If you talk about risk management with MC simulations, there are many applications in civil engineering or other engineering field. Concerning project risk, there are also many applications of Bayesian networks (together with MC) to engineering project considering uncertainties and dependencies.

Risk-based integrity assessment and management is very common in the high-risk industries that are strictly regulated by the government, including but not limited to nuclear plants, natural gas pipelines, etc. Most applications focused on the Monte Carlo simulation integrated with the Parallel computational technique to efficiently evaluate the probabilities of failure. Consider corroded high-pressured pipelines as an illustrative example. Pipeline risk engineers may forecast the time-dependent annual failure probability of each corroded pipeline segments, based on the geometric properties of corrosion defects obtained from inspection and the probabilistic corrosion growth inferred by comparison of inspection results at different times. The pipeline segments with the probability of failure exceeding a predefined threshold are considered critical and treated as candidates for rehabilitation. Hope this clarification may help.

Many companies use Monte Carlo simulation as an important part of their decision-making process.

• General Motors, Proctor and Gamble, Pfizer, Bristol-Myers Squibb, and Eli Lilly use simulation to estimate both the average return and the risk factor of new products. At GM, this information is used by the CEO to determine which products come to market.
• GM uses simulation for activities such as forecasting net income for the corporation, predicting structural and purchasing costs, and determining its susceptibility to different kinds of risk (such as interest rate changes and exchange rate fluctuations).
• Lilly uses simulation to determine the optimal plant capacity for each drug.
• Proctor and Gamble uses simulation to model and optimally hedge foreign exchange risk.
• Sears uses simulation to determine how many units of each product line should be ordered from suppliers.
• Oil and drug companies use simulation to value "real options," such as the value of an option to expand, contract, or postpone a project.
• Financial planners use Monte Carlo simulation to determine optimal investment strategies for their clients’ retirement.

Monte Carlo simulation is the standard approach for aggregating cost distributions of individual elements of major projects and programs for US government agencies since the 1980s. The output is a single distribution representing the total project or program cost from which a point estimate and confidence level can be read. Typically, the 80 percent confidence level is used for budgeting. There are any number of references and examples in the cost analysis literature.

Dear Gunamani ,

You can simulate your project by using the Primavera risk management (PRA) software which is developed by Oracle. This software has two parts; the first part is about uncertainty and the second part is about project risk management.

Should you require further info please let me know.

[email protected]

Energy and oil companies use Monte Carlo simulation in an intensive way.

As our friend Antonio said, MontCarlo simulation is a simulation method that can be used in all kind of project. Practically you can simulate your project to see which results will be achieved if the project completed. 🌹

In addition to aggregating cost distributions as I mentioned earlier, Monte Carlo simulation is used for schedule analysis and cost/schedule analysis. See these papers for examples.

Like other members said the MC application is very broad in scope. First, you should consider what kind of project management problem will be solved using MC, after that you can choose the industry. After you decided, you can start to create the system model, describe the scenario (how the system work) and the parameter of input, process, and output in sequence. The important thing about using MC is the model. You can make an assumption for some condition to make a model more logically (similar to the actual condition but not exactly the same, it is very difficult). Make sure you can measure important parameters from each step from your model. Finally, you can simulate the model to get the result. Validate your model with historical data (please don't generate the data by your self, using some reference from database is necessary), make some adjustments to reduce deviation and then you can use it to predict the future situation (at this step you can generate data by your self using what-if analysis based on the industry situation). To simulate the model you can select appropriate software or you can use excel for a simple model. I hope It can help you.

Thanks for all the answers :) It's helpful information.