Learning Objectives

D-1: Explain the concept of simulation, its advantages, and the key steps in developing a simulation model.

Simulation involves building physical or analytical/mathematical models that attempt to duplicate real-world systems or problems. By doing so, the simulation process enables a manager to study and analyze the real-world system without actually building it. The wide range of business applications for which simulation can be applied also illustrates just how valuable the process can be. Some of the many applications for simulation include production scheduling, employee/worker scheduling, analysis of waiting line systems, inventory planning and control, design of plant layout and distribution systems, and sales processes.

There are eight key steps in developing a simulation model. These steps include (1) develop a model that represents the real-life system or process that is to be studied and analyzed; (2) define the problem; (3) formulate the mathematical model; (4) gather the data needed for the study; (5) translate the model to computerized simulation software; (6) verify and validate the model; (7) experimentation and analysis; and (8) documentation and recommendation.

D-2: Describe Monte Carlo simulation, and set up and solve operations problems using Monte Carlo simulation, by hand and using Excel.

Although simple problems and systems can be studied through manual simulation, most real-world problems and systems are complex and, therefore, require computer simulation models. The Monte Carlo simulation approach uses random numbers to represent key random variables such as demand, lead time, arrival rates, and services times. These variables are then simulated in a series of trials to examine policy decisions. Although simpler examples can be solved by manually using Monte Carlo simulation, for larger problems that are more complex or involve more variables, it is often necessary to employ software tools to solve such problems using Monte Carlo simulation. Excel is a popular choice for solving problems using Monte Carlo simulation, as we demonstrated throughout this module.

› Chapter Outline