Monte Carlo simulation
|
[edit] Introduction
A Monte Carlo simulation is a computational risk analysis tool applied to situations that are uncertain or variable. It is a mathematical way of predicting the outcomes of a situation or set of circumstances by giving a range of possible outcomes and assessing the risk impact of each. It is also referred to as the ‘Monte Carlo method’ or ‘probability simulation’ and is used in many diverse applications such as construction, engineering, finance, project management, insurance, research, transportation and so on.
The name is thought to have been devised by scientists working on the atom bomb in reference to the principality of Monaco – well known for its casinos.
A key characteristic of a Monte Carlo simulation is that it provides a more realistic picture of likely future outcomes by generating a range of possible values, not just a single estimate. In construction, it can be used to predict how long a particular task will take and its likely effect on the programme schedule.
[edit] Mathematical modelling
To begin with, a mathematical model is created using a range of estimates for a particular task. So, for example, a project manager may consider the time it may take to complete a set of tasks by:
- Considering worst case scenarios (ie the maximum expected time values for all variables),
- Considering best-case scenarios (ie the minimum expected time values for all variables).
- Considering the most likely result.
So, for a particular set of tasks on a construction project, the project manager may estimate the following:
| Task | Best case (minimum) | Most likely | Worst case (maximum) |
| Task 1 | 2 weeks | 4 weeks | 7 weeks |
| Task 2 | 3 weeks | 6 weeks | 9 weeks |
| Task 3 | 8 weeks | 13 weeks | 18 weeks |
| Total | 13 weeks | 23 weeks | 34 weeks |
From the table above, it can be seen that the range of outcomes for completing the three tasks ranges from 13 to 34 weeks.
These estimates are inputted into the Monte Carlo simulation which may be run 500 times. The likelihood of a particular result can be tested by counting how many times it was returned in the simulation and a percentage created.
So, it may be that the after 500 simulations, the most likely estimate of 23 weeks completion was only returned 20% of the time (a probability of only 1 in 5). Whereas, completion in 30 weeks was returned 80% of the time (4 in 5), which may be a more realistic basis for the project manager’s decision making.
Note: the extremes may be discounted. It should also be noted that the method is only as good as the original estimates used to create the model. Also, the values outputted are only probabilities but they may give planners a better idea of predicting an uncertain future.
Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.
NB The Green Book, Central Government Guidance On Appraisal And Evaluation, Published by HM Treasury in 2018, suggests that: ‘Monte Carlo Analysis is a simulation-based risk modelling technique that produces expected values and confidence intervals as a result of many simulations that model the collective impact of a number of uncertainties.’
[edit] Related articles on Designing Buildings Wiki
- Code of practice for project management.
- Code of practice for programme management.
- Construction project.
- Construction project manager - morning tasks.
- Contingency theory.
- Game theory.
- Microsoft's six ways to supercharge project management.
- Multi criteria decision analysis.
- Project manager.
- Project execution plan.
- Project manager's report.
- Project monitoring.
- Risk management.
Featured articles and news
ECA progress on Welsh Recharging Electrical Skills Charter
Working hard to make progress on the ‘asks’ of the Recharging Electrical Skills Charter at the Senedd in Wales.
A brief history from 1890s to 2020s.
CIOB and CORBON combine forces
To elevate professional standards in Nigeria’s construction industry.
Amendment to the GB Energy Bill welcomed by ECA
Move prevents nationally-owned energy company from investing in solar panels produced by modern slavery.
Gregor Harvie argues that AI is state-sanctioned theft of IP.
Heat pumps, vehicle chargers and heating appliances must be sold with smart functionality.
Experimental AI housing target help for councils
Experimental AI could help councils meet housing targets by digitising records.
New-style degrees set for reformed ARB accreditation
Following the ARB Tomorrow's Architects competency outcomes for Architects.
BSRIA Occupant Wellbeing survey BOW
Occupant satisfaction and wellbeing tool inc. physical environment, indoor facilities, functionality and accessibility.
Preserving, waterproofing and decorating buildings.
Many resources for visitors aswell as new features for members.
Using technology to empower communities
The Community data platform; capturing the DNA of a place and fostering participation, for better design.
Heat pump and wind turbine sound calculations for PDRs
MCS publish updated sound calculation standards for permitted development installations.
Homes England creates largest housing-led site in the North
Successful, 34 hectare land acquisition with the residential allocation now completed.
Scottish apprenticeship training proposals
General support although better accountability and transparency is sought.
The history of building regulations
A story of belated action in response to crisis.
Moisture, fire safety and emerging trends in living walls
How wet is your wall?
Current policy explained and newly published consultation by the UK and Welsh Governments.
British architecture 1919–39. Book review.
Conservation of listed prefabs in Moseley.
Energy industry calls for urgent reform.
Comments
In undertaking a Monte Carlo risk analysis it should be noted that the variables to which the probabilities are assigned should be independent of each other. As an example the price of reinforced concrete and the price of steel are not necessarily independent of each other.