CIFE
Center for Integrated Facility Engineering
CIFE Seed Proposal Summary Page
2013-14 Projects
Proposal Title: Enhancing Pre-Construction Decision-Making on Sustainable
Commercial Building Projects
Principal Investigator(s): Michael Lepech, Gary Griggs
Research Staff: Kelcie Abraham
Proposal Number: 2013-10
Abstract (up to 150 words):
Architecture, engineering, and construction (AEC) teams need more efficient and effective decisionmaking methods, particularly during pre-construction of sustainability-focused projects when decisions
have the most impact on sustainability performance. The multi-disciplinary nature of decision-making
and the engagement of multiple stakeholders often result in decision problems with multiple objectives,
calling for approaches referred to as multi-criteria decision-analysis (MCDA). There is little consensus on
the best method for pre-construction decision-making. This work proposes the enhancement of preconstruction decision-making for sustainability-focused projects through design and implementation of a
multi-criteria decision-analysis framework. The research, to be conducted with students and interested
CIFE participants, will be used to evaluate the process and decision quality of different MCDA methods
and study the treatment of sustainability objectives. Ultimately, by better integrating sustainability into
pre-construction and developing more rigorous decision-making frameworks that are quick and easy to
use, AEC professionals can help clients achieve sustainability goals with greater efficiency.
1. Motivation
1.1. Research Background
Across the U.S., demand for environmentally sustainable buildings is growing; as shown in Figure 1, the
market for non-residential green buildings has increased rapidly over the past decade. Green buildings are
“structures…that are environmentally responsible and resource-efficient throughout the building’s life
cycle” (US EPA 2012). Often, green buildings are certified through programs like the US Green Building
Council’s Leadership in Environmental and Energy Design (LEED) rating system, which envisions green
buildings as structures that reduce operating costs and increase asset value; decrease waste sent to
landfills; conserve energy and water; are healthier and safer for occupants; and reduce harmful
greenhouse gas emissions (USGBC 2012). As of June 2012, two billion square feet of building space
have been LEED-certified, and LEED is referenced in project specifications for 71% of projects valued at
$50 million and over (USGBC 2012). In 2010, 78% of architecture and engineering firms and 81% of
contractors reported that client demand was driving them toward green building investment (MHC 2010).
Consequently, professionals in the Architecture, Engineering, and Construction (AEC) industry need to
become skilled at developing and delivering green buildings for clients.
Figure 1 The value of the non-residential green building market in the US has grown significantly since 2005; the
share of new construction starts that are green has also increased. Adapted from McGraw Hill Construction (2012)
Over the course of a building project, architecture, engineering, and construction (AEC) consultants
predict and evaluate the performance of many different design and construction options. The daily designconstruction recommendations that these AEC consultants make to the client have significant impacts on
the sustainability of the building throughout its life cycle. (Ugwu and Haupt 2007) With contractors and
designers frequently working under strict budget and schedule constraints, AEC consultants need superior
planning, design, and construction processes to achieve client goals. It is critical that AEC professionals
have the appropriate tools and effective processes to integrate sustainability into their everyday decisionmaking processes.
One of the most active and important stages for decision-making is pre-construction. For the purposes
of this proposal, pre-construction encompasses all of the phases in which changes to the design are
minimal but design and construction details are still being finalized. This means that during preconstruction, decision makers are dealing with decisions that have fewer, better-defined alternatives to
consider and more information to accurately evaluate those alternatives as compared to early-stage
design. However, while decisions may be fewer and easier to model, the ability of decision makers to
change project design and cost decreases and the cost of design changes (in terms of resources and
budget) increases significantly. The MacLeamy Curve, displayed in Figure 2, describes this phenomenon.
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Figure 2 The MacLeamy Curve (MSA-IPD 2012)
1.2. Industry Example
AEC professionals and researchers are actively investigating formal decision processes in practice,
primarily for early-stage design and pre-construction. A case study was conducted with a CIFE member
company during the pre-construction phase of a single large-scale, sustainability-focused project,
executed using Integrated Project Delivery (IPD). The company sought clear communication and desired
a rigorous decision-making process to ensure that high-level performance goals, including cost, schedule,
safety, and sustainability objectives, would be met. The multi-objective, multi-disciplinary nature of preconstruction decision meant that selecting an appropriate decision-making process was key. The team was
concerned with identifying an appropriate process for project-related decisions that had a significant
impact on project cost or schedule and required supporting information from AEC professionals. These
decisions included, but were not limited to, trade partner selection, technology adoption, and detailed
systems comparisons.
For several months during pre-construction, the company implemented two formal, value-based
decision methods: first Weight, Rate, and Calculate (WRC), then Choosing By Advantages (CBA) (Suhr
1999). However, the company found that neither process led to decisions of satisfactory quality and
eventually rejected both. In general, decision makers found that CBA enabled multi-disciplinary
stakeholder participation and added value to decision-making for simple decision problems. However,
decision makers also believed that CBA was inefficient and ineffective for more complex decision
problems and did not adequately clarify decision rationale. The introduction of an online tool, Wecision
Enterprise (DPI 2013), to support CBA improved efficacy, efficiency, and value of information derived
from the decision-making process, but clarity of rationale remained an issue due to the inherent
complexity of decision problems and inconsistencies in factor selection between decisions. These
observations suggest the need for future research concerning the design and implementation of
appropriate tools for pre-construction decision-making on lean projects.
1.3. Research Objectives
This research has two primary goals. The first is to determine which decision-making methods produce
the highest quality decisions – that is, which process is best suited for pre-construction decisions on
sustainability-focused projects. The second objective is to gain insight into the best way to integrate
sustainability objectives into pre-construction decision-making and in turn, to establish a foundation for
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further research to determine how integrated sustainability pre-construction decision-making can be
enhanced.
2. Points of Departure
2.1. Multi-Criteria Decision Analysis (MCDA)
The multidisciplinary nature of AEC decision-making and the engagement of multiple stakeholders often
result in decision problems with multiple objectives. This particular structure of decision-making calls for
a set of approaches referred to as multi-criteria decision-analysis (MCDA). MCDA methods structure and
model the imprecise goals of multi-dimensional decision problems in terms of a set of individual decision
criteria, where each criterion characterizes a single dimension of the problem to be evaluated. The general
framework for most MCDA involves decomposing the decision problem into components, evaluating
each component individually, and reassembling the components to provide overall insights and
recommendations (Seppälä et al. 2002).
AEC professionals and researchers are actively investigating formal decision processes in practice,
primarily for early-stage design and pre-construction. Although treatment of MCDA methods in
construction decision-making literature is fairly limited, a number of researchers have focused on
developing decision support systems that use MCDA to select projects for investment (Lam et al. 2001;
Dey 2006), choose project procurement methods (Kumaraswamy and Dissanayaka 2001; Anderson and
Oyetunji 2003; Mahdi and Alreshaid 2005), and enhance early-stage design (Ugwu and Haupt 2007;
Turskis et al. 2009; Flager et al. 2012). The majority of MCDA methods examined in the literature and
implemented in practice are value-based. Value or utility theory approaches ask decision makers to
develop a numerical score or value for each decision alternative and choose the alternative with the
highest value. Examples include multi-attribute utility theory (MAUT), analytic hierarchy process (AHP),
and simple multi-attribute rating techniques with swing weights (SMARTS) (Lahdelma et al. 2000). CBA
is also considered a value-based method. In the AEC industry, CBA has been applied to decisions about
green roof systems (Grant and Jones 2008), installation of viscous damping walls (Nguyen et al. 2009),
and exterior wall assemblies (Arroyo et al. 2012, 2013).
Although a number of decision-making techniques are currently being investigated and applied
throughout the design and construction process, few researchers have studied pre-construction
applications of value-based methods or the implementation of non-value-based methods on sustainable
building projects. Other processes include goal programming, outranking methods, fuzzy set theory, and
descriptive methods. Research on MAUT, single-value decision analysis (Howard 1966) and the
development of objective functions for sustainability-focused pre-construction decisions has also been
limited.
2.2. Evaluating Decision Methods
Several researchers have developed metrics for comparing and selecting decision processes. Hazelrigg
(2002) defines a framework for considering whether a decision method: (1) allows a user to express
preferences and uncertainties; (2) does not impose constraints or ordering, and; (3) self-consistently rankorders alternatives. Given most complex AEC decisions are collaborative, Chachere and Haymaker
(2011) propose metrics and a process for measuring the clarity of decision rationale, including the teams
involved in the decision, the objectives defined, the alternatives explored, the impacts assessed, the
preferences stated, and the value determined. Senescu and Haymaker (2013) recommend metrics and a
process for measuring the efficiency and effectiveness of communicating process when creating rationale.
Clevenger and Haymaker (2012) identify methods to measure and compare the challenges addressed, the
methods used, and the exploration and guidance achieved. Arroyo et al. (2012, 2013) explored the
characteristics that distinguish viable decision-making methods, particularly for selecting sustainable
alternatives.
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2.3. Sustainability Integration
Decisions and decision-making processes associated with sustainability-focused construction are expected
to be significantly different from the decisions and decision-making processes of traditional construction.
This is due in large part to the often-conflicting nature of sustainability objectives, the relative
unfamiliarity of building owners and AEC professionals with many sustainability concepts and metrics,
and the extended validation process and efficacy timeline associated with most sustainability efforts.
The achievement of sustainability goals can only be known far into the future. Comello et al. (2012)
note that sustainable development and construction is now defined by the criteria used to recognize it: for
example, “pre-construction air quality permit requirements,” “purchase of renewable energy,” “attainment
of LEED certification.” As discussed by Comello, these are not correct formal logic definitions. A major
problem with such concepts of sustainable construction is the fundamental ex post facto nature of
sustainability: in most categories, today’s construction projects may only be deemed sustainable many
years from now. Defining sustainability in such long time frames with little certainty in feedback means
there is little incentive to focus attention on sustainable practices due to the high level of uncertainty in
the returns on such sustainability investments. The absence of definite feedback in the short-term –
positive or negative – significantly complicates the pre-construction decision-making process.
3. Methods
3.1. Charrette Tests
Decision-making charrettes similar to those conducted by Clevenger and Haymaker (2011) and Senescu
and Haymaker (2013) will be conducted with Stanford students and subsequently, industry professionals
from interested CIFE companies. The first charrette will be composed of two mock pre-construction
decision scenarios and will be used to test different decision-making methods identified in the literature
and through the initial industry observations. The decision scenarios will reflect common situations in
pre-construction that are easy to understand and simple to evaluate, with approximately three alternatives.
The first proposed decision problem is the choice between eco-friendly or more traditional jobsite trailers,
which is based on an actual decision from the sustainable campus project. The second problem is the
selection of a steel subcontractor, also based on a decision from the project. The decision scenarios will
include all performance prediction data relevant to the problem, as well as an explicit statement of client
project objectives. Groups of three or four Stanford graduate students will be taken through the exercise
using different MCDA methods. Each group will analyze one decision scenario using the weighted sum
method (also known as weight, rate, and calculate) – the first value-based method that was selected on the
project. This analysis will constitute the business-as-usual case. Subsequently, the groups will analyze the
same decision problem using the analytic hierarchy process, outranking methods, or single-value decision
analysis; the method will be determined randomly. Fuzzy set theory is also being considered. Time and
resources permitting, the group will analyze the second decision scenario using the weighted sum method
and an additional process, different from the second approach to the first decision problem. The use of
two decision scenarios is meant to minimize the learning effect from solving the decision problem using
the weighted sum method.
The groups will conduct their analyses using Microsoft Excel templates provided by the researchers.
These Excel templates will contain macros to track the group’s progress throughout the analysis,
including a complete history of cell value assignments, and compile the relevant data, which are identified
in the next section. Following the each decision scenario, participants will be asked to complete short
group and individual surveys (4-5 questions) related to the process. This is meant to elicit decision maker
satisfaction with process and outcome. The information gathered from the student charrettes will support
preliminary evaluation and comparison of the decision methods and streamlining of the charrette for
testing with industry groups. For the industry charrette tests, the decision scenarios may also be modified
to reflect actual pre-construction decisions on the project.
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3.2. Comparing Decision Methods
The decision methods will be tested against several metrics adapted from the literature for selecting a
decision-making approach. Each method will be considered in terms of five factors:
1.
2.
3.
4.
5.
Stakeholder participation
Clarity of rationale
Efficiency of process
Effectiveness of process
Value of information delivered
This evaluation will be conducted based on the data from the charrettes as well as verbal feedback from
the participants when relevant.
Comparison metrics have been identified from a study conducted by Davey and Olson (1998), who
compared two different multi-criteria group decision support methods and one commercial group decision
support system. Descriptive variables to be determined from the Excel spreadsheets include number of
preference changes, number of solutions changed, time to solution, preference changes per unit time, and
solutions considered per unit time. The exact descriptor of preference in the spreadsheet varies somewhat
with MCDA method. In general, preferences are expressed through value assignments or weighting
schemes (value-based methods), strength of pairwise comparisons (outranking methods), and objective
functions and preference probabilities (single-value decision analysis).
Davey and Olson (1998) also suggest user satisfaction as comparative metric; this will be assessed
qualitatively through the surveys and verbal feedback. Existing decision-making tools are underutilized
and their final conclusions are sometimes mistrusted. Often, the perception decision-makers have of
decision quality is just as important as the . Therefore, when examining the MCDA techniques as applied
to pre-construction decision-making, careful attention should be paid to the preferences of decisionmakers using the tools, including whether they would choose to adopt them. Measures of enhancement
include decision-maker ratings, reductions in decision-making time requirements and meeting lengths,
and the likelihood of adoption for future projects.
3.3. Assessing Sustainability Integration
As discussed previously, evaluating the achievement of sustainability objectives in the short term poses
an interesting challenge. One possible short-term evaluation measure for pre-construction decisionmaking is the satisfaction of the client or owner. Having specified a desired level of “green” or
sustainability performance for the overall project during preliminary design, most clients see their goals
first take shape in the pre-construction phase when design details are finalized, subcontractors are
selected, and construction gets ready to commence. As such, advanced specification and careful selection
of sustainability decision criteria is a major component of the enhanced decision framework with practical
implications. Understanding how criteria specification of sustainability-focused goals influences the
success or perceived success of client sustainability objectives can provide insight into how likely
decision makers are to implement the decision framework. In many regards, notions of client satisfaction
are a practical measurement of achieving sustainability goals for a construction project, particularly in
light of the ex post nature of sustainability, and can relate directly to repeat work for the contracting firms
and economic viability of the project team.
To assess the success of sustainability integration in decision-making, clear sustainability objectives
will be stated for the hypothetical project relating to each decision scenario. For each decision analysis,
the objectives met by the decision solution will be compared against the initial list; a higher number of
achieved objectives indicates better sustainability integration. Some groups may also be provided with a
list of all client goals during the business-as-usual decision analysis and be asked to eliminate those that
are irrelevant in light of the objectives given in the scenario description, whereas others will be asked to
generate an additive list of objectives working only from the scenario description. The hypothesis is that
groups who eliminate goals to produce the decision objectives will achieve better sustainability
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integration (i.e. greater number of sustainability goals met by the decision solution) than those who
generate the decision objectives.
4. Relationship to CIFE Goals
4.1. Schedule Performance
This research will enable the development of decision-making processes that require fewer process
iterations and result in higher client satisfaction with decision quality during pre-construction. Thus, it
allows teams to generate better performance predictions and conduct further analyses as opposed to
spending time in decision-related meetings.
4.2. Cost Conformance
This research will clarify the relationship between explicitly stated project development objectives and
pre-construction decisions that determine project performance. The improvements to decision-making
methods will also allows decision stakeholders to more clearly observe and monitor that the intended
process “checklist” has been followed successfully, and may also lead to improvements in value-formoney.
4.3. Sustainability
The proposed research will lead to improvements in the decision-making process that will help decision
makers to explicitly and systematically consider sustainability objectives in decision-making. As
quantitative sustainability metrics become more widely understood, decision-makers will be more able to
incorporate their knowledge easily into their decision analysis without altering their decision-making
process.
4.4. Globalization
As sustainability goals are implemented for projects all around the world, it is necessary for competitive
construction firms to have flexible, yet robust decision-making methods in place to efficiently and quickly
make decisions at every stage of a sustainability-focused project. The proposed research will lead to
improvements in construction firms translating highly sustainable designs for the built environment into
actual constructed facilities, much of which is now lost during inefficient and confusing pre-construction
decision-making efforts.
5. Industry Involvement
CIFE members and other industry partners will be involved in this project through the development of
decision scenarios and participation in the charrettes. Further testing of the enhanced decision framework
may be pursued on different CIFE member industry projects, and additional follow-up interviews and
surveys may be conducted with industry participants.
This work also leverages the industry experience and relationships of Consulting Professor, Gary
Griggs, as a Co-PI. Professor Griggs joined the CEE faculty after serving as the chairman of ParsonsBrinckerhoff Americas and thus brings an industry oriented-view to the proposed research program. The
proposed research will be Professor Griggs’ first research effort with CIFE.
6. Research Plan, Schedule, and Risks
6.1. Research Plan
1. Student Charrette (May/June 2013): development of Excel templates (user interface and macros),
implementation of charrette tests at Stanford.
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2. IGLC Conference (July 2013): publication of initial observations from sustainable campus project
and general framework for evaluating decision methods in conference proceedings (abstract
accepted; full draft in review), presentation of more detailed evaluation framework and
preliminary findings from student charrettes.
3. Industry Charrette (August/September 2013): modification of charrettes to address real project
decisions, implementation with CIFE members and industry partners on real projects.
4. Charrette Follow-Up (October/November 2013): continued charrette testing to collect any
remaining data on sustainability integration and follow-up with industry participants.
5. Journal Publications (January-March 2014): publications on (1) the evaluation and comparison of
MCDA methods and (2) enhancing sustainability integration submitted to peer-reviewed journals
in construction management, sustainable construction, or decision analysis.
6.2. Risks
1. Software Development – To implement the decision-making software for use by CIFE industry
partners, an easy-to-use graphical user interface (GUI) will need to be developed for the MCDA
tool. The development of this GUI is not within the expertise of the project team. This risk will
be mitigated by hiring a student software developer during the summer (as provided by the
budget).
2. Timeline - The project timeline may be difficult to meet if industry projects cannot be found in
the near-term for study this upcoming summer. Projects must be found that are in the preconstruction stage and have aggressive sustainability goals. This risk will be mitigated by
offering participation to as many CIFE members as possible to find projects that are in the correct
stage of planning and construction.
3. Project Identification - Researchers are dependent on the participation of CIFE’s industry
partners to validate the proposed MCDA method. Industry collaborators must be willing to
provide a set of case study decisions that focus on sustainability goals along with access to
researchers to observe the decision-making process, implement charrettes with participants, and
follow up with inquiries. This risk will be mitigated by offering participation to as many CIFE
members as possible to find the right size and type of sustainability-focused project.
7. Next Steps
Following CIFE seed funding, additional funds will be sought through a proposal to the National Science
Foundation (Directorate of Engineering) to be authored by the PI that focuses on the application and
extension of MCDA techniques to sustainability-focused decision-making beyond the construction
industry. Regardless of additional funding support, this work will continue through 2014 based on student
funding through the Stanford Graduate Fellowship.
8. Works Cited
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by Advantages” in the AEC Industry. Presented at the IGLC 20, San Diego, CA.
Arroyo, P., Tommelein, I. D., and Ballard, G. (2013). Comparing Multi-Criteria Decision-Making
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Clevenger, C. and Haymaker, J. (2012). “The value of design strategies applied to energy efficiency,”
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