Simulation - IntroductionSimulationSimulation is a flexible methodology we can use to analyze the behavior of a present or proposed business activity, new product, manufacturing line or plant expansion, and so on (analysts call this the 'system' under study). By performing simulations and analyzing the results, we can gain an understanding of how a present system operates, and what would happen if we changed it -- or we can estimate how a proposed new system would behave. Often -- but not always -- a simulation deals with uncertainty, in the system itself, or in the world around it. On this page we'll overview different aspects of simulation and on the following pages, accessed by clicking the links at the bottom of the pages, we'll go over more in depth: Simulation ApplicationsSimulation is one of the most widely used quantitative methods -- because it is so flexible and can yield so many useful results. Here's just a sample of the applications where simulation is used:
Simulation ModelsIn a simulation, we perform experiments on a model of the real system, rather than the real system itself. We do this because it is faster, cheaper, or safer to perform experiments on the model. While simulations can be performed using physical models -- such as a scale model of an airplane -- our focus here is on simulations carried out on a computer. Such simulations use a mathematical model of the real system. In such a model we use variables to represent key numerical measures of the inputs and outputs of the system, and we use formulas, programming statements, or other means to express mathematical relationships between the inputs and outputs. Each experiment is called a trial, and a simulation run includes many -- often thousands of -- such trials. When the simulation deals with uncertainty, the model will include uncertain variables -- whose values are not under our control -- as well as decision variables or parameters that we can control. Our simulation model -- often called a risk model -- will calculate the impact of the uncertain variables and the decisions we make on outcomes that we care about, such as profit and loss, investment returns, environmental consequences, and the like. As part of our model design, we must choose how numerical values for the uncertain variables will be sampled on each trial. To learn more, consult Simulation Models. Simulation MethodsComplex manufacturing and logistics systems often call for discrete event simulation, where there are "flows" of materials or parts, people, etc. through the system, and many steps or stages with complex interrelationships. Special simulation modeling languages are often used for these applications. But a great many situations -- including almost all of the examples above -- have been successfully handled with simulation models created in a spreadsheet using Microsoft Excel. This minimizes the learning curve, since you can apply your spreadsheet skills to create the model. Simple steps or stages, such as inventory levels in different periods, are easy to represent in columns of a spreadsheet model. You can solve a wide range of problems with Monte Carlo simulation of models created in Excel, or in a programming language such as Visual Basic, C++ or C#. Running a simulation generates a great deal of statistical data, that must be analyzed with appropriate tools. Professional simulation software, such as Frontline Systems' Risk Solver Platform, allows you to easily create charts and graphs, a wide range of statistics and risk measures, perform sensitivity analysis and parameterized simulations, and even use advanced methods for simulation optimization. Monte Carlo SimulationThe Monte Carlo method was invented by scientists working on the atomic bomb in the 1940s, who named it for the city in Monaco famed for its casinos and games of chance. Its core idea is to use random samples of parameters or inputs to explore the behavior of a complex system or process. Specifically, using a model of the situation, system, or process being looked at, a random sample of each uncertain input (such as sales volume, etc.) is taken. Next the model is recalculated and the key results (such as net profit) saved. this is done repeatedly and the results saved each time. Once complete the range and shape of the results can be examined visually and numerically. Since the 1940's, Monte Carlo methods have been applied to an incredibly diverse range of problems in science, engineering, and finance -- and business applications in virtually every industry. To learn more, consult our Monte Carlo simulation tutorial. Monte Carlo simulation is especially helpful when there are several different sources of uncertainty that interact to produce an outcome. For example, if we're dealing with uncertain market demand, competitors' pricing, and variable production and raw materials costs at the same time, it can be very difficult to estimate the impacts of these factors -- in combination -- on Net Profit. Monte Carlo simulation can quickly analyze thousands of 'what-if' scenarios, often yielding surprising insights into what can go right, what can go wrong, and what we can do about it. Simulation OptimizationOptimization helps you make better choices when you have all the data, and simulation helps you understand the possible outcomes when you don’t. Frontline Solvers enable you to combine these analytic methods, so you can make better choices for resources you do control, taking into account the range of potential outcomes for factors you don’t control. This method, called simulation optimization, helps you make better resource allocation choices “here and now,” in situations with uncertainty. You can learn more on our Simulation Optimization page.
|