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11.2 Risk Quantification

Introduction

Risk Quantification involves evaluating risks and risk interaction to assess the range of possible project outcomes. It is primarily concerned with determining which risk events warrant response by considering the following factors:
  • Opportunities and threats can interact in unanticipated ways.
  • A single risk event can cause multiple effects.
  • Opportunities for one stakeholder may be threats to another.
  • Mathematical techniques can create a false sense of precision

11.2.1 Inputs

  1. Stakeholder risk tolerances
  2. Sources of risk
  3. Potential risk events
  4. Cost estimates
  5. Activity duration estimates

11.2.2 Tools & Techniques

  1. Expected monetary value
  2. Statistical sums
  3. Simulation
  4. Decision trees
  5. Expert judgement

11.2.3 Outputs

  1. Opportunities to pursue, threats to respond to
  2. Opportunities to ignore, threats to accept

 

 

 

 

 


11.2.1 Risk Quantification - Inputs

11.2.1.1 Stakeholder risk tolerances

Different organizations and different individuals have different tolerances for risk.

 

11.2.1.2 to 11.2.1.5 are discussed elsewhere.

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11.2.2 Risk Quantification - Tools and techniques

11.2.2.1 Expected monetary value

Expected monetary value = Risk event probability X Risk event value

The risk event value must reflect both tangibles and intangibles.

 

11.2.2.2 Statistical sums

Statistical sums can be used to calculate a range of total project costs from the cost estimates for individual work items.

This could involve using standard deviation formulae for example:

Triangular distribution

Mean = (a + m + b) / 3 and

Variance = [(b - a) + (m - a)(m - b)] / 18

Beta Distribution

Mean = (a + 4m + b) / 6 and

Variance = [(b - a) / 6]

In order to sum probability distributions, calculate:

 

11.2.2.3 Simulation

Simulation uses a representation or model of a system to analyze the behaviour or performance of the system. The most common form of simulation on a project is schedule simulation using the project network as a model of the project.

Using Monte Carlo analysis, the results of a schedule simulation could provide a statistical distribution of the probability of project completion by a certain date. Traditional mathematical analysis techniques such as CPM and PERT do not account for path convergence and thus tend to underestimate project duration.

 

11.2.2.4 Decision trees

A decision tree is a diagram that depicts key interactions among decisions and associated chance events as they are understood by the decision maker. The branches of the tree represent either decisions (shown as boxes) or chance events (shown as circles).

 

11.2.2.5 Expert judgement

Expert judgement can often be identified in lieu of or in addition to mathematical techniques. For example risk events could be described as having a high, medium or low probability of occurrence and a severe, moderate or limited impact.

 

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11.2.3 Risk Quantification - Outputs

11.2.3.1 Opportunities to pursue, threats to respond to

This is the major output from risk quantification.

 

11.2.3.2 Opportunities to ignore, threats to accept

The process should also document (a) those sources of risk and risk events that project management has decided to ignore or accept and (b) who made the decision to do so.

 

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