1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 PROGRAM RISK MANAGEMENT APPROACH TO REDUCING COST
UNCERTAINTY IN HIGHWAY PROJECTS
Craig R. Wilson
Senior Risk Analyst
Geotechnical and Tunneling Group
Parsons Brinckerhoff
555 Seventeenth Street, Suite 500
Denver, CO 80202
Tel: (303) 390-5893
email: [email protected]
Dan Tran, Ph.D.
Assistant Professor
Dept. of Civil, Environmental and Architectural Engineering
University of Kansas
1530 West 15th Street
Lawrence, KS 66045
Tel.: (785) 864-6851
email:[email protected]
Keith R. Molenaar, Ph.D.
Professor
Dept. of Civil, Environmental and Architectural Engineering
University of Colorado at Boulder
428 UCB
Boulder, Colorado 80309-0428
Tel: (303) 735-4276
email: [email protected]
Submission date: August 1, 2014
Tables and Figures: 7*250 = 1,750.
Total word count: 7,114
39 2 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ABSTRACT
Project cost estimation can be a challenge in highway design and construction projects due to
risks and uncertainties. The initial cost estimates can be prepared in the planning phase and then
revised through the programming and design phases of project development. The accuracy of
early estimates is often characterized by high levels of uncertainty. Cost overruns in highway
design and construction projects are a major problem for state highway agencies. Risk and
uncertainty are primary factors that influence cost escalation and schedule delays. Project risk
management is a tool to manage cost and time overruns in highway projects. However, this
approach considers a project within an organization as an independent component. Although
highway projects are often interconnected with other projects or programs, a limited body of
research and non-proprietary management tools exist to address the cost uncertainty in highway
projects at the program level. This research presents an approach for program risk management
and its application in highway design and construction projects. Monte Carlo-based risk models
were developed to investigate the impacts of risks and uncertainties on cost estimating at both
project and program levels. The model was developed based on a series of interviews with
Washington State Department of Transportation (WSDOT). The results of this study indicate
that the use of program-based risk management can increase cost certainty and provide the
ability to better allocate resources among multiple projects. The proposed approach was
evaluated and verified by experienced professionals in WSDOT to determine its distinct
advantages and disadvantages when compared to the traditional approach of project risk
management. 3 1 INTRODUCTION AND BACKGROUND
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Highway design and construction projects can be extremely complex and have historically
experienced significant cost overruns from conceptual planning estimates (1). The greater the
budgets and the longer the schedules, the more prone to cost overruns a project can be (2). Many
studies highlight cost overrun problems in large highway projects over the past decades (1, 3, 4,
5). Researchers have also pointed out the primary factors that result in project cost overruns
including scope changes, schedule changes, engineering and construction complexities, inflation,
unforeseen events, unforeseen conditions, and market conditions (2, 5, 6, 7, 8). To overcome the
cost overrun issue, highway agencies should effectively identify and manage project risk factors
and cost escalation factors (3, 9). NCHRP Report 574 proposed a systematic approach to identify
cost escalation factors and integrate these factors into the cost estimating and management
process (10). NCHRP Report 658 presents a risk management framework to investigate cost
escalation in highway projects (11). This framework systematically handles risk through the
project development process based on five steps: risk identification, risk assessment, risk
analysis, risk mitigation and allocation, and risk tracking and control.
34 RESEARCH MOTIVATION
35 36 37 38 39 40 A number of risks and uncertainties in highway projects stem from the business and pre-project
planning process at the earlier phases of the project life cycle. As a project develops from the
scoping and preliminary engineering phase through to the construction phase, the project cost
estimating process ca n encounter a number of escalation factors. To obtain an accurate and
consistent cost estimate, agencies should develop a systematic approach that addresses cost
escalation factors across the whole project development process (10). The length of project
Cost estimating and management on large highway projects is a complex and challenging
problem. The process often involves risks and uncertainties. Failing to capture these risks and
uncertainties may lead to cost overruns, schedule delays, and ultimately produce a project and
program that is not of the scope and quality that the traveling public and other stakeholders need.
Project risk management has been investigated in the literature as an effective tool to identity and
manage risk to obtain a project’s objectives, including cost, schedule, and quality (12, 13, 14).
However, a project is not executed alone and it must be considered together with other current
projects within the agency’s program. In a multi-project environment, resource conflicts are a
critical issue and usually happen. The simultaneous management of cost, time, and resource
allocations among projects at the program level is a complex process of balancing the (often
conflicting) interests of multiple participants (15). While a number of studies have focused on
individual project risk management, limited research is available for program risk management.
Recently, NCHRP Report 20-24 (74) concluded that although program risk management is
especially important for the highway industry, guidance for program risk management is lacking
(16). This study aims to examine risk management at the program level in the highway industry.
4 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 development from the planning through the construction phase is a major factor in cost overrun
(17). Inadequate estimating invariably leads to misallocation of scarce resources.
The primary objective of the risk management process is to predict, evaluate, and plan for
potential issues in order to optimize project performance. Another objective of the risk
management process is to complete the process efficiently by generating adequately precise,
accurate, and defensible results (18). It is important to note that there is a significant difference
between a project-level and a program-level risk management approach. Project-level risk
management focuses on achieving specific project performances (e.g., cost, time, quality) by
investigating the specific project conditions, applying the risk management process within the
project, and emphasizing effective management. A program-level risk management approach,
on the other hand, focuses on strategic direction to a set of projects by exploring broader trends
(e.g., national, regional, local, and agency-wide trends). The main objectives of conducting
program risk management are to: (1) identify and analyze inter-project risks; (2) verify project
risk response plans whose actions could affect other projects; (3) determine root causes; (4)
propose specific solutions to risk escalated by project managers; (5) implement response
mechanisms which benefit more than one project; and (6) manage program contingency reserves
(in terms of cost and time) (19). Program management works in conjunction with project
management. Figure 1 shows the hierarchy of management.
FIGURE 1 Hierarchy of management
From an agency perspective, it is expected that program risk management can be a vigorous and
essential tool in the management of risk and uncertainty. Highway projects are often grouped in
programs because of various funding schemes. As a result, risks (e.g., material price escalation)
that are shared across all projects are most effectively managed at the program level. However,
almost all highway agencies only conduct risk management process at the project level. These
agencies rarely look at the impacts of risks and uncertainties on funding across multiple projects.
5 1 2 3 4 One may observe that risks associated with multiple projects at the program level may have
larger impacts than the risks posed to one project alone. The following section discusses a
proposed approach to risk management in highways at the program level.
5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 RESEARCH APPROACH
This study presents risk models to investigate program risk management in the highway industry.
Risk modeling is expected not only to accurately capture the behavior of risks and uncertainties
with a project, but also to properly model the behavior of the project itself. The risk model was
developed based on the four steps below.
Step 1: Developing requisite risk models
This step involves the solicitation of different types of input data from subject matter experts and
other project team members. The process can be carried out by conducting standardized risk
workshops, which are designed to elicit risk information and validate the base cost and schedule
estimates. The outcome of step 1 is the establishment of the requisite risk model. The program
risk model includes all project risk models under the program.
Project Risk Management Model
The project risk management model represents the traditional management of risk from project
managers’ perspectives. When a risk is realized, the funding comes from the project contingency.
In this model, a project manager is a critical factor in the mitigation strategies. The project
manager only has control over the mitigation of the risks. However, the resources for the project
manager are limited relative to the program model in terms of total mitigation and influence.
Risk response strategies in this model are variable among projects (random) and project
contingency is used to deal with the impact of risks.
Program Risk Management Model
The program risk management model is indicative of total program risk management. In this
model, the program manager takes an active role in mitigating risks. The program manager has
the greatest ability to influence project risks through mitigation, and the mitigation strategies are
correlated across all projects. The program manager sets policies regarding risk management
and mitigation strategies. Risk response strategies in this model are the same among projects
(correlated) and management reserve and project contingency are used to deal with the impact of
risks.
Step 2: Model input
The primary inputs for the model include sequences of project activities, uncertainty and risk
factors, and correlation between these factors. Sequencing project activities involve either the
integration of a standard critical path method schedule or the use of a project flow chart. The
6 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 necessary elements contain activity descriptions and durations, activity costs, predecessors and
successors, the various activity relationships, and other data such as time constraints. To simplify
the risk model to make it requisite, activities are often consolidated, condensed, or rolled-up.
Uncertainty inputs, including cost uncertainty, schedule uncertainty, and resource
uncertainty, are the major data in the model. Depending on the available data set, the simple
distributions (i.e., three-point estimate, two-point estimate) or more complex distributions (e.g.,
normal distribution, beta distribution, and lognormal distribution) can be used to represent these
uncertainties.
Correlation of uncertainty and risk events needs to be defined in the risk model. This
correlation describes the behavior of an impact that is related, either positively or negatively, to
another impact. The correlation assessment is important especially when looking across multiple
projects (program risk management). Quite often, the occurrence of an event will cause a related
event to occur in a separate project. Correlation is especially important in modeling cost and
schedule together because a delay in schedule will frequently have a related impact to cost (20).
The input of this data into the model usually occurs in the context of a spreadsheet-based
project estimate and schedule or using sophisticated project scheduling software. The risk data is
input using macros from either within the spreadsheet software or through an add-on to the
project scheduling software. Once all of the data is input into the risk model, the base cost and
schedule are generated. This is simply the traditional deterministic process at the cost and
schedule using basic mathematics in the case of an estimate, or following standard schedule logic
in the case of the project schedule.
Step 3: Running Simulation
Step 3 involves running the model with all of the input data by using the random sampling
simulation such as a Monte Carlo simulation. A model is run a specified number of trials. During
each trial, the model generates a random value for each of the variables within the model based
on its probability distribution function. When one trial is finished, the results are recorded and
another trial is begun. This process is repeated until reaching the number of specified iterations.
Step 4: Model Output
Output data can be extracted from the completed simulation results. To investigate the impact of
program risk management in the model, two different risk response strategies associated with the
aforementioned project risk management and program risk management must be examined.
These risk response strategies represent the different ways that the project manager and the
program manager may respond to risks on the project. These different approaches to risk
responses should be properly integrated in the models to accurately reflect the likely actions of
the parties represented in the model. The primary outputs include (1) an estimate of cost for each
individual project in the program and (2) a total cost of all projects in the program. The
individual project cost is computed by adding the cost of each of the activities in the project
together. The total cost is computed by summing the results of all individual projects in the
7 1 2 3 program. It should be noted that the cost is made up of the deterministic cost (base cost) and the
probabilistic cost (risk cost).
4 DEMONSTRATION: CASE EXAMPLE
5 6 7 8 9 10 11 12 13 14 The risk models were developed to best represent a typical highway construction project. To
develop the model, an extensive review of three projects from the Washington State Department
of Transportation (WSDOT) was conducted: I-90 Two-Way Transit and HOV Operations, SR
167 HOV Lane Projects, and SR 520 Bridge Replacement and HOV Project. These projects were
selected because of the availability of information, the complex nature of each project, and the
diverse issues faced in each project. The data that was reviewed for each of these projects
included the project description, cost and schedule details, and risk registers. Based on the results
of these reviews and a series of interviews with five project and program managers at WSDOT,
the risk model flowchart of a typical design and construction highway project was determined
(Figure 2).
Environmental
Process
ROW/
Utilities/ RR
Scope
Permits
Procurement
Construction
Close-out
Design
15 16 17 18 19 20 21 22 23 24 FIGURE 2 Risk model flow chart For the purpose of demonstration, the values for costs and durations of each task in
Figure 2 were determined based on these three projects and rounded to approximate percentages
of total project cost. These values were reviewed with WSDOT professionals to ensure the
validity of the model assumptions. Table 1 summarizes the base costs and durations. The total
deterministic project cost and duration is $ 5 million and 555 working days, respectively.
TABLE 1 Example of base cost and schedule estimates
Task Description
Scope
Design
Environmental Process
ROW/Utilities/RR
Permits
Procurement
Construction
Close Out
25 Duration
(Days)
Cost ($)
% of Total
Cost
60
120
90
60
60
30
210
15
200,000.0
400,000.0
50,000.0
200,000.0
50,000.0
50,000.0
4,000,000.0
50,000.0
4.0%
8.0%
1.0%
4.0%
1.0%
1.0%
80.0%
1.0%
8 1 2 3 4 The risk and uncertainty factors were determined based on the WSDOT Cost Estimate
Validation Process (CEVP) reports. Table 2 describes the typical risks that were included in the
model.
TABLE 2 Definitions of risk factors
No
5 6 7 8 9 10 Risk Factors
1
Environmental Delay
2
Material Price
Uncertainty
3
External Policy Change
4
ROW Cost Uncertainty
5
Variable Market
Competitiveness
6
Construction Cost
Uncertainty
7
Schedule Uncertainty
8
Permit Delay
9
Procurement Delay
10
ROW Delay
11
Scope Uncertainty
Description
If the project sees a delay during the environmental process due
to complications in the process such as revised requirements,
then the environmental process will be delayed. The project
may have to revisit the design phase.
If there is an unusual increase in the price of construction
materials due to inflation, a key material increase, or other
reason, then it is likely that the construction cost will increase.
If there is a change in the policy affecting design, environmental
process, and construction, then these activities will be delayed.
If there is a difficulty in obtaining right-of-way or increases in
the price of real estate, then there may be an increase in the cost
of right-of-way acquisition.
If there is poor competition in the market at the time the project
is put out to bid, then it is possible that the bids received will be
higher than anticipated, increasing the cost of construction.
If there is some uncertainty in the estimate relating to material
prices or quantities then this will affect the final construction
buy-out cost.
If there is a delay on the construction for any reason, then this
activity will be extended.
If there is a delay in the approvals process, then this activity will
have a longer duration.
If there is some difficulty in obtaining a fair price, then the
project may need to be redesigned and rebid.
If there is a difficulty in obtaining right-of-way or increases in
the price of real estate, then there may be an increase in the cost
of right-of-way acquisition.
If the scope of the project is increased or decreased due to a
change in project requirements, changes in the budget, or other
reasons, then the project will see a change in the cost and
duration of design and construction activities.
Table 3 provides an example of risk inputs, including probability, impact, and types of
distribution use for risk models. It is important to note that for each risk factor, a correlation
among projects needs to be assessed. For example, the number 0, 1/2, or 1 shown in Table 3 was
assigned to each risk to describe the effect of its occurring in multiple projects. It was not as
important which risks were applied to, but it was important that the cross-project correlation be
9 1 2 3 4 included to represent a risk that occurs in similar magnitude across multiple projects in the
program.
TABLE 3 Example of risk data inputs
No
Risk Factors
Probability
1
Environmental Delay
10%
2
Material Price
Uncertainty
10%
3
External Policy
Change
10%
4
5
6
5 6 7 8 9 10 11 12 13 14 ROW Cost
Uncertainty
Variable Market
Competitiveness
Construction Cost
Uncertainty
10%
25%
10%
7
Schedule Uncertainty
25%
8
Permit Delay
10%
9
Procurement Delay
10%
10
ROW Delay
25%
11
Scope Uncertainty
50%
Impact
Environmental Process (0200% Schedule)
Construction (0-30% to
Cost)
ROW (0-10% to Cost)
Construction (0-10% to
Schedule, 0-10% to Cost)
Design (0-10% to
Schedule, 0-10% to Cost)
ROW (0-10% Cost)
Construction (0-10% to
Cost)
Construction (0-50% to
Cost)
Construction (0-25% to
Schedule,0-10% to Cost)
Permit (0-25% to Schedule)
Procurement (0-150% to
Schedule)
ROW (0-20% to Schedule)
Design (0-10% Schedule,010% Cost)
Construction (0-10%
Schedule,0-10% Cost)
Distribution
Type
Correlation
among
Projects
Uniform
½
Uniform
½
Uniform
1
Uniform
½
Uniform
1
Uniform
0
Uniform
0
Uniform
0
Uniform
0
Uniform
0
Uniform
0
In this study, to create a set of projects in the program, the input data including costs,
durations, schedule logic, and risks were used three times. The schedules were staggered to start
every six months to capture the nature of multi-projects in the program. This process generated a
program with three identical projects, each having identical cost, schedule, and risks.
RESULTS AND OUTPUTS
Figures 3 and 4 present the individual project cost and total cost for both project and program
risk mitigation models. Figure 3a shows the individual project cost for the project risk mitigation
risk model. The mean for project cost is approximately $6.27 million and the standard deviation
10 1 2 3 4 5 6 7 8 is approximately $327,000. Figure 3b shows the individual project cost for the program risk
mitigation risk model. The mean for project cost is approximately $6.26 million and the standard
deviation is approximately $259,000. The statistical means for these two models are within 1%
of each other. This finding demonstrates that the bias from inputs was successfully removed. The
standard deviation in the project model is approximately 26% larger than that in the program
model. This result implies a greater certainty in the action of the project managers when viewed
from the program manager’s perspective. The program manager may be more certain to predict
the risk than the project managers.
(a) Individual project cost - Project Model
9 10 11 12 13 14 15 16 17 18 (b) Individual project cost - Program Model
FIGURE 3 Individual project cost
The total cost for each model was computed by summing the results of the three project
total costs in each iteration. This was not done by simply multiplying the one project by three.
Each project was represented separately to model the behavior of different projects in a program.
Figure 4a shows the total cost for the project mitigation model. The mean of the total cost was
$18.80 million. The standard deviation for the program cost was approximately $614,000. Figure
4b shows the total cost for the program model. Figure 4b indicates that the mean of total cost was
approximately $18.78 million and the standard deviation was approximately $526,000.
11 (a) Total cost - Project Model
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 (b) Total cost - Program Model
FIGURE 4 Total cost of all projects in program
Similar to the individual project cost, the mean of total cost for these two models are
within 1% of each other. The standard deviation in the program model is approximately 15% less
than that of the project model. It is expected that the program manager can predict a total cost
with a higher certainty than the project managers.
The model outputs were verified through structured interviews with five risk
management experts in WSDOT who assisted in developing the model in Step 1. The
interviewees agreed that the model was requisite for its purpose and the inputs were sufficient to
accurately reflect the basics of each of the different models. Additionally, the interviewees noted
that the risk models can never be completely inclusive of all factors. The modeling process in
itself is an extraction of an existing set of projects, and inherently, some factors simply cannot be
modeled accurately.
DISCUSSION
The results of the modeling process showed that there were some specific statistical benefits to
the use of program risk management. The increased certainty resulted from the control of project
managers’ risk management activities promoted the program manager with the ability to better
allocate resources among the projects. The program manager also benefited with the reduction in
the amount of funding placed in each of the projects’ contingencies. As a result, the program
manager can reduce the necessary contingency, allocate the remaining funding to other projects
that may need it more, and manage multiple projects in the program effectively and efficiently.
Program risk management would appear to be the best approach to manage the risks from
the program manager’s perspective. Typically, program managers are more experienced than
project managers and can offer better management of risk, or can assist the project managers in
managing risks on their projects. Program risk management provides greater certainty in project
outcomes to the program manager. If the program manager dictates the mitigation strategies,
they are assuming greater risk with the failure of these mitigation strategies.
12 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 The strong relationship between project and program management will help the
organization better manage risks. Understanding this relationship will bring a number of benefits
regarding risk management at the program level such as better planning and coordination, more
efficient and appropriate use of resources, and better prioritization of projects. For example, if
the evolution of each project within the program is tracked and controlled, it is easier to identify
and manage the risks and uncertainties in the entire program. By filtering project and program
risks, resources can be more defensibly reallocated to the critical projects even after these
resources have been assigned to individual projects (21). While competition is the nature of a
multi-project environment, success in performing one project within the program typically does
not ensure entire program success. The competition between projects in the engineering and
construction industry is not in the best interests of the organization as a whole (22). For this
reason it has been argued that increasing cooperation and communication between projects
within the program is crucial in achieving an organization’s objectives.
LESSONS LEARNED
The interviewees were asked to identify lessons learned from their experiences in using program
risk management. The following are the major findings:
 The use of program risk management would allow for more uniform development of a
risk management database.
 The program risk management may be better utilized in certain agencies. Agencies in a
position to standardize risk management procedures would be able to collect risk
information over a period of time in a standardized and easily accessible manner.
Likewise, agencies with similar cost, schedule, and risk practices across all projects could
still allow project managers to maintain control of their projects within the existing
processes, but program risk management of certain large risks could occur at a higher
level without disruption to the project team.
 The program risk management would be more useful in certain project portfolios. For
example, the program risk management would be applicable to projects that were more
uniform in nature, such as simple highway projects, that are not as unique as bridge or
tunnel projects.
 The program risk management approach varied greatly from their existing risk
management approach. The use of program risk management is relatively rare. Program
risk management is a new concept and project risk management is widely the only type
of risk management used in the highway construction industry.
 The main challenges of program risk management include the uniqueness of projects, the
desire for project managers to maintain full control of their projects, and the inability of
program managers to become involved in the details of risk management at the project
level.
CONCLUSIONS
13 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 This study presents some specific statistical benefits to the use of program risk management
through the modeling process. There are differences in the nature and form of project risk
management and program risk management. Projects and programs are interdependent and affect
each other during risk management processes. The results of the project and program risk models
reveal that the increased certainty resulting from the control of project managers’ risk
management activities benefitted the program manager with the ability to better allocate
resources among the projects. The results of the models were evaluated and verified with
WSDOT personnel. Through the structured interviews, it was discovered that despite the
statistical benefits of the program approach, the behavior of individuals within the program was a
crucial hindrance to the benefits of program risk management. Foremost of these behaviors was
the desire of the project manager to maintain full control of the allocation and assignment of
project resources related to the execution of his or her project. Program risk management may
have additional benefits, however, to specific types of projects, programs, or agencies.
Although the results of this study have a potentially positive impact on reducing cost
uncertainties in highway projects through understanding of the impact of risk and uncertainty at
the program level, this study has two main limitations. First, this study focused primarily on a
model that was based on a set of highway construction projects. While the findings are likely
applicable to other construction projects due to their similar construction timeline and similar
project development, it cannot be proven through this research that other construction industries
will have similar benefits and drawbacks of program risk management. Second, the models were
unable to capture all of the relationships between the use of project and program risk
management. For example, specific projects that may have overlapping geography, similar
scope, or other factors may utilize forms of program risk management. The blending of these
different forms would inherently affect the outcome of the program risks.
This study addresses the broad topic of program risk management in which little research
has been done to date, especially in highway projects. Regardless of the advancement made by
this study, there is a need for future research and guidance regarding program risk management.
Some future research topics may include:
 How does the use of variable levels of program risk management help or hinder
successful project completion?
 Can a case study be conducted to compare a program that was managed without program
risk management with a program that was managed with program risk management?
 How can the “correlation between projects in the program” be defined in order to identify
a risk that has a project-only or a program impact? What are the thresholds?
 How does the use of escalation affect the use of program risk management given that a
program runs for a longer period than a project?
It is expected that the research presented in this paper will serve as a starting point to address
future research in risk management at the program level.
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