Factors In A Business Indicate The Need For Simulation (Coursework Sample)

SIMULATION IN BUSINESS
STUDENT’S NAME
TUTOR’S NAME
COURSE TITLE
INSTITUTION NAME
DATE
* Need for Simulation
Simulation is a very essential factor that many businesses need to employ in their systems. Well-designed simulation models are a great help to generating consensus and generating confidence in the design that you create for the business. It helps foresee what lies ahead if you implement a certain model. Many systems have proved to have various properties that call for the need for simulation. Some of these properties are:
* High levels of complexity
* High variation in demand
* Multiple interdependencies
* Severe competition and rationing of resources
Simulation takes a realistic world into a computer and gives an insight on what needs to be done. Computerized simulation provides visual view of the likely consequences when a certain scenario is implemented by doing complex processing and making interdependent decisions. Users are able to gain great insight and understanding of the problem at hand gained from the design, execution of the scenarios and a deep analysis of the outcomes. Simulation can help with finding optimal solutions which gives a high level of confidence. There are many benefits associated with a business employing simulation modeling such as:
1 Your thoughts are distilled. You can clearly articulate the problem.
2 You are able to see the dynamics of your model and gain new insight.
3 You are able to document the required process improvements.
4 The business is able to develop strategies, management plans and actions.
5 The business is able to eliminate any duplicity, flaws and gaps in the systems.
6 Helps examine strategies choices
Ref: Ringberg, H., Roughan, M., & Rexford, J. (2008). The need for simulation in evaluating anomaly detectors. ACM SIGCOMM Computer Communication Review, 38(1), 55-59.
* Monte Carlo simulation
Monte Carlo simulation was invented by scientists who were working on an atomic bomb (1940s). They named this simulation model while working in Monaco city known for its casino games. It uses random parameter/input samples to explore and learn the behavior exhibited by a complex process or systems. The scientists came up with Monte Carlo method to solve physics problems which were complex due to exhaustive numerical evaluations. Monte Carlo proved effective in finding solutions easily and fast. It is used in exploring sensitivity in complex systems such as financial, mathematical and physical models which are stimulated in a loop where statistical uncertainties are included.
Why the name? They named this model as Monte Carlo, a casino in Monaco city where the scientists frequently met. They based this on the chance and random outcomes in the casino games and centralized the modeling technique on the two, just like dice, roulette and slot machines. The Monte Carlo simulation can be used in stochastic modeling, integration, and uniform distribution models. Tasks that can be performed under Monte Carlo analysis include;
* Analysis of data through advanced statistical methods and robust plotting.
* Creating dynamic simulation.
REF:
* Mahadevan, S. (1997). Monte carlo simulation. MECHANICAL ENGINEERING-NEW YORK AND BASEL-MARCEL DEKKER-, 123-146.
* Vose, D. (1996). Quantitative risk analysis: a guide to Monte Carlo simulation modelling.
* Difference between simulation and modeling
Simulation and modeling are closely related computer applications with a major role in engineering and science in the current world. Modeling involves creating of a model acting as a representative of a system or an object with all its properties. Simulation on the other hand is a technique used to study and analyse the real world or an imaginary world by mimicking the same on computer application.
A model helps the user in predicting how changes in a system are affecting other operations or parts of a system. Simulation helps in system optimization allowing one to prevent failure by adjusting the systems parameters. A model is considered to be static while simulation is dynamic since the variables are easy to change. A simulation changes one or more variables in a model and observes the changes. This is only one way modeling cannot change the variables in simulation.
A simulation tries to observe all the possibilities in results by even doing impossible changes. A model will always represent the real world.
A model is a systemic recipe trying to show how to produce a modeled systems behavior data while a simulation takes the recipe and carries it out. This produces the behavior of the modeled system.
REF: H. (2011). Difference Between Modelling and Simulation. Retrieved October 04, 2016, from /difference-between-modelling-and-vs-simulation/
* Justification of Simulation
In the modern world of powerful computers, it often beats logic to use pen and paper. Software developed can perform calculations in a blink of an eye than it takes to use the hand. Simulation finds its justification in the same way by being placed at an upper hand by what it can do.
1 Accurate depiction of reality
Simple analysis can be done manually by anyone. However, increasing complexity in analysis calls for sound measure such as computer based tools. Simulation is used to help in determining the operational status of a system. Using simulation, one is able to include randomness by properly identifying probability distributions extracted direct from study data. Simulation allows interdependence through service events, and also by keeping t...