Cost Risk Analysis
Cost risk analysis is important for ensuring
the success of a project.
Risk analysis is an essential tool for
executing risk work.
Cost risk analysis is important for
identifying and understanding possible
costs and risks that a manager could face
It helps managers to manage risks and
minimize their impact to project or
Cost risk analysis is an important planning tool that helps
an organization to save money, time and builds
organizational reputations (Modarres, 2016).
Effective risk management strategies and policies allow
organizations to identify the strengths, opportunities,
weaknesses, and threats of a project.
Proper planning and implementation of cost risk analysis
procedures and techniques help organizations to prepare
for unexpected events and respond effectively when a
need arises (Larson & Gray, 2015). Therefore, to ensure
project success, it is important to define the procedure for
handling potential risks in order to identify, mitigate and
avoid problems that could impact the success of the
The Importance of Cost Risk
Cost risk analysis is an important technique that
helps business organizations to ensure project
success as well as provides the framework to
identify and prepare for project risks (Garvey, Book
& Covert, 2016).
Cost risk analysis is provided numerous benefits to
an organization. The importance of cost risk
Planning for business success
Helps in business and project preparation
Helps in maximizing results or outcomes
Helps in evaluating project success
In addition, cost risk analysis provides other benefits as listed below;
Defines and break down the company plan or decision or process into its components by
drawing up a flowchart of outputs, inputs, activities, and events.
Calculates and estimates the costs and benefits associated with each component by
including possible direct, indirect, social and financial benefits and costs.
Helps to compare the sum of the benefits with the sum of the costs.
Helps to rank the components into a hierarchy by reflecting their impact on potential
success or failure on the entire process.
Helps in assigning weighting values for each component.
Helps managers to estimate the probability of failure or success for each component.
Helps organizations to multiply the likelihood of failure or success for each component by its
It also helps to compare the risk with costs and benefits associated with the project
The three-point estimate approach uses a
weighting to ensure that the most likely
estimates are weighted four times more
than the other estimates of pessimistic and
optimistic (Garvey, Book & Covert, 2016).
The formula is important in estimating the
cost and time of tasks for the project that a
It helps in estimating the cost and time of
activities for the project in terms of
research and development.
The three-point estimate technique
evaluates total estimate by using an
optimistic estimate, pessimistic estimate,
and the most likely estimate.
Uses of Three-Point Estimate
The 3-point estimate technique is applied in management
applications and information systems for designing of an
estimated probability distribution that represent the outcome
of future events, basing on limited information (Garvey,
Book & Covert, 2016).
The three-point estimate is used during cost estimation of
A 3-point estimate technique is a better estimate as
compared to a single-point estimate
Application for estimating the cost of risks
The three-point estimate technique uses the following steps
to establish estimates.
1) Breaks down the project into an estimable activity list
2) Estimates of E value and standard deviation for all tasks
3) Determines E value for the entire project work as E (Project
work) = ∑ E (Task)
4) The calculation of SD value for the entire project works as SD
(project work) = √∑ SD (Task) 2 .
Therefore, E (O+4M+P)/6
Where E is Estimate, (P) is the pessimistic estimate, (M)
most likely estimate and (O) is the optimistic estimate.
Standard deviation = (P-O)/6
Method of Moments
The method of the moment provides estimates
based solely on the law of a large number.
It assumes independent random variables have a
common distribution mean.
Variables for estimating are selected based on
probability density associated with an unknown
parameter value (Hulett, 2016).
The method uses four steps to link the sample
moments to parameter estimates.
1) If the model has given parameters, the function is
computed for the first moments.
2) The parameters are solved as functions of the
3) Sample moments are computed using a given data.
4) Replacing the distributional moments by the sample
moments to establish the formula for the method of
Monte Carlo Simulation
Monte Carlo simulation is the technique used for analyzing
It is used to analyze the uncertainty associated with risk
events and line item cost estimates.
Monte Carlo simulation develops procedure to handle
This is because projects in initial stages must balance
accuracy demands with the scarcity of details.
Monte Carlo simulation helps managers to establish project
cost and the funds required.
Cost of projects is essential aspect that management must
effectively evaluate to establish a proper framework to
manage projects successfully (Hulett, 2016).
Monte Carlo simulation is important in establishing
contingency and management reserves.
Therefore, an organization should strategically allocate time-
based funds for contingency reserve in order to decrease the
cost of capital (Larson & Gray, 2015).
Monte Carlo simulation provides a cost justification for
treating risks and established response plans.
Monte Carlo simulation provides a clear and sufficient basis
for project contingency and management reserves
Steps of Monte Carlo Simulation
The Monte Carlo simulation used the
following steps to evaluate risks and
1) Awareness to stakeholder
2) Constant management of risk
3) Preparation of initial estimates
4) Determining correlations
5) Building simulation model
6) Run simulations for Monte Carlo (create a
baseline, post-mitigated and pre-mitigated)
7) Produce and communicate results
The Comparison of the
Monte Carlo, three-point estimates and
method of moment techniques are used to
calculate estimates of project costs,
budgets and risks
The three techniques use probability in
order to establish estimates or results.
Estimates are required for Monte Carlo,
three-point estimates and method of
They provide a detailed analysis that
supports the decision-making process in
the presence of uncertainty.
They are used to improve confidence in
scheduling and budget projections.
The contrast of the
The three-point estimate technique does not
mathematically consider complexity.
The three-point estimate uses a weighted average
technique or beta probability distribution approach
to capture estimates for all work packages
(optimistic, pessimistic and normal) (Hulett, 2016).
Monte Carlo simulation provides estimates for
duration and costs and takes uncertainty into
Monte Carlo simulation can deal with high-
dimensional models and provides different answers
for each simulation.
The Monte Carlo simulation process starts with the
Monte Carlo simulation is useful for simulating a
phenomenon with considerable uncertainty in
inputs and systems with a diverse number of
degrees of freedom.
The Method of the moment estimation is solely
based on the law of large numbers.
Monte Carlo simulations, a moment of a
method and three-point estimate are used
to improve the confidence of scheduling
and project budgeting.
Management should apply the best
technique in order to ensure accurate cost,
time and budget estimation.
Proper risk-cost analysis enhances the
success of project implementation and
enhances the process of decision making.
Aveng, T. (2015). Risk analysis. John Wiley & Sons.
Garvey, P. R., Book, S. A., & Covert, R. P. (2016). Probability methods for cost uncertainty
analysis: A systems engineering perspective. Chapman and Hall/CRC.
Hulett, D. (2016). Integrated cost-schedule risk analysis. Routledge.
Larson, E. W., & Gray, C. F. (2015). A Guide to the Project Management Body of Knowledge:
PMBOK (®) Guide. Project Management Institute.
Modarres, M. (2016). Risk analysis in engineering: techniques, tools, and trends. CRC press.