Project Risk Management Budget Report: Monte Carlo Simulation Analysis

This project management report provides a detailed analysis of project risk and budget, utilizing quantitative risk analysis techniques and Monte Carlo simulation. The report begins with an executive summary and defines the project scope. It presents a recommended baseline budget, including 10-30 cost variables, followed by sensitivity analysis to assess the impact of variable changes. The analysis incorporates risk event evaluation, contingency recommendations, and the comparison of baseline budgets against organizational policy. The report employs various tools such as sensitivity analysis, event trees, probability analysis, and fuzzy set theory to evaluate risk factors. The main objective is to calculate numerical probabilities associated with risk factors using Microsoft Excel and Monte Carlo simulation. The sensitivity analysis helps in evaluating the nature of the risks involved, while the event tree analyzes the chronological series of events and their outcomes. The report includes a discussion on quantitative and qualitative risk analysis and the importance of data collection. It also examines the impact of different variables, such as sales price and construction costs, on project outcomes. The report concludes with a summary of findings and recommendations for managing project risks effectively.

Project Management
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Executive summary
We shall be making calculation of the consequence for the involved risk, by carrying out the
financial management’s Risk Evaluation, utilizing the quantitative risk analysis on the numerical
values for the probability. Monte Carlo simulation of the palisade prediction shall be used for
this procedure. For the calculations and numerical values we shall use Microsoft Excel files.
The analysis of this project will be carried out by algorithms and techniques which are as given
below,
1. Probability analysis
2. Sensitivity analysis
3. Fault trees
4. Monte Carlo simulation
5. Fuzzy set theory
6. Influence diagrams
7. Event trees
8. Decision trees
9. Artificial neural networks.
For the analysis of the base line budgets, we shall be making use of the quantise analysis risk
and sensitivity analysis risk, in the initial part of this project. The second module consists of the
Statistics of the risk analysis and the third module consists of Probability of the risk analysis. For
the fourth module, Monte Carlo will be used for the risk analysis simulation. In the fifth and final
module, the contingency analysis of the eestimate, which allows any changes that experience
shows, will be carried out. The fundamental examination of the essential spending plan is $
135,000, $ 115,000, $ 100,000 and unit value 6. Presently, utilizing 9% of the utilization of
expert information, important information and master sentiments, the change of venture spending
plans, under 10% of the business cost, and $ 115,000 because of the likelihood of $ 102,500
because of all out overall revenue and all out benefit of the benefit, investigation will be finished.
The difference in affectability hazard investigation is under 10% of the benefit will be done. Less
than 10% of the profit is the sensitivity risk analysis. Simultaneously, the remaining value is $
64,167, while the sales price ($ 100,000) and the cost of construction ($ 75,000). Other models
are 10% lower (ie $ 65,000), 103,500), its highest value ($ 115,000) and all other, if the sales
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price ($ 135,000) and sales model ($ 135,000). Highest values all are the variables, and then the
project will cost $ 11,167, which is less than -119% less than the 57833 dollar profit shall be
carried out.
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Table of Content
s
Project Scope................................................................................................................................................................... 4
Recommended Baseline Budget........................................................................................................................................ 5
Sensitivity analysis......................................................................................................................................................... 12
Risk Event analysis......................................................................................................................................................... 13
Contingency recommendation........................................................................................................................................ 15
Sensitivity analysis cost variable..................................................................................................................................... 17
Sensitivity risk event analysis......................................................................................................................................... 19
Comparing baseline budget on the organization policy probability.................................................................................23
Conclusion..................................................................................................................................................................... 24
Appendix....................................................................................................................................................................... 25
Reference....................................................................................................................................................................... 30
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Project Scope
The main objective of this project is to calculate the values of the numerical probabilities
with the risk factor by utilizing in Microsoft, the Monte Carlo simulation of the palisade
prediction and by implementing the Quantitative risk evaluation. Sensitive Analysis is one of the
widely used quantitative risk analysis technique and tool to help the risk manager in evaluating
and for analysing the nature of Risk involved. To evaluate the consequences of the project
results, we will examine the changing values in the risk variables. The tools which we shall be
implementing for this project in the quantitative technique evaluation are, Sensitivity analysis
 Event trees
 Probability analysis
 Monte Carlo simulation
 Fuzzy set theory
 Artificial neural networks.
 Fault trees
 Decision trees
 Influence diagrams
The Sensitivity Analysis method is a popular and commonly used analytical methods, which is
utilized in all the different evaluations for the risk management, contains the following factors,
 Identification
 Profit
 Cost
 Schedule
 Analysis
 Investment
 Treatment and control of risk events of the budget
 Duration
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 Baseline estimates
As part of the analysis method, we shall be using different modules in the project. The quantise
analysis risk will be carried out by analysing the base line budgets and evaluating the sensitivity
analysis risk in the First module. The second module will be sued for the Statistics of the risk
analysis. The third module will be used for the Probability of the risk analysis. The fourth
module is the Monte Carlo simulation of the risk analysis. Contingency analysis of the estimate
allows changes in the project and this is implemented and investigated upon, forming the fifth
and the final module of the project.
Recommended Baseline Budget
Quantitative and Qualitative are the two types of Risk Analysis. By making use of the sensitivity
analysis in the project budgets for the risk management, we can evaluate the base line budget by
“Qualitative risk analysis”. The values and numerical techniques to express and measure the
value of uncertainty are done by the “Quantitative risk analysis”. By using this tool, a sense of
false impression of precision and reliability can be created. Inspite of the number of tools and
quantitative techniques available today, we should keep in mind that these are just for assisting in
the process of decision-making but they are not be used in the place of sound judgement. They
will be required in the project at different stages, as some these quantitative techniques and tools
are complex, sophisticated and easily available.
For the base line budgets and basically for the quantitative risk analysis, the below given
categories can be used,
1. Central tendency
2. Frequency distribution
3. Applying descriptive statistics
4. Measure of dispersion
Utilising the quantitative risk measurements, lot of ground work, information collecting, and data
gathering has to be carried out. The time and efforts required for conducting huge data collection
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should be worthwhile and useful if it helps and assists in the risk management process.
Individual and people who have the responsibility of data gathering and the right accurate
method for this information collection, final result will get the validation for the procedure of
quantitative evaluation will depend on the right or wrong way of information collection.
Event Tree
Making use of the Boolean logic to test the chronological series of following events or its results,
an event is evaluated and there is an inductive analytical diagram is known as Even Tree. We
make use of the below parameters in our project for analysing of the data,
 Duration
 Budgets
 Estimation of the budget baseline cost on the risk event on the budget
 Risk analysis on the cost,
 Budget baseline cost to be specified.
Below is the displayed image of our project’s “Event Tree”,
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Fault Tree
Decision Tree
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Monte Carlo simulation
As the probability distribution is 32% for any given factor has the uncertainty inherent for taking
the value range of $632,167, in creating models in this project by utilizing the Monte Carlo tool
for simulating the risk analysis. Every time, a different set of random values is utilized from
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datasets, utilizing the Monte Carlo tool for outcome calculations over and over for the
investment results as a diagram is shown below,
Probability functions of the cost and profit (Bramston, 2012).
Sales Price Profit % change Land Cost Profit % change
-150%
-100%
-50%
0%
50%
100%
150%
monte carlo simulation
Series1 Series2 Series3 Series4 Series5
Series6 Series7 Series8 Series9 Series10
Influence diagram
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Fuzzy set
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Neural Network
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