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Process Simulation in Excel for a Quantitative Management Course
sheet capabilities are very good for sampling simulation methods but ill suited for discrete-event simulation. This paper proposes and discusses how to
use SimQuick, an Excel-based simulation package,
to overcome these limitations and bring discreteevent simulation into the spreadsheet-based quantitative management course.
Process Simulation in Excel for a
Quantitative Management Course
Raymond R. Hill
Department of Operational Sciences
Air Force Institute of Technology
Wright-Patterson AFB, OH 45424
A simulation is a computer model used to evaluate
a system numerically (Law and Kelton, 2000). Stochastic simulations involve random variates to model
variables assumed to follow some probability distribution. When the simulation explicitly models a
system as that system changes over time, the simulation is referred to as a dynamic simulation. Recent
surveys list simulation as among the most popular
and widely used operational research techniques
(Lane, et al., 1993).
ray.hill@afit.edu
Abstract
A nagging limitation of teaching spreadsheet-based
quantitative decision-making courses is the sometimes stilted view of simulation presented, a view
that overly emphasizes sampling simulation at the
expense of process simulation. Without a viable
spreadsheet-based process simulation package, the
best efforts aimed at overcoming this emphasis on
sampling simulation have provided passing references to special purpose simulation packages focused
on process simulation. We introduce and discuss
SimQuick as an alternative approach for teaching
process simulation within the context of a spreadsheet-based approach to teaching simulation. Although an early tool, and somewhat limited when
compared to special purpose simulation packages,
SimQuick provides a viable means for teaching the
process of simulation modeling and reinforcing the
salient features of process modeling as a quantitative technique.
Spreadsheets have long been in use in the business
world while unfortunately ignored among some analysts and engineers. That tendency is rapidly falling
away as the modern spreadsheet truly provides a
diverse and robust environment for most modeling
needs. The modern spreadsheet provides a means to
store and manage data, run statistical analyses, conduct mathematical modeling, import and export
tables and graphs, and interface to other more powerful computer packages. In short, the spreadsheet
is a (nearly) complete analytical toolbox.
Not surprisingly, quantitative management texts focused on surveying quantitative techniques have
nearly unanimously adopted the spreadsheet as the
computing platform of choice. The benefits provided
the course instructor include:
Disclaimer: The views expressed in this article are those
of the authors and do not reflect the official policy of the
United States Air Force, Department of Defense, or the
US Government.
• pre-existing familiarization with the platform;
and
• increased likelihood of student future use of
the quantitative techniques
Teaching new concepts, such as is often the case in
courses involving quantitative techniques, is facilitated by the students’ immediate grasp of the computer tool. The instructor challenge then becomes
one of expanding the students’ abilities to understand and apply the quantitative methods. Then,
because the spreadsheet is so widely available, and
Introduction
A nagging limitation of teaching spreadsheet-based
modeling is providing sufficient coverage of simulation. Simulation is an important quantitative technique but spreadsheet-based approaches sometimes
ignore a good portion of the technique. Basic spread-
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if the instructor has made the proper academic-toapplication connection, the graduated student may
find their problems amenable to spreadsheet modeling and actually use the techniques in practice.
Simulation on Spreadsheets
Analytical simulation falls into two broad categories:
• sampling or Monte Carlo simulation, and
The spreadsheet platform also introduces challenges
for the instructor. These include:
• process simulation (or discrete-event simulation).
• differences between mathematical formulation
and spreadsheet paradigms;
Evans (2001) described these as Monte Carlo or risk
simulation and systems simulation, respectively.
Other simulation applications, such as human-in-theloop, distributed, web-based, and training simulations are excluded from the present discussion.
• too much modeling freedom; and
• limited view of the breadth and depth of quantitative modeling.
The spreadsheet paradigm is one of arrays and matrices of values but not necessarily matrix algebra.
Mathematical models are usually algebraic in representation. The instructor needs to help the student
build the mental mapping between the two paradigms. The spreadsheet also imposes few limitations
on how a user chooses to design a worksheet. This
freedom is carried over into the design and specification of quantitative models. Such expressive freedom can be a source of confusion to the novice modeler. The instructor must avoid overwhelming the
student with spreadsheet capabilities and focus on
effective quantitative modeling methodology. This
expressive freedom can be particularly troublesome
when correct answers are obtained using questionable modeling methods or when the correct spreadsheet model bears no resemblance to an associated
mathematical formulation.
Sampling simulation applications are perfect for the
spreadsheet. Sampling simulation is used to examine risk or uncertainty associated with static models
or simulations that involve activity-scanning approaches. Typical models might be investment models, inventory models, even fairly simply queuing
models are manageable in the spreadsheet (Ragsdale,
2001; Camm and Evans, 1996). The spreadsheet
easily recalculates a model, spreadsheet functions
provide random number generators, and the spreadsheet has plenty of capability to save and analyze
the simulation data generated. Nearly all management science texts include a chapter on simulation,
a chapter usually focused on sampling simulation.
Add-ins like @Risk and Crystal Ball significantly
increase one’s ability to conduct, and teach, sampling simulation, as well as conduct risk analysis in
simulation studies or student projects and theses.
The spreadsheet is a viable modeling platform but
has limitations, in breadth of technique supported
and in the size of models accepted. Advanced users
can of course significantly extend spreadsheet capabilities (like in Excel) with special purpose programming (like VBA), but such topics generally fall
outside the scope of quantitative courses. The survey course student will generally remain a novice
user of quantitative management tools. Thus, the
survey course should provide a basic understanding
of a range of techniques, a sufficient understanding
of the technique’s salient features, and most importantly, an appreciation of what is still unfamiliar with
respect to the technique.
Process simulation for spreadsheets are not quite as
applicable. Process simulation is used to capture
complex system state changes over time, particularly when the state changes are defined by events
within the system. The dynamic interactions and
uncertain event ordering are difficult to capture in a
spreadsheet model without somehow augmenting
spreadsheet capabilities. Typical examples might
include maintenance operations involving demands
on limited resources and unpredictable failure
events, production processes involving looping
within the various workstations within the facility,
or even complex combat models with multiple interacting systems engaged in conflict. These more
complicated, and often more realistic, simulation
applications are either not discussed or just briefly
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mentioned when referencing the more sophisticated
simulation packages. Exceptions include Camm and
Evans (1996), Evans and Olsen (1998), and Laurence
and Pasternack (1998) whose works include student
versions of these more powerful packages. These
exceptions are, however, admittedly limited in the
amount of time and space devoted to discussing how
to use these more powerful simulation packages.
elements, and the connections between the elements.
These specifications are indicated to SimQuick via
particular worksheet tables whose access is controlled via VBA menus within the SimQuick spreadsheet. SimQuick can be described as a first-generation package, replete with significant limitations
when compared to mature, special purpose packages such as Extend, AweSim, ProModel, or Arena.
However, we have found SimQuick, despite its limitations, quite adequate in providing a spreadsheet
tool with which to focus on the salient features of
process simulation thereby providing more complete
coverage of simulation as a quantitative technique.
A stilted view of simulation due to spreadsheet
limitations deprives the survey-course student a full
appreciation of the power and benefits of the simulation as a quantitative technique. To overcome this
limitation, we incorporated SimQuick into the simulation portion of our survey course. In our case,
process simulation follows sampling simulation for
which we use Crystal Ball. See Evans (2000) or
Ragsdale (2001) for details on sampling simulation
using Crystal Ball.
Figure 1 displays the SimQuick control panel. Each
button transfers control to a worksheet containing
the SimQuick tables through which one specifies a
simulation. The only elements available in SimQuick
are those listed on the panel, and described below.
This limitation on SimQuick constructs is a blessing for the survey course as the student must focus
on the process of simulation modeling to successfully employ SimQuick. Further, the student is not
overwhelmed by the large number of modeling
constructs such as one finds in the special purpose
simulation packages. The instructor must however
choose modeling projects that are appropriate for
the SimQuick package.
SimQuick Introduced
SimQuick (Hartvigsen, 2001) is a process-oriented
simulation package for Excel. SimQuick is not an
Excel add-in (such as an .xla file) but rather a spreadsheet template in which a user specifies the elements
of a simulation model, the parameterization of those
Figure 1: SimQuick Control Panel
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Process of Simulation Modeling
1.Formulate the problem to include defining objectives, measurements, and scenarios;
A frustrating aspect of teaching simulation, whether
a component within a survey course, or a full-fledged
simulation course, is reminding students that simulation is applied statistics. Stochastic simulation
output is a function of input random variables and
thus also a random variable and statistics are the
language of random variables. The simulation model
is not the end result, but rather a tool one builds
within the simulation process. The true end product
of a simulation effort, just as in a mathematical
modeling effort, is insight into the system or process under study, insight gained, in this case, via
statistical analysis of simulation output. The
SimQuick design provides a focus on the simulation process.
2.Develop a logical model;
3.Specify probabilistic assumptions; and
4.Implement model on computer.
Evans’ four-step approach is closer to our needs although additional emphasis is given to verifying and
validating the logical model before implementing
the model into SimQuick. This is due to the increased
complexity of a process model versus a static model
as built for a sampling simulation application. We
can use Step 3 to tie back to Crystal Ball, and sampling simulation, through the use of Crystal Ball’s
distribution fitting capabilities to analyze historical
data from which to derive representative probability distributions.
Hartvigsen (2001) lists three steps in using
SimQuick:
A simple flow-chart augmented with additional syntax provides a simple, easy to learn and intuitive
simulation process. Figure 2 is a very simple conceptual model intended to motivate our approach to
teaching simulation. In this example, entities arrive,
wait for a paint station, are painted Red or Blue, and
once painted leave the system. We want to count
how many are painted so we use two buffers, Total
Red and Total Blue. The flowchart elements in Figure 2 list the corresponding SimQuick element type,
a unique name for the element, and any other data
required by the SimQuick element. Linkages among
the elements are indicated by the directed arcs and
specified to SimQuick via the unique names assigned
the element. Figure 2 contains sufficient detail to
directly specify the model to SimQuick. This flowchart in Figure 2 was drawn using the cell formatting and the line drawing facilities within Excel.
1.Conceptually build a model of the process;
2.Enter the model into SimQuick;
3.Test process improvement ideas with the
model
We expand the first step quite a bit when teaching
process simulation with SimQuick to focus more on
the methodology of simulation. This includes increasing the specificity contained within a conceptual model. This facilitates the second step as entering a model into SimQuick is very straightforward
but does require the up-front planning during the
conceptual modeling building process.
Evans (2000) offers the following four step approach:
Figure 2: Paint Process Example
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SimQuick Limitations
elements. For instance, two buffers feeding a
common workstation produces a combine
object effect.
SimQuick represents a new and initial capability for
process simulation within Excel. Not surprisingly
SimQuick has limitations. Understanding and accommodating these limitations helps realize the benefits SimQuick brings to the classroom.
• More than two decision points requires placing SimQuick decision point elements in sequence and determining the correct conditional probabilities.
SimQuick elements are defined via tables within
worksheets of the SimQuick workbook. The tables
currently allow up to 20 elements of each type. Simulations are limited to 10,000 time units and 100 independent replications. There is no facility for removing transient data nor can one specify random
number streams. There is also a limited choice of
probability distributions although those implemented
are quite commonly used in simulation.
Fortunately, each “limitation” of SimQuick provides
an opportunity to reinforce two key concepts to the
student: structured walk through of models for validation, and modeling for effect. A structured walkthrough is usually associated with software engineering but serves the novice modeler well to reinforce
the notion that a simulation, like any computer program it is, will only do what is specified, not necessarily what the modeler thinks it will accomplish.
Modeling effects deals with using the structure and
syntax of the language in such a way that the resulting model sufficiently mimics the intended process—the output data makes sense. For example, a
maintenance process simulation can capture the effect of maintainer transient time, without explicitly
modeling maintainer travel, by properly incorporating travel time into maintenance repair times.
Like more powerful packages, SimQuick drops entities if those entities have no place to move or wait.
Resource usage is prioritized by the order in which
workstations are defined in the Workstation
worksheet and workstations handle as many entities
as resources allow. SimQuick will also do initial error checking before executing the simulation.
SimQuick Teaching Tips
SimQuick limitations mean certain things cannot be
easily modeled. Just like any programming language,
and all simulation packages are programming languages, one must learn to manipulate the language
properly to obtain the desired modeling effect. The
following are some initial lessons learned.
SimQuick Example
Consider the following example:
Two components arrive simultaneously, A and
B. All components are polished. Part B must
be inspected and if required, re-sanded and repolished. Completed A and B components are
paired and assembled into a finished component, Z. Finished components are placed in
holding racks until an hourly delivery truck
arrives to cart off the components. The delivery trucks have limited storage, handling up
to 15 finished components per pickup.
• Emphasize the importance of fully understanding the system under study and then mapping
the desired conceptual model onto the
SimQuick syntax when creating the actual
simulation model.
• Emphasize the importance of fully specifying
the flowchart model and uniquely naming each
piece in the model.
The complete flowchart for this example cannot fit
neatly in single page. Figures 3-6 contain the components of the final flowchart. The reader will note
common elements in the figures. These common
elements are the connecting points between the figures. Figure 3 shows an object entrance and a split
• Use buffers for each workstation. An object
will drop from the simulation if not buffered.
• Emphasize the need to carefully read and grasp
how SimQuick interprets connections between
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into two components, A and B, each moving into a
Buffer to await polishing. Figure 4, extending from
Figure 3 Buffer WPA, is the component A polishing
process. The polishing process time follows a normal distribution, with mean 2 and standard deviation of 0.5 minutes. The workstation handles one
object at a time and does not require special resources. Figure 5, extending from Figure 3 Buffer
WPB, is the component B polishing process which
includes a 5% chance of inspection failure and required re-sanding and re-polishing. The sanding requires a constant 2 minutes. Finally, Figure 6 shows
the final assembly process. Objects from Buffers
FinishA and FinishB combine to form the final product, Z, an operation that requires 1.5 minutes. These
buffers are also the connecting points between Figure 4 and 5, respectively, into Figure 6. Delivery
trucks arrive each hour and take away up to 15 completed components each arrival. Remaining components wait in the Loading Dock buffer. In this model,
all buffer capacities are set to 200 effectively yielding no capacity limitations. Once fully defined and
specified, the user can provide the model information to SimQuick via the proper SimQuick table.
Figure 3: Components A and B Enter System
Figure 4: Component A Polishing Process
Figure 5: Component B Polishing and Reworking Process
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Figure 6: Final Assembly and Packaging Processes
presented on other worksheets. The Examples button provides precisely that, an example completed
table. In this model, the time between arrivals is
modeled as a uniform random variable between 6
and 9 time minutes. A single object arrives and
moves directly to the Login (workstation) element.
Figure 7 is a partial screen shot showing the Entrance element (included in Figure 3), named Arrive, as specified to SimQuick. Note the Entrances
worksheet provides guidance on the acceptable inter-arrival and number of objects arriving distributions, to include a constant value. Similar help is
Figure 7: Specification of Arrival Entrance Element
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Figure 8:Specification of Four of Five Workstation Elements
(Nor(2,.5)), and the destination for once processing
is completed is specified. In the case of the Login
workstation, a nominal time is given while the dual
output destinations serve to split the single arrival
into two components, A and B (since these arrive
simultaneously.)
Figure 8 is a collage from four of the five tables in
which the conceptual model workstations are specified to SimQuick. These tables specify the workstations from Figures 3-5. Note each element has a
unique Name, the processing time may be a constant (0.1 or 2) or some random amount of time
Figure 9: Specification of Buffer Elements in Model
Figure 9 contains a collage of all the buffer elements
defined in the model. Each buffer is provided a capacity (use a large number if capacity is not an issue) and each buffer starts empty. Each element is
provided a unique name and lists a single output
destination. The name specified in the output destination ties directly to the directed arc used in the
model flowchart in Figures 3-6. To conserve space,
neither the Decision Point element nor the Exit elements are shown.
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vide the data for each of the simulation replications,
data which can be easily copied and further analyzed
using Excel statistical analysis tools. In the case of
this re-working example, workstation and delivery
truck utilization results (not included) indicate a
woefully underutilized process (as one might expect
by examining the system description.)
Once the elements are specified to SimQuick, the
number of replications and length of each simulation are provided. When the Run Simulation button
is selected, SimQuick does some error checking and
if all passes, conducts the simulation. The results of
each simulation run, along with cumulative statistics are written to another worksheet accessible via
the View Results button (see Figure 1).
SimQuick Output
SimQuick does not currently provide a means to
specify output measures, the user gets what
SimQuick provides. However, SimQuick does pro-
Figure 10 is a snapshot of the SimQuick output panel.
SimQuick output is predefined; SimQuick provides
general data on each element contained within the
Figure 10: SimQuick Output Sample
model. Inferences for the system are based on this
data provided.
proximately 27% of the time (Fraction time working) and we can infer that 2.4 items required sanding (difference in Work Cycles started for Polishing
A versus Polishing B as well as the Work cycles
started for Sanding B). This represents a 3.7% fail
rate which is very close to the 5% theoretical fail
rate specified for the model. Other data provided
(not shown) indicate a 1% utilization of the Sander
workstation and only 47% of the delivery truck capacity is employed. In class, various embellishments
can be included and the resulting system impacts
As indicated in Figure 10, SimQuick provides the
data for each replication and a summary average
across all the replications. Variance information requires the user save the results worksheet and use
Excel functions to calculate variance information
based on the replication data. For this model, across
the ten replications, an average of 64.4 components
arrive. The Polishing workstations are utilized ap-
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examined using the output from separate SimQuick
runs.
References:
Camm, J. and J. R. Evans. (1996). Management
Science: Modeling, Analysis and Interpretation,
South-Western, Cincinnati, Ohio.
Concluding Remarks
Evans, J. R. and D. L. Olsen. (1998). Introduction
to Simulation and Risk Analysis, Prentice-Hall,
Upper Saddle River, NJ.
In our class, we introduce SimQuick and process
simulation after presenting sampling simulation using Crystal Ball. Our focus has been on the process
of simulation and in particular developing accuracy
in defining the flowchart model of the process. Student feedback has been positive particularly in the
ease of use of SimQuick once the process model is
defined. More importantly, we were able to provide
a familiarization of simulation that balances both
sampling and process simulation.
Evans, J.R. (2000), “Spreadsheets as a Tool for
Teaching Simulation,” INFORMS Transactions on
Education , Vol. 1, No. 1, http://ite.informs.org/
Vol1No1/evans/evans.html
Hartvigsen, David. (2001). SimQuick: Process
Modeling with Excel, Prentice-Hall, Upper Saddle
River, NJ.
Spreadsheets are uniquely suited for sampling simulation and it is quite logical for spreadsheet-based
texts to focus on sampling simulation. As educators
however we must provide simulation familiarization
across the breadth of the analytical simulation spectrum. While limited in capabilities, SimQuick provides a useful spreadsheet-based vehicle for complementing products like Crystal Ball or @Risk and
thus providing a relatively thorough familiarization
of simulation.
Lane, M. S., A. H. Mansour, and J. L. Harpell.
(1993). “Operations Research Techniques: A
Longitudinal Update, 1973-1988,” Interfaces, Vol.
23, No. 2, pp. 63-68.
Laurence, J. A. and B. A. Pasternak. (1998).
Applied Management Science: A ComputerIntegrated Approach for Decision Making, John
Wiley & Sons, New York.
Law, A. M. and W. D. Kelton. (2000). Simulation
Modeling and Analysis, Third Edition, McGrawHill, New York.
Ragsdale, C. T. (2001). Spreadsheet Modeling and
Decision Analysis: A Practical Introduction to
Management Science. South-Western, Cincinnati,
Ohio.
Winston, W. L. and S. C. Albright. (1997). Practical Management Science: Spreadsheet Modeling
and Applications, Duxbury, Belmont, CA
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