DESIGN PROCESS COMMUNICATION …xd549pb6857/Senescu-Design... · CIVIL AND ENVIRONMENTAL...

DESIGN PROCESS COMMUNICATION METHODOLOGY

A DISSERTATION

SUBMITTED TO THE DEPARTMENT OF

CIVIL AND ENVIRONMENTAL ENGINEERING

AND THE COMMITTEE ON GRADUATE STUDIES

OF STANFORD UNIVERSITY

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR THE DEGREE OF

DOCTOR OF PHILOSOPHY

Reid Robert Senescu

June 2011

http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/xd549pb6857

© 2011 by Reid Robert Senescu. All Rights Reserved.

Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.

ii



http://purl.stanford.edu/xd549pb6857

I certify that I have read this dissertation and that, in my opinion, it is fully adequatein scope and quality as a dissertation for the degree of Doctor of Philosophy.

Martin Fischer, Primary Adviser


John Haymaker, Co-Adviser


Terry Winograd

Approved for the Stanford University Committee on Graduate Studies.

Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file inUniversity Archives.

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Reid Robert Senescu

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Abstract

Building project design teams struggle to (1) collaborate around processes within projects, (2)

share processes between projects, and (3) understand opportunities for investment in

improving processes across projects. Overcoming each challenge requires effective and

efficient communication of design processes. Yet, methods for communicating design

processes from the design process communication research field are too cumbersome to be

useful during design, and methods from the project information management research field

focus only on information exchange and not process communication. To address these

limitations, I aggregate findings from organizational science, human computer interaction, and

process modeling fields to develop the characteristics of the Design Process Communication

Methodology (DPCM). DPCM is Computable, Embedded, Modular, Personalized, Scalable,

Shared, Social, and Transparent. Enabling these characteristics, DPCM consists of elements

which represent and contextualize processes and methods that enable designers to capture and

retrieve processes. To test DPCM, I map the elements and method s to the Process Integration

Platform (PIP). PIP is a web tool that enables project teams to organize and share files as

nodes in an information dependency map that emerges as the team works. Results from the

use of PIP in student design charrettes and class projects provide evidence for the power of

DPCM to effectively and efficiently communicate building design processes within project

teams, between project teams, and across project teams. I claim DPCM as a contribution to the

fields of design process management and project information management. DPCM lays the

foundation for commercial software that shifts focus away from incremental and fragmented

process improvement toward a platform that nurtures emergence of (1) improved multi-

disciplinary collaboration, (2) process knowledge sharing, and (3) innovation-enabling

understanding of existing processes.

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Acknowledgments

This dissertation would not exist had I not met my advisor Dr. John Riker Haymaker at a

conference in Montreal in 2006. His enthusiasm for taking a multi-disciplinary approach to

solving construction industry design problems was so contagious, it prompted me to leave my

great job and begin my PhD. Without John’s insightful questions, our research group would

not have developed the innovative multi-disciplinary research that the world needs to address

its economic, social, and environmental challenges. John is a dedicated and loyal mentor, but

also a friend that has made my PhD experience unexpectedly enjoyable.

I would also like to thank Prof. Martin Fischer for his enthusiasm for my research and

Prof. Terry Winograd who introduced me to the mind-boggling philosophy of

phenomenology. I also appreciate Prof. Larry Leifer’s helpful comments as I prepared for my

oral exam and Prof. Jeff Heer for serving as chair. Also, throughout my PhD, Dr. John Kunz

has provided constant guidance, support, and encouraging feedback. Along with Dr. Kunz,

Prof. Ray Levitt’s research on Construction industry processes and organizations provided a

stepping stone for my work.

The validation of my work would have been impossible without the dedication and

ingenuity of software architect, David Anderson. David turned my vision of the Process

Integration Platform (PIP) into a reality while also demonstrating successful collaboration is

possible completely based on phone conversations without ever meeting each other in person.

Zheren Zhang and Nam Wook Kim also contributed to PIP. Also, I would like to thank the

over 200 students who used PIP in five Stanford classes, design experiments, and on their own

research projects.

My PhD was supported by generous funding from Arup. Sir Ove Arup founded his

practice in London in 1946 based on a belief in ‘total design’ - the integration of the design

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process and the interdependence of all the professions involved, the creative nature of

engineering, the value of innovation and the social purpose of design. My research was both

inspired and, I hope, contributes to this vision. In particular, I would like to thank the support

of Ibbi Almufti, Stephen Burrows, Chris Field, Anthony Fresquez, Eric Ko, Jason Krolicki,

Chris Luebkeman, Andrew Maher, Andrew Mole, Jim Quiter, Cole Roberts, Martin Simpson,

and Jeremy Watson. Arup is an inspirational organization of individuals dedicated to the

shaping of a better world.

The Center for Integrated Facility Engineering Technical Advisory Committee

provided additional support for my research. Prof. Ron Wakefield and Prof. Guillermo “red

convertible” Aranda-Mena at RMIT University supported my research in Australia. Over 40

professionals in Australia generously spent time with me explaining their design processes.

I would like to thank my colleagues at the Center for Integrated Facility Engineering:

Caroline Clevenger, Victor Gane, Matt Garr, Robert Graebert, Timo Hartmann, Peggy Ho,

Wendy Li, Tobias Maile, Jennifer Tobias, and Ben Welle. Also, there were a few close

friends that supported me as I made the difficult transition back to academic life: Susie Cho,

Matthew Kennelly, Jordan and Tara Parker, and Stephen Wolf.

Most importantly, I would like to thank my mother and father who always encouraged

me to be curious about the universe around me and always supported my quest to investigate

it. My little sister’s incredible perseverance in attaining her Doctor of Medicine (just one year

ahead of me) has been both a motivation and inspiration.

And finally, I would like to dedicate my dissertation to my two grandfathers, Samuel

J. Rubin and Louis Senescu. These great men planted the entrepreneurial spirit in me. They

taught me to not fear failures, that success comes with diligent work, and that success is the

enjoyment of the journey towards the success itself.

Reid Robert Senescu

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Table of Contents

Abstract ........................................................................................................................ iv

Acknowledgments ......................................................................................................... v

Table of Contents ........................................................................................................ vii

List of Tables ............................................................................................................... xii

List of Illustrations .................................................................................................... xiii

Chapter 1: Introduction ............................................................................................... 1

1 Dissertation Overview ..................................................................................................... 1

2 Perspective on Research to Improve AEC Productivity .............................................. 2

3 Narrowing the Scope of the POP Communication Problem ........................................ 6

4 Intuition for Process Communication ............................................................................ 7

5 Explanation of Dissertation ............................................................................................ 9

6 References ....................................................................................................................... 11

Chapter 2: Relationships between Project Complexity and Communication ...... 14

1 Abstract .......................................................................................................................... 14

2 Introduction ................................................................................................................... 15

3 Points of Departure in Project Information, Complexity, and Communication ..... 16

3.1 Product Organization Process – an ontology for project information ...................... 16

3.2 Complexity – assessing the challenge in modeling projects .................................... 18

3.3 Communication - exchanging information to create facilities ................................. 19

3.3.1 Collaboration within projects ............................................................................. 21

3.3.2 Sharing information between projects ............................................................... 22

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3.3.3 Understanding information generated across the firm or industry..................... 23

3.3.4 Collaboration, Sharing, Understanding - one for all, all for one ....................... 23

4 Research Method - Case Studies .................................................................................. 24

4.1 Interviewee Selection and Agenda ........................................................................... 24

4.2 Developing Criteria for the Two Assessment Methods ............................................ 26

4.2.1 Complexity criteria development ....................................................................... 26

4.2.2 Communication criteria development ................................................................ 27

4.3 Assessing the Case Studies with respect to the Criteria ........................................... 27

4.3.1 Measurement scale for criteria scoring .............................................................. 28

4.3.2 Defining the scope of the case studies ............................................................... 28

4.4 Research Method for Validating Complexity and Communication Assessment

Methods and the Complexity-Communication Relationship ................................... 29

4.4.1 Comparing the scores from the assessment methods with intuitive estimations 29

4.4.2 Evaluating whether the assessment method is calibrated .................................. 30

4.4.3 Evaluating the applicability of the assessment methods .................................... 30

4.4.4 Evaluating the complexity-communication relationship ................................... 30

5 Complexity and Communication Assessment Methods ............................................. 31

5.1 Product-Organization-Process Complexity Criteria ................................................. 31

5.2 Communication Effectiveness and Efficiency Criteria ............................................ 33

6 Results and Discussion .................................................................................................. 37

6.1 Validating the Two Assessment Methods ................................................................ 38

6.1.1 Alignment between intuitive estimations and the scores from assessments ...... 38

6.1.2 Calibrated assessment methods.......................................................................... 39

6.1.3 Applicability of the assessment methods ........................................................... 42

6.2 Discussion of Two Case Studies .............................................................................. 43

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6.2.1 Stadium project in depth .................................................................................... 44

6.2.2 Low-Rise residential project in depth ................................................................ 46

6.3 Discussion of the Complexity-Communication Relationship .................................. 48

6.4 Limitations and Future Work ................................................................................... 51

7 Conclusion ...................................................................................................................... 53

8 References ....................................................................................................................... 54

Chapter 3: Design Process Communication Methodology - improving the

efficiency and effectiveness of collaboration, sharing, and understanding ........... 59

1 Abstract .......................................................................................................................... 59

2 Introduction ................................................................................................................... 60

3 Examples of Collaborating, Sharing, and Understanding Challenges ...................... 63

3.1 Designers Struggle to Collaborate ............................................................................ 63

3.2 Designers Struggle to Share Processes ..................................................................... 64

3.3 Designers Struggle to Understand Processes ........................................................... 65

4 Lack of Effective and Efficient Design Process Communication .............................. 65

5 Synthesizing Existing Concepts to Develop DPCM .................................................... 66

5.1 Organization Science to Enable Adoption ............................................................... 67

5.1.1 AEC requires coordination without standardization .......................................... 67

5.1.2 Form new institutions around processes ............................................................ 68

5.1.3 Use processes to structure information for the matrix ....................................... 70

5.1.4 Knowledge Management without management ................................................. 70

5.2 Human Computer Interaction to Create and Access Processes ................................ 72

5.2.1 Cognitive Science calls for personalized graphical representations .................. 72

5.2.2 HCI advocates information interaction and visualization .................................. 73

5.3 Process Modeling to Represent Process ................................................................... 74

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5.3.1 Process models aimed at improving coordination and planning ........................ 75

5.3.2 Process models aimed at improving automation ............................................... 76

5.4 Gaps in PIM and DPM Research Relative to Characteristics .................................. 76

5.4.1 Improving communication between AEC professionals ................................... 76

6 Theory - Design Process Communication Methodology ............................................ 80

6.1 Elements and Methods Enabling Characteristics ..................................................... 81

7 DPCM Applied to Observed Problems ........................................................................ 86

7.1 Collaboration with PIP ............................................................................................. 86

7.2 Sharing Processes with PIP ...................................................................................... 88

7.3 Understanding Processes with PIP ........................................................................... 89

8 Validation Metrics ......................................................................................................... 90

8.1 Motivation for the Metrics ....................................................................................... 90

8.2 Effectiveness and Efficiency of Capturing Processes .............................................. 91

8.3 Effectiveness and Efficiency of Using Processes ..................................................... 92

8.3.1 Using processes for Collaboration within projects ............................................ 92

8.3.2 Using processes for Sharing between projects ................................................... 93

8.3.3 Using processes for Understanding across the firm or industry ........................ 94

9 Conclusion ...................................................................................................................... 96

10 References ....................................................................................................................... 97

Chapter 4: Communicating Design Processes Effectively and Efficiently .......... 107

Abstract ............................................................................................................................. 107

1 Introduction ................................................................................................................. 108

1.1 Research to Improve Design Processes .................................................................. 108

1.2 Communicating Design Processes ......................................................................... 109

1.3 Overview of this Paper’s Layout and Contributions .............................................. 111

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2 Design Process Communication Methodology .......................................................... 112

3 Validation Method ....................................................................................................... 113

3.1 Summary of Validation Methods for Design Process Improvement Research ...... 115

3.2 Test Setup ............................................................................................................... 118

3.2.1 Mock-Simulation Design Charrette setup ........................................................ 118

3.2.2 Ethnographic-Action research in design class projects .................................... 122

3.3 Techniques for Collecting Data and Measuring Results ........................................ 123

4 Results and Discussion ................................................................................................ 125

4.1 Design Charrettes for Collaboration and Sharing .................................................. 126

4.1.1 Capturing processes effectively and efficiently ............................................... 126

4.1.2 Using processes effectively and efficiently to Collaborate within teams ........ 127

4.1.3 Using processes effectively and efficiently to Share between teams ............... 128

4.1.4 Using processes effectively and efficiently across projects ............................. 131

5 Discussion of Power and Generality .......................................................................... 136

5.1 Internal Validity ..................................................................................................... 136

5.2 External Validity .................................................................................................... 138

5.3 Future Work ........................................................................................................... 139

6 Conclusion .................................................................................................................... 140

7 References ..................................................................................................................... 142

Appendix A: Mock-Simulation Design Charrette Instructions ........................... 148

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List of Tables

Table 2-1. Interview summary. ................................................................................................ 26

Table 2-2. Criteria for measuring a project's product, organization, and process complexity. 31

Table 2-3. Criteria for assessing a project’s communication. .................................................. 34

Table 3-1. Metrics to assess process communication. ............................................................. 95

Table 4-1. Summary of results from the Mock-Simulation Design Charrette. ...................... 130

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List of Illustrations

Figure 2-1. Diagram of the three types of information (product, organization, and process)

and three types of communication (collaborating, sharing, and understanding). ............ 17

Figure 2-2. Assessment of the intuitiveness of results. For each project, the plot shows a

comparison of complexity and communication assessment methods. In general the

analytical assessments (large data points) are relatively close to the intuitive estimations

(small data points). The largest differences occur for the high-rise commercial and

residential, high-rise commercial, and stadium projects (estimates are 1.0 to 1.1 distance

away from analytical assessment). On the other hand, the university building and low-

rise residential were close (0.3). ...................................................................................... 39

Figure 2-3. Assessment of the distribution of complexity and communication criteria. The

number of statements relevant to each criterion varied from 2 to 24. The average score

across all criteria is 2.9, which is expectedly close to three since the one to five scale was

defined based on the interviews. The average of the standard deviations across all

projects is 1.2. This deviation across projects suggests that the assessment criteria are

sufficiently granular to differentiate trends between projects. ......................................... 41

Figure 2-4. Assessment of the comprehensiveness of interviews. The figure shows that there

were in general more interview examples related to project communication criteria than

complexity criteria. In fact, the High School project had so few examples of complexity

criteria that it is not considered in assessing complexity-communication trends. ........... 43

Figure 2-5. The relationship between project complexity and communication. Based on the

six AEC projects assessed, communication problems increase with increased product,

organization, and process complexity. This current trend is unsustainable, because

delivering projects of increased environmental, financial, and social sustainability

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requires increased complexity. AEC requires IT solutions enabling horizontal trends so

the industry can increase complexity without increased communication problems. ....... 48

Figure 2-6. Disaggregated POP complexity and communication trends. The strongest trends

exist between product and process complexity and collaborating. Little evidence exists

that sharing across projects is impacted by complexity. A less sensitive trend exists

between product and process complexity and a firm’s ability to understand information

across the firm or industry. .............................................................................................. 50

Figure 3-1. Comparison of existing research in PIM and DPM with the DPCM characteristics.

The matrix is not intended to cover all research in these two fields, but to show a few

indicative examples of current gaps in the research. While individual research may

address some of these characteristics, the authors have not found a theory that addresses

all of them. ....................................................................................................................... 79

Figure 3-2. The Design Process Communication Methodology. Elements represent and

contextualize a process and methods enable designers to capture and use the process

model. These elements and methods enable the Characteristics. .................................... 85

Figure 3-3. Collaboration in the Process Integration Platform. Users navigate to the

appropriate process level via the hierarchy view (left) or by double clicking folder icons

(right). Users create nodes, upload files to those nodes, and draw arrows to show

relationships between the nodes. Green highlights indicate the node is up-to-date, and red

indicates an upstream file has changed since the node was uploaded. ............................ 88

Figure 3-4. Sharing processes in the Process Integration Platform. Users search information

dependency paths to find processes with the input available and the output desired. Users

can then copy processes to new projects. ........................................................................ 89

Figure 3-5. Understanding processes in the Process Integration Platform. PIP tracks some

process metrics automatically, so users can evaluate the most popular and time-

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consuming processes. Discussion threads are associated with each node, so project teams

can discuss individual files or entire processes. .............................................................. 90

Figure 3-6. Use of the Process Integration Platform (PIP) by students at Stanford University.

PIP is a process-based file sharing web tool that acts as a model for DPCM. Its use

demonstrates that DPCM can be practically applied and tested. ..................................... 97

Figure 4-1. Summary of the Design Process Communication Methodology. DPCM consists of

seven elements that enable design teams to represent and contextualize processes.

Elements contain methods and attributes (not shown) described in more detail in Senescu

et al. (2011b). ................................................................................................................. 113

Figure 4-2. The Process Integration Platform. PIP is a web tool enabling project teams to

organize and share files as nodes in an information dependency map that emerges as they

work. Users navigate to the appropriate process level via the Hierarchy view or by

double clicking frame icons in a Network view. Users create nodes, upload files to those

nodes, and draw arrows to show relationships between the nodes. Green highlights

indicate the node is up-to-date, and red indicates an upstream file has changed since the

node was uploaded. Users can also search dependency paths to find relevant historic

processes from other projects. Each node contains an information ribbon providing

additional process information and the opportunity to rate process productivity or

comment. ....................................................................................................................... 114

Figure 4-3. Mock-Simulation Design Charrette test setup. All teams open up PIP to see the

historic project frames (a). Control teams can open any of the six historic project frames

to view the list of tools used to complete that project (b). Experimental teams see the

same list of tools used to complete the project and their dependencies (c). The design

teams work in the “Team C Classroom” frame, which is initially blank, but then becomes

populated with the tools used to design the classroom. The work of the control teams

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eventually resembles a list similar to (b) and the work of the experimental teams

resembles the network in (c). ......................................................................................... 120

Figure 4-4. Example of a mock-simulation tool. All 20 mock-simulation tools resemble this

Energy Analysis tool. In this case, the Mechanical Engineer finds dependent input values

from the output values of other tools. He then chooses a design by selecting input

independent variables. Clicking the “Analyze” button produces the output values, which

become input to a subsequent tool. ................................................................................ 122

Figure 4-5. Information distribution across projects in a multi-disciplinary design and analysis

class. The shade of each square represents the number of arrows (i.e., dependencies)

from information created by a designer on the top (input) to information created by a

designer on the left (output). Designer names are hidden. A strong single-band diagonal

exists from top left to bottom right because designers depend mostly on information

created by themselves. A wide-band diagonal exists because most designers only depend

on information from within their own team. One exception is the three teams working on

different train stations along the same metro line. The projects are ordered from highest

to lowest project value, suggesting that teams with information distributed across the

team delivered more value (top left) than teams with fragmented and concentrated

information (bottom right). ............................................................................................ 133

Figure 4-6. Frequency and upload latency of information flow between different tools across

all projects in a multi-disciplinary design class. The shade of each square represents the

number of arrows (i.e., dependencies) from information created by a tool on the top

(input) to information created by a tool on the left (output). The size of the square

represents the average latency between when an input file is uploaded and an output file

is uploaded. The visualization enables a manager to understand potentially inefficient

information flows, so that he can invest in improved processes. For example, the large

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dark squares represent information flows that are both frequent and potentially time

consuming. ..................................................................................................................... 135

Chapter 1 Reid Robert Senescu

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Chapter 1: Introduction

1 Dissertation Overview

My research asks: How can design processes be communicated effectively and efficiently

within a project team, between project teams, and across multiple project teams? From my

experience as a structural engineer on building projects and from studying the fields of design

process management and project information management I realized that existing approaches

to communicate design processes are either too cumbersome to be useful during design, and

project information management tools are too simple to capture the essence of a design

process. Building on concepts from organizational science, human computer interaction and

process modeling research, I synthesized the Design Process Communication Methodology

(DPCM) as a simple yet powerful methodology that enables design teams to capture their

processes as they work, so they can then use these process models to collaborate within

project teams, share processes between project teams, and understand processes across the

firm or industry to invest in improvement.

This dissertation follows the three journal paper format whereby chapters 2, 3 and 4

motivate, develop, and validate DPCM. Chapter 2 establishes design process communication

as an important challenge in the Architecture, Engineering, and Construction (AEC) industry.

Chapter 3 explains how existing research does not adequately address this challenge. The

chapter then builds on existing literature to develop the DPCM and proposes how the

methodology can be validated. Chapter 4 provides evidence that DPCM effectively and

efficiently communicates design processes. I then conclude claiming that DPCM, validated


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using the Design Charrette and Ethnographic-Action Research Methods, contributes to the

fields of design process management and project information management.

2 Perspective on Research to Improve AEC Productivity

While the majority of my dissertation focuses on demonstrating how DPCM answers my

research question, this introduction explains my personal story leading up to my research

question, as the question itself reflects a predisposition about how researchers can contribute

to the delivery of increased value per man-hour expended in the AEC industry. Delivering

increased value requires consideration of how any particular research effort fits into not only

existing research fields, but also how it impacts industry by fitting into existing products,

organizations, and processes. Describing the AEC industry using an ontology consisting of

product, organization, and process (POP) component, both inter- and intra-related, provided

me with a lens for assessing and addressing my observations of industry (Garcia et al. 2004).

A research effort aimed at increasing value by addressing just one POP component without

considering the context of the others will be limited in its ability to dramatically impact the

industry. That is, the relationships between product, organization, and process are tightly

intertwined.

Product represents “the physical and abstract concepts that describe the artifact itself,

such as the columns and electrical system of a building.” Organization “is the agency and

agents responsible for design and construction of the artifact.” Process is a series of design and

construction tasks that the organization carries out to design and build the product (Garcia et

al. 2004). Professionals in an organization connect each task to form a process by exchanging

information between professionals. The “process of exchange of information between sender

and receiver to equalize information on both sides” is called communication (Otter and Prins

2002). Professionals exchange information about product (e.g., a reinforced concrete beam),


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organization (e.g., Acme Structural Engineering firm), and/or process (e.g., the tasks outlined

in the reinforced concrete building code for designing a beam). Exchanging information about

a specific process (i.e., communicating process) is itself a process. For at least two decades,

researchers and/or industry have modeled each component of the POP ontology virtually in a

computer (e.g., AutoCAD drawings, Jin and Levitt’s Virtual Design Team (1996), and digital

Gantt charts).

The task of modeling a building (i.e., product modeling) began changing for early

technology adopters with the use of object-based modeling software applications in the late

1990’s. I was exposed to these new object-based software applications with the first version of

Revit Structure in 2005. The industry attached the term Building Information Modeling (BIM)

to any object-based software application and the industry began viewing BIM as a process

itself or as a technology that changes the design process. However, despite the association of

BIM with process change, little commercial development of BIM was process-centric, and

none of the major BIM tools provided explicit support for making processes transparent.1 3d

object-based modeling is still mostly a set of static tools, none of which aid the design team in

explicitly defining, improving, and sharing design processes. Despite the rhetoric that BIM is

a fundamental shift in the design process, the industry generally still plugs the new tools into a

design process that predates computers. When I began this research in 2007, the real impact of

BIM on industry-wide productivity appeared no more disruptive than the introduction of

Computer-Aided Design (CAD). Still, BIM has had two impacts. First, BIM drastically

improved the ability to easily communicate product information to a wider part of an

organization. For example, disciplines could easily understand clashes between different

building parts. Second, the productivity of certain tasks improved drastically mostly on

1 One recently commercialized solution, Scenario Virtual Project Delivery is an exception to this generalization as it does explicitly link process and product models.


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account of defined relationships between objects. For example, the defined connectivity of

beams to columns meant that columns could be moved and beams would follow

automatically. Thus, BIM enabled both improved product communication and improved task

productivity. However, while product communication and some task productivity improved

with the capabilities of simulation and modeling tools during the first decade of the 21st

Century, order of magnitude increases in AEC industry productivity remain elusive.

Methods for communicating organization information (e.g., business colleague

networks, decision-making authority, professionals’ skills, etc.) also changed and became

more important as the organizational structure of AEC companies flattened and projects relied

increasingly on the knowledge of specialized professionals. In 2004, I could search my firm’s

intranet people pages to find professionals with skills to help me perform a task, even if I did

not know anyone that knew that person. With tools like Linked-In, I am not only able to see

the skills of people, but also their relationships to people in my social or professional network.

Though many AEC companies have not fully leveraged this technology to enable their

organizations to communicate the relationships between professionals, the technology exists

and will become increasingly important as the organizations needed to deliver AEC projects

become increasingly complex.

While AEC has new technology for both defining the inter-relationships between

product components and organizational components, the industry still primarily communicates

processes via Gantt charts - a method invented one hundred years ago. And in industry, even

Gantt charts are primarily used solely for communicating the inter-related physical tasks of

construction. Professionals in the AEC industry rarely communicate their own tasks at all or at

least not efficiently. Actually, I frequently spoke informally with project managers to tell them

what I was working on and when acting as a manager, I frequently asked my colleagues what

they were working on. Frequently, I would have to ask the project managers in other


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companies what their colleagues were working on and they would not know, so I would have

to wait for them to ask. Design is a highly interdependent process and designers waste much

time asking what others are working on to avoid working on something that will soon change.

This informal method of communicating process is inefficient.

Despite this inefficiency, the majority of AEC process planning, management, and

visualization research focuses on the construction phase of the building life cycle. It is my

intuition that inefficient and ad hoc design process communication inhibits efforts to improve

the value per man-hour expended in AEC especially since design processes disproportionately

influence the life cycle value of the resulting buildings (Paulson 1976). Two inefficiencies in

particular caused enough personal concern and frustration to prompt my focus on research to

improve design process communication. First, information flow on AEC projects are

inefficient and intermittent (Gallaher et al. 2004). Extrapolating from the report by Gallaher et

al., the global AEC industry wastes $138 billion annually due to inefficient information flow

(Young et al. 2007). This inefficient information flow cannot be improved without first

communicating the processes responsible for the inefficiency.

Second, the financial, environmental, and social impacts of completing an AEC

project are large and increasing. Buildings consume 70% of the U.S.’s electricity and 40% of

raw materials (United States Green Building Council 2007). Exploding population growth

requires more building, requiring more resources, and increasing their relative financial

impacts. More buildings potentially increase environmental impacts. As population density

increases, a building project has increased social impacts. Managing the multi-disciplinary

tradeoffs needed to maximize financial, environmental, and social value requires design

process communication between disciplines.


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From the above paragraphs, I conclude that new technology has enabled the industry

to communicate product and organization, but not process. Also, project teams define product

component relationships and organizational component relationships, but not the relationships

between the tasks of design professionals. Third, while task productivity may have improved,

overall processes have not improved. Finally, the financial, social, and environmental impacts

of unproductive design processes are especially large and increasing. Improving the value of

the buildings delivered per man-hour expended requires improved design processes which

require process communication (Ford and Ford 1995). If I could have spent 95% of my time as

a structural engineer designing structures, I probably would have remained a structural

engineer. But, I spent much of my time inefficiently or ineffectively communicating process,

not an especially interesting task, and so, I began researching methods for improving the

effectiveness and efficiency of communicating processes. Project productivity cannot increase

until the industry has a method for communicating product, organization, and process. This

revelation led to the focus of my research question on process communication.

My intuition that the industry needed more research on process communication was

based on literature and experience, but I had little claims for generality of the problem. Also,

early in my PhD research my colleagues emphasized the importance of scale. Did the

problem I described only apply to large projects? Is this a new problem or at least an

increasingly important problem? What is the impact of product, organization, and process

complexity on communication?

3 Narrowing the Scope of the POP Communication Problem

My question about POP communication and its relationship with complexity led me to

interview 36 professionals working on eight different AEC projects in Australia. As described

in Chapter 2, these interviews confirmed that communication problems increase with


7

increased project complexity. Though not validated in this dissertation, the interviews led me

to believe that new building information modeling technology enabled improved

communication of product, even on complex buildings. While I did not add BIM maturity to

my assessments of communication and complexity, I am confident that I could have used an

assessment method such as the BIM Maturity Matrix (Succar 2010) to demonstrate that the

effectiveness of product communication increased with increased usage of BIM. Also, I found

no need to explore methods for communicating organization - interviewees did not complain

about their inability to communicate organizational information. Process communication, on

the other hand, seemed to inhibit productivity both for the project teams I interviewed and for

the projects I worked on as a structural engineer. Thus, while Chapter 2 considers

communication of all POP information both in design and construction, Chapters 3 and 4

scopes the research to communication of design processes.

4 Intuition for Process Communication

As described in Chapter 3, the design process improvement literature has spent much time

developing increasingly effective methodologies for communicating design process. In

particular, I saw firsthand the educational value of Narratives in project-oriented design

classes. Narratives are a method for communicating projects by representing product,

organization, and process as a network of nodes linked to digital files (Haymaker 2006). Yet,

during all of my interviews and professional experience, I never saw Narratives or other

methodologies for communicating design processes applied in industry. This absence is not

due to the lack of tools capable of effectively communicating design processes. Microsoft

Visio and Microsoft Project are widely available and most of the methodologies described in

the literature could be applied using these existing tools. Despite a few attempts to pilot the

use of Narratives on professional design projects, use of the Narrative-building tool did not


8

grow in the industry. In general, designers are not willing to spend time communicating their

processes formally. One of the primary reasons for this lack of formal process communication

is that communicating processes does not benefit designers at the instant they are designing.

Thus, it is not sufficient to merely have the methodology and tools to effectively communicate

processes. The act of communication must also be efficient. This necessity for methodologies

and tools to enable both effective (i.e., able and/or accurate) and efficient (i.e., quick and/or

with little effort) process communication is considered throughout the dissertation and is a

fundamental part of my research question.

However, from the interviews and my own experience, there is a benefit to

communicating process both for the individual, for the team, for other teams, and for the entire

firm and industry. Throughout my dissertation, I call the type of process communication that

occurs within teams, collaboration; between teams, sharing; and across the entire firm or

industry, understanding. Most process communication research focuses on just one type of

communication, but all areas agree that communication requires (1) Capturing, (2)

Structuring, (3) Retrieving, and (4) Using processes. Benkler (2002) describes these steps as

part of the information-production chain needed for collaboration in the peer-production

model. Knowledge management research describes these steps as needed for sharing of

processes across projects (Carrillo and Chinowsky 2006; Javernick-Will and Levitt 2010;

Kreiner 2002). Finally, innovation literature cites these steps as required for companies to

understand their processes to make strategic investments in process improvement (Hargadon

and Sutton 2000). The importance of leveraging process communication to benefit

collaboration within the team, sharing of processes between teams, and understanding of

processes across the firm or industry is the final and perhaps most important premise

embedded in my research question. Considering these three types of process communication


9

simultaneously differentiates my research from previous efforts in the project information

management and design process management research fields.

I observed that digital files are the core deliverables of AEC professionals, and

consequently, I hypothesized that the dependencies between these files are indicative of design

processes. I hypothesized that communicating the dependencies between files enables

sufficient interpretation of design processes to improve collaboration, process sharing, and

understanding of processes.

This observation led me to the intuition that designers could communicate their

processes as they managed their information in digital files. Thus, I developed DPCM – a

methodology that enables design teams to save and exchange their digital files as a node in a

file dependency map. By defining the dependencies between files as they exchange

information, designers can communicate their processes within teams, between teams, and

across the firm or industry.

5 Explanation of Dissertation

In Chapter 2, I apply complexity and virtual design and construction research to develop a

method for assessing product, organization, and process complexity. Then, through project

team interviews, I develop a communication assessment method. The method assesses (1)

collaboration within projects, (2) sharing between projects, and (3) understanding of processes

across the firm or industry. I applied these two assessment methods to seven case study design

and construction projects in Australia. Through case study interviews, I validate the usefulness

of the methods and provide evidence of the existence of a trend between increased POP

complexity and increased communication challenges. The two assessment methods provide

the opportunity for teams to learn from and improve upon their communication strategies. By


10

increasing the awareness of the relationship between complexity and communication, Chapter

2 describes the industry problem to motivate and provide foundation for the development of

more efficient and effective communication tools. Chapters 3 and 4 contribute to this

development.

Whereas Chapter 2 discusses product, organization, and process, Chapter 3 scopes the

communication problems to designers (1) struggling to collaborate around processes within

projects; (2) share better processes across projects; and (3) understand the best opportunities

for investment in improving processes across projects. Overcoming each challenge requires

communication of design processes. Chapter 3 aggregates findings from organizational

science, human computer interaction, and process modeling fields to develop the Design

Process Communication Methodology (DPCM). DPCM enables efficient and effective design

process communication through a Computable, Embedded, Modular, Personalized, Scalable,

Shared, Social, and Transparent environment for capture, structure, retrieval, and use of

processes. To test DPCM, the research maps the methodology to software features in the

Process Integration Platform (PIP). PIP is a web tool that enables project teams to organize

and share files as nodes in an information dependency map that emerges as they work.

Chapter 3 closes with evidence of the testability of DPCM and proposes metrics for evaluating

DPCM’s efficiency and effectiveness in communicating design process.

Chapter 4 validates DPCM using the Mock-Simulation Design Charrette Method and

Ethnographic-Action Method applied to a project-based design class. Results demonstrate that

DPCM accurately captures design processes with little effort. When collaborating within

project teams, process clarity and information consistency result in little rework, and positive

iteration enables consideration of multi-disciplinary design trends. With DPCM, designers

share processes between project teams and use the shared processes without committing

process mistakes. DPCM enables the understanding of processes across projects, which


11

provide insights into: how integrating information between project team members impacts

product value; and opportunities for investment in improved information flows.

These findings provide evidence for the power of DPCM to effectively and efficiently

communicate building design processes through collaboration within project teams, sharing of

processes between project teams, and understanding processes across projects to strategically

invest in improvement. I claim that DPCM, validated using the Design Charrette and

Ethnographic-Action Research Methods, contributes to the fields of design process

management and project information management. In terms of broader impact, DPCM lays

the foundation for commercial software that shifts focus away from incremental and

fragmented process improvement toward a platform that nurtures emergence of (1) improved

multi-disciplinary collaboration, (2) process knowledge sharing, and (3) innovation-enabling


6 References

Benkler, Y. (2002). "Coase's penguin, or, Linux and the nature of the firm." Yale Law Journal,

112(3), 367-445.

Carrillo, P., and Chinowsky, P. (2006). "Exploiting knowledge management: The engineering

and construction perspective." Journal of Management Engineering, 22(1), 2-10.

Ford, J.D., and Ford, L.W. (1995). "The role of conversations in producing intentional change

in organizations." The Academy of Management Review, 20(3), 541-570.

Gallaher, M. P., O'Connor, A. C., Dettbarn, J. L., Jr., and Gilday, L. T. (2004). "Cost analysis

of inadequate interoperability in the U.S. Capital facilities industry." National Institute

of Standards and Technology, GCR 04-967.


12

Garcia, A. C. B., Kunz, J., Ekstrom, M., and Kiviniemi, A. (2004). "Building a project

ontology with extreme collaboration and virtual design and construction." Advanced

Engineering Informatics, 18(2), 71-83.

Hargadon, A., and Sutton, R. I. (2000). "Building an innovation factory." Harvard Business

Review, 78(3), 157-166.

Haymaker, J. (2006). "Communicating, integrating and improving multidisciplinary design

narratives." Second International Conference on Design Computing and Cognition, J.

S. Gero, ed., Springer, Netherlands, 635-653.

Javernick-Will, A., and Levitt, R. E. (2010). "Mobilizing institutional knowledge for

international projects." Journal of Construction Engineering and Management,

136(4), 430-441.

Kreiner, K. (2002). "Tacit knowledge management: The role of artifacts." Journal of

Knowledge Management, 6(2), 112-123.

Otter, A. F. d., and Prins, M. (2002). "Architectural design management within the digital

design team." Engineering, Construction and Architectural Management, 9(3), 162-

173.

Paulson, B. C. J. (1976). "Designing to reduce construction costs." Journal of the Construction

Division, 102(4), 587-592.

Jin, Y. and Levitt, R.E. (1996). "The virtual design team: A computational model of project

organizations." Computational & Mathematical Organization Theory, 2(3), 171-195.

Succar, B. (2010) "Building information modelling maturity matrix." Handbook of research

on building information modelling and construction informatics: Concepts and

technologies, J. Underwood and U. Isikdag, eds., IGI Publishing, 65-103.


13

United States Green Building Council. Last accessed June 2007, http://www.usgbc.org.

Weber, M. (1947). The theory of social and economic organization, The Free Press, Glencoe,

IL.

Young, N., Jr., Jones, S.A. and Bernstein, H.M. (2007). "Interoperability in the construction

industry." SmartMarket Report: Design & Construction Intelligence, McGraw Hill

Construction.


14

Chapter 2: Relationships between Project Complexity

and Communication

Reid Robert Senescu, Guillermo Aranda-Mena, and John Riker Haymaker

1 Abstract

The Architecture Engineering Construction (AEC) industry delivers increasingly complex

projects but struggles to leverage information technology to facilitate communication on these

projects. To begin to address this challenge, the project information management research

field needs methods to assess communication, complexity, and their relationships. First, the

authors apply complexity and virtual design and construction research to contribute a method

for assessing product, organization, and process (POP) complexity. Second, through project

team interviews, the authors contribute a communication assessment method to assess

collaboration within projects, sharing of information between projects, and understanding of

information generated across the firm or industry. Applying the two assessment methods to

case studies, the authors validate the applicability of the methods to AEC industry and

contribute the establishment of a trend between increased POP complexity and increased

communication challenges. The two assessment methods provide the opportunity for teams to

learn from and improve upon their communication strategies based on project complexity. By

increasing the awareness of the relationship between complexity and communication, the

paper aims to motivate and provide foundation for the development of more effective and

efficient communication tools.


15

2 Introduction

The Architecture Engineering Construction (AEC) industry delivers increasingly complex

projects to meet the financial, social, and environmental goals of stakeholders. Ideally, project

teams would continue to effectively and efficiently communicate despite this complexity. Yet,

even with the increased availability and pervasiveness of Information Technology (IT), project

teams still struggle to communicate (Eckert and Clarkson 2004; Haymaker and Chachere

2011; Luiten and Tolman 1997; Senescu and Haymaker 2008; Senescu et al. 2010). These

struggles limit the ability of project teams to manage complexity to achieve stakeholder goals.

The authors adopt the information processing view of a firm to elucidate the AEC

project as a network of information exchanges (Weber 1947; Galbraith 1974). AEC Projects

consist of an organization implementing a process to deliver a product, such as a building

(Garcia et al. 2004). This product-organization-process (POP) ontology provides a useful lens

for looking at the network of information exchanges on a project. Complexity theory helps

quantify the challenge of representing these projects (Homer-Dixon 2000). Communication

research provides guidance as to how people must exchange these representations by (1)

collaborating within projects, (2) sharing of information between projects, and (3)

understanding of information generated across the entire firm or industry (Senescu and

Haymaker 2009). Despite the importance of effective and efficient communication in

managing POP complexity, the project information management research field lacks a method

to assess the relationship between complexity and communication.

This paper reviews this POP, communication, and complexity research and

synthesizes findings from literature and case study interviews to develop a method for

assessing communication and a method for assessing project complexity (Figure 2-1). To

develop the communication assessment method, the authors collected empirical data through


16

interviews with AEC professionals. In contrast, the authors base the POP complexity

assessment method on complexity literature, and in this case, the interviews confirm the

relevance of this literature to AEC projects.

This paper contributes a method for measuring project complexity and a method for

measuring the effectiveness and efficiency of communication. The two assessment methods

provide the opportunity for teams to learn from and improve upon their communication

strategies. Application of the two assessment methods to the interviews results in the second

contribution: evidence of a trend between increased complexity and increased communication

challenges. By establishing this trend, the authors aim to motivate and provide foundation for

the development of more effective and efficient communication tools.

3 Points of Departure in Project Information, Complexity, and

Communication

3.1 Product Organization Process – an ontology for project information

Using a “POP ontology” to define the project comes from research on virtual design and

construction. The authors choose this ontology because professionals in the industry can

control product, organization, and process to achieve their firm’s goals. Product represents

“the physical and abstract concepts that describe the artifact itself, such as the columns and

electrical system of a building.” Organization “is the agency and agents responsible for design

and construction of the artifact.” Process is the design and construction tasks that the

organization carries out to build the artifact (Garcia et al. 2004). In application, the POP model

generally only lists POP objects without explicitly defining the relationships between the

objects. For example, the entire project team rarely uses parametric product models because of

the opacity of the relationships between product objects (Gane and Haymaker 2010). Also, the


17

relationships (i.e., knowledge sharing networks, decision-making authority, complementary

skills, etc.) between professionals in AEC are rarely transparent (Haymaker et al. 2010). And

finally, professionals are rarely aware of the relationships between their own tasks, let alone

how their tasks interrelate with the entire project team (Eckert and Clarkson 2004; Senescu

and Haymaker 2009). Simply listing objects is conveniently simple, but this lack of

communicating POP relationships is limiting, because the true value of a model comes from

the relationships between the objects (Rechtin 1991).

Figure 2-1. Diagram of the three types of information (product, organization, and process)

and three types of communication (collaborating, sharing, and understanding).


18

The original intent of POP models was to develop increasingly detailed relational

models to “elucidate and eventually mitigate potential risks to project success” (Garcia et al.

2004). As project complexity increases, developing relational POP models becomes

increasingly difficult. Developing a method for assessing the complexity of the product,

organization, and process (i.e., POP complexity) provides a first step toward implementing

this relational POP model.

3.2 Complexity – assessing the challenge in modeling projects

Managing project complexity is a critical factor impacting project success and the application

of conventional management systems to complex projects is inappropriate (Allen 2008;

Baccarini 1996). This paper builds on the following complexity definition (Homer-Dixon

2000) to develop a measurable method for assessment:

1. Multiplicity – number of components.

2. Causal Connections – number of links between components (to the extreme, there is

causal feedback where a change in one component loops back to affect the original).

3. Interdependence – the larger the module that can be removed from the complex

system without affecting the overall system’s behavior, the more resilient and less

complex the system.

4. Openness – to outside environments, not self-contained, difficult to locate boundary.

5. Synergy – the degree to which the entire system is more than the sum of the parts.

6. Nonlinear behavior – the effect on the system is not proportional to the size of the

change on a component.

This fundamental point of departure for the more detailed criteria described in Section 5.1

defines complexity more specifically than other AEC research (Allen 2008; Allen et al. 1985;

Baccarini 1996). Specifically, this paper addresses Baccarini’s call for work that not only


19

explicitly defines complexity but also that determines the relationship between project

complexity and the degree of integration, which can also be assessed by assessing

communication. Outside of AEC, Maier et al. (2008) consider complex product development,

but while many of their communication factors such as “autonomy of task execution” are

indicative of the project’s complexity, complexity itself is not explicitly defined and the

relationship between communication and complexity is not investigated. Otter and Emmitt

(2008) also discuss communication in the context of complexity, but do not explicitly define

the latter.

Before addressing this need to relate communication to complexity in the AEC

context, this paper must first develop the two methods for assessment. As discussed in Section

3.1, firms control product, organization, and process to achieve their goals. Managing POP

requires the communication of POP information. By breaking down complexity using the

same lens used to assess information exchanges, the authors can compare how POP

complexity impacts the communication of POP information. The previous methods of

assessing a project’s complexity do not consider POP explicitly, so these previous methods do

not permit a granular explanation of the mechanism causing complexity to impact

communication.

3.3 Communication - exchanging information to create facilities

Professionals can exchange information about product (e.g., a reinforced concrete beam),

organization (e.g., Acme Structural Engineering firm), and/or process (e.g., the steps outlined

in the reinforced concrete building code for designing a beam). Exchanging information about

a specific process is itself a process. The “process of exchange of information between sender

and receiver to equalize information on both sides” is called communication (Otter and Prins

2002). This definition is consistent with “sharing of meaning to reach a mutual understanding”


20

(Otter and Emmitt 2008) and as a “cognitive and social process by which messages are

transmitted and meaning is generated” (Maier et al. 2008).

AEC literature distinguishes knowledge from information and data (Otter and Prins

2002). This distinction is also common throughout the knowledge management field of which

Foss et al. (2010) provide an overview. The assessment of communication does not require

this distinction, because people must exchange all three types. Furthermore, the distinction is

not useful, because the definitions of each are relative both personally and temporally. For

example, a person may possess information (aware of the data’s relevance and purpose), but

the rest of the project team, ignorant of the relevance and purpose, may consider the

information merely data. Also, people forget the relevance and purpose of data quickly,

therefore relegating current information to future data. Consequently, this paper calls data,

information, and knowledge simply information. Exchange of any information is called

communication (Luiten and Tolman 1997).

While not distinguishing between the exchange of data, information, and knowledge,

this paper does propose to distinguish between three types of information exchange: 1) within

project, (2) between projects, and (3) across firm or industry. Considering all three types of

information exchange explicitly is important, because otherwise, there are “cost-benefit

mismatches” in communication. That is, many efforts to improve communication do not

consider that “the person responsible for recording information is typically not the person who

would benefit from the information once it is recorded” (Eckert et al. 2001). Also, team

members frequently have conflicting obligations to the project and to the firm (Dossick and

Neff 2010). That is, it is possible that within-project communication is excellent, but that

communication between projects or the gathering of information from across the entire firm is

poor. Thus, distinguishing between three types of communication is useful: (1) Professionals

exchange information within project teams in order to collaborate; (2) professionals exchange


21

information between project teams with the intent to share; and (3) professionals gain

understanding of information generated on projects across the firm or industry. Including

these three types in the communication assessment method encourages a holistic approach to

improving communication. The following paragraphs explain the challenges and opportunities

for improvement of each type of communication and relate each type to POP complexity.

3.3.1 Collaboration within projects

Research on the exchange of information within a project is found in collaboration literature

(Kvan 2000) and project information management literature (Froese and Han 2009). Laufer et

al. (2008) and Otter and Emmitt (2008) provide an overview of literature on within-project

communication in AEC, and Laufer claims, but does not provide evidence for, a link between

communication challenges and complexity on construction projects.

Recently, product communication has improved as firms more deeply adopt Building

Information Modeling (BIM) and increasingly exchange BIM between companies on a

project. However, firms have difficulty using BIM for anything more than geometric

coordination (Taylor and Bernstein 2009). While BIM may precipitate more tightly coupled

technology within the project, decision making is often divided between different companies

on the project (Dossick and Neff 2010). Collaboration research does not examine whether

complexity has a negative impact on the type of information exchange that would enable

BIM’s use beyond geometric coordination (e.g., design decision making informed by BIM-

supported performance analysis). Taylor and Bernstein call for more research on the impact of

project complexity on the evolution of BIM. As BIM is fundamentally a product

communication tool, an explanation of the impact of complexity on communication would

contextualize Taylor and Bernstein’s work and provide a foundation for further development

of BIM software applications that can accommodate increasingly complex projects.


22

3.3.2 Sharing information between projects

In addition to the challenges of exchanging information within projects, knowledge

management literature focuses on the exchange of information between projects (Carrillo and

Chinowsky 2006; Javernick-Will and Levitt 2010; Kivrak et al. 2008). For example, Conklin

(1996) describes a “project memory system” to define this knowledge and make it available to

other projects. According to Conklin, the project memory system is necessary for knowledge

sharing between projects, because organizations lack ability “to represent critical aspects of

what they know.” Once this system enables knowledge acquisition, Conklin claims the

knowledge must be structured. Hansen et al. (1999) describe two aspects of knowledge

structuring: codification and personalization. Codification relies on IT tools to connect people

to reusable explicit knowledge (Javernick-Will et al. 2008). Personalization relies on

socialization techniques to link people so they can share tacit knowledge. IT can provide the

general context of information and point to individuals or communities that can provide more

in depth knowledge. Knowledge management is not just acquisition and structuring (Kreiner

2002). Will and Levitt (2008) address the additional importance of the future ability of others

to retrieve the collected knowledge to use it. Aiming to improve this retrieval and reuse,

Fruchter and Demian (2002) developed a technical solution called CoMem. With CoMem,

professionals share information between projects by seeing an overview of product

information on other projects, zooming and filtering that information, and then retrieving

detailed product information on demand.2

Conklin, and Fruchter and Demian validate their technical implementations of

information sharing research on simple problems. To assess the state of current information

2 As discussed in Section Chapter 2:3.3, while the knowledge management field describes the capturing, structuring, retrieving, and use of knowledge, this knowledge is frequently indistinguishable from information that is shared between projects and then interpreted by professionals to become knowledge.


23

sharing and its relationship with POP complexity, industry needs methods for assessing the

impact of project complexity on the ability of organizations to share information between

projects.

3.3.3 Understanding information generated across the firm or industry

The third type of communication examined in this paper is the exchange of POP information

from the project level to the firm or industry level. The authors call this gathering of

information from projects across the firm or industry, understanding. In particular, the paper

focuses on the gathering of POP information that enables corporate-level professionals to

sufficiently understand project information such that they can make strategic decisions about

investment in product, organization or process communication technology. Innovation

literature commonly discusses understanding to describe how companies make strategic

investments to innovate. For example, innovation requires a “knowledge-brokering cycle”

consisting of (1) capturing good information, (2) keeping information alive, (3) imagining new

uses for old information, and (4) testing these new uses (Hargadon and Sutton 2000). Industry

lacks a method for assessing its ability to understand POP information across the firm or

industry. Consequently, industry struggles to strategically invest in technology enabling

improved communication of product, organization, and/or process.

3.3.4 Collaboration, Sharing, Understanding - one for all, all for one

Research and industry address these different types of communication independently. For

example, companies have different systems (both technically and organizationally) for project

team collaboration, knowledge management, and research and development. However, at the

same time one is exchanging information to collaborate within a project, that person can also

contribute to sharing information across projects and to understanding of information from

across the firm or industry. The authors intuit that an opportunity exists to simultaneously


24

communicate information within teams, between teams, and across the firm or industry.

However, with current IT, POP complexity stifles the achievement of this communication

convergence. Project information management research requires a communication assessment

method, a complexity assessment method, and a comprehension of the complexity-

communication relationship to motivate and provide foundation for the development of more

effective and efficient communication tools.

4 Research Method - Case Studies

The previous section established a need for a communication assessment method, a

complexity assessment method, and comparison of the relationship between communication

and complexity. This section explains how the authors performed case study interviews. Then

the section explains how the authors developed the two assessment methods. After

establishing a research method to develop the assessment methods, the section discusses how

the authors scored the case studies to validate the assessment methods and evaluate the

communication-complexity relationship.

4.1 Interviewee Selection and Agenda

Using the criteria described by Yin (2003), the authors selected case study projects and

professionals within the selected projects, and prepared an interview agenda. The validity and

reliability of the collected empirical data lies within a constructivist, not a positivist, paradigm

(Crotty 1998). Thus, results are not deterministic but still insightful due to careful selection of

projects and interviewees. Interviews occurred at seven offices in three Australian cities

(Table 2-1). Two of the offices are part of the same firm. At each office, questions focused on

one project, though the authors also interviewed professionals unassociated with the project,

but possessing relevant information about how the office exchanges information within


25

projects, between projects, and across the firm or industry. Also, some of the interviewees

worked on the project at the office but represented, for example, the owner. Interviewees’

roles included architect, structural engineer, drafter, project manager, BIM coordinator,

quantity surveyor, and design technology director.

The authors formulated open-ended questions related to technology, business

processes, knowledge management, information management, communication, and decision

making. Interviews were semi-structured and focused on topics of interest to interviewers and

interviewees rather than on prepared questions. Generally, the authors focused on a particular

process employed by the interviewee on the project, because this focus led interviewees to

discuss specific information that they exchanged with other professionals within the project

and between projects. The authors also asked how professionals exchanged project

information to executives overseeing multiple projects across the firm and how executives

gathered information from projects across the firm or industry.

The information-centric discussion enabled the authors to use the same interviews for

validation of the complexity assessment method and both development and validation of the

communication assessment method. The open-endedness of the interviews enabled the authors

to assess complexity through specific discussions of information rather than the interviewees

own assessment of the complexity of their project. Whereas project complexity is independent

of interviewee perception, the communication assessment is dependent on the interviewees’

perception. In the communication assessments, the open-endedness of the interviews enabled

the authors to determine the communication problems of greatest concern to the interviewees

without being biased by the authors’ preconceived notions of communication problems in the

industry.


26

Table 2-1. Interview summary.

Company Type Geographic Distribution

Project Type Interviewees

Multi-Disciplinary Engineering Firm Global University Building 6

Multi-Disciplinary Engineering Firm Global Commercial and Residential High-Rise

7

Architecture and Multi-Disciplinary Engineering Government Office

Multiple offices in state

Call Center and Offices 8

Architecture Firm A few offices in Oceana

Commercial High-Rise 2

Construction General Contractor Global Stadium 9

Architecture Firm One office High School 1

Architecture Firm Global Low-Rise Residential 3

4.2 Developing Criteria for the Two Assessment Methods

For each assessment method, the authors developed criteria that the authors then used to score

the communication and complexity of the case studies. This section discusses the research

method for the development of the criteria.

4.2.1 Complexity criteria development

The authors developed the complexity criteria by applying Homer-Dixon’s (2000) synthesis of

complexity literature (Section 3.2) to product, organization, and process. Developing the

criteria was straightforward in that complexity literature views any system as objects

connected by relationships. In assessing product complexity, the objects are building objects.

For organizations, the objects are professionals. For process, the objects are tasks. For

example, Homer-Dixon’s concept of multiplicity (see Section 3.2) is applied to develop a

criterion for product complexity: quantity of building components and level of detail at which


27

building components are considered by the project team. Each complexity criterion is a

modification of Homer-Dixon’s criteria such that it fits the POP ontology in the AEC context.

4.2.2 Communication criteria development

To develop criteria for the communication assessment method, the authors repeatedly and

randomly chose two interviews and brainstormed ways the two interviews differed. This

cross-case searching tactic prompted the development of new unanticipated criteria

(Eisenhardt 1989). For example, one collaboration criterion emerged when authors compared

one project team that repeatedly asked for information from other team members (pulled) with

another project team whose infrequent requests for information resulted in responsive, not

proactive, collaboration. By repeatedly comparing random pairs of interviews in this way, the

authors assembled a list of communication criteria. Aggregating the list, each communication

criterion fell into one of three types of communication, which the authors later found to be

consistent with the literature discussed in 3.3. That is, professionals exchange product,

organization, or process information through three types of communication: (1) collaboration

within project teams, (2) sharing information between project teams, and (3) understanding

information across the firm or industry.

4.3 Assessing the Case Studies with respect to the Criteria

The previous section described how the authors developed the criteria that make up the two

assessment methods. This section explains the research method for scoring each case study

with respect to each criterion. As the scoring is relative to the case studies considered by the

authors, the section ends with a discussion of how the authors determined the case study

scope.


28

4.3.1 Measurement scale for criteria scoring

Requiring a method for assessing case studies with respect to complexity and communication,

the authors developed a one to five measurement scale. A “five” represented the most complex

instance encountered in the interviews and a “one” the simplest. Similarly, a “five”

represented the worst communication encountered and a “one” represented the best

communication. The scale was relative to the interviews conducted. That is, if interviews

included a villager building his own grass hut, a “one” would have represented a much simpler

P, O, or P than the simplest project in this research effort. The intent of quantifying this

measurement scale was to identify trends, not absolute levels of complexity and

communication, nor mathematically defined correlations.

4.3.2 Defining the scope of the case studies

The authors assessed complexity at the project level within the scope of the interviewee’s

responsibility. For example, when talking to two professionals about one project, the authors

averaged findings between the two professionals. If the first professional’s scope involved a

small product at a low level of detail (e.g., a “one” score for the multiplicity criterion) and the

second professional’s scope involved a large product at a high level of detail (e.g., a “five”

score for the multiplicity criterion) the two interviews would be averaged and the project

would be assigned a medium (e.g., “three”) for the product multiplicity criterion.

The method for defining scope varied for the three types of communication. The

authors assessed the interviewee’s collaboration on the project, but ignored statements made

about collaboration between others. Thus, an engineer may have small scope and interact

simply and collaborate effectively with one other person, even though the overall project may

have high complexity and horrible communication. The authors assessed sharing with respect

to how the team learned from other project teams or vice versa. The authors assessed the level


29

of understanding of information gathered across the entire firm by assuming that the

interviewees on each case study were representative of the entire firm. In some cases,

interviewees not specifically involved in a project contributed to the assessment of sharing and

understanding.

4.4 Research Method for Validating Complexity and Communication

Assessment Methods and the Complexity-Communication Relationship

The previous sections established research methods to develop the criteria that make up the

assessment methods and a scoring method to evaluate the case studies with respect to each

criterion. To score each case study, the first author coded each interview using the methods

applied by Erdogan (2008). For all the interviewee statements that the first author considered

relevant to one complexity or communication criterion, the author scored the statement on the

one to five scale. Some statements applied to multiple criteria. This section explains how the

author applied this analytical scoring method to validate that the two assessment methods are

consistent with intuitive estimation, calibrated, and capable of comprehensively acquiring

data. The section closes with an explanation of the method for evaluating the relationship

between complexity and communication.

4.4.1 Comparing the scores from the assessment methods with intuitive estimations

Before analytically scoring each interviewee statement, the first author intuitively estimated a

score for each project with respect to each criterion on a one to five scale. This method is

subjective but provides a check for the detailed analysis method to ensure significant issues

are not missed or skewed by the detailed analysis.


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4.4.2 Evaluating whether the assessment method is calibrated

A useful assessment method accurately reveals insightful differences between projects (Yin

2003). If all criteria score the same or are highly skewed to one end of the assessment scale,

the method cannot reveal insights into the differences between projects. Thus, the authors

validated the assessment methods by calculating the mean score and standard deviation of the

scores for each criterion. A calibrated assessment method will have an average of all the

criterion means close to three (halfway on the one to five scale) and an average standard

deviation of greater than one, which demonstrates that different statements had different

scores. Both calculations validate that the assessment method is calibrated.

4.4.3 Evaluating the applicability of the assessment methods

An assessment method for which it is too difficult to comprehensively acquire data is not

useful for assessing complexity and communication. Thus, the authors calculated the number

of statements collected for each criterion to demonstrate that the assessment methods can be

comprehensively applied to case study projects

4.4.4 Evaluating the complexity-communication relationship

The authors assessed trends between complexity and communication by plotting the mean

scores for each case study. In evaluating trends, the authors only considered case studies with

an average of at least one relevant statement for every two criteria for both complexity and

communication. The authors made this decision, because the high school case study lacked

sufficient complexity data for a meaningful mean score. Thus, the authors did not use the high

school project for evaluating a trend, but did use this case study’s data for validation of the

assessment method.


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5 Complexity and Communication Assessment Methods

5.1 Product-Organization-Process Complexity Criteria

As discussed in more detail in Section 3.2, the criteria for assessing complexity come from the

aggregation performed by Homer-Dixon (2000). These criteria provide a useful method for

addressing the project information management needs of AEC (Froese and Han 2009).

Extending Froese’s application, this paper disaggregates complexity assessment further by

applying it individually to a project’s product, organization, and process. Table 2-2 provides a

definition for each criterion and an example from interviewee statements.

Table 2-2. Criteria for measuring a project's product, organization, and process complexity.

Complexity Criteria Label

Very Simple =1. Example Interview Statementi

Very Complex = 5. Example Interview Statementi

Pro

duct

Multiplicity not many components, low level of detail. We model a diffuse sky, one floor, one façade, varying only height.

many components, high level of detail. We performed energy analysis within a millimeter of detail.

Causal Connections one component has no impact on others. We focus collaboration on a few crunch points, e.g., floor-to-floor height.

one component impacts many others. Choosing controls and sensors is not trivial: on/off vs. dimming, sensor locations, etc. [all have many impacts on performance].

Interdependencies functions independently, resilient, separable. We use packaged systems for HVAC and electrical.

does not function independently, not resilient, integrated. As so many things are highly sensitive to it, they know they need to look at solar load more carefully.

Openness not sensitive to environments beyond product scope or control. To design the elevator, all I need is building height and how much space I have.

sensitive to environment beyond product scope or control. Our design goal is to come in 2nd or 3rd in tendering.

Synergy building is simply the sum of its parts. We have an assembly code parameter and/or data set for each element in the model.

Need to assess everything together. The bits between disciplines are where the real innovation occurs.


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Nonlinear linear, easy to predict whether stakeholder goals will be met, even with precedence-based design. We use back-of-hand calcs to approximate ducts and shaft sizes.

emergent/non-linear, requires intense performance-based design and even then, still difficult to predict. It was hard to predict how long it would take to do roof construction, [because there are so many factors and it's unprecedented].

Org

aniz

atio

n

Multiplicity single discipline, few people. For Scheme Design, there were no models, design was generic, no participation of engineers.

multiple firms, hundreds of people. We are doing design of all services to Detailed Design [and then sub-contractors take over].

Causal Connections hierarchical chain of command, one person affects only the person above. [He didn't know about the construction sequence or why his supervisor prioritized certain areas over others; he just responded to requests].

highly networked, one person impacts multiple other people. MEP, facades, ESD, acoustics, and building physics [all participated in design decisions].

Interdependencies people can work independently. The Senior Engineer decides [by himself] and considers [options] based on experience.

people need each other to work. In Design Development, we ditched our own model of slabs and instead, used the architect's model [because we couldn't work independently].

Openness independent self-contained group. The developer gave us a spreadsheet showing their value function for the return on the apartments [there was just one stakeholder with a clear delineation of the organizational extent of the project].

project group is ill-defined, because of heavy government and/or citizen involvement. [Contractor] gives 80% of what the [government owner representatives] are expecting and we need to make sure it's ok with [the government's facility] operator.

Synergy team with distinct and separated responsibilities. It's my job to figure out how to arrange and stack panels on site [he then went into details of exactly his job versus other people's responsibilities].

a team working together is more than the sum of its parts. There is a fine line between deciding what the discipline should take responsibility for versus what I should handle.

Nonlinear one person does not have a huge impact on everyone else. The QA sheet gives a sense of ownership to the crew so they take the quality of the panels seriously [a large check and balance system existed, so a failure by one person would not be catastrophic].

everyone is on the critical path, no one is easily replaceable. [Specific other engineer] could probably find it quickly, but she is out.

Pro

cess

Multiplicity few tasks. We decided ahead of time what options to consider [so, the process was straightforward]

many tasks. Designed MEP, facades, ESD, acoustics, and building physics [all participated in design decisions].

Causal Connections non-iterative. We [architects] work the services into the building and then hold [the engineers] to it. We don't let [the engineers] change their mind.

iterative (necessarily, positive). Cost planning is iterative with the architect.


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Interdependencies tasks can be performed independently and broken up into smaller and smaller tasks. We established zones in underfloor space for each discipline "so we could work independently and speed up iterations."

tasks need to be performed all at once and cannot be broken up. Models were needed for constructability, because we're building from top down (not typical), so we need to work out geometry and construction together.

Openness easy to scope the tasks and predict the tasks in advanced. [It seemed to them unconscionable that they would miss these deadlines; that nothing would prevent them from meeting the deadlines, and I saw the date on the drawing sets had the same day as the scheduled delivery].

unknown factors make it difficult to know in advance what tasks will be required. 75% of the rooms randomly vanished in Revit, requiring 5 days work to replace.

Synergy two tasks sum as two (e.g., interface between the two tasks are seamless). We knew thermal performance was ok for 100% glazing so then, we just find glazing that works for daylighting [as opposed to a process that considered interactions between thermal and daylighting].

two tasks sum as greater than the whole (e.g., due to transactions between the two). Then, the fire consultants said they needed huge fans. Working out with the architect how to accommodate [geometrically] the new fans took 7-10 man days and put us over budget.

Nonlinear each task is more or less isolated, errors are not cumulative. The process is that everyone fills out a field for each object they create in the Revit model.

unforeseen large repercussions, a small error in a small task can have huge repercussions on future tasks. It's intense to make changes. You move wall and have to check all drawings.

i brackets [ ] represent explanation or description by interviewer. Otherwise, statements are paraphrased words of the interviewee, unless quoted.

5.2 Communication Effectiveness and Efficiency Criteria

The authors developed the communication criteria by categorizing differences between

interviews. Table 2-3 provides a label and definition for each criterion and examples from

interview statements. The contrasting example statements in the table demonstrate how each

criterion was developed to reflect differences in the case studies using the cross-case searching

tactic (Eisenhardt 1989). Contrasting statements led to the development of criteria that differed

in how directly or indirectly the criteria assessed communication and in how the criteria

assessed communication effectiveness or efficiency. For example, the criterion labeled speed

directly assesses relative efficiency of collaboration. On the other hand, the criterion labeled

info pull assesses how often the organization asks for information they need. The info pull


34

criterion is indirectly indicative of collaboration effectiveness, because not asking for

information (i.e., remaining ignorant of others information) takes zero time, but is indicative

of ineffective exchange of information (unless the other people know to give you the

information they require). Another example of an indirect measurement is the options

criterion. For example, the interior designer on one project team presented just one option for

the interior design that would then be approved or rejected by the owner representative. In

contrast, the cost estimator on another project provided feedback to the other disciplines on

thirty design options. This criterion indicates indirectly the extent to which the teams

collaborated to consider multi-disciplinary tradeoffs between different design options versus a

case of collaboration where a single option progressed until it was determined to be

insufficient by a particular discipline. Aggregated, the criteria provide a direct and indirect

assessment of the effectiveness and efficiency of how the professionals collaborated within the

team, shared information between teams, and understood information across the entire firm or

industry.

Table 2-3. Criteria for assessing a project’s communication.

Communication Criteria Label

Great Communication = 1. Example Interview Statementi

Poor Communication = 5. Example Interview Statementi

Col

lab

orat

ion

Speed fast information exchanges. [With these three semi-automated steps] overall scheme design takes 3-5 man days.

time consuming information exchange. Takes about 100 days to get coordinates of every piece of steel out of the model.

Clarity explain product, organization, or process clearly. Sketchup allows clients to connect with what we design.

unclear product, organization or process explanation. The documentation is "appalling." The industry relies more and more on shop drawings. The designer just creates schematics and relies on detailing and coordination after design.

Consistency consistent information between team members. There was consistency between discipline assumptions.

inconsistent information between team members. Sometimes the .dwg is revised or people e-mail files [instead of saving the file correctly] and that screws things up.


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Integrated Decisions

considers multiple disciplines in decision making. Multi-disciplinary workshops really help to integrate ideas.

decision making silos. The architect was not concerned about anything but aesthetics.

Common Process Knowledge

the entire team knows the processes within each discipline. The architect is using ArchiCAD to IFC, but we haven't really used it [i.e., Engineer knows the architect's processes even if disconnected from their own process].

no knowledge of people's processes outside discipline. We purposefully delete [permanently] annotations [showing design rationale] after submitting the drawings.

Standardized well defined standard for digital information exchange. We created a BIM process manual.

no standard, all different formats. The modelers are not consistent, so it's difficult to extract information for cost.

Planned planned coordination. At the beginning of the project, we ask what are the key things that should be coordinated.

reactive coordination. Fire consultants weren't on board early even though we knew the building would need performance-based smoke ventilation, [this caused problems].

Incentivized incentivize to consider multi-disciplinary tradeoffs and strive toward a global optimum. Acoustician says [engineer] wants to expose ceiling for thermal mass, even though acoustics is not good [but acoustician recognized other goal was more important]

incentivized to optimize within discipline silos. Structural engineer had no incentive to make all the panels similar, so constructability was difficult.

Options considers many options. We costed about 30 different options for [the building].

considers only one option. They produce 3D renderings of the selected option …for verification…[but the owner representative wasn't happy with results].

Connected team is well-connected. We hold workshops regularly to integrate ideas.

few connections between the team. [The manager complained], the consultants give us Navisworks files, but they are "useless to us." [In the same office, an engineer said she is using Navisworks on the same project.]

Info Pull proactively explain what information is needed from other people. The mechanical engineer knows what he needs and lets others know via conversations or e-mail.

rarely requests information from others. We changed shop detailers and they didn't give us the ProSteel model, only the Navisworks file…in the future we need to get the detailers to give us the information we needed [in the format we need].

Sh

arin

g

Transparency all information created is organized transparently in a single location and the information is linked to the process and organizations that created it. We have a directory of [interoperability] scripts that are reusable.

all information created is temporary, the process is never documented. We highlighted the potential problem verbally….and then, later, we got blamed for it (we didn't have a record of us warning about it).


36

Awareness exchange processes with global community. [The engineer] presented at [the research institute] about Grasshopper, so the company shares processes via community talks.

little awareness of processes outside the project, company, and city. I am predicting actual performance within 3%. "No way they could use more energy than predicted." [either the engineer is unaware of the huge differences between prediction and actual performance common in industry, or the industry is unaware of the extremely advanced technique employed by this engineer].

Dissemination disseminate processes so other teams can adopt them. Wiki helps explain how to use the server.

don't disseminate processes. The truth is we have no idea how we transfer knowledge to the organization as it went from 4 to 18 architects

Externally Connected

well connected with communities outside project. We are working with a [professor in Brisbane] and with the IAI in Scandinavia

little personal contact with communities outside project. "BIM is a software product" so everything needs to be compatible between all disciplines [contrasting with general view of BIM as a method and general acceptance that BIM is useful without complete compatibility, suggesting he is not well connected with the global BIM community.]

Standardized Processes

when possible, processes are standardized. Now, we're running our own scripts on servers, solidifying our basic processes.

processes that could be standardized are not. Setting full procedures [for standard parametric modeling techniques] is very expensive….but it's scary because students [or newly graduated] create geometric messes that would kill a project.

Un

der

stan

din

g

Measurable Success

easy to measure process success. Profit measures success.

difficult to measure process success. [The person managing investment in new design process technology, when asked about how they will know if their latest investment is successful] "Yes, it would be a good idea to have a measure of success" [they didn't have any].

Measured Time measured time spent on tasks. Before coordinating penetrations took 2 days [in Navisworks], now we have a customized process that takes half a day.

did not measure time spent on tasks. We can't quantify the cost of running BIM, nor the benefit of BIM…nor the cost of "appalling" documentation

Process Documentation

documented some aspect of the process. The government is investing in reducing energy, so our mechanical spec will be used in many schools, so that is why we could spend the time to document our mechanical design process.

no documentation and no knowledge of the process. We start with an image of how the building should be, and then the process should just make that a reality. All the creation happens in the mind [the process is just producing what's in the mind at the beginning].


37

Categorization categorized projects for comparison of process. We looked at the ten best and ten worst projects [in terms of profit and then evaluated which ones used BIM]

no categorization, so impossible to compare. [Some companies simply had no formal way of categorizing projects]

Incentivized To Innovate

incentivized to innovate. We have a new revenue stream where our modelers go into sub's offices and model for them.

no incentives to innovate. It's our traditional policy to keep paper, so we just do that. Besides, it's [her] job [implying she would not have anything to do without paper].

Process Language formal computable language for describing the process. I set up facade configurations. Then, Radcalc is a perl code that reads xml. The code parses the xml and feeds it into Radiance, and it analyzes all the options in the servers in Melbourne.

informal, non-computable method for describing process. no language or grammar. process description is likely interpreted differently by different people. [When asked about a particular energy analysis process, the engineer tried to find the answer in the project folder, but frustrated, said] This folder is "such a disaster. Someone deleted the presentation folder….This is such a mess…I can't remember what we did."

Vertical Understanding

vertical understanding of process throughout the organizational hierarchy and buy-in to the importance of process improvement. 64 bit computers is not a big deal. We just upgrade.

process is not understood at different levels of the organizational hierarchy. PM viewed BIM as only a software product and didn't want to upgrade until it was compatible with all disciplines.

Investing methodical about investment decisions. We analyze when we need to upgrade and investigate options for computers in detail.

investing in new technology is random. We make decisions about investing in software or script development to go between them “somewhat randomly.”

i brackets [ ] represent explanation or description by interviewer. Otherwise, statements are paraphrased words of the interviewee, unless quoted.

6 Results and Discussion

This section presents the results from applying the assessment methods on seven case studies.

The authors calculated the results by applying the research method described in Section 4. The

section uses two example case studies to illustrate how using the two assessment methods

together provides insight into the mechanisms responsible for causing complexity to impact

communication.


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6.1 Validating the Two Assessment Methods

This section provides evidence that the two assessment methods provide results that align with

intuitive estimates; that the two methods are calibrated to the case studies; and that the two

methods can be comprehensively applied to case studies.

6.1.1 Alignment between intuitive estimations and the scores from assessments

The first validation of the two assessment methods came from comparing the intuitive

estimates with the assessment-based scores. The assessment methods should produce more

precise results than the intuitive estimations, so large deviations between what is expected

intuitively would reveal potential problems with the assessment methods or the intuitive

estimations. Plotting each project’s complexity on the x-axis and communication on the y-axis

(Figure 2-2), the difference between the analytical assessment (large data points) and the

intuitive estimate (small data points) for each project is the distance between the two points on

the graph. The average difference was only 0.72, demonstrating reasonable alignment between

the intuitive estimations and the scores from the assessments.


39

Figure 2-2. Assessment of the intuitiveness of results. For each project, the plot shows a

comparison of complexity and communication assessment methods. In general the analytical

assessments (large data points) are relatively close to the intuitive estimations (small data

points). The largest differences occur for the high-rise commercial and residential, high-rise

commercial, and stadium projects (estimates are 1.0 to 1.1 distance away from analytical

assessment). On the other hand, the university building and low-rise residential were

close (0.3).

6.1.2 Calibrated assessment methods

Across all seven projects, the authors applied the 42 criteria (18 complexity criteria in Table

2-2 and 24 communication criteria in Table 2-3) 398 times to interview statements. A valid

assessment method should not have highly skewed scores across these criteria or the same

scores for all the criteria. Balanced and varied scores enable insights to be drawn from the


40

assessment method – an important feature of a useful assessment method (Yin 2003). Figure

2-3 (a) shows the number of interview statements that the authors considered relevant for

assessing the project with respect to each criterion. For each criterion, the authors measured

the mean and standard deviation of scores across the seven projects (Figure 2-3 (b)). The mean

score of all the criteria was 2.9 with a standard deviation of 0.6. This calculation revealed that

each criterion was well balanced with about as many statements scoring higher and lower than

three. The average standard deviation within each criterion was 1.2, revealing that in general

each criterion was sufficiently granular that variations existed between projects. However, two

exceptions exist. There are only two statements relevant to Nonlinear Organization and four

statements relevant to Categorization. For both criteria, the examples come from a single

project, so the standard deviation is zero. This exception probably arose from insufficiently

thorough interviews that failed to explore these criteria, rather than a problem with the criteria

themselves. Since in general Figure 2-3 reveals that the assessment method is balanced around

three and varied away from three, the assessment method is well calibrated and potentially

useful in discovering trends.


41

Figure 2-3. Assessment of the distribution of complexity and communication criteria. The

number of statements relevant to each criterion varied from 2 to 24. The average score across

all criteria is 2.9, which is expectedly close to three since the one to five scale was defined

based on the interviews. The average of the standard deviations across all projects is 1.2. This

deviation across projects suggests that the assessment criteria are sufficiently granular to

differentiate trends between projects.


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6.1.3 Applicability of the assessment methods

An assessment method for which it is too difficult to comprehensively acquire data is not

useful for assessing complexity and communication. Thus, the authors calculated to what

extent the case study interviews covered the project complexity and communication criteria

comprehensively. A higher number of statements per criterion signified a more comprehensive

interview. Averaged across all projects, there were 0.9 statements per project complexity

criterion and 1.6 statements per project communication criterion. That is, on average each

project had just less than one statement for each project complexity criterion. As there were 18

complexity criteria, each project had on average 16 statements related to complexity.

For the High School project, there were only 0.3 statements per project complexity

criterion (Figure 2-4). As this value was below 0.5, interviews on this project did not offer a

comprehensive view of the project complexity, increasing the potential inaccuracy of the

project’s complexity assessment. Further supporting this potential inaccuracy, the intuitive

estimate for complexity was 0.8 points higher than the analytical assessment (the third largest

difference). Also, this was the only project where only one person was interviewed.

Consequently, the authors disregarded this project’s complexity and communication

assessment, and it is not used for evaluating trends. However, because of the comprehensive

set of statement examples for the other six case studies, the authors conclude that in general

the two assessment methods can be applied comprehensively.


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Figure 2-4. Assessment of the comprehensiveness of interviews. The figure shows that there

were in general more interview examples related to project communication criteria than

complexity criteria. In fact, the High School project had so few examples of complexity

criteria that it is not considered in assessing complexity-communication trends.

6.2 Discussion of Two Case Studies

As the previous section demonstrated that the two assessment methods produce results that are

aligned with intuitive estimates, calibrated, and able to be comprehensively applied, this

section discusses the results from applying the assessment methods to two representative case

studies (the stadium and low-rise residential). The discussion explains the reasoning behind

the criteria scores assigned to each example (criteria are indicated in parenthesis) and the

mechanisms by which complexity impacted communication. The authors chose these two case

studies because they are at the two extremes of all the case studies examined: the stadium is

complex with many communication challenges and the low-rise residential is simple with

good communication.

As the criteria scores for the stadium and low-rise residential case studies are relative

to the other cases discussed in the paper, the section describes these examples in the context of


44

two graphs. Figure 2-5 shows the mean criteria scores for complexity and communication for

each case study. Figure 2-6 shows a more nuanced relationship between complexity and

communication by disaggregating project complexity by product, organization, and process;

and communication by collaboration, sharing, and understanding. Section 6.3 discusses in

more detail the overall trends revealed by these two graphs.

6.2.1 Stadium project in depth

The stadium project was a complex product because of its size and geometrical non-

uniformity (product multiplicity). Interviewees stated repeatedly that every panel in the

stadium was unique. Organizationally, the project was complex in some respects and simple in

others (see top middle plot in Figure 2-6). The organization was complex in that the project

involved government representatives, many sub-contractors and fabricators, and many

different designers (organizational multiplicity). Also, it was vulnerable to a wide variety of

public organizations outside the immediate project team (organizational openness). However,

the organization was simple in that team members had delineated roles (organizational

synergy) with clear lines of hierarchy (organizational causal connections). The process was

complex, because design was influenced by constructability, making it difficult to separate

contractor and designer tasks (process interdependence). Because decision makers were

unclear, frequent iteration occurred (process causal connections). This complexity

corresponded with many communication problems. For example, the aspect of the

organization that was complex inhibited team members from collaborating across discipline

silos to make decisions. In one case, the contractor submitted an interior design option to an

owner representative for approval. Neither the owner representative nor other stakeholders

participated in the decision making, and the contractor lacked incentive to consider multi-

disciplinary tradeoffs (collaboration – incentivized). At the same time, the team considered

just one design option (collaboration – options) in one month (collaboration – speed). The


45

project complexity also stifled efforts to maintain consistency (collaboration – consistency) in

information management systems (the first author observed a request to find an AutoCAD file

that took 30 minutes to fulfill).

Sharing process information across projects was difficult, because the process was not

documented. A Design Manager for the Contractor stated, ‘he would like to link memos to

build a story of what happened’ (sharing – transparent). In one case, the Drafter explained how

he needed to come up with a system for managing information from scratch. This example

demonstrated that the firm did not disseminate processes across projects (sharing –

dissemination), perhaps because of organizational and process complexity. On the other hand,

for information more easily standardized, such as material costs and productivity rates, the

firm had a database shared across projects (sharing – standardized processes).

The ability of the contractor to understand the project to invest in improvement lagged

only slightly behind the other projects. During a discussion about the cumbersome process of

finding paper documents, the interviewee explained paper was both tradition and policy and

many jobs were solely dedicated to managing the paper (understanding – incentivized to

innovate). It is more difficult for a large complex organization to quickly migrate to digital

systems. Also, according to the project manager, ‘we can’t quantify the cost of running BIM

nor the benefit of BIM so it’s difficult to make a value proposition for introducing new

technologies’ (understanding – measured time). Measuring complex and unique processes is

more difficult than measuring simple frequently repeated processes. On the other hand, a

quality assurance professional clearly strived toward ensuring no roof leakage and to provide

traceability if leakage occurred (understanding – measurable success). This definition of

success and documentation of its achievement allowed the firm to compare different project

processes and outcomes. Of course, while this professional had a well-scoped and measurable

goal, the complexity of the project made many other goals more difficult to measure.


46

6.2.2 Low-Rise residential project in depth

The low-rise residential project was relatively simple and communication excellent. The low-

rise residential project contained many pre-packaged HVAC and electrical systems common

across all the apartments, but also functioning independently (product interdependence). Also,

the client was a real estate developer, contrasting with the many stakeholders on the stadium

project. The client measured project success based on the number of apartments built on the

site. This product goal was relatively unaffected by the outside environment (product

openness) and clear to the entire team making collaboration easier (collaboration – clarity).

Also, the site was highly constrained. Few feasible layout options existed and so, iteration was

unnecessary (process causal connections). Also, the organization was hierarchical. For

example, the geotechnical consultant gave a report to the architect and then, the architect gave

the same report to the structural engineer (organization causal connections). The report did not

have implications for the entire project, nor did the structural engineer have to work closely

with the geotechnical engineer to develop a sophisticated foundation strategy. The process was

also relatively simple since it seemed unimaginable to the team that they would miss drawing

submission deadlines; all previous deadlines were met. This confidence and consistency

suggests that it was easy to scope and predict tasks (process openness).

The team used Google Sketchup to communicate and reported that the tool effectively

allowed the owner to connect with their designs (collaboration – clarity). It is easier for

architects to use tools such as Sketchup to communicate designs of simpler buildings than

complex buildings (Figure 2-6 top left). They reported taking a week to create apartment

layouts, because they worked closely with the structural engineers and developed an integrated

design together (collaboration – integrated decisions).

The interview focused less on sharing, but some examples existed where the firm

created standardized processes for going between for example, parametric modeling tools and


47

Microsoft Excel (sharing – standardized processes). Also, a senior associate in the office

provided ample evidence that he was aware of global technology trends in the industry

(sharing – awareness).

Relative to other projects, the architecture firm understood their current processes and

invested in improvement. For example, the firm categorized BIM projects and non-BIM

projects (understanding – categorization). They considered tangible BIM investment costs

such as hardware, software, human resource costs (understanding – measured time), and even

when including these costs, measured increased profit on BIM projects. The firm also invested

in BIM technology at the corporate level (understanding – vertical understanding), though the

investments themselves were not especially methodical (understanding – investing).


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Figure 2-5. The relationship between project complexity and communication. Based on the

six AEC projects assessed, communication problems increase with increased product,

organization, and process complexity. This current trend is unsustainable, because delivering

projects of increased environmental, financial, and social sustainability requires increased

complexity. AEC requires IT solutions enabling horizontal trends so the industry can increase

complexity without increased communication problems.

6.3 Discussion of the Complexity-Communication Relationship

The previous section uses two case study examples to demonstrate how the authors applied the

assessment methods to the case studies. The discussion of the case studies also illustrates the

difference between a complex project with poor communication and a simple project with


49

good communication. Assessing the six case studies reveal a general trend between increased

complexity and increased communication problems (Figure 2-5) – the second contribution of

this paper. Because the complexity and communication assessment methods consist of three

types of information and three types of communication, respectively, the assessment methods

enable comprehension of how each complexity type impacts each communication type. This

disaggregation of the assessment methods reveals a more nuanced set of smaller trends (Figure

2-6). For example, a trend exists between product and process complexity and collaboration

problems (top left and top right of Figure 2-6). However, the middle column of Figure 2-6

suggests little trend between the impact of organization complexity on communication. Also,

the middle row reveals little trend between POP complexity and sharing, suggesting that the

complexity of one project is not indicative of how companies share information across

projects. One explanation for this observation is that the architecture firm that designed the

simple low-rise residential is a large global firm that also works on many complex projects.

Discovering a relationship between project complexity and sharing may require interviewing

multiple project teams within the firm.

Unlike sharing between projects, a trend exists between how product and process

complexity impacts a firm’s ability to understand information across projects, but this trend is

less steep than the collaboration trend. The stadium project supports this observation because

the project was so complex that it was both difficult and there was little incentive to

understand the project. But for the low-rise residential building, the project’s simplicity and

the ability of the firm to understand information on the project may have been circumstantial.

Again, more thorough investigation of complex projects by the same firm would be needed to

confirm that understanding increases with project complexity.


50

Figure 2-6. Disaggregated POP complexity and communication trends. The strongest trends

exist between product and process complexity and collaborating. Little evidence exists that

sharing across projects is impacted by complexity. A less sensitive trend exists between

product and process complexity and a firm’s ability to understand information across the firm

or industry.


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6.4 Limitations and Future Work

Unlike previous studies outlined by (Maier et al. 2008), the results presented above avoid

measuring communication purely on quantity of information exchanged. Also, the authors do

not attempt to assess complexity by counting product, organization, and process objects.

Instead, the authors assess complexity qualitatively, and similar to Maier et al., assess

communication effectiveness and efficiency. The case study interview approach leads to

qualitative analysis potentially more subject to authors’ bias than methods that count

information on projects or survey project participants. Instead, the paper applies inductive

logic, not seeking to be hypothetico-deductive. That is, the interview approach limits the

authors’ ability to claim a particular linear (or exponential) correlation between complexity

and communication or perform causality analysis, because there are just six case studies with

scores based on qualitative information.

As opposed to relying on the survey method employed by Maier (2008) to assess

communication or an information counting approach to assess complexity, the interview

method revealed communication problems unknown to the project teams and was able to

assess complexity relative to other projects. For example, in one interview, the manager

complained, the consultants give us Navisworks files, but they are "useless to us." In the same

office, an engineer said she is using Navisworks on the same project. By interviewing the

team, as opposed to surveying, the authors could assess that the team struggled to collaborate

effectively, because they were not connected even though they were collocated. Thus, the

Connection criterion permitted assessment of their struggle to communicate their processes

and organizational skills. With respect to complexity, the architects on the low-rise residential

building discussed the process of laying out apartments as complicated because of the

geometric challenges in maximizing the number of apartments. But the case study interview

method enabled the authors to compare this relatively simple process (perceived by the


52

interviewees as complex) with the university building that had to layout many different types

of rooms while simultaneously taking into account other goals such as minimizing energy.

Thus, the interview approach both revealed communication problems unknown to the

interviewee and avoided assessments biased by interviewees’ narrow context of complexity.

The advantages of the qualitative approach lead the authors to suggest that future work

should apply the two assessment methods to more case studies to develop more robust

conclusions about complexity-communication trends. Additionally, recording interviews and

then blindly coding statements with respect to criteria would provide stronger evidence for the

trends observed in this paper. In particular, more case studies are needed to assess the

existence of trends between the disaggregated project complexity and communication data

points in Figure 2-6. The case study interview approach could be combined with a quantitative

approach that could claim with statistical significance the correlation between communication

and complexity. Determining the steepness of the complexity-communication curve through

quantitative methods would also allow researchers to correlate complexity and communication

with other methods of assessing projects.

In particular, more research is needed to assess whether the implementation of product

communication tools such as BIM software applications or organization communication tools

such as intranet-based people pages are more likely to enable excellent communication despite

complexity. While this paper did not assess the maturity of such technology on the different

case study projects, interviews provided insight on the state of IT. Many interviewees

described how BIM enabled them to better communicate the building product. While

communicating information about the organization may be challenging, interviewees

infrequently cited this challenge as a major problem. On the other hand, many interviewees

cited how their daily work was negatively impacted by poor process communication


53

technology. These observations are speculative and future work is needed to assess the

additional impact of technology adoption on complexity and communication.

7 Conclusion

This paper describes two contributions to project information management literature. First, the

paper contributes a project complexity assessment method and a communication assessment

method. The paper develops the first method by extending others’ definition of complexity to

apply to products, organizations, and processes. The authors develop the communication

assessment method by revealing differences from interview statements. The breadth of project

types and the case study scores demonstrating calibration and comprehensive applicability to

the case studies provide evidence that the methods are generalizable to different types of

products (i.e., from low-rise residential to stadium) and organizations (i.e., from architecture

firm to general contractor). The paper also demonstrates through the low-rise and stadium

project examples how the methods enable assessment of the mechanisms by which product,

organization and process complexity impacts collaboration, sharing, and understanding.

Second, the paper contributes evidence of a trend between a project’s product, organization,

and process (POP) complexity and communication.

This trend between increased project complexity and poor process communication is

unsustainable as the AEC industry must resort to more complex solutions to provide more

financial, social, and environmental value to stakeholders. By identifying this trend, this paper

aims to motivate future research that discovers types of interventions (e.g., innovative

communication tools, project contract structures, professional education programs, etc.) that

decouple the relationship between increased complexity and increased communication

problems. The complexity and communication assessment methods provide a foundation for


54

developing and measuring the impact of these interventions on the sensitivity of

communication effectiveness and efficiency to POP complexity.

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Chapter 3: Design Process Communication Methodology -

improving the efficiency and effectiveness of

collaboration, sharing, and understanding

Reid Senescu, John Haymaker, and Martin Fischer

1 Abstract

Architecture, Engineering, and Construction (AEC) designers struggle (1) to collaborate

within projects, (2) share processes across projects, and (3) understand processes across the

firm or industry. Overcoming each of these challenges requires communication of design

processes. The paper aggregates concepts from organizational science, human computer

interaction, and process modeling fields to develop the Design Process Communication

Methodology (DPCM). DPCM is a social, technical, and representational environment for

communicating design processes that is Computable, Embedded, Modular, Personalized,

Scalable, Shared, Social, and Transparent. To apply and test DPCM, the research maps the

methodology to software features in the Process Integration Platform (PIP). PIP is a process

communication web tool where individuals exchange and organize files as nodes in

information dependency maps in addition to folder directories. The paper provides evidence of

the testability of DPCM and proposes metrics for evaluating DPCM’s efficiency and

effectiveness in communicating design process. DPCM lays the foundation for commercial

software that shifts focus away from incremental and fragmented process improvement toward

a platform that nurtures emergence of (1) improved multi-disciplinary collaboration, (2)

process knowledge sharing, and (3) innovation-enabling understanding of existing processes.


60

2 Introduction

Design processes disproportionately influence the life cycle value of the resulting products

(Paulson 1976). While the total cost of design is relatively small, the design phase of a project

greatly influences total project value. Also, final project value generally increases with the

number of different design options considered (Akin 2001; Ïpek et al. 2006). Yet, despite

information technology advances during the last half of the 20th Century, the number of design

options explored for any one design decision is typically less than five and almost always a

small percentage of the possible design space available (Flager et al. 2009; Gane and

Haymaker 2010). Not surprisingly, the construction value per man-hour expended actually

decreased from 1964 to 2003, while the rest of the American non-farm industry more than

doubled (United States Department of Commerce, 2003). Research leading to new information

management systems can improve design processes to increase project value per man-hour

expended. Improving design processes requires not just isolated technological improvements

but also process change within companies. This change requires process communication (Ford

and Ford 1995). The Design Process Communication Methodology (DPCM) contributes to the

project information management (PIM) and design process management (DPM) research

fields by laying the foundation for the development of commercial software that

communicates design processes to increase the value per man-hour expended by the

Architecture, Engineering, and Construction (AEC) industry.

Organizations within the AEC industry create information to represent the product

through a process (Garcia et al. 2004). The process can be viewed through three lenses:

conversion, flow, and value generation (Ballard and Koskela 1998). The authors choose the

information flow lens because of the relatively large potential for capturing information paths.

The design process is then organizations exchanging information that leads to a plan for the

building Product. Jin and Levitt’s Virtual Design Team (1996) similarly apply this


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information processing view of the organization to the AEC industry – first described by

Weber (1947) and later adopted by March and Simon (1958) and Galbraith (1977). This

research scopes the focus further to only include digital information exchange from scheme

design to construction documentation on projects involving complex (Homer-Dixon 2000;

Senescu et al. 2011a) products, organizations, and/or processes.

This section uses this information processing lens to describe how the AEC industry

can improve design processes through improving three types of process communication:

1. The organization can collaborate more effectively and efficiently within the project

team. In this case, the organization does not significantly change the topology of

information exchanges on a project, but executes the exchanges better through

improved comprehension of the project team’s processes. For example, the

information may be more consistent throughout the project or a particular project team

member may make a discipline-specific decision with more insight about how that

decision impacts other disciplines.

2. One project team may share a process between project teams. For example, a team

may learn about a process employing more effective software that they then

implement on their project.

3. A team may consciously develop an improved process. Developing improved design

processes requires investment, which requires a claim that the return will be an

improvement on the current state. Organizations must understand their current

processes across the firm or industry to strategically invest in process improvement.

For example, a team may understand that across the firm, they repeatedly count

objects in their building information model and then manually enter quantities into a

cost estimating spreadsheet, and so they invest in developing a script that performs the

process automatically.


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The AEC industry struggles to collaborate around processes, share processes, and understand

processes effectively and efficiently as project complexity increases (Senescu et al. 2011a).

Considering all three types of information exchange (i.e., communication) explicitly

and in unison is important, because otherwise, there are “cost-benefit mismatches” in

communication. That is, many previous communication improvement efforts do not consider

that “the person responsible for recording information is typically not the person who would

benefit from the information once it is recorded” (Eckert et al. 2001). Also, team members

frequently have conflicting obligations to the project and to the firm (Dossick and Neff 2010).

Holistically considering the three communication types, this paper asks: how can design

processes be communicated efficiently and effectively within project teams, between project

teams, and across a firm or industry?

This paper first describes three observed design process communication challenges that

motivated this research (Section 3). Then, the authors look to the PIM and DPM research

fields to find a solution to the observed challenges. Not finding a solution, the authors describe

in Section 3 how these two research fields lack a methodology that both effectively and

efficiently communicates design processes. Describing the literature review, Section 6

aggregates concepts from the organizational science, human computer interaction, and process

modeling research fields to develop DPCM, which describes a representational, technical, and

social environment for process communication. From the literature review, the authors

conclude that DPCM should be Computable, Embedded, Modular, Personalized, Scalable,

Shared, Social, and Transparent. Section 5.4 explains how PIM and DPM process

communication or information management methodologies do not exhibit these

characteristics. Sections 6 through 8 explain DPCM and propose a method for validating its

impact on design process communication.


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3 Examples of Collaborating, Sharing, and Understanding

Challenges

While Senescu et al. (2011a) provides evidence of the generality of communication challenges

in the AEC industry, this section uses the design of a university business school campus to

provide three examples to provide context to the problems DPCM addresses. The first author

gathered these observations directly and through interviews during his role as structural

engineer on this project.

3.1 Designers Struggle to Collaborate

When designing the campus, researchers identified six discrete stakeholder groups with 29

project goals. The design team evaluated seven mechanical heating/cooling options with

respect to these goals. They divided one building into five different zones and assigned five of

the seven mechanical options to these zones. The team created a Microsoft Excel spreadsheet

to gain consensus on the mechanical heating/cooling decision. The spreadsheet showed the

underfloor air distribution system as the best choice. In the same project folder, the design

team also created AutoCAD files with floor plans to communicate the mechanical systems in

the various zones to owner representatives. These AutoCAD files showed that the designers

frequently chose options other than the underfloor air distribution.

The problem was not that the design team chose the incorrect systems or that their

process for designing the mechanical systems was inconsistent. The problem was that the

process was opaque to everyone but the mechanical engineers. The mechanical engineers

saved the spreadsheet and the AutoCAD files in the project folder with no representation of

the dependencies between any of the supporting files responsible for causing this apparent

inconsistency. If the team knew the process, they would have known that the AutoCAD files

were the most up-to-date and not intended to be dependent on the spreadsheets. Instead, the


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apparent inconsistency between the two documents inhibited acoustics, lighting, and structural

engineering consultants to maintain information consistency and to comprehend information

dependencies. This inefficient collaboration caused negative rework (Ballard 2000). It also

inhibited effective multi-disciplinary system integration, which was necessary for meeting

many of the stakeholders’ environmental sustainability goals. Designers do not work in an

environment where the project team communicates information dependencies, which makes

collaboration within project teams challenging.

3.2 Designers Struggle to Share Processes

The stakeholders explicitly communicated the importance of material responsibility when

choosing structural systems (Haymaker et al. 2008). 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 environmental impacts of materials in their design

decision, the structural engineer was unable to find a process for conducting an environmental

impact analysis comparing the concrete and steel options. Several months later, the structural

engineer met a researcher in California who had worked in Australia to develop a process for

performing model-based assessments of the environmental impact of construction materials

(Tobias and Haymaker 2007). The Australian research center had worked directly with the

Australian offices of the same engineering firm at the time of the project.

In this case, 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 firm in Australia had


65

already performed this process. This example illustrates that designers struggle to efficiently

and effectively share processes between projects teams.

3.3 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 campus library, daylighting consultants created video

simulations of sunlight moving across a space. The process required much manual

manipulation of geometry and materials to reformat the information, so each new software

package could interpret the data representing the building. This process was not productive as

the consultants spent 50% of their time on these non-value adding tasks and considered only

2-3 options resulting in a sub-optimal design.

The individual consultants lacked incentive to invest time in process improvement.

Their tools did not capture their process (and the resulting lack of productivity), place them in

peer communities to improve the process together, nor provide transparent access to other

processes that could form the basis for improvements. Also, managers lacked a transparent

method for understanding process productivity rates and therefore, could not develop a

monetary justification for encouraging process innovation. AEC organizations struggle to

understand their processes across the firm or industry to strategically invest in increased

process productivity.

4 Lack of Effective and Efficient Design Process Communication

Within design process management, the design rationale (Moran and Carroll 1996a) and

design process improvement (Clarkson and Eckert 2005) research field have already

developed effective design process communication methodologies to overcome the challenges

faced by the university building design team. Yet, these research methodologies have not been


66

adopted by industry (Conklin and Yakemovic 1991; Moran and Carroll 1996b). This lack of

adoption is not due to the lack of tools capable of effectively communicating design processes.

Rather, the lack of adoption stems from the lack of incentive for designers to communicate

processes at the instant they are designing. Thus, it is not sufficient to merely have the

methodology and tools to effectively communicate process. The act of communication must

also require little effort; it must be efficient.

This need for efficiency prompted the authors to also investigate the project

information management research field as PIM focuses on improving the efficiency of

information exchange (Froese and Han 2009). The authors’ intuition is that communicating

information exchange would have been sufficient for addressing the university building design

team’s challenges. However, PIM does not address the need to communicate information

exchanges for collaboration, leveraging information systems to benefit sharing across projects

(Malone et al. 1999), and understanding of processes by the firm and industry (Ballard and

Koskela 1998; Hartmann et al. 2009). PIM lacks methodologies for effectively (i.e., able

and/or accurate) communicating processes, whereas DPM literature describes methodologies

for communicating processes, but lacks a sufficiently efficient (i.e., quick and/or with little

effort).methodology for industry to adopt these methodologies.

5 Synthesizing Existing Concepts to Develop DPCM

To address the lack of effective and efficient methodologies for communicating design

processes in the PIM and DPM fields, the authors synthesized concepts from organizational

science, human computer interaction and process modeling research fields to develop

characteristics for the Design Process Communication Methodology. The authors chose these

three fields because of the importance of developing a methodology that would: be adopted by

organizations; facilitate the creation and accessibility of design processes in a computer; and


67

specify a grammar for representing the processes. The authors reviewed 92 papers in these

three research fields by utilizing a snowball approach. After explaining the origins of the

characteristics using a subset of the most influential papers, Section 5.4 evaluates some

examples of PIM and DPM research efforts with respect to the characteristics.

5.1 Organization Science to Enable Adoption

This section first explains why highly interdependent tasks inhibit process standardization and

so, process documentation should be embedded. Research on institutions suggests that

technology should be transparent, social, modular, and shared to best allocate human capital

and creativity. Institutional research on matrix organizations suggests hierarchically structured

information is not suitable in AEC, which the authors interpret to mean information should be

represented in a way that makes process transparent. Finally, Knowledge Management

research calls for embedding and socializing of design process knowledge acquisition,

structuring, and retrieval so processes can be shared.

5.1.1 AEC requires coordination without standardization

Standardization permits coordination when situations are relatively “stable, repetitive and few

enough to permit matching of situations with appropriate rules” (Thompson 1967). In AEC,

the International Alliance for Interoperability developed the Industry Foundation Class (IFC)

to standardize data schema for describing buildings. The Georgia Tech Process for Product

Modeling (GT-PPM) and Integrated Delivery Manuals (IDM) also depend on a standard

design process (Lee et al. 2007; Wix 2007). The new capabilities of simulation software, the

complex demands of stakeholders, and the global nature of design teams make design

processes increasingly complex, dynamic, and based on performance (not precedence).

Organizations with variable and unpredictable situations inhibit process standardization.

Instead, coordination must be achieved by “mutual adjustment,” which “involves the


68

transmission of new information during the process of action” (March and Simon 1958).

Extrapolating to design processes, coordination should occur by embedding the process

documentation in the minute-to-minute work of designers rather than by developing standard

coordination methods. This lack of embedment inhibited the project team described in Section

3.1 from collaborating to make a mechanical system decision, because they were not aware of

each others’ processes. This concept explains why process standards have been relatively

unsuccessful in practice and why convergence to a single product model has not emerged in

AEC.

5.1.2 Form new institutions around processes

Institutionalism research explains relationships between firms and information. In Coase’s

(1937) model for the firm, a firm forms when the gains from setting up the firm including

organizational costs are greater than setting up a market including transaction costs. The open

source software institution does not fit within Coase’s model, and so, Benkler (2002) proposes

the alternative peer production model. Benkler claims that this emerging third type of

institution “has certain systematic advantages over the other two in identifying and allocating

human capital/creativity.” In describing the necessary conditions for processes to be

implemented and shared in this peer production model, Benkler breaks down the “act of

communication” into three parts. First, someone must create a “humanly meaningful

statement.” Second, one must map the statement to a “knowledge map,” so its relevance and

credibility is transparent. Finally, the statement must be shared. In utilizing these advantages

and conditions, a process communication environment can mimic the success of the open

source software industry.

Berger and Luckmann’s (1967) explanation of the firm provides insight as to how to

instantiate Benkler’s peer production model. Berger and Luckmann explain that many menial


69

tasks take much effort to complete. They argue that “habitualization” is human nature, because

it “frees energy” for creativity and “opens up a foreground for deliberation and innovation.” In

building design, habitualization is possible, because many individual tasks are repeated.

Thompson’s “standardization” is difficult, because the same collection of tasks (i.e., a process)

rarely occurs more than once with the same actors. Berger and Luckmann argue that

habitualization of tasks is the reason why institutions form, because institutions can invest in

technology to perform standard tasks, providing an advantage over the sole practitioner. A

larger institution that collectively develops more institutional habits can then focus more on

creative endeavors. For these institutions to exist, “there must be a continuing social situation

in which the habitualized actions of two or more individuals interlock” (Berger and Luckmann

1967). But what happens when the quantity and diversity of tasks and actors is so great that

these social institutions do not occur naturally? Individuals in the organization must

continuously waste energy on tasks that from an institutional perspective seem habitual, but

from the perspective of the individual are unique (e.g., the daylighting consultants in Section

3.3 thought they were the only ones performing the tasks). Can technology facilitate “social

situations” where “individuals interlock” to create reciprocal typification? Habitualization

(i.e., recognition of one’s own repetitive tasks) combined with reciprocal typification (i.e.,

when two people recognize each other’s habits) are critical for the formation of a peer-

production institution. Technology is needed to socialize (i.e., promote collective engagement)

and share (i.e., distribute among the organization) information exchange and make

typification transparent, so institutions can form around common processes. For example, a

community focused on finding the environmental impacts of structural materials could have

made the process described in Section 3.2 habitual within the organization. To reach this

point, however, the community must first find a way to socialize and share this process.


70

5.1.3 Use processes to structure information for the matrix

Programmers in the open-source software movement are simultaneously part of Benkler’s peer

production model and Coase’s traditional firm. Designers also exist within this peer

production model and the traditional AEC matrix organization. The matrix organizations in

large AEC design firms generally form by project, by geography and/or by discipline, but the

firm stores information hierarchically in folder directories. Just as Davis and Lawrence (1977)

claim that new business conditions required a change to the matrix organization, analogously,

expanded uses of digital information require a deconstruction of the hierarchical information

structure. Information now serves multiple purposes. A project team uses a building object

such as a window for architectural rendering, daylighting analysis, and energy analysis.

Designers exert much effort to create this object and so, it no longer belongs to just one

project, but is utilized on multiple projects. In addition, with increased computer power and

demand to view tradeoffs, more designers exchange more information, more frequently. As

shown by the mechanical system design problem, it is difficult to maintain information

consistency. Organizing the information by dependency brings the transparency needed for

consistency.

5.1.4 Knowledge Management without management

An organization’s knowledge is a resource. In this knowledge-based theory of the firm, the

organization is a social community that transforms knowledge into economically rewarded

products and services (Grant 1996; Khanna et al. 2005). Conklin (1996) describes a “project

memory system” to define this knowledge and make it available to others. The project

memory system is necessary, because organizations lack ability “to represent critical aspects

of what they know.” Whereas Conklin generally applies this system to capturing knowledge

from meetings, the same lessons apply to capturing design process knowledge. A process

communication environment that acts as “an evolutionary stepping stone to organizational


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memory” would allow designers to track information exchanges on a project (e.g., in

Australia) from which designers on another project (e.g., in California) could deduce design

process knowledge. Preserving organizational knowledge requires more than just “capturing

lots of information.” The knowledge must be made sharable by capturing and structuring the

knowledge in ways “that create and preserve coherence and ‘searchability’” (Conklin 1996).

Once Conklin’s stepping stone from project to organization enables knowledge

acquisition, the knowledge must be structured. Hansen et al. (2005) describe two aspects of

knowledge structuring: codification and personalization. Codification relies on information

technology tools to connect people to reusable explicit knowledge (Javernick-Will and Levitt

2010). 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 to individuals or communities that can provide more in depth knowledge. Knowledge

management is not just acquisition and structuring (Kreiner 2002). Javernick-Will and Levitt

(2008) address the additional importance of the future ability of others to retrieve the collected

knowledge.

The lack of design process knowledge sharing inhibited the successful material

environmental impact analysis process in Section 3.2 from being utilized by other project

teams outside Australia. This sharing also exists in Benkler’s peer production model. Yet,

Benkler’s model requires minimal if any management. Combining the peer production model

with knowledge management research provides guidance for developing an environment for a

self-perpetuating acquiring, structuring, and retrieval of design process knowledge that is

completely embedded in the design process and requires minimal management.


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5.2 Human Computer Interaction to Create and Access Processes

HCI specifies how to facilitate the designer’s interaction with the digital representation of the

process. Cognitive Science research develops models for predicting how humans will behave,

but these models will not predict perfectly (Winograd and Flores 1987) and so, it is also

important to research and describe how humans interact with computers (Winograd 2006). In

the former, researchers use cognitive models for Artificial Intelligence (AI) whereas the latter,

HCI, focuses on designing computer systems for humans; not to model humans. However, in

the Human-Information Interaction (HII) and Information Visualization (two branches of

HCI) fields, the distinction between AI and HCI blurs. For example, HII applies Information

Foraging Theory, which itself draws on Cognitive Science research (Pirolli 2007).

Programmers best develop systems for a user to search and comprehend information through

descriptive research on how to model human searching and comprehending; HCI benefits

from AI research. At the same time, the programmer can only model human searching and

comprehending behavior through iteratively testing how humans search and comprehend; AI

also benefits from HCI research.

This section takes this mutually beneficial perspective on HCI and AI. First, Cognitive

Science research calls for personalized process views. Next, the section discusses practical

implications of Cognitive Science with respect to HII and Information Visualization. These

branches of HCI provide insight to make the communication environment sharable, scalable,

social and transparent.

5.2.1 Cognitive Science calls for personalized graphical representations

“The power of the unaided mind is highly overrated…The real powers come from devising

external aids that enhance cognitive abilities” (Norman 1993). Can a technical environment

enhance a designer’s abilities to collaborate, share, and understand? “Solving a problem


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simply means representing it so as to make the solution transparent” (Simon 1981). To

illustrate, Norman (1993) presents the ticktacktoe game. As a mathematical word problem,

finding a solution is difficult, but represented graphically in the game ticktacktoe, the solution

is obvious. Similarly, the best representation of airline schedule depends on the user’s

objective; a communication environment should represent information according to the user’s

task (Norman 1993). In terms of processes, the graphical representation of information

dependencies should be personalized to the user’s skills. For example, more analytical-

minded decision makers (as measured by the Witkin GEFT) made better decisions when

presented with a graph representation of information dependency as opposed to an

“Interaction Matrix” (nearly identical to the Design Structure Matrix used in AEC (Steward

1981)). The decision-making performance of heuristic type individuals was less sensitive to

the graphical presentation of the information dependencies (Pracht 1986). More personalized

views of the mechanical system design process in Section 3.1 would have permitted process

transparency and a more collaborative design decision.

5.2.2 HCI advocates information interaction and visualization

This section seeks to find “how information environments can best be shaped for people”

(Pirolli 2007). Providing methods for achieving this goal, Information Visualization is the

“use of computer-supported, interactive, visual representations” of abstract, non-physical data

to amplify cognition (Card et al. 1999). For example, the human eye processes 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”

(Card et al. 1999). Therefore, visualizations to aid search and pattern detection should use

features that can be automatically processed. Designers will be able to better draw meaning

from information dependency graphs if the graphs use images, process views at appropriate


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scales, and spatial layouts indicative of topology (Card et al. 1999; Nickerson et al. 2008).

These strategies will make the environment more transparent.

The capabilities of the human eye also influence information scent – the perceived

value of choosing a particular path to find information (Pirolli 2007). To promote an accurate

and intense scent for the designer to find useful shared processes, search results should show

the actual information dependency graphs. Also, the environment should track the most useful

processes and prioritize these processes in search results.

Heer et al. (2007) shows that social groups will reveal more patterns than the same

number of individuals. Combining conversation threads with visual data analysis helps people

to explore the information broadly and deeply, suggesting a promising opportunity for

supporting collaboration in design activities. The environment should allow the community to

point to specific locations in the graphs to discuss patterns socially.

5.3 Process Modeling to Represent Process

Process modeling research creates a formal grammar for communicating processes to

collaborate, share, and understand. Austin et al. (1999) provide an overview of process

modeling techniques used to communicate the building design process. AEC researchers

delineate process modeling research by different views of the process or by the objectives of

the modeling. For example, Ballard and Koskela (1998) view engineering processes through

conversion, flow, and value generation and hypothesize that transparency of these views will

result in design success from the perspective of that view. AEC researchers develop

generalized process models with the intent of supporting new working methods, identifying

gaps in product information models, and informing new information models (Wix 2007).

Process models may also aim to facilitate collaboration, share better practice, or communicate

decisions. Though process modeling research frequently overlaps multiple objectives, the next


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sections are organized according to research aimed at improving coordination and planning,

and automation. This literature claims models should be embedded, scalable, shared,

transparent, social, and computable.

5.3.1 Process models aimed at improving coordination and planning

Narratives attempt to overcome the challenges of multi-disciplinary, iterative, and unique

design processes (Haymaker 2006). To facilitate coordination, Narratives create task-specific

views of information flow (consistent with the views suggested by Norman in Section 5.2.1).

Haymaker et al. also express the need to facilitate coordination by representing the status of

information. While the Narratives research calls for embedding of process modeling into the

design process, and identifies and facilitates the need to make the source, status, and nature of

the information dependencies transparent, these concepts are not validated.

As opposed to Narrative’s graph view which communicates a planned or historically

implemented process, the Design Structure Matrix (DSM) uses a matrix view to plan and

algorithms to improve the process. Originally, DSM tracked the dependencies of activities

(Steward 1981), but the Analytical Design Planning Technique (ADePT) extends DSM by

utilizing Data Flow Diagrams (Fisher 1990) and IDEF0 (Austin et al. 1999) to model not just

tasks but also information flow between tasks (Austin and Baldwin 1996; Austin et al. 2000;

Baldwin et al. 1998). An important part of both modeling techniques is their ability to take a

complex system and scale it down into sub-systems.

Embedding such process descriptions in the design process may have permitted the

owner representatives to be more confident in the mechanical system decision by quickly and

accurately comprehending the process. Similarly, the vision of Integrated Practice includes “a

world where all communication throughout the process are clear, concise, open, transparent,

and trusting: where designers have full understanding of the ramifications of their decisions”


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(Strong 2006). Thus, the process, not just product models, should be shared with the entire

project team, and the information on which decisions are dependent should be transparent.

5.3.2 Process models aimed at improving automation

Comprehending how project teams coordinate aids development of automated information

flow, so recent process modeling efforts support both goals. “Interoperability exists on the

human level through transparent business exchanges” (American Institute of Architects 2007).

The importance of associating people with information exchange to develop automation is

analogous to Hansen’s claim that knowledge must be social, not just codified.

IDMs aim to provide a human-readable integrated reference identifying “best

practice” design processes and the data schemas and information flows necessary to execute

effective model-based design analyses (Wix 2007). IDMs recognize that information must be

tracked at varying scales of detail. To help identify best practice processes, the environment

must also promote sharing by using metrics so designers can evaluate processes. IDMs

contrast with Narrator’s focus on designer communication, but are similar to Geometric

Narrator, which emphasizes the use of process models to perform modular computations on

information (Haymaker et al. 2004).

5.4 Gaps in PIM and DPM Research Relative to Characteristics

This section identifies gaps in PIM and DPM Research with respect to the characteristics

described above.

5.4.1 Improving communication between AEC professionals

Several methods have advanced the design of process models for use by AEC professionals in

practice. Narrator and Geometric Narrator form two fundamental points of departure.

Geometric Narrator enables a designer to build a modular, computable process but lacks a


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general infrastructure to easily share these processes (Haymaker et al. 2004). Narrator plans

processes and visually describes processes retroactively (Haymaker 2006). Narratives

incorporate Ballard’s information flow view via information dependency arrows and the

conversion view by showing the tool used to transform the information. Narrator addresses

some of the sharing deficiencies (via a searchable database) of Geometric Narrator, but at the

expense of its computable power and embedment in the design process. Neither Narrator nor

Geometric Narrator can be shared widely on the Internet nor personalized to create views to

individual users.

More powerful at process planning, DSM similarly plans the design process through

task dependencies, but also more explicitly identifies iteration and includes methods for

scheduling activities to minimize rework (Eppinger 1991; Steward 1981). Though these task

sequence optimization methods are computable algorithms, it is not within DSM’s scope to

automate information flow, nor act as an information communication tool that links to

particular information. Austin et al. (1999) demonstrated the ability to model 10,015 data

flows on a hospital project, which required 40 hours to capture, though 91% of the data flows

came from a generic process. While this Analytical Design Planning Technique focused on

process modules that could be shared and reused across projects, most DSM research instead

focuses on optimizing the ordering of design tasks without much concern for the effort and

difficulty required to map out task dependencies. “While people have a tacit understanding of

when a process plan is no longer relevant, it is difficult to describe the relationship between

the process plan and the process that actually occurs… Process models are typically generated

to plan, i.e., before the project, and hardly any company goes to the trouble of comparing the

model with the process that actually exists. Process post mortems are rarely done, because

everybody is busy moving onto the next project…” and there are rarely lessons learned about

the process itself (Clarkson and Eckert 2005).


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DePlan (Choo et al. 2004) attempts to tie ideal schedules derived from ADePT with

the realistically possible execution of those plans based on constraints and resource limitations

as described by LastPlanner (Ballard 1999). DePlan makes the plan dynamic, but there is still

much additional overhead, and it is not integrated with information management systems, so it

is likely relied upon only weekly - not embedded in design.

Critical Path Method (Kelley and Morgan 1959), Lean Production (Howell 1999;

Koskela 1992; Krafcik 1988), Last Planner (Ballard 1999), and Virtual Design Team (Jin and

Levitt 1996) are all fundamentally process planning techniques - not embedded in design.

While they offer insights to DPCM as process planning and control methodologies, they do

not include concepts for communicating digital information and are not discussed here in more

detail.

The Information Value Based Mining for Sequential Patterns (VMSP) is intended for

embedment in the design process to capture design process knowledge (Ishino and Jin 2006).

Ishino and Jin wrote a customized tool that captures changes in a CAD tool, and attempts to

derive design rationale from those changes. VMSP requires intense customization of software

tools and is not scalable to the hundreds of tools used in professional practice (a problem

typical of many of the tools proposed by Moran and Carroll (1996a)). While also intended to

be embedded in design and addressing shared processes, ActivePROCESS may also suffer

from scaling issues when applied to problems more complex than the simple block design

scenario because of the detail with which engineers would need to document all their design

moves (Jin et al. 1999).

Decision Dashboard (DD) improves design rationale transparency by communicating

options, alternatives, and criteria (Kam and Fischer 2004). DD contains some abilities to

compute values associated with the process nodes from information contained in related nodes


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but does not intend to automate information flows. DD does not easily support multi-user

sharing. DD models design rationale, an important aspect of the design process to

communicate. However, DD does not address research findings that demonstrate that design

rationale systems are rarely implemented in practice, because designers struggle to document

their rationale when performing design (Conklin and Yakemovic 1991; Ishino and Jin 2002;

Moran and Carroll 1996b). Finally, DD focuses on one decision at a time and does not address

how to organize the thousands of decisions made on a typical project; it is not scalable.

Figure 3-1. Comparison of existing research in PIM and DPM with the DPCM characteristics.

The matrix is not intended to cover all research in these two fields, but to show a few

indicative examples of current gaps in the research. While individual research may address

some of these characteristics, the authors have not found a theory that addresses all of them.


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As opposed to efforts such as GT-PPM (Lee et al. 2007) aimed at modeling processes

to develop a product model standard, DPCM calls for a computable web of individual

interoperability solutions. Many of the current process modeling approaches to improving

interoperability are formulated at an abstract level to define general data exchanges and

processes, and have limited value as a project-specific design guidance and management tool.

That is, software developers, not designers, use these process models, and they are therefore

not intended to be transparent, and sharable from the perspective of the typical designer.

Generally, PIM research efforts focus on modular and computable methods (Figure

3-1) that enable more efficient exchange of information. The methods are either themselves

intended to be embedded in the design process or are intended to facilitate the development of

software embedded in the design process. DPM research focuses on process transparency for

planning before design or story-telling afterwards and is more convincingly scalable to real

projects. DPCM aims to satisfy all the characteristic gaps in the existing DPM and PIM

research.

6 Theory - Design Process Communication Methodology

Gaps exist between the characteristics exhibited by the methodologies described in PIM and

DPM literature and those characteristics recommended by organizational science, human

computer interaction, and process modeling research. Modeling research recommend for a

communication environment and what the existing PIM and DPM literature has contributed.

These points of departure provide important characteristics for the development of a process

communication environment. This section describes elements of DPCM that represent and

contextualize the processes and methods that describe how designers capture and use these

processes by interacting with a computer. The main contribution of this paper, the DPCM, is

the combination of the elements and methods that enable the Characteristics.


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6.1 Elements and Methods Enabling Characteristics

Embedded

Users use the environment simultaneously to organize and exchange information as well as

communicate processes.

They Save or Open Information, and can Open old versions of information. Each

Information node contains a list of previous Versions of the files. The Status of the

information can be up-to-date, being worked on, or out-of-date. These information

management elements and methods encourage the users to use the environment as the primary

means of exchanging information while they work. The ability to effortlessly Draw arrows

after saving a file embeds process capturing in this information management work flow.

Scalable

The environment scales to the tens of thousands of files exchanged on the largest construction

projects, and also scales across the industry to apply to many different types of projects.

The environment enables scaling within a project by providing access to

representations of information dependencies through a Frame. A Frame is a type of Node that

itself contains views onto a collection of other Contained Nodes. Unlike the nodes which exist

in a single non-hierarchical network, the frames are organized hierarchically. Thus, the user

can choose to Open each frame via a Hierarchy or Network type of Window. This hierarchical

organization enables the representation of processes at multiple levels of detail and ensures

that users are not overwhelmed by visualizations of networks containing dozens of nodes not

relevant to their task.


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The environment scales across the industry, because it uses a discretization and format

of information common across the industry: the File and the URL. As every project uses

digital files or URL’s to describe some aspect of the building, the environment can be utilized

across the industry.

Personalized

The environment personalizes communication to each user.

As the Frame is simply a view onto nodes, a single Node can exist within multiple

frames. Thus, designers can create custom views of the nodes and their relationships that are

comprehensible and relevant to them. They just Drag and drop nodes into their personal

frames without affecting how others see the nodes.

Transparent

The environment enables the comprehension of processes by the designers.

The environment achieves transparency through arrows between information and

frame nodes. Each arrow represents information Dependency. That is, the End Node is

dependent on the Start Node if information contained within the Start Node was used to create

the information in the End Node.

The environment additionally enables transparency by assigning each node an

information Ribbon. The Ribbon contains a Description of the information contained within

the node. For each Frame node, the Ribbon displays the difference in time between the most

recently uploaded file and the oldest file, indicating the latency since the initiation of the

process, the Duration. The Ribbon also shows how many times (Times viewed) users opened


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the Frame – an indication of the popularity or importance of the process. Also, each

Information node has a Time stamp showing when the file was last uploaded, and what Tool

was used to create the file based on the file suffix. All nodes have a Title and an Actor that

denotes the person responsible for the node.

Social

The environment promotes social engagement with project information and dependencies.

Within each node’s Ribbon users can Post comments about the information and the

processes in the Discussion thread. They can also Rate the process in terms of its productivity.

Shared

The environment facilitates the distribution of processes.

Users can Search Dependency paths and individual Nodes. Also, users can easily

share their views of processes with others, because each Frame has a URL that can be sent to

other users.

Modular

The environment enables users to combine several parts of other processes into a new process.

It also allows geographically separated users to work on different parts of a process and then

combine their work. This modularity contrasts with strategies aimed at representing all project

information within a single type of data schema and instead encourages discrete modules of

information dependent on each other.


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Users can thus, mix and match Process modules containing all of the above elements

and Duplicate the Process modules and customize them to specific projects.

Computable

The environment enables users to attach Scripts to a Dependency that would automate

information flow from the Start Node to the End Node. Defining each dependency as a

computable relationship between two pieces of information enables the gradual development

of improved interoperability between tools.


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Figure 3-2. The Design Process Communication Methodology. Elements represent and

contextualize a process and methods enable designers to capture and use the process model.

These elements and methods enable the Characteristics.


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7 DPCM Applied to Observed Problems

This section demonstrates how by operationalizing DPCM as the process-based file sharing

tool, the Process Integration Platform, DPCM addresses the three types of communication

challenges described in Section 3.

7.1 Collaboration with PIP

If PIP had been available to the mechanical engineering team on the university building

project, they could have used PIP to collaborate around their digital files. After logging in, the

user sees two personalized home page Windows: a hierarchy view on the left of the screen and

a network view on the right (Figure 3-3). In this case, the mechanical engineer wants to use an

Architecture Model file and a Daylighting Analysis file as input to an energy analysis. The

engineer navigates through the Frame hierarchy to a more detailed process level showing the

architecture and daylighting models. This hierarchical organization of frames enables the

process to be scaled to many files. He drags and drops the Information Nodes containing the

Architecture Model file and the Daylighting Analysis file into his Energy Analysis frame. The

Frames are thus personalized in that the same Information Node containing the Architecture

Model file exists within the context of the Daylighting Analysis frame and within the context

of the Energy Analysis frame. The mechanical engineer then double clicks on each file to

open it on his desktop. The ability to Open and Save files directly in PIP Information Nodes

enables process capturing to be embedded in the design process. He imports the Revit model

into his energy analysis tool. Looking at the daylighting analysis results, he manually enters

the energy required for artificial lighting into the energy analysis tool. After completing the

energy analysis, he double clicks in the graph view to Create an Information Node and Saves

the energy analysis file to that node. As he used the architecture model and daylighting

analysis as input to the energy analysis, he also Draws Arrows from those two nodes to the


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new energy analysis node to represent this Dependency and make the Process transparent.

Now that the energy analysis is complete, he uses the results to create a decision matrix in

Microsoft Excel. He uploads the Excel file to a new node and draws an arrow to it. When the

architectural design changes, prompting the upload of a new energy analysis file, the

downstream decision matrix file Status is no longer up-to-date (indicated by red highlight),

because it was created based on an out-dated energy analysis file. If based on this new energy

analysis, the mechanical engineers decide on a displacement ventilation system and create an

AutoCAD file dependent on the new energy analysis, the rest of the project teams now know

to integrate their designs with the AutoCAD file and not the out-dated decision matrix. Using

PIP makes the mechanical design process transparent to the entire project team, so they can

comprehend information relationships, consider tradeoffs, and make related information

consistent.


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Figure 3-3. Collaboration in the Process Integration Platform. Users navigate to the

appropriate process level via the hierarchy view (left) or by double clicking folder icons

(right). Users create nodes, upload files to those nodes, and draw arrows to show relationships

between the nodes. Green highlights indicate the node is up-to-date, and red indicates an

upstream file has changed since the node was uploaded.

7.2 Sharing Processes with PIP

In addition to facilitating collaboration, other teams can also share design processes with the

structural engineer on the university building project allowing calculation of the

environmental impact of materials. Since PIP is web-based, sharing is enabled by the

structural engineer searching for a Process where a project team started with input “Arch .ifc”

to denote an architecture model with an Industry Foundation Class file format and produced

“LCA,” life cycle assessment (Figure 3-4). The results display three projects and the engineer

browses to find the most relevant process. The engineer can Duplicate the relevant Process

module and paste it within the university building frame to be used as a planning template,

which can then be populated with project-specific information.


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Figure 3-4. Sharing processes in the Process Integration Platform. Users search information

dependency paths to find processes with the input available and the output desired. Users can

then copy processes to new projects.

7.3 Understanding Processes with PIP

With PIP, professionals can understand the processes across the firm or industry, so they can

identify popular inefficient processes and strategically invest in improvement. Each Node has

a Ribbon containing information that describes the process within the frame or the information

contained within the node. PIP offers a process-centric discussion forum for users to Post

Comments and Rate process productivity (Figure 3-5). By socially discussing processes, a

community of designers can discuss where the firm should invest in process improvement. A

community of daylighting consultants could see that their process is Viewed often, but that the

process Duration is long. They could discuss the inefficiencies of the process and decide to

collectively program a script to extract information from a Revit file and convert it to a format

that would be interoperable with the daylighting analysis software. The consultants could then

save that Script in PIP and drive computable information flows automatically.


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Figure 3-5. Understanding processes in the Process Integration Platform. PIP tracks some

process metrics automatically, so users can evaluate the most popular and time-consuming

processes. Discussion threads are associated with each node, so project teams can discuss

individual files or entire processes.

8 Validation Metrics

8.1 Motivation for the Metrics

Validating DPCM requires measuring the efficiency and effectiveness for each type of

communication. Process communication requires (1) Capturing, (2) Structuring, (3)

Retrieving, and (4) Using processes. Benkler (2002) describes these steps as part of the

information-production chain needed for collaboration in the peer-production model (Section

5.1.2). Knowledge management research describes these steps as needed for sharing of

processes across projects (Section 5.1.4) (Carrillo and Chinowsky 2006; Javernick-Will and

Levitt 2010; Kreiner 2002). Finally, innovation literature cites these steps as required for

companies to understand their processes to make strategic investments in process

improvement (Hargadon and Sutton 2000).


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Historically, these different types of communication were independent. Companies

have different systems (both technical and organizational) for project management, knowledge

management, and research and development. Yet for each type, the literature suggests steps

for improving collaboration, sharing, and understanding that are similar. Because of this

similarity, at the same time one is exchanging information to collaborate within a project, that

professional can also contribute to sharing processes across projects and to strategic

understanding of processes across the firm or industry. Thus, Section 8.2 measures capturing

and structuring of processes and Section 8.3 measures the retrieving and using of processes

within projects, between projects, and across the firm or industry. The sections combine

capturing and structuring into simply capturing, and retrieving and using into simply using,

because capturing and using provides sufficient granularity for assessment.

8.2 Effectiveness and Efficiency of Capturing Processes

In typical design projects it is difficult to determine the theoretical or ideal information

dependencies. Measuring how accurately the process model matches the actual process is

nearly impossible. However, in a controlled design experiment, the theoretical information

dependencies are known, and capturing effectiveness can be measured as the:

(1.1.1) Percentage of true dependencies captured by the process model.

A communication method that captures a high number of dependencies in a controlled

environment should also capture a relatively high number of the dependencies on an industry

project.

Design projects consist of “production work that directly adds value to final products,

and coordination work that facilitates the production work” (Jin and Levitt 1996). An efficient

method for communicating process will not decrease the amount of time spent on production

work nor increase the amount of time spent on coordination work. That is, capturing processes


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accurately should not cause any burden on other aspects of the project. Two measurements

indicative of burden are:

(1.1.2) Frequency of value-adding information transfer between designers;

(1.1.3) Number of design iterations.

Design iteration and exchange of information between designers are valuable parts of

production work. When design teams are burdened with managing information, they iterate

and exchange information less frequently.

In the University Building example, project managers planned the process through a

series of milestones. The milestones provided a coarse view of process resulting in the capture

of zero information dependencies. The authors hypothesize that applying DPCM would

capture a much larger percentage of dependencies without the burden caused by previous

methods which required hours of effort invested early in the project (Austin et al. 1999).

8.3 Effectiveness and Efficiency of Using Processes

Once DPCM captures processes, designers can use the processes for the three types of

communication.

8.3.1 Using processes for Collaboration within projects

The ability of a team to collaborate effectively around a process can be measured by the:

(2.1.1) Number of local iterations;

(2.1.2) Number of statements about design trends.

These two metrics both indicate multi-disciplinary collaboration effectiveness. Without

collaboration, teams will optimize locally within their discipline silos. Successful design

solutions require global consideration of multi-disciplinary tradeoffs and the resulting iteration

that enables the best solutions to be found (Akin 2001; Ïpek et al. 2006).


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Inefficient project teams perform negative rework without ever completing an

internally consistent and complete design (Ballard 2000). The lack of up-front collaboration

means most problems are resolved during construction when the cost of resolution is highest.

Thus, the efficiency of collaboration around a process can be measured by:

(2.1.3) Number of complete and accurate design options produced.

Throughout the design process, a team that collaborates efficiently will produce multiple

design options as they iterate toward a final design. During the design process, efficiency can

be measured by:

(2.1.4) Internal consistency of design assumptions.

For example, in the mechanical engineering problem, the structural engineer may have

assumed no underfloor air distribution in his structural design based on the HVAC Decision

Matrix file, while the electrical engineer may have assumed he could place all his wires in the

underfloor space based on the Mechanical Zoning plans. This inconsistency would delay the

completion of an accurate design option. These types of inconsistencies cause statements of

confusion (See Section 3.1), so collaboration effectiveness can also be assessed by the:

2.1.5 Number of expressions of confusion.

Together these metrics allow researchers to assess the relative ability of different

communication methodologies to impact the efficiency and effectiveness of collaboration

within projects.

8.3.2 Using processes for Sharing between projects

Effective use of other projects’ processes requires retrieving productive processes.

Researchers need a scoring system to evaluate processes. The actual scoring system used may

vary depending on the goals of the project. Clevenger and Haymaker (2011) provide one


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method for evaluating design processes, though the actual scoring method used can vary as

long as the same method is used to evaluate the comparison of the process communication

methodologies employed. The ability of a methodology to enable the effective sharing of

processes between project teams is indicated by the:

(2.2.1) Score of projects selected to imitate.

For example, many processes for leveraging building information models to perform life cycle

assessments of structural systems may exist. An effective communication methodology will

enable project teams to effectively retrieve and use the best processes. However, retrieving

and attempting to use an appropriate process is insufficient. A project team must be able to use

another project’s process efficiently. Efficient use of a shared process should minimize:

(2.2.2) Number of errors made implementing the shared process.

Errors may include redundant steps such as using more tools than required, using tools

incompatible with other tools, or missing critical analysis. For example, the structural engineer

on the university building project may retrieve the Australian LCA process in Figure 3-4, but

if the structural engineer forgets a critical part of the process, then the methodology does not

enable efficient sharing of processes.

8.3.3 Using processes for Understanding across the firm or industry

AEC companies consider IT investments to be costly and risky, yet investments proceed based

on “gut feel” without understanding current processes and how the specific investment will

improve them (Marsh and Flanagan 2000). An effective process communication method

enables the firm or industry to effectively use their understanding of current processes to

strategically invest in process improvement. Unlike the above communication types, the

authors evaluate effective understanding qualitatively by investigating the ability of a

communication methodology to answer the following questions:


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1. What are the most important types of information on projects?

2. Who are the most critical individuals on projects?

3. What information flows between tools are most common?

4. What are the latencies between tools or between people?

5. How well is information distributed within the team?

6. What is the relationship between information distribution and project performance?

Of course, some insights require case studies or ethnographic research, and other insights can

be derived more efficiently through IT-based communication methods embedded in projects.

The authors measure understanding efficiency as the time required to achieve the insights. The

time is trivial for DPCM as data visualization tools provide nearly instantaneous access to the

process information.

Table 3-1. Metrics to assess process communication.

Process Communication Steps

Effectiveness Efficiency

Capturing (1.1) Percentage of dependencies captured

(1.2) Frequency of value-adding information transfer between designers

(1.3) Number of local iterations

Using within projects (2.1.1) Number of local iterations

(2.1.2) Number of statements about design trends

(2.1.3) Number of complete and accurate design options

(2.1.4) Internal consistency

(2.1.5) Number of expressions of confusion

between projects (2.2.1) Score of projects selected to imitate

(2.2.2) Number of errors made implementing a shared process

across firm or industry

Time required to gain insight Insights provided by process information


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9 Conclusion

The DPCM contributes to PIM and DPM research fields by laying the foundation for the

development of commercial software that communicates design processes to increase the

value generated per man-hour expended by the AEC industry. The paper makes a case for the

need for such a methodology both based on three examples of communication struggles in

practice and by a review of the DPM and PIM research fields. The authors develop DPCM by

synthesizing concepts from organizational science, human computer interaction, and process

modeling research to conclude that a communication environment should be Computable,

Embedded, Modular, Personalized, Scalable, Shared, Social, and Transparent. However,

current research efforts do not exhibit these characteristics and thus, industry lacks a method

for effectively and efficiently communicating process. In particular, prior research focuses

insufficiently on embedding process communication in minute-to-minute work, fostering a

social community around processes, personalizing process views, and sharing processes.

Elements that represent and contextualize process and methods for capture and using of

process enable these eight characteristics.

The paper validates the legitimacy of the DPCM theory by proposing metrics for

comparing it with other communication methods. Also, PIP shows that developers can

implement the theory, and that such an implementation addresses the three types of

communication struggles observed in practice. Providing additional evidence of the testability

of DPCM, over 200 students used PIP in class projects, design charrettes, and on graduate

student research projects (

Figure 3-6). This adoption of the tool demonstrates both the perceived usefulness of DPCM

and the ability of future research to measure the impact of DPCM on communication

effectiveness and efficiency. This future research will provide further evidence that DPCM

can lay the foundation for commercial software that shifts focus away from incremental and


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fragmented process improvement toward a platform that nurtures emergence of (1) improved

multi-disciplinary collaboration, (2) process knowledge sharing, and (3) innovation-enabling


Figure 3-6. Use of the Process Integration Platform (PIP) by students at Stanford University.

PIP is a process-based file sharing web tool that acts as a model for DPCM. Its use

demonstrates that DPCM can be practically applied and tested.

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Chapter 4: Communicating Design Processes Effectively and

Efficiently

Reid Robert Senescu and John Riker Haymaker

Abstract

Existing design process management methodologies for communicating design processes

cannot be efficiently implemented. Project information management methodologies developed

to efficiently exchange information do not effectively communicate processes. Consequently,

the Architecture, Engineering, and Construction industry struggles to: (1) collaborate within

projects, (2) share processes between projects, and (3) understand processes across projects

to strategically invest in improvement. This paper contributes a set of metrics and an

accompanying test method that validate a methodology’s ability to communicate processes

effectively and efficiently to overcome all three of these communication challenges. Second,

the paper uses the set of metrics and the test method to validate the Design Process

Communication Methodology (DPCM). Results demonstrate that designers employing DPCM

accurately capture processes with little effort. When collaborating within project teams,

process clarity and information consistency result in little rework, and positive iteration

enables consideration of multi-disciplinary design trends. Designers share processes between

project teams and use the shared processes without committing process mistakes. DPCM

enables the understanding of processes across projects to provide insights into the

relationship between design integration and project performance as well as opportunities for

strategic investment in improved processes. Thus, this paper both validates DPCM’s ability to


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improve design processes through effective and efficient process communication and enables

future researchers to validate new process improvement methodologies.

1 Introduction

The Design Process Communication Methodology (DPCM) aims to increase the effectiveness

and efficiency with which Architecture, Engineering, and Construction (AEC) industry design

professionals communicate processes. Senescu et al. (2011b) explain the operationalization of

DPCM through the development of the Process Integration Platform (PIP) - a web tool that

enables project teams to organize and exchange files as nodes in an information dependency

map that emerges as they work. Senescu et al. established both theoretical and practical

justification for DPCM as a contribution to the design process management (DPM) and

project information management (PIM) research fields. This paper first contributes a set of

metrics and an accompanying test method for comparing DPM or PIM methodologies. Then,

this paper validates that DPCM enables the effective and efficient communication of processes

within project teams, between project teams, and across project teams.

This introduction first explains how the DPM and PIM research fields attempt to

improve design processes. Next, this section explains three types of process communication

that can improve design processes: collaboration within project teams, sharing of processes

between teams, and understanding of processes across projects. Then, the introduction closes

with an overview of the rest of the paper.

1.1 Research to Improve Design Processes

Design processes are often unproductive (Flager and Haymaker 2007; Gallaher et al. 2004;

Navarro 2009; Scofield 2002; Young et al. 2007). The design process management research

field addresses this lack of productivity through the design rationale (Moran and Carroll 1996)


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and design process improvement research (Clarkson and Eckert 2005). The research

frequently attempts to improve design processes by first validating descriptive and predictive

process modeling methods using industry observation and case studies (Cross and Roozenburg

1992). DPM research then lays the foundation for normative research that proposes new

methods aimed at directly improving industry design processes. Researchers attempt to

validate these new methods by demonstrating increased value per man-hour expended. For

this validation, DPM researchers either use a research method such as a design experiment

(Clayton et al. 1998), or they directly apply the method in industry using a research method

such as Ethnographic-Action (Hartmann et al. 2009).

Both the design rationale and design process improvement literature frequently

attempt to improve design processes through more effective and efficient methods of process

communication (Ford and Ford 1995). As digital files are the primary deliverable of AEC

design professionals, the authors intuit that maps of the dependencies between these files

would be indicative of design processes. This intuition that design processes can be

communicated through visualizing information dependency maps led the authors to

investigate the project information management research field. PIM attempts to improve

design processes by improving the efficiency with which project teams exchange information

by aiding the management of a project’s information systems (Froese and Han 2009).

PIM focuses on methods for efficiently exchanging information within a project and thus

improving process productivity. On the other hand, DPM attempts to reach similar

productivity gains by focusing on improving the effectiveness of process communication.

1.2 Communicating Design Processes

The AEC design process consists of organizations exchanging information that lead to

drawings of a building product (Garcia et al. 2004). Communication is the “process of


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exchange of information between sender and receiver to equalize information on both sides”

(Otter and Prins 2002). Combining the two definitions, professionals communicate processes

by exchanging information that describes how professionals exchange information. Design

processes can be improved through three types of effective and efficient process

communication:

1. The organization can collaborate more effectively and efficiently within the project

team. In this case, the organization does not significantly change the topology of

information exchanges on a project, but simply executes the exchanges better through

improved comprehension of the project team’s processes. For example, the

information may be more consistent throughout the project or a particular project team

member may make a discipline-specific decision with more insight about how that

decision impacts other disciplines.

2. One project team may share a process between project teams. For example, a team

may learn about better software that they then implement on their project.

3. A team may consciously develop an improved process. Developing improved design

processes requires strategic investment, which requires a claim that the return will be

an improvement on the current state. Organizations must understand their current

processes to invest in improvement across projects. For example, a team may

understand that they repeatedly count objects in their building information model and

then manually enter quantities into a cost estimating spreadsheet, and so they invest in

developing a script that performs the process automatically.

The AEC industry struggles to collaborate, share, and understand design processes effectively

and efficiently (Senescu et al. 2011a; Senescu et al. 2011b).


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Each types of process communication challenge requires consideration of how

designers: (1) Capture, (2) Structure, (3) Retrieve, and (4) Use processes. Benkler (2002)

describes these steps as part of the information-production chain needed for collaboration

when team members are not aligned by the goals and hierarchy set by a single firm.

Knowledge management research describes these steps as needed for sharing of processes

across projects (Carrillo and Chinowsky 2006; Javernick-Will and Levitt 2010; Kreiner 2002).

Finally, innovation literature cites these steps as required for companies to understand their

processes to make strategic investments in process improvement (Hargadon and Sutton 2000).

However, the literature lacks a unifying methodology enabling the development of technology

to support design teams in employing these four steps necessary for the three types of

communication.

1.3 Overview of this Paper’s Layout and Contributions

DPCM provides this unifying collaboration-sharing-understanding methodology by enabling

the communication of processes within project teams, between project teams, and across

multiple project teams. Section 2 summarizes DPCM. To model DPCM and validate it, the

authors developed the Process Integration Platform. PIP is a web tool that enables project

teams to manager and exchange files as nodes in an information dependency map that emerges

as they work. This paper validates the impact of DPCM with the Mock-Simulation Design

Charrette (MSDC) and a modified Ethnographic-Action method (Hartmann et al. 2009) as

described in Section 3. After presenting validation results (Section 4), the paper concludes

with an explanation of how the set of metrics, accompanying test method, and DPCM

contribute to DPM and PIM through validation of DPCM’s impact on design process

communication effectiveness and efficiency (Section 5 and 6).


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2 Design Process Communication Methodology

DPCM lays the foundation for commercial software that fosters effective and efficient process

communication. DPCM consists of elements (Figure 4-1) that represent and contextualize the

process and methods that describe how designers capture and use these processes by

interacting with a computer. One example of an element is the Dependency, which represents

information from one file being used to create information in another file. One example of a

method is Draw Arrow, which is how the designer defines the Dependency. The elements and

methods enable the characteristics derived from the points of departure as described in detail

in Senescu et al. (2011b). For example, the element, Dependency, enables process

Transparency. The method, Draw Arrow, enables the capturing of process to be Embedded in

minute-to-minute work as designers organize and exchange files.


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Figure 4-1. Summary of the Design Process Communication Methodology. DPCM consists of

seven elements that enable design teams to represent and contextualize processes. Elements

contain methods and attributes (not shown) described in more detail in Senescu et al. (2011b).

3 Validation Method

Before it was possible to validate DPCM, the authors operationalized DPCM using the Agile

Development Method (Cohn 2004) as a framework for mapping DPCM to the Process

Integration Platform’s internet application features. PIP is a web tool that enables project

teams to manage and exchange information files as nodes in an information dependency map

(Figure 4-2). After PIP became usable, the authors shifted toward the Ethnographic-Action

Method (Hartmann et al. 2009) to further develop PIP, which follows the close study of user

behavior advocated by Kohavi et al. (2007).


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Figure 4-2. The Process Integration Platform. PIP is a web tool enabling project teams to

organize and share files as nodes in an information dependency map that emerges as they

work. Users navigate to the appropriate process level via the Hierarchy view or by double

clicking frame icons in a Network view. Users create nodes, upload files to those nodes, and

draw arrows to show relationships between the nodes. Green highlights indicate the node is

up-to-date, and red indicates an upstream file has changed since the node was uploaded. Users

can also search dependency paths to find relevant historic processes from other projects. Each

node contains an information ribbon providing additional process information and the

opportunity to rate process productivity or comment.

This section summarizes previous research methods used to validate design process

improvement research. Building on these previous efforts, the section describes the test setup,

metrics, and data collection system used to validate DPCM.


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3.1 Summary of Validation Methods for Design Process Improvement

Research

Senescu et al. (2011b) discusses and compares DPM and PIM, concluding that previous

research lacks a methodology enabling both effective and efficient design process

communication. In response, that paper proposed DPCM. However, the extent to which

DPCM is effective and efficient remained an open question, so this section builds on previous

design process improvement work to synthesize two research methods appropriate for

evaluation of DPCM: Mock-Simulation Charrette method (MSDC) and Ethnographic-Action

Method.

The first method for validating DPCM comes primarily from the Charrette Test

Method, which combines the architectural notion of charrette (a short, intense design exercise)

with the software usability testing common in the human computer interaction research field.

Clayton et al. (1998) developed the charrette method “to provide empirical evidence for

effectiveness of a design process to complement evidence derived from theory.” The method

permits:

multiple trials which increases reliability;

repeatable experimental protocol;

objective measurements;

comparison of two processes to provide evidence for an innovative process.

Researchers can widely apply this method to design computing research questions, but they

must customize the method to their particular question. For example, Clevenger and

Haymaker (2009) create a customized charrette called the Design Exploration Assessment

Methodology, which enables designers to use Energy Explorer (a Microsoft Excel


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spreadsheet) to quickly generate and record design alternatives to provide quantitative

measurements of different design strategies.

The authors similarly customized the charrette method with additional inspiration

from research outside of AEC. For example, Heiser and Tversky (2006) performed A-B

experiments with students and concluded that showing students diagrams with arrows caused

the students to describe equipment with functional verbs as opposed to nouns and adjectives

describing the equipment structure. Also, students with text descriptions containing functional

verbs were more likely to draw arrows. Heiser and Tversky did not make normative claims

about whether, for example, teams should use more arrows when collaborating with each

other, but, unlike Clayton’s implementation of the Charrette Test Method, they described a

cognitive phenomenon by recording user language. Using instant messaging, the MSDC also

records language to evaluate the impact of DPCM on, for example, designer confusion.

Another inspiration for MSDC was GISMO – a method that aims to improve decision

making by graphically displaying information dependencies. Pracht (1986) demonstrated that

business students made decisions leading to higher net income for their mock companies when

presented dependencies in graphical form. Though not applied to a design problem, the

validation method for GISMO presented quick, quantitative results demonstrating that a new

computer-aided process resulted in students making more effective decisions to achieve a

clearly defined goal.

Inspired by these validation methods, the authors developed the MSDC method which

customizes Clayton’s method while still leaving it sufficiently general such that other

researchers could use the method to compare their DPM research with DPCM. The authors

also validated the DPCM using student class projects, because compared to the charrettes,

student class projects (1) work on a time scale more on par with professional projects; (2) have

more freedom to choose processes and tools like in professional projects; (3) work at a level of


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product detail similar to scheme design in professional projects; (4) tackle more of the

“wicked” and complex problems with more ambiguous design goals faced by professionals

(Bachman 2008; Rittel and Webber 1973; Senescu et al. 2011a). These attributes of student

design projects translates to more intertwined production and coordination work. This

intertwining makes it difficult to isolate and measure impacts on coordination work, which

inhibits the ability to use the method for validation of collaboration and sharing. However,

these drawbacks do not inhibit the authors’ ability to draw insights about their design

processes to validate the impact on process understanding.

Thus, the authors selected the MSDC to validate collaboration and sharing and the

Ethnographic-Action method in class projects to validate understanding. The authors utilized

the Ethnographic-Action method, because of its success in validating the development and

implementation of information systems (Hartmann et al. 2009). In particular, Beylier et al.

(2009) demonstrated how a research method nearly identical to Ethnographic-Action

successfully validated the KALIS methodology, which, similar to DPCM, embedded process

sharing capabilities into an information management tool. Like the KALIS research method,

both the MSDC and Ethnographic-Action research can be categorized as part of a

comprehensive study in the Design Research Methodology’s “Descriptive Study II stage to

investigate the impact of the support and its ability to realize the desired situation” (Blessing

and Chakrabarti 2009). MSDC and Ethnographic-Action use the information processing view

of design to validate research that aims to support design (Blessing and Chakrabarti 2009).

Alternatively, adopting the CIFE Horseshoe research framework (Fischer 2006), MSDC and

Ethnographic-Action are specific research methods used in the testing task to validate the

DPCM theory presented in Senescu et al. (2011b).


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3.2 Test Setup

3.2.1 Mock-Simulation Design Charrette setup

The authors chose students as opposed to professionals for the charrettes, because it was easier

to recruit student volunteers and previous charrettes found no correlation between professional

experience and performance in the charrette (Clevenger 2010). The author incentivized

students to participate with a pizza party and a prize to the team that completed an accurate

design of the classroom with the highest Net Present Value (NPV).

Students signed up for different sessions during a three month period without knowing

whether the sessions were going to be control or experiment cases - a between-subject

experiment design. Each session consisted of one team of five students. The first author

randomly assigned each student to a design role: Architect, Environmental Consultant,

Mechanical Engineer, Structural Engineer, and Cost Estimator. The first author presented a

summary of the information in the remainder of this section to each session via a PowerPoint

presentation (see Appendix) and a tutorial.

The teams were chosen to design the next generation of green classrooms and their

goal was to collaborate to maximize the NPV. The teams calculated NPV using a subset of the

20 Microsoft Excel mock-simulation tools available.

The teams had access to the project frames of six historic projects (Figure 4-3a). Prior

to the charrette, the first author completed the six different designs in each project frame using

six different subsets of the 20 tools. Thus, the teams could choose what tools to use for their

new classroom project by looking at the tools from the historic project frames, or they could

choose the tools from a project frame containing all 20 tools. The teams did not need to use all

the tools and certain tools are not interoperable with other tools.


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For example, the structural engineer could choose to use GreenStructuralAnalysis.xls,

StructuralAnalysis.xls, GravityAnalysis.xls, SeismicAnalysis.xls, FoundationDesign.xls,

and/or SuperStructuralAnalysis.xls. Many of these tools were redundant.

SuperStructuralAnalysis.xls conducted both a gravity and seismic analysis, so using the

Gravity, Seismic and SuperStructural tools would have been inefficient. On the other hand,

only GreenStrucutralAnalysis.xls outputted the Environmental Cost of Structural Materials, so

if the Cost Estimator depended on this value for the GreenNetPresentValueCalc.xls, the two

designers would have had to collaborate to ensure they use these two interoperable tools. The

various tools received different inputs and gave different outputs. Some also used different

units, allowed simulation of different types of designs, and took a different amount of time to

analyze. The variety of tools simulated the real choices of professional designers and the

interoperability problems of complex software tools.


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Figure 4-3. Mock-Simulation Design Charrette test setup. All teams open up PIP to see the

historic project frames (a). Control teams can open any of the six historic project frames to

view the list of tools used to complete that project (b). Experimental teams see the same list of

tools used to complete the project and their dependencies (c). The design teams work in the

“Team C Classroom” frame, which is initially blank, but then becomes populated with the

tools used to design the classroom. The work of the control teams eventually resembles a list

similar to (b) and the work of the experimental teams resembles the network in (c).

Each designer inputted independent variables into one or more of the mock-simulation

tools. The mock-simulation tools then analyzed the input values to output performance values

(Figure 4-4). The conversion of inputs to outputs did not correlate with first principle

predictions of building performance but did follow general trends based on actual analysis.

This lack of correlation was acceptable because the intent of MSDC was to model the

coordination of design work; the work performed between simulations. The actual input and

output values had little absolute significance, which was preferable to using real simulation


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tools because MSDC nullified the domain specific skills and experience of the test participants

and focused instead on the coordination design work impacted by the design process

management research.

Two differences existed between control and experiment groups. The control group

could not draw arrows. They simply exchanged files with each other in a list similar to most

teams in professional practice save files in Windows Explorer. The first author instructed

experimental groups to draw arrows to show the dependency between tool files. This

difference measured the impact of DPCM on collaboration

In addition, the control group could search and view information on historic projects,

but not information dependencies (Figure 4-3b). The experimental group could search and

view historic projects’ information dependencies (Figure 4-3c). This difference enabled the

measurement of the impact of DPCM on sharing.

The teams began the charrette with a ten minute meeting to plan their design process.

Simulating the typical non-collocated, asynchronous project team, the participants dispersed

and sat at different computers and communicated only via instant messaging and PIP.


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Figure 4-4. Example of a mock-simulation tool. All 20 mock-simulation tools resemble this

Energy Analysis tool. In this case, the Mechanical Engineer finds dependent input values from

the output values of other tools. He then chooses a design by selecting input independent

variables. Clicking the “Analyze” button produces the output values, which become input to a

subsequent tool.

3.2.2 Ethnographic-Action research in design class projects

The authors selected student projects as opposed to a professional project, because student

projects: (1) do not require the usability, reliability, security, and legacy integration required


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by professionals; and (2) enable simultaneous comparison of multiple projects of similar scope

within a three month period. Four professors chose to use PIP as the primary process capturing

and file sharing web tool in five classes, but the results presented in this paper came from use

in one Multi-disciplinary Design and Analysis course taught in the winter of 2010. 32 student

designers in this class worked on eight teams of three to five students over three months. The

designers adopted specific roles similar to the charrettes and professional practice. Each team

presented a design solution for a real project for which they received a grade.

3.3 Techniques for Collecting Data and Measuring Results

The authors instrumented PIP to measure the effectiveness and efficiency of (1) Capturing, (2)

Structuring, (3) Retrieving, and (4) Using processes. This section describes how the authors

measured and/or calculated each result in Table 4-1.

PIP stores the DPCM attributes and logged most user actions in a MySQL database.

Each Excel tool records design values every time a designer calculated output values. The

designers used logged instant messaging to communicate with each other. The first author told

students they could only exchange design values using PIP and the first author did not observe

cases of work not uploaded into PIP, so it is unlikely that designers performed much work that

was not recorded.

The authors calculated the percentage of dependencies captured with a VB algorithm

that matched the input values of uploaded files with the output values of previously uploaded

files. If there was a match and an arrow was drawn, then the dependency was captured. If a

match existed and an arrow was not drawn, then the dependency was not captured. If no

matches were found, the authors manually investigated the dependency. As the authors did not

know what information influenced decision making, false-positives (i.e., cases where a user

draws an arrow without using information from the start node) could not be calculated.


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Each tool contained a Visual Basic (VB) macro recording design options calculated in

each tool. Another VB macro aggregated all the tools uploaded to PIP and calculated the total

number of local iterations per person.

The first author compiled all logs of instant messages and blind coded the chats for the

number of statements about trends and of confusion. An example of a trend statement was

“cement weight has a lot more environmental cost than steel.” A statement of confusion

example was “sorry I was confused, hold on. Let me redo the structure calcs.”

Inconsistency occurred when an input value in one tool did not match the output value

in a previous tool. A VB algorithm checked the consistency between dependent files.

Occasionally, the algorithm incorrectly identified inconsistent variables, and so the first author

manually checked the variables the tool identified as inconsistent. A high percentage of

inconsistent variables could occur because the designers simply copied a number incorrectly

or because they used the wrong precedent file to retrieve an output value

Whereas inconsistency looks at information transfer between any tools, the number of

complete and accurate designs reflected a global iteration that includes an NPV calculation.

The first author emphasized to students that they cannot simply fabricate values. For example,

if a designer inputs Total Energy as 145 MJ/year into the NPV tool, a corresponding energy

analysis tool must have an output of 145 MJ/year. Otherwise the NPV calculation was invalid

and the global iteration was incomplete or inaccurate.

The effectiveness of sharing required an assessment of whether teams chose better

historic projects to mimic. Before looking at what historic projects the teams mimicked, the

authors developed an equation that calculates a score for the process employed in the previous

projects. The score considers number of tools required, number of arrows, rating (1 to 5 stars),

discussion posts, process duration, and times viewed. Looking at the tools used, teams


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frequently combined process modules from more than one previous project, so the authors

calculated the mean process score of projects mimicked.

Once evaluating the effectiveness of the teams’ retrieval of historic projects to mimic,

the authors looked at the efficiency with which the teams applied these historic processes. The

number of missing tools is calculated with respect to the tools required to complete the NPV

calculation. Frequently, designers were confused and wasted time trying tools not necessary

for their NPV calculation, which prompted the calculation of the number of redundant tools

used. The number of non-interoperable tools used reflects pairs of tools that the designer

attempted to use together, but that were incompatible, because the output format of one

variable did not match the input format of the variable in the dependent tool.

Applying these metrics to the charrettes resulted in non-normally distributed data.

Consequently, p-values of counts are calculated using the non-parametric permutation method

(Hothorn et al. 2010). For binary calculations such as whether a global iteration was

completed accurately, the authors used a binary two sample test called the Fisher Test (R

Development Core Team and contributors worldwide). Neither method depends on an

assumed distribution. The authors considered a p-value less than 0.10 as statistically

significant; i.e., the authors are at least 90% confident that DPCM caused the observed

differences between control and experimental teams.

4 Results and Discussion

This section presents results (Table 4-1) according to the four steps required for

communication: (1) Capturing, (2) Structuring, (3) Retrieving, and (4) Using processes.

Capturing and structuring is combined into one activity validated by the charrettes to enable

effective and efficient Collaboration, Sharing, and Understanding. Retrieving and Using is


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also combined and is evaluated within projects, between projects, and across multiple projects.

Senescu et al. (2011b) explains the rationale behind the metrics.

4.1 Design Charrettes for Collaboration and Sharing

The time designers worked on their classroom designs varied between 57 and 67 minutes, with

just a one minute average difference between the four control and four experiment teams. PIP

crashed for experiment team D, so their results are not reported. From California, the authors

also led a charrette with a team of professionals located in Singapore and Australia. Technical

difficulties and a temporarily absent team member limited this session to 30 minutes, so this

session is not included in the results.

4.1.1 Capturing processes effectively and efficiently

DPCM’s ability to communicate relies on accurate process capturing. Individuals employing

DPCM captured 93% of the 132 actual information dependencies between the tools.

Moreover, the same designer was responsible for seven of the nine missing arrows. The

authors interpret this result as evidence for the power of DPCM to capture process effectively.

In conventional practice and in the design charrette teams without DPCM, designers simply

list files with zero capturing of the dependencies between the files.

As discussed in the Introduction and extensively in Senescu et al. (2011b), many

previous DPM research efforts demonstrate the ability to capture process effectively. One of

DPCM’s unique contributions is the efficiency with which process capturing occurs. This

efficiency is important since designers use the information exchange method requiring the

least effort (Ostergaard and Summers 2009). After uploading a file to share with teammates,

the designer effortlessly draws an arrow from the files containing information that the designer

used for the newly shared file. By recording the time between file upload and arrow drawing,

the authors confirm that capturing is trivially efficient. Furthermore, no evidence exists that


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suggests a burden on the teams drawing arrows. Designers employing DPCM exchanged

about the same number of files with others. Designers also iterate more frequently in their

tools and discussed trends more frequently. These results suggest that capturing processes did

not reduce the time spent on value-added production work and thus, DPCM enables not just

effective but also efficient process capture.

4.1.2 Using processes effectively and efficiently to Collaborate within teams

Capturing process effectively and efficiently does not necessarily equate to usefulness. After

all, other DPM research captures the rationale behind decision-making or dependency

relationships at the variable level. DPCM only captures the dependencies between aggregated

groups of variables (i.e., files) and does not require specification of the rationale behind the

process nor the extent or type of dependency. Does DPCM’s limited definition of process

capture still enable effective and efficient use of process for collaboration within projects?

Effective collaboration entails consideration of multi-disciplinary design tradeoffs.

Isolated in discipline-specific silos, designers will iterate to optimize designs only within their

discipline as opposed to working collaboratively to optimize globally. The design charrette

results provide evidence that DPCM positively impacts collaboration, because teams

employing DPCM discussed trends 37% more frequently (p=0.06) and designers accordingly

iterated 60% more (p=0.02). The authors interpret these statistically significant results to mean

that DPCM enables the designers to collaborate around their processes more effectively within

the project team.

The teams with DPCM also collaborated more efficiently. The three teams employing

DPCM completed four accurate classroom designs. The four teams without DPCM completed

zero accurate designs. Both sets of teams worked for about one hour uploading and

downloading files, discussing designs, etc., but the teams without DPCM never completed an


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accurate calculation of their classroom’s NPV based on analysis performed in other tools. This

result is analogous to a professional project team working for three months performing

analysis and discussing designs, but never actually producing drawings that a construction

team could use to build the design. The collaboration was inefficient, because it did not

produce a design.

Two other results support the conclusion that teams with DPCM collaborated more

efficiently. First, teams without DPCM inconsistently transferred information from one tool to

another five times more frequently (p~0). The authors conclude DPCM enabled design teams

to work more consistently with each other. Consistent with this result, designers without

DPCM instant messaged each other expressions of confusion with three times more frequency

(p=0.12). Frequently, these expressions of confusion were followed by rework. Both this

confusion and the related information inconsistency provide additional evidence that teams

collaborate more efficiently when employing DPCM.

4.1.3 Using processes effectively and efficiently to Share between teams

Teams looked at previous projects’ processes to choose tools to use or they chose tools from a

separate frame containing all 20 tools. Effective use of shared processes should result in the

selection of better processes to mimic with DPCM than without. Every time a team attempted

a new NPV calculation (i.e., an attempted global iteration); the authors calculated the score of

the processes they mimicked based on the historic processes. The authors normalized the

scores, so exclusive use of the best historic process resulted in a score of one. The

experimental teams on average chose precedent projects that scored 17% higher than the

control groups. The variation of the experimental teams’ was 0.17 versus 0.06 for the control.

Thus, the teams with DPCM more consistently retrieved processes with higher scores

(p=0.03).


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Once the teams chose precedent projects to mimic, they mixed and matched them and

used them for their classroom design process more efficiently. For example, 20% of the time a

team without DPCM used a tool, the tool performed a redundant calculation to a tool that the

team had already uploaded. Teams with DPCM used redundant tools just 6% of the time,

suggesting that DPCM made the processes from previous projects more transparent, so they

did not waste time on tools they did not need. Also, teams with DPCM never missed a tool nor

used a tool that was not interoperable with another tool. Teams without DPCM missed eight

tools that were necessary for completing a design. For example, one team without DPCM

never performed an energy analysis, suggesting that they had trouble learning from previous

projects, which always included some type of energy analysis tool. 5% of the time, teams

without DPCM chose tools not interoperable with another tool they had chosen. Choosing an

inappropriate tool or missing a tool altogether is a process mistake that detrimentally impacted

the efficiency with which the team used historic processes. Teams with DPCM made six times

fewer process mistakes per tool used (p~0), which the authors interpret as evidence that

DPCM enables teams to efficiently use processes shared between teams.


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Table 4-1. Summary of results from the Mock-Simulation Design Charrette.

Process Communication Metrics No DPCM DPCM p

Capturing Processes

Effectiveness n = # of true dependencies 151 132

% of dependencies captured 0% 93% -

Efficiency

n = # of people 20 15

# of files uploaded per person 4.6 4.8 -

# of local iterations per person 26 43 0.02

# of statements about trends per person 3.3 4.5 0.17

Using Processes

Collaboration within projects

Effectiveness

n = number of teams 4 3

# of statements about trends per team 16.3 22.7 0.06

n = number of people 20 15

# of local iterations per person 27 43 0.02

Efficiency

n = completed global iterations 8 8

# of complete & accurate designs 0 4 0.08

n = total number of variables transferred

367 277

% of inconsistent variables 21% 4% ~0

n = number of people 20 15

# of statements of confusion per person 0.45 0.13 0.12

Sharing processes between projects

Effectiveness

n = attempted global iterations 11 9

mean process score of projects mimicked

0.63 0.74 0.03

Efficiency

n = # of files uploaded 92 72

# of redundant tools used 18 4 -

# of missing tools 8 0 -

# of non-interoperable tools used 5 0 -

# of total process decision mistakes 31 4 ~0

Understanding processes across multiple projects

See Figure 4-5 and Figure 4-6 and related discussion in Section 4.1.4.


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4.1.4 Using processes effectively and efficiently across projects

During the ten-week Multi-Disciplinary Design and Analysis class, 32 students on eight

project teams used PIP to upload 1222 files, and they downloaded files 1939 times (an average

of 60 downloads per designer). They drew 2057 arrows to capture the dependencies between

the files they created. This usage data provides evidence that students used PIP as a primary

file sharing tool on their projects.

DPCM enables effective and efficient understanding of design processes across the

eight projects. Unlike the quantitative evidence provided for collaboration and sharing, this

section provides qualitative evidence by demonstrating that employing DPCM provides

insights into design processes. DPCM enables teams to visualize the degree of information

distribution across a team (Figure 4-5). The darkness of a square in Figure 4-5 (see legend)

indicates the number of arrows drawn between information created by e.g., Jones and

information created by e.g., Smith. The relatively dark diagonal suggests that designers most

frequently depend on information created by themselves. However, some teams depend on

information distributed evenly across the team (top left) whereas others have information that

is more fragmented or concentrated (bottom right). The former suggests that the teams in the

top left have integrated their designs, because generally the designers created information

dependent on other designers. The latter (bottom right) suggest the potential for fragmented

designs, because designers created much information independent of information created by

others on the team. It is important to interpret this graph as indicating only the potential of

integration problems, because it is possible that the difference between the top left and bottom

right reflects the teams on the bottom right prefer communicating verbally, using paper

documents or that the tools they used or projects they work on did not require as much

integration.


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DPCM also enables understanding of how information flows across multiple projects.

For example, three project teams worked on different train stations along the same rail line.

Figure 4-5 shows that one primary liaison exchanged information between these teams.

Though not shown in the figure, DPCM enables the overlay of average information latency

between people where information latency is the difference in time between uploads of

dependent files. Graphing information latency revealed that Metro Station 3 used information

from Metro Station 1, but that the Metro Station 1 team then updated that information. The

Metro Station 3 team never used that updated information. Again, this situation just reveals a

potential problem. Still professional designers frequently create spreadsheets that are then

used on subsequent projects. Years later, construction of the building may reveal an error in a

design calculation that has since propagated to other projects. DPCM enables understanding of

the process by which this information propagates between projects.

As the teams are sorted by project value (normalized final presentation grades

provided a proxy for project value), a trend exists between the degree of information

distribution and the project value delivered. The authors make no claims about the statistical

correlation between higher value and more distributed information in PIP. Rather, Figure 4-5

provides evidence for the power of DPCM to enable managers to answer questions such as:

Does the amount of information distribution across projects correlate with client satisfaction,

project profit, or change orders during the construction phase? Project managers can use

Figure 4-5 as a live project dashboard enabling interpretation of large quantities of blank

squares as potential integration problems. Employing DPCM thus provides effective

understanding of design integration across projects.


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Figure 4-5. Information distribution across projects in a multi-disciplinary design and analysis

class. The shade of each square represents the number of arrows (i.e., dependencies) from

information created by a designer on the top (input) to information created by a designer on

the left (output). Designer names are hidden. A strong single-band diagonal exists from top

left to bottom right because designers depend mostly on information created by themselves. A

wide-band diagonal exists because most designers only depend on information from within

their own team. One exception is the three teams working on different train stations along the

same metro line. The projects are ordered from highest to lowest project value, suggesting that

teams with information distributed across the team delivered more value (top left) than teams

with fragmented and concentrated information (bottom right).

AEC companies consider IT investment to be costly and risky, yet investments

proceed based on “gut feel” without understanding current processes and how the specific

investment will improve them (Marsh and Flanagan 2000). The AEC industry wastes $138


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billion annually due to poor interoperability, but few companies have the tools to understand

their detailed inefficiency problems (Young et al. 2007). Just as Figure 4-5 shows the

frequency of information flow between people, the shade of the squares in Figure 4-6 shows

the frequency of information flow between tools. A global building design firm uses over 200

tools to assess AEC project impact on the environment, so understanding the best tools to use

together is not trivial (Ayaz 2009). From Figure 4-6, a manager can see that across the eight

projects, designers frequently used an AutoCAD file to create a 3D Revit model and on

average about 20 days passed between the uploading of the AutoCAD file and the uploading

of the Revit file. This square represents the process of students frequently acquiring 2D plans

from their professional project mentors, and then, building up 3D models in Revit that they

could feed to other analysis. As this process was frequent and time consuming, a manager

would want to invest in improving this process, perhaps by creating a program that

automatically created 3D models from 2D plans. In practice, a manager could immediately

decide against process investments involving information flows that are not time consuming

or infrequent (small, light squares) and immediately focus attention on improving the

information flows of common, time consuming processes (large, dark squares). Thus, DPCM

enables effective understanding of the latency and frequency of information flows between

tools across multiple projects.


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Figure 4-6. Frequency and upload latency of information flow between different tools across

all projects in a multi-disciplinary design class. The shade of each square represents the

number of arrows (i.e., dependencies) from information created by a tool on the top (input) to

information created by a tool on the left (output). The size of the square represents the average

latency between when an input file is uploaded and an output file is uploaded. The

visualization enables a manager to understand potentially inefficient information flows, so that

he can invest in improved processes. For example, the large dark squares represent

information flows that are both frequent and potentially time consuming.


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5 Discussion of Power and Generality

5.1 Internal Validity

Internal validity is the “extent to which the structure of a research design enables us to draw

unambiguous conclusions from our results” (Vaus 2001). The ability to conclude that the

research design validates DPCM is contingent on an acceptance that PIP accurately models the

abstract DPCM. The features in PIP map directly to DPCM, but it is possible other researchers

could develop different technical instantiations of DPCM. As discussed more thoroughly in

Senescu et al. (2011b), the authors mapped DPCM to PIP using the Agile Software

Development (Cohn 2004). After PIP became usable, the authors shifted away from the Agile

Development Method toward the Ethnographic-Action Method (Hartmann et al. 2009). For

example, at first the Node element in DPCM included an attribute containing an image of the

information referenced by the node, but this feature was never requested by students, so the

authors iterated and removed this attribute from DPCM. Also, students did not insist on

automation between nodes, and so the Computable characteristic was deprioritized. Students

did require the notion of a “home frame” where students could personalize their views onto

different project frames. The authors iterated to find that this notion was consistent with HCI

literature emphasizing the importance of personalizing visualizations of information and so,

the attribute is included in DPCM. This iterative research process bound the abstract

methodology (DPCM) with the usable technological model (PIP) and ensured that test results

from PIP apply to DPCM.

This PIP-DPCM coupling enables overall conclusions to be made about DPCM, but

not granular conclusions about the relative importance of individual Characteristics, elements,

and methods. This paper does not provide evidence that PIP is transparent, social, scalable,

embedded, shared, computable, and modular, nor that the elements and methods enable these


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Characteristics (see Senescu et al. (2011b)). Rather, this paper provides evidence that

collectively, elements and methods aimed at enabling these Characteristics results in effective

and efficient communication. A more exhaustive literature review and application of the

Ethnographic-Action method to practice would likely reveal more Characteristics, elements,

and methods that may result in even greater effectiveness and efficiency. And similarly, it is

possible DPCM includes some components that contribute only marginally to effectiveness

and efficiency. For example, few students utilized the method for searching dependencies, so

the validation provides no evidence that this particular method is necessary for process

communication. However, Senescu et al. (2011b) provides theoretical justification for DPCM

in its entirety, and this paper validates that DPCM does enable effective and efficient

communication – a theoretical contribution to PIM and DPM and a practical contribution to

industry.

Another potential limitation to the power of the results lie in the internal validity of

the charrette method due to the limitations in eliminating “demand characteristics” –

indications to participants about the researchers’ hypothesis. It is possible that Control and

Experimental teams knew of each other and consequently tried to act like “good subjects”

(Goodwin 2009). Previous attempts to change the charrette to a within-subject design failed

both because participants were unwilling to design the classroom twice and it was difficult to

nullify the impacts of learning. Though qualitative and subjective, observations of chats and

the general morale of the teams suggested that the challenge of the design task and incentive

to win the prize were sufficient to ameliorate the desire on the part of the students to appease

the authors’ expected outcome. This conclusion is consistent with a finding that found that the

desire to perform well when evaluated by peers (it was clear to students that results would be

made public) overpowers the desire to confirm the hypothesis of the researcher (Rosnow et al.

1973).


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Experimenter bias may also have impacted results. The authors tried to minimize their

impacts on the designers’ performance by using a scripted presentation for each group.

Furthermore, many of the results had p-values below 0.05 combined with differences between

control and experiment of greater than 50%. It is unlikely that individuals desire to appease

the researcher or experimenter bias could account for such dramatic differences. A claim for

internal validity is further enhanced by the repeatability of the charrettes.1

5.2 External Validity

External validity is the “extent to which results from a study can be generalized beyond the

particular study” (Vaus 2001). Two main features of the validation method limit the extent to

which conclusions can be generalized. First, the charrettes and the class projects are merely

models for the complexity of real project environments. Second, the participants are students

as opposed to professionals. In both cases, the emphasis on coordination work reduces the

limitations of the validation methods. While the production tasks performed individually differ

greatly from practice, the patterns of information exchange in the class and in the charrette do

not appear to differ from industry projects. Furthermore, a similar application of the charrette

method by Clevenger (2010) showed that the experience and skills of professionals did not

influence their ability to assess the relative impact of different variables on energy

performance. In fact, the professionals in that study could not identify important variables for

energy performance with any more accuracy than random guessing. Thus, it is not surprising

that the authors found that improved collaboration and sharing did not lead to a building with

higher NPV’s. This lack of correlation between process effectiveness and product outcome is

consistent with the other findings from other researchers’ charrettes. For example, when asked

1 Researchers can visit http://www.cafecollab.com, register, and view the class project and charrette results and download the charrette setup and repeat the experiments presented in this paper.


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to select the type of variables (e.g., window area, building geometry, etc.) with the greatest

impact on energy efficiency, professional responses approached random guessing immediately

after they chose the most impactful variable (Clevenger 2010). Similarly, a charrette involving

structural designs found that student participants’ ability to pick optimal designs drops from

96% of optimal when deciding between two variables to 76% for six variables (Flager 2011).

The MSDC required decisions for several dozen variables (the amount varied with the process

chosen), suggesting that any charrette results showing near optimal solutions were probably

due to randomness and not skill. This suggestion does not imply that professional designers

make random design decisions, but that the charrette isolates coordination work from

production work, mitigating the relevance of design decisions. The charrette intends to

measure how designers exchange information, not the quality of the information they

exchange. Thus, the authors only claim that DPCM enables effective process communication,

not necessarily improved product outcomes.

While the authors did not specifically measure collaboration in the student class

projects, the students clearly adopted PIP for collaboration. This adoption demonstrates that

the methodology could be applied in practice, even if the authors cannot claim that

professional projects would see the same dramatic differences between conventional

information management methods and DPCM. The class projects do not demonstrate adoption

of DPCM for sharing across projects. Also, it is beyond the scope of this proposal to measure

the impact of process understanding on investment decisions on process improvement.

5.3 Future Work

Despite the qualifications attached to the external validity, the power of the findings presents a

strong case for future work to make more generalized conclusions. The repeatability of the

charrettes enables future researchers to compare DPCM with other DPM methods while the


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accessibility of PIP enables its application to future industry case studies to compare with

other PIM research.

This research provides two additional opportunities. First, the application of DPCM to

AEC education and research may enable students and researchers to learn more from each

other when using PIP. Second, DPCM enables the replacement or supplementation of

ethnographic research methods used to study the process of design. Just as social and

professional networking sites have revolutionized the ability of organizational scientists to

apply social network analysis to modern communities, a tool such as PIP enables design

researchers to capture the social interactions and information relationships and apply social

network analysis algorithms to this data. This paper presents a tiny fraction of the results from

the 120,000 actions recorded by 387 different user names in PIP between April 2009 and April

2011. Much opportunity exists to use this data to study how teams exchange information.

6 Conclusion

Architecture, Engineering, and Construction designers (1) struggle to collaborate within

projects, (2) share better processes between projects, and (3) understand processes across

projects to strategically invest in improvement. Overcoming each challenge requires

communication of design processes. The Design Process Communication Methodology

(DPCM) enables effective and efficient design process communication. To test DPCM, the

research maps the methodology to software features in the Process Integration Platform (PIP).

PIP is a web tool enabling project teams to organize and share files as nodes in an information

dependency map that emerges as they work. This paper contributed a set of metrics and an

accompanying test method to measure the impact of DPCM on the effectiveness and

efficiency of four steps required for process communication: (1) Capturing, (2) Structuring,

(3) Retrieving, and (4) Using processes. The authors measure effectiveness and efficiency


141

using PIP and contrast these measurements with results from a conventional information

management method that does not show the dependencies between information.

Using PIP in the Mock-Simulation Design Charrette (MSDC), the authors conclude

that DPCM captures and structures processes effectively, because the student designers

capture 93% of the true information dependencies in the controlled design experiment. The

capturing and structuring is efficient, because it places no measurable burden on the design

teams

DPCM had a statistically significant impact on the number of iterations performed by

designers and the frequency of discussion of multi-disciplinary trends. The design teams

without DPCM did not complete any accurate designs, whereas teams with DPCM completed

four accurate designs. These results provide evidence for the effectiveness and efficiency with

which DPCM enables collaboration within teams.

To select a process, the student design teams viewed the information created by

historic projects. When viewing historic projects employing DPCM, teams selected better

projects to mimic. Once they selected a project, teams with DPCM used the newly shared

processes more efficiently, because they committed few process mistakes. Teams with DPCM

shared processes between teams effectively and efficiently.

DPCM enables the understanding of processes across projects, which provide insights

into: the relationship between information distribution among designers and project

performance; and opportunities for investment in improved information flows.

These results demonstrate the power of DPCM to effectively and efficiently

communicate design processes within projects, between projects, and across projects. DPCM

contributes to filling a gap between two research fields: (1) Project information management

research enables the efficient exchange of information, but does not effectively communicate


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process; (2) Design process management research effectively communicates processes, but

with methods too inefficient to be adopted in practice. DPCM lays the foundation for

commercial software that shifts focus away from incremental and fragmented process

improvement toward a platform that nurtures emergence of (1) improved multi-disciplinary

collaboration, (2) process knowledge sharing, and (3) innovation-enabling understanding of

existing processes.

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Appendix A Reid Robert Senescu

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Appendix A: Mock-Simulation Design Charrette Instructions

To ensure uniformity between all the student design teams participating in the charrette, I

followed the following script at the beginning of each charrette:

1. Open PowerPoint to “You Can Start This Now” slide. 2. Sign into PIP. 3. Check that everyone has:

a. Windows b. Microsoft Excel c. Firefox ver. 3.5 or higher

4. Check that everyone: a. Enabled Macros in Excel b. Enabled Popups in Firefox: Tool, Options, Content, uncheck Block Pop-up

Windows 5. Tell students to sign in to Gmail, so they can use Gchat.

a. Confirm everyone accepted your invitation b. Include them in one conversation and send test message.

6. Go through slides 1-9 7. Go through each step of the instructions. 8. Note that they can search and navigate via hierarchy 9. Remind them that to see others work they just need to hit refresh 10. Tutorial

a. Show the tools they can choose from b. Show existing projects and the TeamXBoeing797 project

i. Show how they can see comments on projects ii. Boeing 727 would be a good project to learn from

c. Choose Boeing727 to follow. d. Show that there are results from design competition e. Open up 012-Aerodynamics-01.xls from Boeing 727 f. Change values and save to desktop g. Upload to pip h. Show that you can also get tools from Tools folder i. Goto Tools and get structural file j. Show that they need to meet D/C ratio k. Save to desktop, emphasize file name convention with iteration l. Jump ahead to NPV in Boeing 727 project m. Show how cost estimator can provide feedback to the rest of the team n. Explain that they can iterate in big global iterations like in the example, or if

they want, they can iterate between two tools, but they need to number their iterations in the file name.

11. Slides 13-15. Charrette Instructions and Rules 12. Kickoff meeting


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I presented the following slides to each team:


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151


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After the kickoff meeting, the students returned to their individual computers and began

designing the classroom.

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