Project Costing with Monte Carlo Simulated Probabilities

Name: Project Costing with Monte Carlo Simulated Probabilities
Rating: 4.3 (40 reviews)

Using @RISK to quantify project contingency considering price and quantity variations and risk events.

Created byFernando Hernandez

Last updated 3/2020

English

What you'll learn

How to calculate probabilistic contingency using Monte Carlo simulation, considering all kinds of risks on any type of project. The objective is to demonstrate a number of basic and intermediate tools from @RISK that are relevant to current practices used in the world of project management. The goal is to show how @RISK tools can be used to represent quantitative – not qualitative – risk analysis.

Course content

5 sections • 24 lectures • 2h 6m total length

Introduction to the course2:32
We introduce the course and give you tips on how to best learn techniques on the subject. Make sure you download the resources: A detailed step-by-step PDF document containing 79 pages with all the course material and instructions and the two versions of the Excel workbook, solved and unsolved files.
Using a Probabilistic Approach2:04
Introduction: The module objective is to demonstrate a number of basic and intermediate tools from @RISK that are relevant to current practices used in the world of project management.
Contingency Reserve Usage7:59
Contingency and management reserves are established to respond to risks so that risks do not compromise the project.
Considering Event Risks6:33
Defining event risks that may or may not occur and may have an impact on the project.

Inserting Variability To Project Activities7:52
Using @RISK we use three parameter points: a minimum, a most likely or mode value and a maximum in order to recognize the varying nature of activities.
Inserting Variability To Project Activities: Part 23:40
We continue using a PERT function to insert unit price and quantity variation on activities.
Structuring The First Event Risk: Client Breach3:56
A typical category of event risk that either happens or not. It is binary in nature.
Structuring The Second Event Risk: Subcontractors Going Out Of Business: 17:49
Handling a risk that is not binary in nature. The bankruptcy of any number of sub-contractors is a mult-frequency type of risk.
Structuring The Second Event Risk: Subcontractors Going Out Of Business. Part 24:23
Inserting a COMPOUND distribution to correctly handle multi-frequency risks with varying impacts for each potential bankruptcy.
Structuring The Third Event Risk: Weather Related Delays6:13
A third type of event risk, called Multiple Frequency Single type of event. An event may occur or not a number of times during a certain time period or project.
Completing Details To Run The First Simulation7:08
Defining outputs and simulation settings before running a simulation.

Interpreting Simulation Results: Histogram8:13
After a simulation has been run we start interpreting histograms for output variables.
Interpreting Simulation Results: Tornado Graphs4:33
The idea of using tornado graphs is to perform sensitivity analysis. They display a ranking of the input distributions that impact a certain output.
The Riskmakeinput Function7:53
This powerful function allows synthesizing several input variables into a single bar on a tornado chart.
Interpreting Tornado: Change In Output Mean7:23
A favorite tornado that does not use coefficients and is preferred by many decision makers.
Interpreting Tornado: Regression Coefficients8:04
A tornado graph charting "regression coefficients" and showing the magnitudes of the bars "normalized" by the standard deviation.
Interpreting Tornado: Scatterplots1:44
Scatterplots show the relationship between two different input variables.
Interpreting Tornado: Correlation Coefficients (Spearman Rank)2:05
A tornado chart with Spearman Rank Correlation Coefficient assumes non-linearity on the calculation of such an index.
Interpreting Tornado: Regression Mapped Values2:07
This type of tornado graph normalizes bars expressed in units of standard deviation change in the respective input.
Interpreting Tornado: Contribution To Variance3:05
This tornado graphs shows the amount of change in the output attributable to each input.
Interpreting Tornado: Spider Graph – Change In Output Mean2:33
A spider graph shows how the value of the output statistic changes as the sampled input value changes.

Requirements

Intermediate knowledge of Excel.

Description

We present here an integrated business case for calculating a simplified project cost estimation. An integrated case for calculating a simplified project cost and its probabilistic contingency reserve estimation. Quantification of price and quantity risk as well as event risks. The objective of presenting this example is to demonstrate a number of basic and intermediate tools from @RISK that are relevant to current practices used in the world of project management. The goal is to show how @RISK tools can be used to represent quantitative – not qualitative – risk analysis.

Who this course is for:

Project-related professionals and students: Project managers, cost estimators, cost engineers, project planners and schedulers.

Project Costing with Monte Carlo Simulated Probabilities

What you'll learn

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Course content

Introduction and Defining the Problem4 lectures • 19min

Traditional Approaches to Contingency Estimation1 lecture • 5min

Structuring The @RISK Model7 lectures • 41min

Interpreting Results10 lectures • 48min

Advanced Settings2 lectures • 14min

Requirements

Description

Who this course is for: