 67-75). New York: American Society of
Mechanical Engineers.
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