1. Problem Definition
When
estimating the cost for a project, product or other item or investment, there is always uncertainty
as to the precise content of all items in the estimate, how work will be
performed, what work conditions will be like when the project is executed and
so on. These uncertainties are risks to the project. Some refer to these risks as
"known-unknowns" because the estimator is aware of them, and based on
past experience, can even estimate their probable costs. The estimated costs of
the known-unknowns is referred to by cost estimators as cost contingency.
2. Feasible
Alternatives
AACE International, the Association for the
Advancement of Cost engineering, has defined contingency as
"An amount added to an estimate to allow for items, conditions, or events
for which the state, occurrence, or effect is uncertain and that experience
shows will likely result, in aggregate, in additional costs. Typically
estimated using statistical analysis or judgment based on past asset or project
experience.
In general,
there are four classes of methods used to estimate contingency.
a.
Expert
judgment
b.
Predetermined
guidelines (with varying degrees of judgment and empiricism used)
c.
Simulation
analysis (primarily risk analysis judgment incorporated in a simulation such as
Monte-Carlo)
d.
Parametric
Modeling (empirically-based algorithm, usually derived through regression
analysis, with varying degrees of judgment used).
3. Develop the outcomes for each alternative
Of the four methods, in
order to complete the formulation
of the proposed budget the results to be obtained is an estimate of
the value of the price can be expected between low cost and high cost
or one of them.
a. Expert judgment; Expert judgment involves
consulting with human experts to use their experience and understanding of a
proposed project to provide an estimate for the cost of the project.
b.
Predermined
guidelines; The guidelines are used as a reference to
estimate the price. basics in join empiriscism
c.
Simulation
analysis; risk analysis
is part of every decision we make. We are constantly faced with uncertainty,
ambiguity, and variability. And even though we have unprecedented access to
information, we can’t accurately predict the future. Monte Carlo simulation
(also known as the Monte Carlo Method) lets you see all the possible outcomes
of your decisions and assess the impact of risk, allowing for better decision
making under uncertainty.
d. Parametric
modeling; The algorithmic method involves the use of equations to perform
software estimates. The equations are based on research and historical data and
use such inputs as Source Lines Of Code (SLOC), number of functions to perform,
and other cost drivers such as language, design methodology, skill-levels, risk
assessments, etc.
4. Acceptable
Criteria
the
method chosen should be consistent with the first principles of risk management
in that the method must start with risk identification, and only then are the
probable cost of those risks quantified. In best practice, the quantification
will be probabilistic in nature (Monte-Carlo is a common method used for
quantification).
5. Analysis and comparison of the alternatives
Step by step to be
taken as a measure of the proposed budget is
the reference price is obtained from multiple sources.
Of all the three
categories of sources made minimum cost,
mean cost and maximum
cost then simulated using Palisade @
Risk software. Below
is an example calculation software
in question.
6. Select
the preferred alternative
P5 to P95 estimate is a range
that will be proposed to the budget, at Pertamina we usually use the P90.
7. Performance
Monitoring & Post Evaluation of Result
It is
known that in order to propose
a budget value,
can be specified with a simulation analisys
in this case we can see using the software @
Risk and analisys
to create a simulation
must have some reference
sources for reference prices can be simulated.
Reference
Wikipedia. Cost contingency. Retrieved from : http://en.wikipedia.org/wiki/Cost_contingency
Parametric Cost Estimating Handbook. Estimation Methodologies. Retrieved from : http://cost.jsc.nasa.gov/pcehhtml/pceh.htm
Qfinance. Analysis Using Monte Carlo Simulation. Retrieved from : http://www.qfinance.com/asset-management-checklists/analysis-using-monte-carlo-simulation
Palisade. Monte Carlo Simulation. Retrivied from: http://www.palisade.com/risk/monte_carlo_simulation.asp
