57R-09: Integrated Cost and Schedule Risk Analysis Using ...web.aacei.org/docs/default-source/toc/toc_57r-09.pdf · An approach to examine the impact of risk upon a project schedule

57R-09

INTEGRATED COST AND SCHEDULE RISK ANALYSIS USING RISK DRIVERS AND MONTE CARLO SIMULATION OF A CPM MODEL SAMPLE

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AACE International Recommended Practice No. 57R-09

INTEGRATED COST AND SCHEDULE RISK ANALYSIS USING RISK DRIVERS AND MONTE CARLO SIMULATION OF A CPM

MODEL

TCM Framework: 7.6 – Risk Management

Rev. July 9, 2019 Note: As AACE International Recommended Practices evolve over time, please refer to web.aacei.org for the latest

revisions.

Any terms found in AACE Recommended Practice 10S-90, Cost Engineering Terminology, supersede terms defined in other AACE work products, including but not limited to, other recommended practices, the Total Cost Management

Framework, and Skills & Knowledge of Cost Engineering. Contributors: Disclaimer: The content provided by the contributors to this recommended practice is their own and does not necessarily reflect that of their employers, unless otherwise stated. July 9, 2019 Revision: Dr. David T. Hulett, FAACE (Primary Contributor) Waylon T. Whitehead (Primary Contributor) James E. Arrow, DRMP William A. Banks, Jr. David C. Brady, P.Eng. DRMP Larry R. Dysert, CCP CEP DRMP FAACE Hon. Life

John K. Hollmann, PE CCP CEP DRMP FAACE Hon. Life Donny Lai John R. Schuyler, PE CCP DRMP Edward J. Thomas Warner W. Uhl

June 18, 2011 Revision: Dr. David T. Hulett (Primary Contributor) Christopher P. Caddell, PE CCE Tommy Clarke Dr. Ovidiu Cretu, PE Kevin M. Curran Michael W. Curran Patrick B. Egger Ricardo Garcia da Roza John M. Hale Dennis R. Hanks, PE CCE

John K. Hollmann, PE CCE CEP Donald F. McDonald, Jr. PE CCE PSP Oscar A. Mignone Stephen P. Warhoe, PE CCE CFCC Robert F. Wells, CEP Dr. Trefor P. Williams Ronald M. Winter, PSP David C. Wolfson Rashad Z. Zein, PSP

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AACE® International Recommended Practice No. 57R-09

INTEGRATED COST AND SCHEDULE RISK ANALYSIS USING RISK DRIVERS AND MONTE CARLO SIMULATION OF A CPM MODEL TCM Framework: 7.6 – Risk Management

July 9, 2019

Copyright © AACE® International AACE® International Recommended Practices

Single user license only. Copying and networking prohibited.

TABLE OF CONTENTS Introduction ................................................................................................................................................................... 2

Scope ......................................................................................................................................................................... 2

Purpose ...................................................................................................................................................................... 2

Background ................................................................................................................................................................ 2

Recommended Practice ................................................................................................................................................. 3

Preconditions for a Schedule Risk Analysis ................................................................................................................ 3

A CPM Schedule that Complies with Industry Practices ........................................................................................ 3

Risk Data Collected to Improve Data Quality ........................................................................................................ 4

Properties of Risk Drivers .......................................................................................................................................... 5

Risk Drivers and Discrete Risks .............................................................................................................................. 5

Effect of Duration Uncertainty with 100% Likelihood ........................................................................................... 6

Effect of a Risk Driver with Less Than 100% Likelihood ......................................................................................... 7

Assigning Risk Drivers to Multiple Activities .......................................................................................................... 8

Risk Drivers Inherently Model How Activity Durations Become Correlated ......................................................... 9

Assigning Risks in Parallel or in Series ................................................................................................................. 11

Case Study - Model of Offshore Gas Production Platform Project .......................................................................... 12

Modeling Uncertainty .......................................................................................................................................... 13

Modeling Systemic Risks as Risk Drivers .............................................................................................................. 14

Schedule Risk Results Using Risk Drivers in a Monte Carlo Simulation ............................................................... 15

Prioritizing Risk Drivers for Management Action ................................................................................................ 16

Risk Treatment Using Prioritized Risks .................................................................................................................... 17

Integrating Cost and Schedule Risk Analysis ........................................................................................................ 18

Summary ...................................................................................................................................................................... 22

Contributors................................................................................................................................................................. 24

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57R-09: Integrated Cost and Schedule Risk Analysis Using Risk Drivers and Monte Carlo Simulation of a CPM Model

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INTRODUCTION Scope This recommended practice (RP) defines and explains the integration of cost and schedule risk analysis using a Monte Carlo simulation of a critical path method (CPM) resource-loaded schedule. It explains in some detail the use of risk drivers [1] to represent the identified risks to a project’s cost and schedule in an integrated approach. There are generally three overall purposes of such an exercise:

• To estimate the probability of finishing on or before the schedule date and on or under the cost estimate.

• To determine the amount of cost and schedule contingency needed to provide a chosen degree of confidence of hitting both targets.

• To identify the risks that cause any estimated overrun. Using root-cause risks to generate the simulation results permits the prioritization of identified and quantified risks by their impact on the schedule. This focus on risks facilitates identification of effective risk treatment options and estimates of the post-treatment results. Purpose This document is not intended to be a standard. This document is intended to provide a guideline for integrated project cost and schedule risk analysis using risk drivers in the context of conducting a Monte Carlo simulation-based schedule risk analysis of a CPM project schedule that most practitioners would consider to be good practice. This RP illustrates some of the most important features of risk drivers and compares the method to other risk analysis methods also described in AACE International recommended practices. Alternative methods to evaluate contingency include those relying on parametric estimation such as those described in Recommended Practice 42R-08 [2]. The reader is also encouraged to read recommended practices 44R-08, Risk Analysis and Contingency Determination Using Expected Value [3] and 64R-11, CPM Schedule Risk Modeling and Analysis: Special Considerations [4]. Background In integrated cost and schedule risk analysis, the platform for the analysis is usually a summary analysis schedule reflecting the project plan at a summary level of detail. The integration of cost and schedule is made possible because the total cost, expressed without contingency, is loaded onto the activities as time dependent (labor and rented equipment) and time independent (material and equipment to be installed) resources. The risk driver method of representing the identified risks (project specific or systemic risks) characterizes those risks by:

• Their probability of occurring with some tangible impact.

• A probability distribution of impact factors, to be applied to activities’ scheduled duration and cost variance during a Monte Carlo simulation, representing each risk’s impact on activity durations if it occurs.

• The activities that the risk affects if it occurs. Risks are represented by their probability of occurring, and their impact on activity duration and cost (burn rate for labor, total cost for materials). The difference between risk drivers and discrete risks is in the use of multiplicative factors to represent the impact of a risk on the duration of the affected activities.

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The method is consistent with Recommended Practice 40R-08, Contingency Estimating – General Principles, [5] that specifies several first principles including: “starts with identifying risk drivers” and “links risk drivers to the activities they affect.” The risk driver described in this RP identifies risks’ importance to the cost and schedule objectives. The effect of uncertainty (i.e. background noise reflecting inherent variability, estimating error and estimating bias if present) is included to derive the total impact to the cost and schedule from these two sources. An approach to examine the impact of risk upon a project schedule is the expected value method as described in Recommended Practice 44R-08 [3] and 65R-11 for integrated cost-schedule risk analysis [7]. This method is based on estimates of the probability of the risks occurring and of the overall impact (in days or months) on the final finish date if the risk occurs. The expected value method often does not use the project schedule as a platform, which is a drawback since the calculation of the risks’ influence on the final completion date is very difficult, particularly with risks affecting parallel paths in the schedule. In contrast, Monte Carlo simulation uses the logic-driven CPM schedule as its platform. Risk drivers can be applied to all activities in the schedule that they influence. As a consequence, some risks will be assigned to multiple activities and some activities will be influenced by multiple risks. This method models the holistic effect of risks as they cause durations of all activities they affect to expand and contract. Costs also increase or decrease because duration variation during simulation indirectly affects labor-type resource costs. Importantly, during simulation the critical path may vary as some risks occur and others do not. The risk driver method uses simulation to estimate the project schedule and cost using risk data to calculate the finish date according to dynamic CPM principles. The risk driver approach incorporates the interactions and interdependencies of the project plan. The analyst does not have to resolve questions about whether the risk occurs on a risk-critical path or to estimate the impact of risks occurring frequently, or infrequently, since the Monte Carlo simulation accounts for all these considerations. Additionally, the analyst does not have to estimate and impose correlations between activity durations since the risk driver method generates these during simulation, as shown below. If the risks are truly root causes of variations rather than just symptoms of a more basic cause, those drivers are likely to be independent of each other and not to be correlated. Finally, using the root cause risks as drivers of the simulation facilitates risk prioritization for timely and effective risk treatment decisions by management. The target audience for this recommended practice includes risk practitioners and project managers who need to become aware of contemporary methods of using root cause risks, represented by risk drivers, to perform integrated cost and schedule risk analysis. Others involved in making decisions about risk treatment actions should also be aware of the benefits of using risk drivers. RECOMMENDED PRACTICE Preconditions for a Schedule Risk Analysis A CPM Schedule that Complies with Industry Practices

Quantitative risk analysis that is based on Monte Carlo simulation of a CPM schedule has advantages and poses some challenges. The schedule should be reviewed against easily-obtained scheduling practices such as that written by the US Government Accountability Office.

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Project schedules are dynamic models of the project plan, since any risk event that affects any activity’s duration may cause a delay in the finish date.1 Schedule uncertainty, represented by inherent variability, estimating error or estimating bias, can be built upon by adding risk drivers on top of the uncertainty to illustrate the build up from the deterministic schedule to a fully risk loaded schedule. Working with the project schedule allows detailed modeling about each identified risk and assigning its impact to specific activities or to general categories of activities. The impact may affect durations and/or costs. The simulation software working with the CPM schedule computes the detailed implications for the finish date and cost of uncertainty and many risks potentially occurring simultaneously. Frequently, the schedule available for analysis is a detailed contractor’s schedule. That schedule may have many more activities than are needed to model the strategic effect of risk on the project’s finish date. This schedule also may be incomplete or omit scope outside of the contractor that is needed for the project to be finished and total cost to be considered. Often, schedules submitted for analysis are not compliant with project scheduling industry practices (such as avoiding dangling activities, using constraints and lags, exhibiting realistic total float and having continuous and believable critical paths). In such cases, analysis cannot proceed until these deficiencies have been rectified. To perform schedule simulation for quantitative risk analysis, dynamic models, where the milestone dates and total cost are determined by activity durations and schedule relationship logic, are required. A new summary analysis schedule can be developed and validated by plenary stakeholders to ensure all project scope is identified. The summary analysis schedule needs to be inclusive of all work since the analyst cannot tell in advance of the analysis which paths will turn out to be critical when risks are considered. The main paths in the summary schedule need to reflect the more detailed plan with realistic float values.2 That schedule needs to include sufficient detail to represent the many interdependencies that occur in complex projects. The analysis is carried out against the summary schedule. Note that time is needed to gather all the data, carry out the analysis, prepare reports and the like. In that period, there may have been progress or changes, which needs to be assessed and the analysis may need to be revised or revalidated with this latest data. Risk Data Collected to Improve Data Quality

Collecting good-quality data about project risks is crucial to the success of the risk analysis. Typically risk workshops, confidential risk interviews, or a combination of the two methods are used.

• Risk workshops gather knowledgeable people into a room to identify and quantify the risks’ probability and impact on activities’ costs and durations. Under the leadership of a skilled facilitator the discussion may lead to a consensus on a set of risks with usable data that benefits from the synergy of those in the room. Often, however, people find that sharing honestly and openly in a workshop setting is difficult, particularly if there are risks that cannot be discussed because they are unpopular, may conflict with management statements or customer requirements, imply the project is in default of the contract terms, or for other reasons. These risks are sometimes called “unknown knowns” since they are known but cannot be mentioned, as referred to in the article “There are Known Knowns.” [6] Within organizations with a relatively low level of risk management maturity, [7] discussing these risks in risk workshops may be difficult for some who fear reprisal from management. Other issues that negatively affect workshop efficiency include: groupthink (suppressing dissent), the “Moses factor” (i.e. an influential person such as the project manager who overwhelms others), and cultural conformity (i.e. decisions that match the organization’s norms). [8]

1 In this RP the risk drivers will be described as potentially delaying the finish date or increasing cost. Of course, risks can be opportunities that may conceptually shorten activity durations resulting in an earlier finish date. In general, many risks described by those on the project are either all or mostly threats, so for ease of explanation most of the discussion is written as if risks are threats to project completion targets. 2 Float values may not replicate those in the detailed schedule if that schedule is not compliant with industry practices.

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Documents

57R-09: Integrated Cost and Schedule Risk Analysis Using ...web.aacei.org/docs/default-source/toc/toc_57r-09.pdf · An approach to examine the impact of risk upon a project schedule