Specifications for a Social and Technical Environment for Improving
Design Process Communication
R.R. Senescu & J.R. Haymaker
Center for Integrated Facility Engineering, Stanford University, Stanford, CA USA
ABSTRACT: The architecture, engineering, and construction (AEC) industry can generate tremendous value
by improving design processes. Building Information Modeling (BIM) and simulation tools provide potential
for such process innovation. Observed case studies and design management literature suggest that processes
can only improve through collaboration, sharing and understanding (defined here as Design Process Communication). Industry has not widely adopted process communication techniques, though other research fields
provide points of departure for promoting better communication. Organization Science studies show how information and knowledge is exchanged within institutions. Cognitive Science explores how the brain manages
information, suggesting improved information management methods. Human Computer Interaction explains
how computers best aid and supplement these human and organizational capabilities. Process Modeling research shows how to best visualize design processes. The paper links this research to develop a specification
of a social and technological environment - a Design Process Communication Methodology. The environment
is incentivized, usable, transparent, modular, sharable, personalized, transparent, embedded, computable, and
scalable. The Methodology aims to improve building design process efficiency and effectiveness from concept to construction documentation. REDUCE to 150 words
1 INTRODUCTION
This research aims to derive financial, environmental, and social value from more efficient and effective design processes. Design processes disproportionately influence the life cycle value of the
resulting products (Paulson 1976). Thus, while the
total cost of design is relatively small, the design
phase of a project greatly influences total project
value. Despite major advances in information technology over the past fifty years, the value per manhour expended, productivity, actually decreased
from 1964 to 2003 (United States Department of
Commerce 2003). As construction project’s final
value is most influenced by design, the architecture,
engineering, and construction (AEC) industry can
improve value per man-hour expended most directly
by improving design processes.
Design is an aggregation of many information
exchanges between people within and between organizations. Jin and Levitt’s Virtual Design Team
(1996) similarly applied this information processing
view of the organization to the AEC industry first
described by Weber in 1920 (1997) and later adopt-
ed by March and Simon (1958) and Galbraith
(1977).
Inefficient and ineffective information exchange contributes to construction productivity
stagnation. The National Institute of Standards and
Technology (NIST) report (Gallaher et al. 2004)
confirms that information flow on AEC projects are
inefficient and intermittent. Extrapolating, McGraw
Hill estimates the global AEC industry wastes $138
billion annually due to software interoperability
problems, which comes at the expense of meeting
financial, environmental, and social project goals
(Young et al. 2007).
At the same time poor information exchange
reduces a project’s total value, the cost of resources
required to obtain that value is increasing. Buildings
consume 70% of the U.S.’s electricity, 40% of raw
materials, and 12% of water (USGBC 2007). Exploding population growth will require more building which requires more resources, increasing their
relative cost. More buildings increase society’s negative impact on the environment. As populations expand and population density increases, a single project impacts more stakeholders. The AEC industry
has a responsibility to improve design processes to
ensure limited resources are applied optimally with
respect to financial, social, and environmental value.
At least three methods exist that permit organizations to improve design processes:
(1)
Design firms use more tools, to decide between more building technology options, to
meet the performance goals of more stakeholders. These trends provide an opportunity
to design more valuable buildings, but traditional collaboration methods limit this realization. To improve design processes in this
complex environment, designers need a
methodology that enables collaboration.
(2)
Project teams around the world face the same
financial, environmental, and social challenges, and continuously develop new and
different methods to overcome these challenges. To learn from how others address
these problems, design teams need a methodology that promotes sharing.
(3)
Developing new design processes requires
investment, which requires a claim that the
return will be an improvement on the current
state. In other words, if a design team does
not understand the inefficiency and ineffectiveness of their current process, they will not
invest in improvement. Design process innovation requires a methodology for design
process understanding.
Collectively, this research defines collaborating,
sharing, and understanding, design processes communication. This paper extracts findings from Organizational Science, Cognitive Science, Human
Computer Interaction, and Process Modeling research fields. It links these findings to develop a design process communication methodology that specifies
an
organizational
and
technological
environment necessary for improving the efficiency
and effectiveness of design processes. The paper
then describes tool derived from this methodology,
describes metrics for evaluating the methodology,
and briefly describes preliminary results from using
the method.
The next section gives specific examples of
how current design process communication limits
design process improvement.
2 OBSERVED PROBLEM
2.1 Case Studies
This paper uses the design of the Stanford Graduate School of Business (GSB) campus to provide examples of challenges faced by multi-disciplinary de-
sign teams. As structural engineer on this project,
the first author gathered design process information
directly and through interviews.
2.1.1 Designers struggle to collaborate
When designing the GSB, researchers identified
six discrete stakeholder groups with 29 project
goals.1 The design team evaluated seven mechanical
heating/cooling options with respect to a subset of
these stakeholder goals (Figure 1).2 They divided
one building into five different zones and assigned
five of the seven mechanical options to these zones
(Figure 2). The process of assigning these cooling/heating technologies to the building is complex.
According to the matrix the under floor distribution
system is the best choice. However, the floor plan
shows that in many cases other options prevailed.
Predicting how multiple options perform with respect to multiple goals in different contexts requires
the design team to synthesize information from multiple tools that output multiple measurements.3
The result of this informal process caused the
owner representatives to, in their own words, “feel
lost with so many options for the mechanical system.” The representatives described the decision as
“mixed and unclear” with “a lot of data.” The representatives “expressed their concern about inequity in
the mechanical system decision, specifically the potential inequity between faculty offices.” The owners did not understand how to interpret the data with
respect to stakeholder goals. The representatives also felt they “need more data” for “stronger justification” of the mechanical system decisions. The designer process was not sufficiently transparent such
that the owners found the design team’s recommendation convincing.
Comprehending this decision was too complex
just within mechanical engineering. In reality, the
decisions also impact acoustics, lighting, and structural engineering. With current tools, both the owners and designers did not systematically consider the
complex impacts of one decision on multiple disciplines. The multi-disciplinary design team did not
maintain consistency among information. The design team struggle to comprehend and manage their
information dependencies; they struggle to collaborate.
2.1.2 Designers struggle to share processes
The stakeholders communicated the importance
of material responsibility when choosing structural
1 Many projects have substantially more stakeholder groups.
For example, on a hospital project we found 41 stakeholder
groups with 33 goals.
2 At this point in design, geometry was fixed. A recent stadium project produced 201955 variations (Flager 2009).
3 The firm designing this mechanical system recently found
that they use 197 sustainability design tools and 189 structural
design tools.
Figure 1. An abridged decision matrix showing three of seven options and a subset of goals. The matrix suggests that underfloor air distribution best meets the goals, but of course, different systems are
best in different context. The designers do not have a tool to represent this complex interaction.
systems. The structural engineer created schematic
Revit Structure models of steel and concrete options.
The engineering firm had recently purchased Athena, software that uses a database to output the environmental impact of building materials. Despite a 3d
object oriented model (containing a database of
structural materials and quantities), a database of the
environmental impacts of those materials, and a desire by the stakeholders to consider material responsibility in their design decision, the structural engineer was unable to conduct an environmental impact
analysis comparing the concrete and steel options.
Several months later, the structural engineer met a
researcher in California that had worked with the
Cooperative Research Centre for Construction Innovation in Australia to develop a process for performing model-based assessments of the environmental
impact of construction materials. In fact, the research centre worked directly with the Australian offices of the same engineering company.
In this case study, a clear demand for an improved
process existed in the California office. The engineer
could not find a design process to compare options
with respect to stakeholder goals, even though researchers in California and engineers from the same
company in Australia had already performed this
process (Tobias and Haymaker 2007). A 2008 survey (Senescu and Haymaker 2008) confirmed the
generality of this problem: designers struggle to
share processes.
2.1.3 Designers struggle to understand processes
With the goal of informing the design team’s decision regarding the quantity and size of louvers on
the south façade of the GSB, daylighting consultants
created video simulations of sunlight moving across
a space (Figure 3). Looking to improve the realism
of the output, the consultants discovered they could
use the process described in (Senescu and Haymaker
2008). As described in Senescu and Haymaker
(2008), this process was inefficient, no one was developing an improved process, and the author could
not find an improved solution. The consultants’ supervisors, software developers, and their clients had
no methods for understanding how an improved
process could increase profits or design quality.
With an investment of $2400, Senescu and Haymaker (2008) estimates $32,400 of added value annually. Senescu extrapolates that further investment
could add $97 million of value to the firm annually
through more efficient and effective analysis processes.
The current tools provide an opportunity to improve the efficiency and effectiveness of the lighting
design process. Yet, individual consultants are not
incentivized to invest their time in process improvement. Their tools do not track their process
(and the resulting inefficiency), place them in a
small peer community to improve the process together, nor provide transparent access to other pro-
cesses that could form the basis for improvements.
Also, managers lack a transparent method for understanding the inefficiency and therefore, lack a monetary justification for encouraging development of alternatives.
Figure 2 Mechanical zone plans did not convince the owners
that the this layout was correct. The zone plans were not consistent with other documentation, which made it difficult for
other disciplines to coordinate with the mechanical engineers.
Figure 3. Snapshot of a QuickTime video. The consultants
use this video to show the architects how different louver configurations affect daylighting in the GSB.
3 RESEARCH TASKS
The previous section describes problems we observed in industry. The next section looks to aggregate research in other fields to develop a specification for a social and technical environment, which
we hypothesize will improve the efficiency and effectiveness of design processes through better communication. This specification of the Design Process
Communication Methodology is the contribution of
this paper. Using the Agile Software Development
method, we use the specification to create a processbased information management web tool, called the
Process Integration Platform (PIP). We discuss preliminary results of using a PIP prototype in this paper. Senescu (2008) describes PIP in more detail.
4 POINTS OF DEPARTURE
4.1 Organization Science
This paper takes the information processing view
of the organization first adopted by Weber 1924, and
later expanded by March and Simon 1956 and Galbraith 1977. Jin and Levitt’s Virtual Design Team
(Jin 1996) first adopted this view of the organization
to provide a transparent view of business processes
in AEC.
In this section, (1) we first explain why highly interdependent tasks inhibit process standardization.
This research prompts a claim that process documentation must be embedded and transparent. (2)
Then, via research on the Institution, we explain that
technology must be transparent, social, and shared
to best allocate human capital and creativity. (3) In
addition, Institutional research on matrix organizations suggests that hierarchically structured information is not suitable in AEC. This research leads us
to claim that the representation of information in
matrix organizations must be transparent and personalized to each individual. (4) And finally, we
look at Knowledge Management research to see the
value proposition for the emergence, structuring, and
sharing of design process knowledge. This research
emphasizes the importance of embeddedness of
knowledge emergence; transparency of information
structuring; and social sharing.
4.1.1 Coordination without standardization
According to Thompson (year), standardization
permits coordination when situations are relatively
“stable, repetitive and few enough to permit matching of situations with appropriate rules.” In AEC,
the IAI developed IFC’s to standardize data schema
for describing buildings (International Alliance for
Interoperability). GTPPM (process based methods
for developing product data models), Integrated
Delviery Manuals, and others also depend on a
standard design process (Lee et al. 2007)(IDM Reference). The new capabilities of simulation software,
the complex demands of stakeholders, the dynamic
and global nature of design teams make design process increasingly complex, dynamic, and performance (not precedence) based. March and Simon
(1958) argued at the time that organizations with
variable and unpredictable situations prevent process
standardization. Instead, coordination must be
achieved by “coordination by mutual adjustment,”
which “involves the transmission of new information during the process of action.” We extrapolate
that coordination must occur by Embedding the process in the hour-to-hour work of designers rather
than by developing standard methods for coordination.
The technical environment must track reciprocal
interdependence to allow teams to better coordinate
by mutual adjustment. These tools must allow humans with bounded rationality to visualize and understand these reciprocal interdependencies. Processes must be transparent without requiring
standards. This organization science research may
explain why such standards have been relatively unsuccessful in practice and why we fail to see a convergence to a single product model in AEC.
4.1.2 Institution Forming
Coase described a model for explaining the development of a firm versus a market (Nature of the
Firm, 1937). (Benkler 2002) proposes the alternative
peer production model to explain the open source
software institution. Benkler claims that this emerging third type of institution “has certain systematic
advantages over the other two in identifying and allocating human capital/creativity.” We seek a design
process communication methodology that utilizes
these advantages to mimick success in the open
source software industry.
Benkler, in describing necessary conditions for
“information-production and information exchange
chain” in this peer production model, breaks down
the “act of communication” into three parts. He first
describes that someone must create a “humanly
meaningful statement.” Second, they must map the
statement to a “knowledge map,” so that its relevance and credibility is transparent. Finally, the
statement must be shared. Each of these parts make
up the “information-production chain.”
Bergner’s explanation of the firm provides insight
as to how Benkler’s peer production model for this
third type of institution could form. Bergner explains
that many menial tasks take much effort to complete
(see lighting case study in Section 2.1.3). Bergner
(1967) argues that “habitualization” is human nature, because it “frees energy” for creative decision
making and “opens up a foreground for deliberation
and innovation.”4 Sharing previous processes allows
this creativity through modular combination of existing ideas (Hippel and Krogh 2003), and reduces
the burden of redundant activities, so individuals can
focus on value adding innovation.
In fact, Bergner argues that this habitualization of
activities is the very reason why institutions develop
in the first place. Organizations can invest in technology to perform standard tasks, providing an advantage over the sole practitioner. A larger institution (or the greater the number of participants in the
technology) which collectively can develop more
institutional habits, can then focus more on creative
endeavors.
Bergner says that for institutions to exist, “there
must be a continuing social situation in which the
habitualized actions of two or more individuals interlock” (p.57). But what happens when the quantity
and diversity of actions and actors is so great that
these social institutions do not occur naturally? Individuals in the organization must continuously
waste energy on activities that from an institutional
perspective, seem habitual, but from the perspective
of the individual are unique. Can we use technology
4
Thompson’s standardization refers to collections of information exchanges, whereas Bergner’s habitualization could
involve just the habit of performing a single information exchange.
to facilitate “social situations” where “individuals
interlock” to create reciprocal typification5? This
paper claims that technology is needed to socialize
information exchange and make typification transparent, so that communities (i.e. institutions) can
form around common tasks (i.e. habits).
4.1.3 Structuring Information for Matrix Organizations
Programmers in the open-source software movement were simultaneously part of Benkler’s peer
production model and Coase’s more traditional firm.
Designers will also exist within a peer production
model and the traditional AEC matrix organization.
Davis (Davis and Paul 1977), observing the
emergence of the matrix organization structure in
the 1960s, proposed three conditions necessary for
an organization to switch to a matrix structure.
First, environments required organizations to focus
on more than one goal with equal weight. Second,
as complexity requires increased coordination,
transaction costs (see Williamson, but reference
original) of typical hierarchical organizations become prohibitive, making it more efficient for coordination to transcend a single organizational structure. Third, pressure to utilize resources more
efficiently and meet high quality standards. Large
design companies generally formed matrix organizations, aligned by project, by geography and/or by
discipline, but the way in which they stored information remained hierarchical. Companies structured
their information according to project, discipline,
phase, etc. Just as Davis required that new conditions required a change in structure, we analogously
argue that changes in the conditions of information
storage similarly require a deconstruction of the typical hierarchical structure of information.
First, information now must serve more than one
purpose. A building element object such as a window must be used for an architectural rendering, for
a daylighting analysis and for an energy analysis.
Much effort is exerted to create this window object,
and so, it no longer belongs to just one project, but
must be used on multiple projects. The information
about the window must be transparent to the project
team. This transparency requires a non-hierarchical
structure. Information now has many purposes and
so, it is inappropriate to structure it in just one way.
It must be personalized.
Second, the time it took to find and share information used to be relatively small. But with increased computer power and increased demand to
5
Whereas habitualization refers to the recognition of one’s
own repetitive tasks, typification is the recognition of other’s
habits. Reciprocal typification is when two people recognize
each others habits Baumer, E., and Tomlinson, B. (2006). "Institutionalization through reciprocal habitualization and typification." LECTURE NOTES IN COMPUTER SCIENCE, 3825, 122..
view tradeoffs, information exchange now has a relatively high transaction cost. As was shown in the
collaboration problem example, it is difficult for designers today to keep information consistent. Thus,
it is key that the relationship between information be
transparent.
4.1.4 Knowledge Management
Grant states that an organization’s knowledge is a
resource (Grant 1996). In this knowledge-based theory of the firm, the organization is a social community that transforms knowledge into economically
rewarded products and services (taken from Will
2008, quoting Grant 1996 and Khanna 2005). AEC
project teams transform knowledge into designs that
they then supply to clients to be built.
Conklin (Conklin 1996) explains a “project
memory system” to explicitly define this knowledge
and make it available to others. He observes that the
project memory system is necessary, because organizations lack the ability “to represent critical aspects
of what they know.” Like Conklin, we propose an
environment that acts as “an evolutionary stepping
stone to organizational memory.”
Whereas Conklin (1996) generally applies this
system to capturing knowledge from meetings, we
focus on capturing design process information. We
propose an environment that tracks information exchanges on a project to deduce knowledge about the
design process to be applied on other projects. Thus,
we are not just interested in obtaining tacit and explicit knowledge that already exists. Conklin’s stepping stone from project to organization can also lead
to knowledge emergence.
Once knowledge emerges, it must be structured.
Hansen (Hansen et al. 2005) describes two aspects
of knowledge management: codification and person-
alization. According to Will (2008), Codification
relies on information technology tools to connect
people to reusable explicit knowledge. Personalization relies on socialization techniques to link people
so they can share tacit knowledge. Information
Technology can provide the general context of
knowledge and then, point people to individuals or
communities that can provide more in depth
knowledge. We specify an environment where design process knowledge is structured through information dependencies and linked to people that have
even more in depth knowledge about the process.
Kreiner (Kreiner 2002) states that knowledge
management is not just emergence and structuring.
Will (Will and Levitt) uses Kreiner’s term
knowledge mobilizing to address the additional importance of the future ability of others to find the
collected knowledge. Knowledge management is
not just emergence, structuring, and pushing information, but also the ability to pull relevant
knowledge when needed. We call this pushing and
pulling of knowledge, sharing.
But notice that these three components (emergence, structuring, and sharing) of knowledge management are the same three factors required for
Benkler’s peer production model! Yet, in Benkler’s
model, there is minimal if any management. We
look at knowledge management research to provide
guidance for developing an environment for a selfperpetuating emergence, structuring, and sharing of
design process knowledge with minimal if any management.
4.2 Process Modeling
Now we look at Process Modeling….to be completed
4.3 Human Computer Interaction
Not complete
Norman (1993) writes “the power of the unaided
mind is highly overrated…The real powers come
from devising external aids that enhance cognitive
abilities.” Can we develop a tool that enhance designer’s abilities to collaborate, share, and understand?
Car writes of six ways that visualizations amiplify cognition. By visually representing dependencies
, designers can offload this memory and conetrate on
other things. By grouping related information together, less time is spent searching. Patterns in dependencies may emerge which wouldn’t exist without the information. By using perception to monitor
changes.
The human eye can process information in two
ways. Controlled processing, like reading, “is detailed, serial, low capacity, slow…conscious.” Automatic processing is “ superficial, parallel…has
high capacity, is fast, is independent of load, unconscious, and characterized by targets “popping out”
during search.” According to , coding techniques to
aid search and pattern detection should use features
that can be automatically processed.
Groups of users spurred by storytelling and
shared memories spent more time exploring and asking deeper analysis questions than individuals (Heer)
Also, “lines joining nodes” may enlarge one’s
working memory and “lead to dramatic improvements of cognitive functions.” Thus, even if the designer is conscious of the relationships between information, the representation of those relationships
graphically may allow the designer to comprehend
and act upon the relationships more effectively.
5 DESIGN PROCESS COMMUNICATION
METHODOLOGY
This section aggregates the points of departure into
the Design Process Communication Methodology in
Table 1. The methodology specifies a social and
technical environment for design process communication.
Table 1. The Design Process Communication Methodology’s specifications for a social and technical environment.
Characteristic
Specification
Distributed
Store all project information on a network.
Allow users to find process modules from previous projects.
Embedded
Information relationships must emerge from designer activities.
Track information exchange during the design process.
Modular
Break processes into segments of information exchanges.
Source
Benkler
Bergner
Thompson
Grant
1996
Hippel
Allow knowledge to be added with minimal transaction cost
Personalized
Organize information differently for different people.
Associate people with information exchanges
Social
Form communities around design processes.
Transparent
Represent design processes through information exchanges between designers
Davis
1977
Hansen
2005
Bergner
Jin
Provide metrics around processes that indicate credibility.
Contextualize processes with project information
1996
Benkler
Benkler
Inform designers about the typical processes followed by other designers.
Bergner
Organize information according a graphical web of information dependency rather than a
hierarchy where information can only lie in one locaton.
Alert designers when information that they used changes.
Shared
Davis
1977
Davis
1977
Ability to search processes via paths of information exchanges
Allow searching across projects
Scalable
Aggregate information exchanges across all projects.
TBD
This will roughly triple in size as I write the other pods
TBD
Figure 4: Metrics for evaluating the Design Process Communication Methodology
6 VALIDATION METRICS
Senescu (2008) describes the Process Integration
Platform (PIP) developed according to the specifications in Table 1. We intend to validate the Design
Process Communication Methodology by measuring
the impact of PIP on design process efficiency and
effectiveness. The validation will confirm or challenge the theoretically-founded specifications derived from the points of departure above.
Do designers with the design process communication methodology perform more efficiently? Senescu and Haymaker (2008) describes in detail metrics for measuring efficiency and effectiveness. By
tracking the amount of time spent on each task, the
authors determine the percentage change in design
process efficiency due to introduction of the methodology. The percentage change in efficiency is defined as:
∆ Efficiency = ∆ (Value Added Time / Total
Process Time)
Equation 1
From this equation, the authors compare the relative efficiency of various processes currently in
practice and measure whether P.I.P. improves efficiency. Senescu and Haymaker (2008) also explains
how to measure design process effectiveness:
∆ Effectiveness = 50% x ∆ MACDADI + 25%
x ∆ Designer Review + 25% x ∆ Option Generation
Equation 2
We will run several design charettes and case
studies to obtain data for these metrics. Figure 4
shows hypothetical results from the planned tests.
For example, the data may suggest that the theoretically founded Design Process Communication
Methodology improves Efficiency 26% and Effectiveness 17% (show arrow for effectiveness).
7 PRELIMINARY RESULTS
We introduced a prototype of PIP to a multidisciplinary building analysis class at Stanford University. The student group aimed to perform multiple analysis to decide between atrium options for the
Stanford GSB library design. Mimicing practice,
each student was responsible for a particular type of
analysis. Rather than using e-mail or a common
folder directory, the students used the
prototype tool shown in Figure 5 as the primary
means for collaboration. For example, when the
student performing energy analysis, needed to know
how much energy would be required for lights, he
double clicked on the daylighting analysis node to
open a daylighting sub-process and find the information required. The energy analysis student then
Figure 5: High level results of the use of a prototype tool by a multi-disciplinary analysis class at Stanford.
drew an arrow to the energy analysis node to represent this dependency. He then saved the files he
created as a sub-process beneath the energy analysis
node. This exercise proves conceptually that it is
possible to collaborate via information dependency
maps. Within the next month, the research will
demonstrate design process sharing and implement
the metrics described in Section 6.
8 CONCLUSION
Designers in AEC struggle to collaborate, share and
understand. To address these challenges, this paper
aggregated findings in organizational science, process modeling, cognitive science, and human computer interaction research fields to develop specifications for a social and technical environment, the
Design Process Communication Methodology. We
presented metrics for evaluating the impact of this
methodology on design process efficiency and effectiveness. Preliminary results show that the implementation of the social and technical environment is
possible. Future research will use the metrics presented to measure impact. The findings will either
confirm or challenge the applicability of this other
research to the design process communication problems in AEC.
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