This document provides an introduction and overview of NetRisk, a GPM-based schedule risk analysis software by PMA Technologies. It discusses key features such as qualitative and quantitative risk analysis, duration sampling, floating and pacing, prime risks, sensitivity analysis, and comparing different risk scenarios. Case studies are presented demonstrating features like duration ranging calculations, floating and pacing, using advanced properties for SNE constraints and GPM planned dates, correlations, and comparing CPM to GPM. The document emphasizes that NetRisk models risks in a stochastic network differently than deterministic CPM.

1. PMATechnologies
INTRODUCTION TO NETRISK
GPM-Based Schedule Risk Analysis
PRESENTED BY
Francisco Cruz, PE, PMP, RMP
PMA Consultants

2. PMATechnologies
GPM planned dates are favored over SNE constraint dates1
Activities and activity nodes are encoded with stochastic rules2
Any risk that, if occurring, impacts multiple activities, may occur with
a different probability and impact on each activity
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Risks resulting from common, contemporaneous decisions to start
activities on dates later than early dates are modeled
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Criticality and cruciality are combined to measure importance5
Adverse weather and weather-event risks are modeled6
GPM algorithms are extended for stochastic networks7
INTRODUCING GPM RISK
Modeling & Analysis
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NETRISK – GPM-BASED SCHEDULE RISK ANALYSIS
Qualitative Risk Analysis
- Interactive Risk Register
- Probability & Impact Matrix
- Risk Breakdown Structure
Quantitative Risk Analysis
- Duration Sampling, Floating and Pacing
- Global Distributions and Assignment Groups
- Introducing Prime Risks
- Sensitivity Analysis
- Advanced Properties
- End Node Diagram (View Schedule at Selected
Percentile)
- Scenario Modeling (Easy Comparison of
Different Scenarios)
Risk Manager - Simplified and Intuitive Risk Analysis Platform
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RISK MANAGER - OVERVIEW
Risk Register
Risk Breakdown Structure (RBS)
Rules
Sampling
Branches
Run
Results
Simplified and intuitive risk analysis platform for analysts and non-analysts
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QUALITATIVE RISK ANALYSIS
Risk Register
Instinctive Risk Register
that allows a 2-way
assignment (i.e., risk-to-
activity or activity-to-risk).
Intuitive Qualitative Property Box
permits easy population of uncertainty
information about the project.
The NetRisk Manager
allows users to export
input/output data at any
time during the analysis.
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QUALITATIVE RISK ANALYSIS
Probability and Impact Matrix
Gear Icon opens the
Qualitative Thresholds
window .
Matrix used to define
the probability and
impact factors in the
probability and impact
scales. Probability and
Impact factors will
determine and
automatically feed each
Risk Rating.
There are 6 pre-defined Themes for the PxI Matrix,
which include Fire, Forest, Horizon, Ice, Pond, and
Rainbow.
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QUALITATIVE RISK ANALYSIS
Risk Breakdown Structure
NetRisk provides two RBS to
classify the different types of
identified risks.
Two hierarchy levels add
more granularity to the risk
taxonomy.
Probability Distributions can also
be assigned to the specific
categories for a global risk
allocation.
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QUANTITATIVE RISK ANALYSIS
Duration Sampling, Floating and Pacing
Global Range is a shortcut
to ranging activity
durations when there is
limited detail, often in the
concept and schematic
stages of a project. It is also
used to quickly assess the
overall risk in a project.
What Probability
Distribution Function
Should be Used?
Duration Ranging captures activity
duration risk limited to estimating
uncertainty or estimating judgment. It
allows users to specify low, mode and high
durations for activities as either a percent
variance from the mode or differential
values from the mode.
Pacing: A scheduling strategy that, based
on the predicted (deterministic) duration
of an activity, involves shifting an
activity within the float then existing at
that juncture in the realization if the then-
existing float exceeds the activity
deterministic float by a specified
threshold.
Floating: A scheduling strategy that
considers the predicted (deterministic)
float of an activity and that involves
shifting an activity within is predicted float
range to a planned date at the onset of a
realization.
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QUANTITATIVE RISK ANALYSIS
Prime Risks
Unique to NetRisk, Prime Risks have
different probabilities of occurrence or
p parameters vis-à-vis each associated
activity. In other words, Prime Risks
allows the user to assign
different probability parameters for the
same risk to various activities.
A Group enables the user to cluster
together two or more activities
assigned to a particular risk, which has
a common likelihood but may have
different impact values for different
activities.
Associating impact risks to
activities or vice versa is
accomplished via the
NetRisk Manager.
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QUANTITATIVE RISK ANALYSIS
Rules (Activity Correlation)
Relative to correlated sampling among
activity durations, where an activity is
affected either positively (opportunity) or
negatively (threat) by another, the user
can model the relationship with a
correlation coefficient.
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QUANTITATIVE RISK ANALYSIS
Rules (Risk Activity Pair Correlation)
The Risk Activity pair can be correlated
with another Risk Activity Pair with either
Impact Coefficient (IC) or Likelihood
Coefficient (LC)
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QUANTITATIVE RISK ANALYSIS
Run
Rules for Convergence
enable the simulation to be
stopped when enough
realizations have been run
on a schedule model.
Pre-Treatment and Post-
Treatment scenarios are
investigated by selecting and
implementing alternatives to
modify high-priority risks from
the SRA simulation model to
achieve alternate simulation
results that improve the
probability threshold.
NetRisk offers both Monte
Carlo sampling, and Latin
Hybercube sampling.
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QUANTITATIVE RISK ANALYSIS
Results
NetRisk provides
Distribution graphs
(histograms and S-curves)
for each selected activity,
link, milestone, and
benchmark in the project
model. The graphs can be
maximized to facilitate
their evaluation.
A comprehensive list of
output is available to the
user to perform sensitivity
analysis, prime risk
sensitivity, driving links
and gap analysis.
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QUANTITATIVE RISK ANALYSIS
Sensitivity Analysis
- NetRisk Criticality is displayed through criticality indices, and it represents how much time each
activity was on the critical path during the simulation.
- NetRisk Cruciality is Williams’ cruciality, or the correlation between sampled activity duration
and project duration, for all realizations.
- Priority Index- For each activity, it shows the significance of an activity on the project’s duration
by taking its criticality and enhancing it by its cruciality. Priority Index = (1+Cruciality)* Criticality
Sensitivity Analysis is performed using
Pearson’s Product Moment and
Spearman’s Rank Correlation to account
for relationship between variables when
creating correlated series, Pearson’s
correlation measures relationship
between Activity Duration & Project
duration and Spearman’s Correlation
measuring the correlation between two
values calculated by sorting (ranking)
each value in the pair.
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QUANTITATIVE RISK ANALYSIS
Easy Comparison of Different Scenarios
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This comparison highlights
different treatment strategies
applied to increase likelihood
of achieving the deterministic
project completion.

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GPM & CPM RISK
SOFTWARE COMPARISON
GPM Risk as Embodied CPM Risk as Embodied
Schedule view relies on time-scaled LDM networks Minimalist schedule view that relies on logic GANTT charts
Planned dates can be used to model SNE constraint dates SNE constraints cannot reflect the network stochastic nature
A risk occurs with unique probability/impact per activity-risk pair A risk occurs with the same probability on all impacted activities
Longest path, sampled path & shortest path logic constructs Longest path & sampled path logic constructs
Floating and pacing risks are modeled as random risks Neither floating nor pacing risks can be modeled
Automated risk removal for risk sensitivity analysis Manual, one-by-one risk removal for risk sensitivity analysis
Multiple simulations within the same file for easy comparisons One simulation per file complicates comparison of results
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1 Any project or contract schedule that is not risked through its life
cycle does not conform to scheduling best practices
2 Any schedule that does not expressly reserve reasonable schedule
margin does not conform to best practices either
3 The CPM optimism bias impacts CPM risk analysis results in that ‘p
dates’ are biased early/are optimistic
4 GPM planned dates are better suited to risk modeling than
deterministic SNE constraint dates
5 Activity durations should be ranged using benchmarking
6 With schedule margin as critical path float, early completion
schedules are the new normal
7 There is a new sheriff in town!
TAKE-AWAYS
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NETRISK CASE STUDIES
1 Schedule Risk Analysis (Risks + Uncertainty)
2 Floating & Pacing
3 Using Advanced Properties
- SNE Constraint Date
• Make Planned Dates
• Maintain Planned Dates
• Remove Constraint
• Ignore Constraint
- GPM Planned Dates (Maintain vs Override Use Early Dates)
5 CPM vs GPM Comparison
4 Correlations
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CASE STUDY # 1
LGA EES Substation Schedule Risk Analysis
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CASE STUDY DISCUSSION POINTS
• Sampling – Duration Ranging Calculations
• Distribution Types
• Simulation Type – Latin Hypercube vs Monte Carlo Simulation
• Convergence
• Simulation Results – Distribution, Criticality, Cruciality, Priority
Index, Risk Sensitivity, Driving Links and Mean Gaps
• Compare Scenarios
• View Schedule at Selected Percentile
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DURATION RANGING CALCULATIONS
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CASE STUDY # 2
Floating & Pacing
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FLOATING CALCULATIONS
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PACING CALCULATIONS
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CASE STUDY # 3
Using Advanced Properties
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SNE CONSTRAINTS CALCULATIONS
Make Planned Date SNE Constraint
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SNE CONSTRAINTS CALCULATIONS
Maintain SNE Constraint Date
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SNE CONSTRAINTS CALCULATIONS
Remove Constraint
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SNE CONSTRAINTS CALCULATIONS
Ignore Constraint
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GPM PLANNED DATES (MAINTAIN VS
OVERRIDE, USE EARLY DATES)
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Maintain GPM Planned
Dates Cumulative Curve
Use Early Date
Cumulative Curve

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CASE STUDY # 4
Use of Correlations
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USE OF CORRELATIONS
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THE LAW OF LARGE NUMBERS
Independent Variables – Distribution gets narrower as number of
observations increase.
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INDEPENDENT VS CORRELATED VARIABLES
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Independent Scenario has
a narrower distribution
Correlated Scenario has a
broader distribution

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INDEPENDENT VS CORRELATED
CASE STUDY COMPARISON
Pre-Treatment A –
Independent Variables
Pre-Treatment B –
Correlated Variables
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When one Activity picks a
lower duration in an
iteration, the other
Correlated Activity picks
up a lower duration
When one Activity picks a
higher duration in an
iteration, the other
Correlated Activity picks
up a higher duration

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CASE STUDY # 5
CPM vs GPM Comparison
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CPM (EARLY PLANNED DATES) VS GPM
(PLANNED DATES)
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Early Planned
Dates (CPM)
Planned
Dates (GPM)

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1 Visual Risk
2 High-priority User Requests
3 Automated Risk Removal in Risk Sensitivity Analysis
4 Full Stochastic Network Modeling
5 Weather Risks
6 Full Interoperability with Cost Risk Software
7 Integrated Resource Leveling During Simulation
TAKE-AWAYS
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Learn more about NetRisk by visiting:
pmatechnologies.com/netrisk