Simulation models

19 Feb

Reply to the three posts 1 and 1a is from one author and 2 from a different one.  Answer and follow conversation in a min of 300 words with APA references

1-Simulation models are a way to use a computer to model a real life situation. They are designed specifically to incorporate uncertainty in their variables. Uncertainty is modeled by using random generation for inputs. This then provides a distribution of results, much like sampling. Using simulation allows the tester, designer, analyst to generate many different scenarios and then chose the best in order to drive business decisions.

Because simulation models can be adjusted, they can then be used to model variation in operating conditions on a specific system. The model can simulation actual systems thus allowing the designer to ask a lot of questions and design the simulation around those questions without building an actual system.

As discussed above, simulation models use inputs from randomly generated numbers. This allows the analyst to generate scenarios and show most-likely outputs, as well as best case and worst case outputs of those scenarios.

The entire foundation of simulation models are probability distributions. This is due to the fact that random number generation is used for at least one input in the model. Thus, the model shows potential values of inputs as well as the probability of those values. Different models are built from the different types of probability distributions:

Discrete versus continuous

Symmetric versus skewed

Bounded versus unbounded

Nonnegative versus unrestricted

Real-world applications of simulation models are grouped into four main areas:

Operations models: warranty costs based on the time of uncertainty until failure

Financial models: Future cash flows, future stock prices, and future interest rates

Marketing models: product life-cycle, customer behavior, sales and advertising

Games of chance: gambling games, dice, cards

Albright, S. C., Winston, W. L., & Albright, S. C. (2015). Business analytics: data analysis and decision making. Stamford, CT, USA: Cengage Learning

2- Good information in your post. Can you give me an example of a business who would benefit from using this type of modeling? How would it help? Are there in negatives?



1a- A really great practical application is a case study of a hospital which used an operating room (OR) model in order to revamp and revitalize their OR efficiency. They conducted a simulation model using ARENA software in order to maximize use of OR rooms and staff. their inputs included: the patient waiting time in holding areas, their internal process, staff scheduling, room availability and the time (of day). The biggest challenge was the data that the research team had. They built a probabilistic model off of existing data, so it had to be accurate and it had to ensure the data selected was of sufficient quality to build an accurate model. This then caused the team to have to read through and select a multitude of patient records which took time. The end result was a hospital that received the highest accreditation score of any public hospital at the time.

I think this is a good example of how simulation modeling can be used in a real world application; it also shows the challenges of ensuring that the simulation model has the appropriate inputs of sufficient quality in order to produce quality results.

Render, B., Stair, R. M., & Hanna, M. E. (2012). Decision analysis. In Quantitative analysis for management (11th ed., pp. 555). Upper Saddle River, NJ: Prentice Hall.


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Posted by on February 19, 2017 in academic writing, Academic Writing



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