Simulation modeling is the most widely used operations research/systems engineering technique for designing new systems and improving the performance of existing ones. Yet many so-called simulation studies result in: (1) systems that don’t meet their crucial performance requirements, (2) unnecessary capital expenditures or operating expenses, and (3) the potential for loss of human life. These failures are usually caused by the lack of technical training or relevant experience on the part of the analysts. Many people view simulation modeling as just being a complicated exercise in computer programming, when, in fact, sound simulation studies also require a technical background in simulation methodology (model validation, choosing simulation input-probability distributions, basics of random-number generators, design and analysis of simulation experiments, etc.), stochastic modeling (e.g., queueing theory), probability, and statistics. A university course that focuses on the use of a particular simulation-software product or vendor training, although important, is definitely not sufficient for project success. This course will jump-start your use of simulation by quickly identifying the most-common reasons for failure of simulation projects and then providing you with the best modeling approaches and simulation methodologies for avoiding these pitfalls. Versions of this course have been given for organizations such as Booz Allen & Hamilton, Clorox, Joint Warfare Analysis Center (JWAC), Michigan Simulation Users Group, Military Operations Research Society, Sandia National Labs, U.S. Army, and U.S. Navy.

“Great summary of the pitfalls in a simulation study!”  Manufacturing Engineer, General Motors

“Very superb presentation.”  Edward Williams, Professor, University of Michigan, Dearborn


What You Will Learn (look to the right for more detail):

  1. Ten Pitfalls Related to Modeling and Validation Process
  2. Five Pitfalls Related to Use of Commercial Simulation Software
  3. Three Pitfalls Related to Modeling of System Randomness
  4. Five Pitfalls Related to Design and Analysis of Simulation Experiments
  5. Best Modeling Practices and Simulation Methodologies for Avoiding These Pitfalls