What is a Monte Carlo Simulation?

The video explains Monte Carlo simulation, a technique named after Monte Carlo's gambling tables, used in project management to assess schedule and budget risks. It involves estimating activity durations and probabilities, represented as probability distributions, often using the Beta function. The Monte Carlo method uses computing power to simulate project durations, providing a probability distribution for the project's end date. Challenges include making accurate estimates and the method's assumption of activity independence. Despite its complexity, understanding Monte Carlo is crucial for project managers focused on risk management.

What is the primary use of Monte Carlo simulations in project management?

• To calculate tax returns

• To design casino games

• To assess schedule and budget risks

• To predict weather patterns

Which distribution is commonly used in project management for Monte Carlo simulations?

• Normal distribution

• Poisson distribution

• Beta function

• Exponential distribution

What does a triangular distribution suggest in the context of project management?

• A delay is more likely than finishing early

• No delays are possible

• A delay and finishing early are equally likely

• Finishing early is more likely than a delay

What is the main challenge in using Monte Carlo simulations?

• Training staff to use the method

• Understanding complex mathematical equations

• Making accurate estimates of distributions

• Finding the right software

What assumption does the Monte Carlo method make about project activities?

• They are all dependent on each other

• They are independent of each other

• They are all completed simultaneously

• They are all delayed equally

Why is it important for project managers to understand Monte Carlo simulations?

• To avoid using any simulations

• To impress their colleagues

• To ensure all projects are completed on time

• To make informed decisions about risk management

What is a key limitation of Monte Carlo simulations?

• They require too much data

• They assume activities are independent

• They are too expensive to run

• They are only useful for small projects