Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter.
Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.
OCR for page 90
90 A Guidebook for the Evaluation of Project Delivery Methods
permits/approvals has received a rating of +. The same risk factor, under a DB delivery method,
is seen as unfavorable from the agency's point of view because the agency thinks that the DB con-
structor is not the best party to obtain various permits and approvals (such as environmental
permits). Therefore, a rating of - is assigned. Another risk factor in the hypothetical example,
"design defects," has a rating of - under the DBB arrangement because in this delivery method
the agency is responsible for the accuracy of design. A DB approach, on the other hand, is rated +
because it transfers this risk to the constructor.
If the choice of a project delivery method has no effect on a particular risk factor, then a rating
of 0 will be assigned. In rating each risk factor, one can refer to the contents of Chapter 3 of this
guide, where the advantages and disadvantages of various project delivery methods in relation to
24 pertinent issues are documented.
No attempt is made at this stage of the Tier 3 analysis to quantify the impact of these risk fac-
tors (in terms of $ value or project delay). After the matrix is developed and the risk factors rated,
the evaluation team can review the outcome and see if any project delivery method seems supe-
rior in terms of its capacity in dealing with these risk factors. For example, a review of the matrix
in Table 6.1 may suggest that DB is the better choice for the owner agency because of the num-
ber of favorable ratings that it obtained.
Preparation of the risk-allocation matrix and rating the risk factors can be accomplished in
a reasonable amount of time. If the outcome suggests a "most appropriate" project delivery
method, then the decision is finalized and the results, along with justification, are documented.
If, after going through the process, the choice is still not clear, then the Tier 3 process should con-
tinue on to the second phase--the quantitative analysis.
Quantitative Analysis
The quantitative approach should be attempted only if the qualitative approach does not result
in a clear delivery method choice for a project. As shown in Figure 6.3, it is suggested that the
Tier 3 quantitative analysis occur at the conclusion of the preliminary engineering phase, after
the agency has conducted the FTA-mandated probabilistic risk analysis of project cost and sched-
ule. The risk analysis is a major undertaking that requires hundreds of person-hours over the
course of several weeks. Also, the outcome of the risk analysis can inform the project delivery
method selection process (see Figure 6.4). The quantitative phase of Tier 3 would then be con-
tingent on the availability of the complete risk analysis. If this risk analysis is not a requirement
(for example in projects that do not apply for federal funding), then it is suggested that the proj-
Quantitative
Risk Analysis Cost/Schedule Approach
(See Fig. 6-5)
- Ranked Risk Factors
- Risk Factor 1
- Risk Factor 2
...
Figure 6.4. Risk analysis outcome as an input to project delivery
method selection.
OCR for page 91
Tier 3--Optimal Risk-Based Approach 91
ect delivery method selection decision be made without this phase as the cost of this phase could
be prohibitive.
The outcome of the probabilistic risk analysis required by the FTA consists of a distribution
(range of possible values) for project cost and duration. Also, a list of the most important risk
factors, ranked according to their impact on budget or schedule, is provided as part of the risk
mitigation report. Usually, the number of these ranked risks is limited (e.g., in several risk assess-
ments conducted by the project management oversight (PMO) consultants on behalf of the FTA,
the list of significant risk factors included 10 to 15 risk factors). The FTA analysis follows the logic
of Pareto's law (also known as the 80-20 rule and the law of the vital few), which states that for
many events, 80% of the effects come from 20% of the causes. In the context of project risks, rel-
atively few risks are responsible for most of the project cost or schedule overruns. The project
cost distribution and the list of ranked risks serve as inputs to the process of selecting the best
project delivery method. For each ranked risk, a distribution of risk costs is usually estimated.
The highest ranked risks are those with large expected values and large ranges (an indication of
high variability in the risk factor).
The proposed process, called the quantitative approach in this work, will involve estimating the
effect of each major risk factor on the agency's budget, given a specific delivery method. The process
starts by reviewing all the risk factors and selecting the risk factors whose value will be affected by
the choice of project delivery method. Only the risk factors that are sensitive to the project deliv-
ery method will be selected for further analysis. For each of these risk factors, the range of cost will
be estimated under a given project delivery method. This estimation can best be accomplished by
some of the same experts involved in the risk analysis. Figure 6.5 provides an example of a hypo-
thetical project in which four major risk factors have been identified as the risk factors that are
affected by the choice of project delivery method and the two remaining candidates for delivery
method are DBB and DB. The risk factors are the following: permits, utility relocation, differing site
conditions (DSC), and third-party issues. The cost of each risk is estimated using a triangular distri-
bution, although many other distributions can be used depending on the nature of the risk factor.4
The sum of these risk costs will give the distribution for the total risk costs. There are statistical
methods that can be used to calculate this sum with relative ease. Comparison of distributions of
these total risk costs will give the owner agency a valuable tool for assessing the effect of project
delivery method on project cost. A similar approach can be used to assess the effect of risks on
project schedule. If the purpose of the risk analysis is to examine the effect of delivery method on
project duration, all the distributions depicted in Figure 6.5 would have durations on the X-axis
and the total effect will be the total impact on project schedule instead of on project cost.
The quantitative analysis is a powerful tool for comparing competing project delivery methods.
It focuses on those differences between project delivery methods that affect cost and schedule and
provides a consistent way of evaluating each project delivery method vis-à-vis major risk factors
affecting the project. This analysis allows the decision-maker to document the reasons for the
selection of a specific project delivery method. The drawback of this analysis is its dependency
on the availability of expensive risk analysis results and the higher skill level required for pricing
out each risk under various project delivery methods. However, the choice of the project deliv-
ery method is a natural outcome of a risk analysis exercise because one of the most important
benefits of any risk analysis is risk allocation/mitigation. A properly selected project delivery
method is an effective risk mitigation instrument that can help keep project costs low and min-
imize project delays.
4
In a triangular distribution, the range of possible values is estimated with a lower bound (optimistic), an upper
bound (pessimistic), and a most-likely value. The triangular distribution is commonly used in probabilistic risk
analysis because of its simplicity.
