Introduction to Monte Carlo Simulation

This 8-hour course answers the questions:

• What is Monte Carlo Simulation?
• What type of data do I need for Monte Carlo Simulation?
• Should I use EXCEL, MINITAB or a commercial package like Crystal Ball?
• What should I expect from Managers/Customers when I present my analysis?

Monte Carlo Simulation is a method that has many applications and flavors. First of all, “simulation” refers to the fact that we build an artificial model of a real system in order to study and understand the system. The “Monte Carlo” part of the name alludes to the randomness inherent in the analysis. The name “Monte Carlo” was coined by [physicist Nicholas] Metropolis (inspired by [Stanislaw] Ulam’s interest in poker) during the Manhattan Project of World War II, because of the similarity of statistical simulation to games of chance, and because the capital of Monaco was a center for gambling and similar pursuits. In short, The Monte Carlo method provides approximate solutions to a variety of mathematical problems by performing statistical sampling experiments on a computer. The method applies to problems with no probabilistic content as well as to those with inherent probabilistic structure. When you leave this course you will be able to build a Monte Carlo simulation for your particular process or problem using either EXCEL, MINITAB or Crystal Ball.

Course Outline:

1. History/Background
2. Discrete & Continuous Monte Carlo Simulation
3. The need for Random numbers
4. Monte Carlo simulation in EXCEL™ & Crystal Ball™
5. In-class examples
   1. Barber shop simulation
   2. Buffon needle problem
   3. Forecasting Manufacturing Demand
   4. Improving business processes (RFP, Contract processing,
   5. Design job flow, Design Development Process
   6. Risk of not achieving a deadline
   7. Tolerancing with Monte Carlo simulation
   8. Calculating Max Stress
   9. Safety Risk Monte Carlo Simulation

Introduction to Discrete Event Modeling and Simulation

Modeling and Simulation (M&S) is widely utilized to represent physical systems and processes in order to reduce costs and increase performance. It can be utilized as a decision tool in order to support trade-off analysis and develop cost-effective solutions early in design or during product sustainment. This course will provide an introduction to discrete event simulation. The course will teach participants the basics of M&S, guide them through building a basic and advanced models and illustrate how to effectively performance a trade-off analysis. The course will illustrate concepts through several comprehensive examples utilizing the Flexsim simulation package. Each student will be expected to bring a laptop capable of running Flexsim (see flexsim.com). A temporary license shall be provided to students during the class.

Course Outline:

TBD