Simulation is one of the most widely used quantitative analysis tools. To simulate is to try to duplicate the features, appearance, and characteristics of a real system. We will build a mathematical model that comes as close as possible to representing the reality of the system. Physical models can also be built to test systems.
Advantages of simulation
1) It is relatively straightforward and flexible.
2) Recent advances in computer software make simulation models very easy to develop.
3) Can be used to analyze large and complex real-world situations.
4) Allows “what-if?” type questions.
5) Does not interfere with the real-world system.
6) Enables study of interactions between components.
7) Enables time compression.
Disadvantages of simulation
1) It does not generate optimal solutions; it is a trial-and-error approach.
2) It requires managers to generate all conditions and constraints of real-world problem.
3) Each model is unique and the solutions and inferences are not usually transferable to other problems.
Monte Carlo Simulation
When systems contain elements that exhibit chance in their behavior, the Monte Carlo method of simulation can be applied. The basis of the Monte Carlo simulation is experimentation on the probabilistic elements through random sampling.
Some examples/Applications
1) Inventory demand.
2) Lead time for inventory.
3) Times between machine breakdowns.
4) Times between arrivals.
5) Service times.
6) Times to complete project activities.
7) Number of employees absent.
Steps of Monte Carlo Simulation
1) Establishing a probability distribution for important variables.
2) Building a cumulative probability distribution for each variable.
3) Establishing an interval of random numbers for each variable.
4) Generating random numbers.
5) Actually simulating a series of trials.
