August 1996 Volume 23 Number 4
Second Look at Simulation Software
Non-traditional uses can yield unexpected benefits
By Jerry Banks and Van Norman
The traditional uses of simulation have been discussed by many authors [Banks
and Norman, 1995; Law and Kelton, 1991; Pegden, Shannon and Sadowski, 1995;
and Schriber, 1991]. However, there are other non-traditional uses of simulation
software that produce unexpected benefits. This article presents an expanded
set of simulation uses that are commonly considered, plus some non-traditional
uses and their benefits. The listing was developed by examining many past
The traditional uses of simulation go beyond providing a look into the future.
These include the following:
Better understanding. Managers often want to know why certain
phenomena occur in a real system. With simulation, you determine the answer
to the "why" questions by reconstructing the scene and taking
a microscopic examination of the system to determine the cause or causes
of the phenomena. This is difficult to accomplish with a real system because
you frequently can't see or control it in its entirety.
Also, by compressing or expanding the time scale, simulation allows you
to speed up or slow down phenomena so that you can thoroughly investigate
them. You can examine an entire shift in a matter of minutes if you desire,
or you can spend two hours examining all the events that occurred during
one minute of simulated activity. Time compression and expansion helps the
decision maker to understand "why."
Animation features offered by many simulation packages take this a step
further by allowing the user to see the facility as if it were running.
Depending on the software used, you may be able to view the simulated operations
from various angles and levels of magnification, even 3-D.
Visualizing system dynamics adds to understanding and aids in the detection
of design flaws that appear credible when seen just on paper or in a 2-D
Explore possibilities. Simulation lets you test every aspect
of a proposed change or addition without committing resources to their acquisition.
This is critical, because once the hard decisions have been made, the bricks
have been laid or the material handling systems have been installed, changes
and corrections can be extremely expensive.
Simulation can be used to specify requirements for a system design. For
example, many firms use simulation to define complex control algorithms
and then implement the rules in the actual system. Additionally, the specifications
for a particular type of resource in a complex system to achieve a desired
goal may be unknown. By simulating different capabilities for the resource,
the requirements can be established.
Additionally, a great advantage of using simulation is that once you have
developed a valid model, you can explore new policies, operating procedures
or methods without the expense and disruption of experimenting with the
real system. Modifications are incorporated in the model, and you observe
the effects of those changes on the computer rather than the real system.
Simulation, in many firms, is integral to the continuous improvement process.
The cost of a typical simulation study is usually less than 1 percent of
the total amount being expended for the implementation of a design or redesign.
Since the cost of a change or modification to a system after installation
is so great, simulation is a wise investment.
Diagnose problems. The modern enterprise is very complex; so
complex that it's impossible to consider all the interactions taking place
at a given moment. Simulation allows you to better understand the interactions
among the variables that make up such systems. Diagnosing problems and gaining
insight into the importance of these variables increases your understanding
of their effects on the performance of the overall system.
Long throughput times, missed production targets, late jobs and other production
problems keep many managers awake at night. It's easy to forget that congestion
is an effect rather than a cause. However, by using simulation to perform
bottleneck analysis, you can discover the cause of the delays in work-in-process,
information, materials or other processes. Once the cause is determined,
an appropriate solution can be tested via simulation and implemented.
Build consensus. Some people operate with the philosophy that
talking loudly in meetings, distributing fancy reports and presenting computerized
output convinces others that a proposed design is valid. In many cases these
designs are based more on an individual's preconceived notions than on factual
analysis. Simulation provides objective understanding about how a system
actually operates -- a much firmer basis for design than relying on subjective
In these days of downsizing and cut-backs, many people are fearful. Using
simulation to make objective decisions can help to alleviate these fears.
Since decisions are made objectively, employees are less likely to be drawing
any sort of inference when you approve or disapprove of designs.
Train the team. Simulation models can provide the basis for
excellent training. Used in this manner, the team makes decisions at set
points during the progression of the model. The team, and individual members
of the team, can benefit by their mistakes, and learn to operate better.
This is much less expensive and less disruptive than on-the-job learning.
When Not To Use Simulation
To be fair, we should point out that there are times when simulation is
not an appropriate tool. In fact, if a closed-form mathematical model is
available, simulation should not be used since it takes more time and is
probably more expensive. Also, because simulation models are inherently
descriptive they offer little in the way of prescriptive advice concerning
how to manage or modify operations. Consequently, if a different type of
model (linear programming, for example) can be used, it is more appropriate
to do so.
For instance, if we are trying to plan production levels for the next year,
a detailed, minute-by-minute, simulation of the entire factory is probably
inappropriate. A linear program using aggregate production capacities would
not only provide "what if" information, but would also suggest
"what's best." The problem with most prescriptive models is that
they often do not accurately represent the complex interactions and dynamics
of the real-world system.
There are many uses of simulation that are non-traditional. Oftentimes,
these non-traditional uses may be more important than the traditional ones.
Modeling the system contributes to understanding. In a complex
system, no one person knows all the details. The modeling effort benefits
everyone's understanding of the unfamiliar components, the relationship
of the components to the entire system, and the impact of an individual
sub-system on the entire system.
In a manufacturing environment, one individual might be acquainted with
metal cutting operations, whereas another understands the material handling
devices, and yet another is an expert only in the information flow system.
If all of these people are participating in the simulation activity, either
as modelers or through inclusion in the review sessions given by the modelers,
they become more familiar with other components of the system. Many opportunities
for improvement arise when all involved can see the big picture.
An example from consulting pertains to a warehousing and distribution center
that was not achieving the desired throughput. In this case our client was
another consulting firm serving as the system integrator. The simulation
model indicated that the resources were not being used anywhere near their
limits. The problem seemed to be in the algorithm that controlled stock
The system integrator continued to provide us with revised picking algorithms,
but none were significant improvements. In desperation, they asked us, the
simulation analysts, to devise an algorithm since we had developed a very
thorough understanding of the system. The algorithm developed by our analyst
was a significant improvement over that developed by the system's integrators,
although it still did not achieve the desired throughput.
Data collection leads to understanding. There is often so much
noise in raw data that it is difficult, sometimes impossible, to "see
the forest for the trees." Looking at histograms and other graphical
output can provide a much clearer picture of what is happening. For example,
an understanding that the repair time for a resource follows a distribution
that is skewed to the right might help understand why long waiting lines
sometimes arise. Further, this understanding might lead to some alternatives
for reducing these long waiting lines, even before the model is complete,
or the simulation analysis has taken place.
During the construction of a simulation model on a recent consulting engagement,
we were trying to match our model's predicted output with the output of
an actual heat treating process. Unfortunately, the output of the model
greatly exceed the actual output. We had accurate data on the time between
batches from an automatic recording device and had assurances that the center
ran two machines after a major shutdown. Because of the discrepancy, we
spent several months collecting actual output data.
Eventually, we realized that although the operation was running two furnaces
as we had been told, the output was low the first few days after a shutdown
due to restart problems. This fact had never been entered into the model
(or into capacity planning, for that matter). When added to the model, the
predicted output was in complete agreement with the actual and we learned
something significant about the system.
Visualize the idea. Consider the case in which there are disagreements
between managers concerning the desirability of a course of action. One
manager says to go straight, another says go to the left, and the third
says go to the right. Once a manager commits to a direction, it is a show
of weakness to change to the direction championed by another manager. An
impasse can be reached, and no action is taken, perhaps to the detriment
of the firm. Simulation can avoid this impasse, or even break through it
if it has been reached. After verification and validation a simulation model
can become reality. This reality can then be used to resolve disputes as
well as become a specification for the manner in which the system will be
Not only does simulation help with the visualization by managers, but with
hourly workers and supervisors that must be convinced that an idea has merit.
In fact, it is quite possible that a system will fail unless all of those
involved buy into it. New methods may be perceived as risky and threatening
to hourly personnel. Some of this fear is warranted. The workers wonder
if they will be able to make 100 parts per day under the new system. They
wonder what will happen if they can only make only 80 parts per day. These
workers may become resistant to change.
Process debugging. Oftentimes, when validating a model of a
rather complex manufacturing facility, the simulation appears to run significantly
better than the actual facility. Usually this is considered a "bug"
that needs to be corrected. However, if the "bug" can be replicated
in the actual facility, this may be an opportunity for improvement. We refer
to this as "process debugging."
This has happened to us on several occasions. Once, this discovery led to
a significant reduction in throughput time. The model assumed that a single
cart was used to move work from a department having many parallel machines.
The resulting cycle time was 30 percent less than the actual time, and we
looked for the "error." When we found that each of the parallel
machines had its own cart and made the appropriate change in the model,
the cycle times matched. Later, we realized that the actual department could
function with a single cart. Thus, the "error" in the simulation
study became a recommendation that was unexpected.
Process debugging can lead to big opportunities for improvement in the actual
system. A simulation model makes it easy to "debug" the process
by finding out which assumptions are being violated and what the impact
will be by improving them. The simulation model also pinpoints the changes
that will make the most impact. [For more on process debugging, see Hopp
and Spearman, Chapter 9. ]
1. Banks, J., and V. Norman (1995), "Justifying Simulation in Today's
Manufacturing Environment," IIE Solutions, November.
2. Hopp, W.J., and M.L. Spearman (1996), "Factory Physics: Foundations
of Manufacturing Management," Richard D. Irwin, Burr Ridge, IL.
3. Law, A.M., and W.D. Kelton (1991), "Simulation Modeling and Analysis,"
2nd ed., McGraw-Hill, New York.
4. Pegden, C.D., R.E. Shannon, and R.P. Sadowski (1995), "Introduction
to Simulation Using SIMAN," McGraw-Hill, New York.
5. Schriber, T.J. (1991), "An Introduction to Simulation Using GPSS/H,"
John Wiley, New York.
Jerry Banks is a professor in the School of Industrial and Systems Engineering
at the Georgia Institute of Technology and associate director for research
at the Center for International Standards and Quality at Tech. Van Norman
is president of AutoSimulations, Inc. in Bountiful, Utah.
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