Simulation Models
Under what conditions would you choose to use a simulation model? What is the
simulation process? Explain the pros and cons of using simulation modeling. Respond to at
least two of your classmates’ postings.
In this discussion post, we are asked to explain the simulation process and describe
under which conditions would a person choose to use a simulation model. Finally, we
are asked to explain the pro’s and con’s of using simulation modeling.
According to Lawrence and Pasternack (2002), the simulation processes develops
models to evaluate systems numerically over certain periods of interest. The purpose
of simulations is to predict system characteristics that tend to be used to choose the
most appropriate policy from a set of alternatives that can be considered. In simple
words, simulations have the purpose to predict what is to come by considering
variables influencing a project or an outcome in a certain combination. Simulations
end up analyzing the results and making a decision whether or not to go along with
the alternative that has been simulated.
The four key steps to the simulation process include:
• Defining the problem
• Developing the Simulation Model
• Running the Simulation Model and obtaining the Results
• Communicating the Results
Simulation modeling is performed when the subject being analyzed is new or in a
constant state of change such as the weather simulations, or where the variables
change greatly from period to period and cannot be assessed using an algorithm to
solve the solution. Simulation models, however, take into account the randomness
and interdependence through probability distributions assembled from the study data
and provides a method to “look” into the future.
Some of the pro’s associated with simulation modeling is it allows experimentation in
a short amount of time by performing sensitivity and what if analysis on the
material. The use of random number mapping also reduces cycle time of collecting
data and allows the model to be processed multiple times in order to determine a
mean response. This also results in the simulation testing being cheaper and faster as
once a basic model is created, it can be altered and modified to fill in the intricacies of
the process much quicker than physically making changes to a product.
Cons with simulation model can be seen as with any other modeling, an errant key
stroke has the potential to alter the results of the simulation and provide the user with
wrong results, care must be taken to ensure accurate information is used. Also, when
modeling simulation models, the user must have sound modeling and good
programming skills in addition to statistical knowledge in order to accurately design
the model and determine the length and number of cycles the simulation must run in
order to obtain a good prediction of the future. As with any model, users must
understand that in most cases, the models and theories are not often 100% accurate.
Reference
Lawrence, J., & Pasternack, B. (2002). Applied Management science: Modeling,
Spreadsheet Analysis, and Communication for Decision Making (2nd ed.) [with
accompanying CD-ROM]. Hoboken, NJ: John Wiley & Sons, Inc.