6.1 Schedule Risk Principles, Risk Drivers, and Three-Point Estimates

AACE Planning and Scheduling Professional PSP

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1 Chapter 0: Introduction & AACE PSP Exam Structure

0.1 Current AACE PSP Exam, Eligibility, and Certification Facts 0.2 Planning and Scheduling Professional Roles and Competencies 0.3 Cost Engineering and Scheduling Terminology per RP 10S-90

2 Chapter 1: Scope Definition & Planning Fundamentals

1.1 Work Breakdown Structure (WBS) Architecture & RP 33R-15 1.2 Organization & Resource Breakdown Structures (OBS, RBS, RAM) 1.3 Project Planning and Activity-Logic Development 1.4 Contract Requirements and Delivery Constraints 1.5 Stakeholder Identification, Needs, and Plan Review 1.6 Constructability Methods and Value Engineering 1.7 Resource Identification and Project Variables 1.8 Scope, Goals, Project Plan, and Phase Definition 1.9 Cost Breakdown Structure and Cost Estimate Development 1.10 Phase Relationships, Baseline Plan, Forecasts, and Risk/Recovery Planning

3 Chapter 2: Critical Path Method Mechanics & Network Logic

2.1 Activity Definition and Logic Relationships 2.2 Activity Durations, Productivity, and Work Calendars 2.3 Forward and Backward Pass Mathematical Mechanics 2.4 Float Calculations and Critical-Path Identification

4 Chapter 3: Advanced Network Techniques & Schedule Constraints

3.1 Schedule Constraints and Their Impact on Dates and Float 3.2 Advanced Logic Modeling: Ladders and Fragnets 3.3 Schedule Levels of Detail and Schedule Basis Documentation 3.4 Schedule Scope and Schedule Specification 3.5 Breakdown-Structure Relationships and the Cost Estimate Model 3.6 Stakeholder Feedback and Types of Schedules 3.7 Activities, Milestones, Schedule Quality, and Basis Documentation

5 Chapter 4: Resource Management & Leveling

4.1 Resource Planning, Allocation, and Histograms 4.2 Resource Leveling vs. Resource Smoothing 4.3 Cumulative Resource S-Curves & Productivity Tracking

6 Chapter 5: Baseline Management & Schedule Updating

5.1 Baseline Establishment, Forecasts, and Controlled Revision 5.2 Progress Measurement and Data Date Mechanics 5.3 Out-of-Sequence Logic: Progress Override vs. Retained Logic 5.4 Schedule Update Review and Quality Verification per RP 53R-06 5.5 Cost/Resource Management, Schedule Change, and Feedback 5.6 Acceleration and Recovery Scheduling per RP 54R-07 5.7 Control Schedules, Variance Analysis, Forecasts, and Management Summaries

7 Chapter 6: Schedule Risk Analysis & Monte Carlo Simulation

6.1 Schedule Risk Principles, Risk Drivers, and Three-Point Estimates 6.2 Monte Carlo Simulation, Criticality, Correlation, and Merge Effects 6.3 Risk Outputs, Confidence Dates, Sensitivity, and Contingency

8 Chapter 7: Earned Value & Earned Schedule Management

7.1 Earned Value Foundations: PV, EV, AC, and Variances 7.2 Traditional EV Performance Indices & Late-Project Quirks 7.3 Earned Schedule (ES) Theory, SV(t), and SPI(t) 7.4 Independent Schedule Forecasting & IEAC(t)

9 Chapter 8: Forensic Schedule Analysis & Delay Claims

8.1 Delay Classification, Criticality, and Contract Questions 8.2 RP 29R-03 Taxonomy, Data Validation, and Method Selection 8.3 TIA, Windowed Analysis, and Subtractive Models 8.4 Concurrency, Pacing, and Float: Technical Analysis Before Entitlement

10 Chapter 9: Executive Communication & Practical Scenario Applications

9.1 Official PSP Memo Writing Framework 9.2 Structured Technical Problem-Solving for PSP Scenarios 9.3 Current Exam Strategy and Core Calculation Review

Free exam prep AACE Planning and Scheduling Professional Practice Questions 100 questions Study Guide 48 topics

6.1 Schedule Risk Principles, Risk Drivers, and Three-Point Estimates

Key Takeaways Explain This Topic AI

A deterministic CPM produces one result from one set of durations; a risk model samples uncertainty to produce a distribution of possible completion dates.

Three-point estimates must use clearly defined optimistic, most-likely, and pessimistic assumptions rather than arbitrary percentages around the baseline.

Classic PERT uses an expected duration of (O + 4M + P) divided by 6 and an approximate standard deviation of (P − O) divided by 6.

A risk driver combines a probability of occurrence, an impact distribution, and the activities affected if the risk occurs.

Model selection and contingency confidence must reflect data quality, decision purpose, risk tolerance, and documented assumptions. Last updated: October 2026

6.1 Schedule Risk Principles, Risk Drivers, and Three-Point Estimates A deterministic Critical Path Method (CPM) schedule calculates dates from one duration, one calendar, and one logic model for each activity. It is essential for coordination, but its single finish date is conditional: the date is true only if the modeled assumptions occur. A probabilistic schedule risk analysis repeatedly samples uncertain inputs and shows a range of completion outcomes. AACE RP 57R-09 addresses integrated cost and schedule risk analysis using risk drivers and Monte Carlo simulation of a CPM model. It is guidance, not a universal standard, and it emphasizes choosing a degree of confidence appropriate to the decision. AACE RP 65R-11 is a different document: it concerns integrated expected-value methods. Do not cite RP 65R-11 as the title for Monte Carlo CPM simulation. Two different things to model

Input Meaning Example treatment Base-duration uncertainty Normal variation even when no named event occurs Sample an activity duration range Discrete risk event An event may occur or may not occur Sample occurrence first, then sample impact if triggered Common-cause risk driver One risk affects several related activities Apply one correlated driver to all mapped activities Logic or calendar risk The execution model itself may change Model alternatives explicitly or analyze scenarios Treating every risk as independent activity padding usually overstates some paths, understates common-cause effects, and hides the source of contingency. The model should preserve traceability from each material risk to its affected work. Building three-point estimates For an uncertain duration, define: Optimistic (O): a credible short outcome under favorable but feasible conditions; Most likely (M): the modal or most plausible outcome under the stated execution plan; and Pessimistic (P): a credible long outcome under unfavorable but feasible conditions. The labels do not automatically mean P1, P50, or P99. If the organization intends the endpoints to represent particular percentiles, it must say so. The team should document crew size, quantities, production rates, calendars, weather treatment, access assumptions, and whether named risk events are included. Otherwise, two experts can use the same three numbers while describing different uncertainties. Classic PERT approximation The traditional PERT approximation is: T e = O + 4 M + P 6 T_e = \frac{O + 4M + P}{6} T e = 6 O + 4 M + P σ ≈ P − O 6 \sigma \approx \frac{P - O}{6} σ ≈ 6 P − O For O = 12, M = 18, and P = 36 working days, the expected duration is 20 days and the approximate standard deviation is 4 days. These are model outputs, not guarantees. The standard-deviation expression is a PERT approximation; it is not the standard deviation of every possible beta distribution sharing the same three inputs. Triangular distribution A triangular distribution uses minimum, mode, and maximum directly. Its mean is: μ = O + M + P 3 \mu = \frac{O + M + P}{3} μ = 3 O + M + P It can be useful when a bounded range and a mode are all that can be defended. A beta-PERT form can place more weight near the mode. Neither distribution is automatically “correct” merely because data are scarce or abundant. Historical data may support an empirical distribution, while expert judgment may still support either triangular or beta-PERT after calibration. Discrete risk drivers Suppose an environmental permit has a 35% chance of causing delay. A traceable model can represent: a 35% occurrence probability; an impact distribution conditional on occurrence; and the activities and logic interfaces affected. In iterations where the risk does not occur, its added impact is zero. In iterations where it occurs, the model samples the defined impact. If the event affects design, procurement, and field mobilization together, mapping a common driver preserves that dependence better than applying three unrelated random paddings. Correlation and model quality Correlated inputs matter. Weather may affect several civil activities during the same period; a late vendor drawing may affect fabrication and testing together. Ignoring correlation can produce an unrealistically narrow or otherwise distorted output distribution. Conversely, adding arbitrary correlation everywhere can overstate tail risk. Before trusting the output, verify that the deterministic CPM is usable: logic is complete, calendars are intentional, constraints are justified, resource assumptions are understood, and the status date is correct. Simulation does not repair a defective network; it repeats that defect thousands of times. Interpreting results The output usually includes a completion-date distribution, percentile dates, criticality or schedule-sensitivity measures, and risk-driver rankings. These answer different questions. A P80 date is the date met or beaten in about 80% of modeled iterations, given the model assumptions. A high criticality index indicates frequent critical-path participation; high correlation indicates strong association with completion variation. Neither statistic by itself proves causation or prescribes a mitigation. The practical discipline is to state assumptions, run sensitivity checks, and explain limitations. A risk model supports a decision; it does not convert uncertain inputs into certainty.

Test Your Knowledge

For O = 12 days, M = 18 days, and P = 36 days, what does the classic PERT approximation produce?

Expected duration 18 days and standard deviation 6 days

Expected duration 20 days and approximate standard deviation 4 days

Expected duration 22 days and variance 4 days

Expected duration 24 days and approximate standard deviation 12 days Check Answer Need a hint? AI

Test Your Knowledge

How should a 35%-probability permit-delay event be represented in a traceable risk-driver model?

Increase every activity duration by 35%.

Always insert the maximum delay so the baseline is conservative.

Trigger the event in roughly 35% of iterations and sample its conditional impact only when triggered.

Ignore the event because CPM cannot represent uncertainty. Check Answer Need a hint? AI

Test Your Knowledge

Which statement about three-point distribution selection is most defensible?

The choice should reflect the meaning of the inputs, available evidence, and sensitivity testing; no one distribution is universally required.

Triangular is mandatory whenever historical data are unavailable.

Beta-PERT is mandatory whenever historical data exist.

The deterministic duration must always equal the optimistic value. Check Answer Need a hint? AI

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