The book "How to Measure Anything" by Douglas W. Hubbard has a chapter on using Monte Carlo simulations to do project planning and gather estimates. It's successfully been used for project planning large complex projects like building nuclear power plants, or projects that NASA or the Navy or similar have done.
The approach is slightly different then the article above describes. Instead it has each engineer go through calibration exercises until they can fairly accurately produce 5th and 95th confidence interval estimates. Then each engineer provides 5th and 95th confidence interval estimates for each item that needs to be worked on. Those confidence intervals are kept separate. You can then run a Monte Carlo simulation where each piece of work is weighted randomly assigned to each engineer that provided estimates for that particular item, and randomly picks a number based off their provided confidence interval estimate on how long it ends up taking them to complete the item for this particular simulation.
I was on a small team that used the above technique. We were in a large company trying to launch a new product, and given manufacturing lead time, and seasonality of the market demand, it was very important that we could provide a good estimate to the business when we could have the software portion of the MVP completed. The business provided us with what they thought the MVP features were, we further added in engineering tasks that weren't business facing, but needed to be completed. We confidence interval estimated those, and then also confidence interval estimated our personal vacation days, sick days, as well as a bucket of "unidentified work". The 80th percentile Monte Carlo simulation put us out a little more then a year. Our actual delivery was off by only a week from the Monte Carlo tp80; I don't remember in which direction, but it wasn't consequential to the business.
The approach is slightly different then the article above describes. Instead it has each engineer go through calibration exercises until they can fairly accurately produce 5th and 95th confidence interval estimates. Then each engineer provides 5th and 95th confidence interval estimates for each item that needs to be worked on. Those confidence intervals are kept separate. You can then run a Monte Carlo simulation where each piece of work is weighted randomly assigned to each engineer that provided estimates for that particular item, and randomly picks a number based off their provided confidence interval estimate on how long it ends up taking them to complete the item for this particular simulation.
I was on a small team that used the above technique. We were in a large company trying to launch a new product, and given manufacturing lead time, and seasonality of the market demand, it was very important that we could provide a good estimate to the business when we could have the software portion of the MVP completed. The business provided us with what they thought the MVP features were, we further added in engineering tasks that weren't business facing, but needed to be completed. We confidence interval estimated those, and then also confidence interval estimated our personal vacation days, sick days, as well as a bucket of "unidentified work". The 80th percentile Monte Carlo simulation put us out a little more then a year. Our actual delivery was off by only a week from the Monte Carlo tp80; I don't remember in which direction, but it wasn't consequential to the business.