Description:
Today, simulation is widely accepted in the world of business to predict, to explain, to train, and to help identify optimal solutions. Simulation is used extensively in manufacturing to model production and assembly operations, develop realistic production schedules, study inventory policies, analyze reliability, quality, and equipment replacement problems, and design material handling and logistics systems. It is used in designing and evaluating computer and communication networks and scheduling resources in complex projects. Simulation also finds extensive application in both profit-seeking service firms such as financial and retail companies, and in non-profit service organizations such as health care, government, and education. These applications might involve the study of customer waiting-line behavior, evaluating surgical schedules, and designing efficient work flows in offices. Rather recently, due to increased availability and power of personal computers, simulation capabilities have been linked with spreadsheets to allow managers and knowledge worker to evaluate risks of financial investment, marketing, real estate, and other common types of business decisions.
Simulation models may be either deterministic or probabilistic. The type of simulation model in this project is probabilistic because it account for randomness in the data or the systems it represents. Some common examples of data that exhibit random behavior and are modeled probabilistically in simulation models are consumer sales, machine operating times until failure, customer service times, and project activity completion times.
There are two distinct types of probabilistic models: Monte Carlo simulation models and system simulation. Monte Carlo simulation is basically a sampling experiment whose purpose is to estimate the distribution of an outcome variable that depends on several probabilistic input variables. Monte Carlo simulation is often used to evaluate the expected impact of policy changes and risk involved in decision making. Systems simulation, on the other hand, explicitly models sequences of events that occur over time. Thus, inventory, queuing, manufacturing, and material-handling problems are among the types of situations addressed with systems simulation.
You also use Monte Carlo simulation to solve the project problem.
Application of Risk Analysis Problem:
Your group is a decision-making team and is considering a $10 million expansion project. Estimates of key input factors are (all uniformly distributed):
Market size: 100,000 to 350,000 tons
Selling price: $375 to $575
Market growth rate: 0 to 6% per year
Market share: 3 to 17%
Total investment required: $7 million to $10.5 million
Useful life of the new facility: 5 to 15 years
Residual value of facility: $3.5 million to $5 million
Operating costs: $320 to $550 per ton
Fixed costs: $250,000 to $375,000
Analyze the risks associated with this project.
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