Monte Carlo simulation - Revision history

Designing Buildings at 10:46, 10 September 2020

2020-09-10T10:46:15Z

← Older revision		Revision as of 10:46, 10 September 2020
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	* Contingency theory.		* Contingency theory.
	* Game theory.		* Game theory.
−	* Microsoft's six ways to supercharge project management	+	* Microsoft's six ways to supercharge project management.
		+	* Multi criteria decision analysis.
	* Project manager.		* Project manager.
	* Project execution plan.		* Project execution plan.

Designing Buildings at 10:22, 10 September 2020

2020-09-10T10:22:35Z

← Older revision		Revision as of 10:22, 10 September 2020
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	Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.		Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.
		+
		+	NB [https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/685903/The_Green_Book.pdf 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.’

	= Related articles on Designing Buildings Wiki =		= Related articles on Designing Buildings Wiki =
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	* Risk management.		* Risk management.

−	[[Category:Theory]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]	+	[[Category:DCN_Definition]] [[Category:Theory]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]

Editor at 18:10, 25 October 2019

2019-10-25T18:10:24Z

← Older revision		Revision as of 18:10, 25 October 2019
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	* Contingency theory.		* Contingency theory.
	* Game theory.		* Game theory.
		+	* Microsoft's six ways to supercharge project management
	* Project manager.		* Project manager.
	* Project execution plan.		* Project execution plan.

Designing Buildings at 08:16, 22 February 2019

2019-02-22T08:16:56Z

← Older revision		Revision as of 08:16, 22 February 2019
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	= Introduction =		= Introduction =

−	A Monte Carlo simulation is a ~~computerised~~ risk analysis tool applied to situations that are uncertain or variable. It is a mathematical way of predicting 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~~, the environment, and oil~~ and ~~gas~~.	+	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.		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.
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	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.		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.

−	Mathematical modelling	+	= Mathematical modelling =

−	To begin with, a mathematical model is ~~typically~~ 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:	+	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:

−	1 Considering worst case scenarios (ie the maximum expected time values for all variables) ~~and~~	+	# 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).
−	~~2 Consider~~ best-case scenarios (ie the minimum expected time values for all variables).	+	# Considering the most likely result.
−		+
−	~~3 Consider~~ the most likely result	+

	So, for a particular set of tasks on a construction project, the project manager may estimate the following:		So, for a particular set of tasks on a construction project, the project manager may estimate the following:
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	From the table above, it can be seen that the range of outcomes for completing the three tasks ranges from 13 to 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 ~~for a total of~~ 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.	+	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.		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~~: completion in 13 weeks was only returned 1% of the time while completion in 34 weeks was returned in 100% of the simulations~~.)	+	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.
−		+
−	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.	+
−		+
−	~~The Monte Carlo simulation predicts what could happen and the likelihood of it happening and is designed to allow for better decision making in an uncertain world~~.	+

	Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.		Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.

Editor at 19:32, 20 February 2019

2019-02-20T19:32:48Z

← Older revision		Revision as of 19:32, 20 February 2019
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	The Monte Carlo simulation predicts what could happen and the likelihood of it happening and is designed to allow for better decision making in an uncertain world.		The Monte Carlo simulation predicts what could happen and the likelihood of it happening and is designed to allow for better decision making in an uncertain world.
		+
		+	Palisade @RISK for Excel from Palisade Corporation is just one of the available software programmes able to undertake Monte Carlo simulations.

	= Related articles on Designing Buildings Wiki =		= Related articles on Designing Buildings Wiki =

Editor at 19:25, 20 February 2019

2019-02-20T19:25:45Z

← Older revision		Revision as of 19:25, 20 February 2019
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	= Related articles on Designing Buildings Wiki =		= 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.
		+	* Project manager.
		+	* Project execution plan.
		+	* Project manager's report.
		+	* Project monitoring.
		+	* Risk management.

	[[Category:Theory]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]		[[Category:Theory]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]

Editor at 19:22, 20 February 2019

2019-02-20T19:22:53Z

← Older revision		Revision as of 19:22, 20 February 2019
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	{\|		{\|
−	\| [[File:~~Monte Carlo large~~.jpg]]	+	\| [[File:Monte_Carlo_large.jpg\|link=File:Monte_Carlo_large.jpg]]
	\|}		\|}

−	Introduction	+	= Introduction =

	A Monte Carlo simulation is a computerised risk analysis tool applied to situations that are uncertain or variable. It is a mathematical way of predicting 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, the environment, and oil and gas.		A Monte Carlo simulation is a computerised risk analysis tool applied to situations that are uncertain or variable. It is a mathematical way of predicting 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, the environment, and oil and gas.
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	{\|		{\|
−	~~\|width="25%"~~\| Task	+	\| Task
−	~~\|width="25%"~~\| Best case (minimum)	+	\| Best case (minimum)
−	~~\|width="25%"~~\| Most likely	+	\| Most likely
−	~~\|width="25%"~~\| Worst case (maximum)	+	\| Worst case (maximum)
	\|-		\|-
	\| Task 1		\| Task 1

Editor: Created page with "{| | File:Monte Carlo large.jpg |} Introduction A Monte Carlo simulation is a computerised risk analysis tool applied to situations that are uncertain or variable. It is a ..."

2019-02-20T19:22:21Z

Created page with "{| | File:Monte Carlo large.jpg |} Introduction A Monte Carlo simulation is a computerised risk analysis tool applied to situations that are uncertain or variable. It is a ..."

New page

{|
| [[File:Monte Carlo large.jpg]]
|}

Introduction

A Monte Carlo simulation is a computerised risk analysis tool applied to situations that are uncertain or variable. It is a mathematical way of predicting 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, the environment, and oil and gas.

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.

Mathematical modelling

To begin with, a mathematical model is typically 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:

1 Considering worst case scenarios (ie the maximum expected time values for all variables) and

2 Consider best-case scenarios (ie the minimum expected time values for all variables).

3 Consider the most likely result

So, for a particular set of tasks on a construction project, the project manager may estimate the following:

{|
|width="25%"| Task
|width="25%"| Best case (minimum)
|width="25%"| Most likely
|width="25%"| 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 for a total of 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: completion in 13 weeks was only returned 1% of the time while completion in 34 weeks was returned in 100% of the simulations.)

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.

The Monte Carlo simulation predicts what could happen and the likelihood of it happening and is designed to allow for better decision making in an uncertain world.

= Related articles on Designing Buildings Wiki =

[[Category:Theory]] [[Category:Construction_management]] [[Category:Construction_techniques]] [[Category:Cost_/_business_planning]]