Concurrent Engineering http://cer.sagepub.com
Resistance Based Modeling of Collaborative Design Karen J. Ostergaard and Joshua D. Summers Concurrent Engineering 2007; 15; 21 DOI: 10.1177/1063293X07076273 The online version of this article can be found at: http://cer.sagepub.com/cgi/content/abstract/15/1/21
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CONCURRENT ENGINEERING: Research and Applications
Resistance Based Modeling of Collaborative Design Karen J. Ostergaard and Joshua D. Summers* Department of Mechanical Engineering, Clemson University, Clemson, SC 29634-0921, USA Abstract: This study presents a new model for collaborative design that is analogous to electrical circuits with current (rate of design artifact synthesis and analysis), voltage (knowledge that drives the design process), and resistance (barriers to the exchange of design information). An electric analogy is used to capture the flow of information throughout the design process, it maps well to existing models of collaboration, and it provides a mechanism to capture resistances to flow. Specifically, the resistances are identified from a collaborative design taxonomy. Currently, these resistances are qualitatively captured based upon empirical evidence, but they may in the future be calibrated with additional experimental investigations. The model is illustrated through a simple example. Extensions and an assessment of the model are provided. Key Words: collaborative design model, engineering design, electric analogy, concurrent engineering.
1. Introduction Companies in a wide range of industries are finding that success in the modern marketplace requires effective competition in global markets with reduced cost and lead-time [1–4]. The concept of collaborative design has emerged both as an effect of globalization and as a prospective tool for enabling this new engineering design approach [5–7]. Collaborative design is defined as an interactive team structure composed of actors, both humans and artificial reasoning systems, working to achieve a common design goal via shared ideas, expertise, responsibilities, and/or resources. Despite the ongoing research in collaboration, the true opportunities and limitations are not well understood, and therefore the actual gains of applying collaborative design are not clear. Models may be used to facilitate improvements of the collaborative design process by describing or predicting the scenario of design only when the models themselves are fully understood [8]. This study intends to increase this understanding and promote development of tools or methods to aid collaborative design. Briefly, a collaborative design taxonomy is presented and general models of
*Author to whom correspondence should be addressed. E-mail:
[email protected] Figures 2 and 8 appear in color online: http://cer.sagepub.com
collaboration are reviewed. A taxonomy of collaborative design issues has been developed to classify factors that impact the collaborative design process [9–11]. The taxonomy, which is characterized by an orthogonal organization of issues and by spanning the potential space of collaborative design, is used as a guide in evaluating whether collaborative design models are comprehensive. Further, the classification of factors is a useful aid in planning studies of the design process as it highlights variables that should be controlled in such studies. A review of actor based, task based, and information based collaborative design models is presented, highlighting the current understanding of collaboration. Finally, a new model of collaborative design is proposed and illustrated through a simple example. A novel collaborative design model is presented, developed as a natural progression from the taxonomy, with additional influence from the review of existing models. The structure of the model is based on an analogy to electrical circuits with a focus on the relationship between information flow and resistance to that flow in collaborative engineering design. This helps to clearly highlight areas of resistance; areas where one would expect to find the greatest need for collaborative design tool development. This new model should facilitate a more efficient application of collaborative design. An application of the model is presented to a design case to clarify the model and demonstrate its relevance.
Volume 15 Number 1 March 2007 1063-293X/07/01 0021–12 $10.00/0 DOI: 10.1177/1063293X07076273 Downloaded from http://cer.sagepub.com at PENNSYLVANIA STATE UNIV on April 16, 2008 ß 2007 SAGE Publications © 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
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2. Background One view of collaborative design is based upon the flow and accumulation of information. In order to explain collaborative design, information flow may be analyzed and the factors that introduce resistance to the process identified. A first step to create this understanding of information flow was the development of the taxonomy to classify issues affecting collaborative design [10]. That research and the work presented in this article to construct an associated collaborative design model are directed toward eventual development of agentbased collaborative support tools structured upon a fundamental understanding of the collaborative process. This section introduces the collaborative design taxonomy, discusses models of collaboration, and provides a view of information flow in collaboration. 2.1 Collaborative Design Taxonomy The development of a collaborative design taxonomy is believed to aid in the identification and organization of collaborative design issues. The taxonomy is used to illustrate and extract the relationships and dependencies among the factors, such as in developing experimental user studies of the collaborative design process. While some research efforts have been made to organize particular issues in collaborative design, such as the classification of conflicts [12], a thorough organization of issues in collaborative design does not appear to exist in the literature. Taxonomies have also been applied in general engineering design research, such as for engineering decision support systems [13], mechanical design problems [14], idea generation methods [15], and design requirements [16]. Six top-level attributes of collaboration are identified from the literature, thus composing the top level of the taxonomy: team composition, communication, distribution, design approach, information, and nature of the problem. This taxonomy and associated references are illustrated in Table 1. The attributes are further sub-divided based on the established literature. The full taxonomy includes six primary-level attributes, twenty-seven secondary-level attributes, twenty-eight tertiary-level attributes, and forty-four evaluatable taxons. This taxonomy is illustrated here in outline format. The base leaf taxons are found in parentheses. These terminating taxons represent the lowest developed level in the taxonomy. It is at this level that a collaborative design situation may be described with specifying values for all the taxons. 2.2 Models of Collaboration Collaborative design models to date have typically focused on the development of actor centered models
AND
J. D. SUMMERS
Table 1. Collaborative design taxonomy [10]. Team Composition Group Size (number) Culture (type) Individual Personality (type) Expertise (level) Team member relations ( positive, neutral, negative) Leadership styles (autocratic, consultative, collective, participative, leaderless) Nature of the Problem Type (novel, routine) Concurrency (serial, parallel) Coupling (level) Abstraction (level) Scope (number of domains) Complexity (level) Information Form (design artifact, background) Management Ownership (set, get, change, validate, inherit) Permission to change (set, get, change, validate, inherit) Security (set, get, change, validate, inherit) Change propagation (set, get, change, validate, inherit) Perceived level of criticality (level) Dependability Reliability (level) Completeness (level) Communication Mode Verbal (Boolean) Written (Boolean) Graphic (Boolean) Gestures (Boolean) Quantity Frequency (number) Duration (time) Syntax Language (type) Translators (number) Proficiency of the team Techniques (level) Technology (level) Dependability of resources Reliability (level) Availability (level) Intent (inform, commit, guide, request, express, decide, propose, respond, record) Distribution People Geographically (co-located, distributed) Organizationally (within boundaries, outside boundaries) Temporally (same time zone, different time zones) Information Geographically (co-located, distributed) Organizationally (within boundaries, outside boundaries) Temporally (same time zone, different time zones) Design Approach Design tools (consistently applied, applied occasionally, not applied) Evaluation of progress (self-assessment, assessed by outside parties) Degree of structure (company policy, chosen by team, free) Process approach (generative, variant) Stage (clarification of task, conceptual, embodiment, detail)
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Resistance based Modeling of Collaborative Design
and computer support systems [17–21]. Computer actors, or agents, are defined as autonomous programs that are reactive, collaborative, social, and/or mobile [22]. Actor-centered models focus on the participants in collaborative design. Others have modeled collaborative design with a focus on the process [23–26]. These models are typically event-based with an emphasis on decisionmaking. A third approach is to model the information in collaborative design, focusing on the exchange, interpretation, and collection of information. This often leads to a focus on design information as it flows through the process [27–30]. The main reason for developing general design models is to improve the design process via explanations and/or predictions. The goal of this new collaborative design model is to explain the flow of information such that components that introduce or impact resistance are identifiable in a manner that is comprehensive (captures all relevant aspects), easy to understand, and provides support to manage, predict, and analyze collaborative processes. An ideal, functional collaborative design model should serve to explain the process and facilitate a more efficient application of collaborative design [31]. Since models allow complex systems to be understood and their behavior predicted within the scope of the model, a comprehensive and intuitive collaborative design model should enable a more efficient application of collaborative design. Specifically, a model is a description of a system that accounts for its known or inferred properties and may be used for further study of its characteristics [32]. Depending on its intended purpose, a model may be descriptive (describing reality) or prescriptive (suggesting approaches). Models may be characterized by their vocabulary, or the expressiveness; the degree to which the model describes the specified system.
The correctness or validity of the representation provided by a model is also important. The observed or experimental underpinnings of empirical models may make them more readily accepted in practice than theoretical models [33]. Both empirical and theoretical models require validation. The utility of collaborative design models may be impacted by these characteristics. 2.3
Information Flow
The collaborative design model presented here has been developed as a natural progression from the taxonomy with additional influence from related research. Refer to Figure 1 for an example of data flow in the analysis portion of a design in Boeing’s integrated product design environment (IPDE) [34]. Along these paths, the information flow may encounter resistance. For instance, when the information describing the aircraft geometry is passed from the CATIA and the loftsman to the Boening ‘Paver’ system to refine the surface, some information is lost in the translation. Additional types of resistance in this exchange could include bandwidth transmission issues, scheduling problems of computational tasks, coordination between design departments, or uncertainty with respect to who might be responsible for ultimate decision making on the geometry (e.g., does the loftsman, fluid dynamist, or the structure analyst act as the final arbitrator in disputes?). In another application the fundamental premise is that people receive, organize, create, and structure information concurrently and over time [35]. Davis et al. have conducted information flow studies in an effort to uncover the different modes of coordination and information exchange and to identify the causes and consequences of information flow breakdowns. The resulting information flow map is illustrated in Figure 2. In this model, it is clear that different
A
B
Figure 1. Information flow in a portion of Boeing’s IPDE [34]. Downloaded from http://cer.sagepub.com at PENNSYLVANIA STATE UNIV on April 16, 2008 © 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
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Figure 2. Information flow map [35].
stakeholders, such as the R&D Board (upper lefthand corner) or Engineering Services Support (center) each generate and share different types of information such as technical data results and manufacturing documents. Each of these documents, or pieces of information, is shared with other groups which, in turn, generate additional documentation. The information flow in this model can be serial, parallel, or cyclic. Furthermore, with each exchange or generation of information, there may be different issues that retard the process, such as scheduling problems within the support and analysis groups or conflicts between objectives between marketing and quality assurance. The information network can also be affected by updates to information packets which have been previously used to make decisions which should be revisited. However, these updates may not be broadcast to all affected parties. These are just a few illustrations of different types of resistances that may be encountered in this model of the information flow in a collaborative design situation. Considering these basic concepts of information flow, it is believed that it is constructive to illustrate the collaborative design process using analogy to
electrical circuits. The use of this analogy should clearly highlight areas of resistance, enable analysis and planning of collaborative design, and highlight where the greatest need for collaborative design tools exists.
3. Resistance Model This section introduces the proposed model for collaboration. The structure and components required for extending the ‘information flow’ analogy to electrical circuit modeling is discussed. This is followed by an explanation of the relationships in the model. Supplemental concepts, such as capacitance, switches, and voltage and current sources follow. Finally, aspects that require further investigation, such as the interdependence of resistances analogous to resistances of alloys, are identified. 3.1 Structure and Components The concepts of passive and active knowledge supplement the taxonomy to facilitate synthesis of a fully descriptive model of the collaborative
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Resistance based Modeling of Collaborative Design
design process. Active knowledge, also called design data or foreground information, is explicit design knowledge that captures the values of all attributes of design objects. Active knowledge is embodied in design artifact representations. Product design specifications, CAD models, and bills of material are examples of active knowledge. Passive knowledge, also referred to as design knowledge or background information, is expertise that enables the creation and manipulation of active knowledge. Examples of passive knowledge include designer experience, books or other resources, and procedures. Some simple definitions of electrical components are useful for introducing the analogy to information flow in collaborative design [36]. Refer to the full text for a more thorough explanation of electrical circuitry. The fundamental electric quantity is charge (q). Current (I ) is the rate of flow of charge passing through a region of space. The work per unit charge, or effort, associated with the motion of charge between two points is voltage (V ). When electric current flows through circuit elements, it encounters a certain amount of resistance (R). The level of resistance depends on the properties of the resistor material. Current flowing through a resistance element will cause energy to be dissipated in the form of heat. Ohm’s law, shown in Equation 1, relates these quantities. Conductance (G) is the inverse of resistance, and Ohm’s law can be restated as shown in Equation 2. V¼I R I ¼ G V:
active knowledge. The level of resistance depends on the properties of the collaborative design factor. The structure of this model provides users with a familiar representation that has significant inferential value. The equivalent electrical component for each taxonomy component can be derived directly from the taxonomy structure and supporting literature [9,10]. Quantifiable measures of the electrical properties may be obtained through empirical studies aimed at determining the effect of each component on the transfer of passive knowledge to active knowledge. The taxonomy serves as an initial guide in designing these studies. The electrical circuit analogy may be extended to the additive properties of resistors and conductors in parallel or series. These properties are briefly reviewed here. Consider Kirchoff’s rule of conservation of charge. When current encounters resistors in series, the same current flows through each resistor while the voltage drop is proportional to the equivalent resistance. In electrical circuits, the voltage drop represents the energy required to move charge from one point to another. This does not indicate a consumption of voltage. Therefore, a voltage drop in terms of the collaborative design model does not mean that passive knowledge is consumed. Rather, it is a measure of the required passive knowledge. In series, the equivalent resistance is the sum of the individual resistances, given by Equation 3. Note that this means that the equivalent resistance of a series connection is always greater than any individual resistance.
ð1Þ ð2Þ
Considering these definitions in the context of the collaborative design process, the following analogous relationships are proposed. Accumulation of active knowledge, such as product specifications or CAD models, can be represented as charge. This accumulation represents the design artifact as a collection of model instances. Current, then, is the rate of accumulation of active knowledge. This represents the development and/or changes in view of the design artifact, such as building product specifications, creating a CAD model, or creating a new view of data for use by participants in the design with differing needs (creating a bill of material from a CAD assembly changes the view from the designer’s view to the planner’s view). Voltage is the effort that drives the current flow. Thus, passive knowledge may be represented as voltage since it enables the creation and manipulation of active knowledge. For example, experience with past design cases enables the creation of specifications for a variant design. Collaborative design factors, as outlined in the taxonomy, act as elements of resistance, or possibly conductance, by influencing the rate accumulation of
Req ¼
n X
Ri :
ð3Þ
i¼1
When resistors are connected in parallel, there is an equal potential difference across each resistor. The current is then equal to V/Req where equivalent resistance is defined in Equation 4. In this case, Req is always smaller than the smallest individual resistance. These properties will be applied to collaborative design in the following section. n X 1 1 ¼ : Req Rn i¼1
3.2
ð4Þ
Explanation of Relationships in Model
The lowest-level factors, or leaves, detailed in the taxonomy characterize the resistance encountered by each task in a collaborative design project. The actual values of these resistances are not currently known, but may be derived from empirical studies. The resistances composing a single task are considered to be encountered in parallel since the same voltage is
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Completeness of Information
High
Figure 3. Resistances combined in parallel to describe a single task.
"
Rtask
N X 1 ¼ Ri i¼1
Low
Low
High Resistance
Figure 4. Estimated resistance related to completeness of information.
Dictatorial Leadership style
applied across each of the resistors (Figure 3). That is, the same level of passive knowledge is available for handling each collaborative design resistance in a given task. For clarification, consider the design task: create product design specifications (PDS). This task is characterized by a particular level of each of the issues listed in the taxonomy (for the given task, the team has a particular level of team member relations, the design problem is either novel or routine, the team has a particular distribution, etc.). A single body passive knowledge, or the team’s collective body of experience, resources, etc., is utilized in the creation of the PDS. Equation 5 defines the equivalent resistance for a single task, Rtask, such that all factors in the taxonomy with a resistance value are represented in the equivalent resistance.
Representative
Anarchic
#1
Low
:
High
ð5Þ
In Equation 5, i denotes the factor in the taxonomy (e.g., individual personality, group culture, personnel distribution). N denotes the total number of resistance elements from the taxonomy that are valid for a particular design task. For any design task, N will be less than seventy-seven, the total number of lowest level factors in the taxonomy. For example, the taxonomy branch of information/perceived level of criticality contains leaves: high, medium, and low. The resistance introduced by only one of these factors is included in the equivalent resistance for a particular task. While the actual resistance values have not been identified, the resistance to the accumulation of active knowledge introduced by a task of high complexity is predicted to be greater than the resistance introduced by a less complex task. Examples of expected relationships are given in Figures 4 and 5. As the completeness of information used in the design increases, the design model begins convergence to a single solution. Figure 4 shows this relationship of inverse proportionality between resistance and completeness of information. Relative to the leadership styles [22], the information flow is expected to be less restricted when a single actor is responsible for each stage of the design process than when multiple actors are responsible. Figure 5 shows this approximated relationship. Each sequence of tasks may be described as series or parallel according to synchronicity. Resistors in series, as shown in Figure 6, represent tasks that are conducted
Resistance Figure 5. Estimated resistance related to leadership style.
I a
Rtask1
Rtask2
b
Figure 6. Tasks conducted in series.
in sequence. Brainstorming design concepts and then evaluating those concepts are examples of series tasks. Equation 6 gives the equivalent resistance. Req ¼ Rtask1 þ Rtask2 :
ð6Þ
The total ‘voltage drop’ is proportional to the sum of Rtask1 and Rtask2 while current is unchanged. This means that a quantity of passive knowledge (V ¼ I Req) is required to increase the active knowledge via tasks 1 and 2. Consider the example of building a product design specification given earlier. To complete this task, a certain level of passive knowledge is required. This may include knowledge about the methodology to build a PDS, experience with similar designs, formulae related to mechanical properties of the design, and/or other knowledge. The voltage drop quantifies this level of passive knowledge. According to Kirchoff’s conservation laws, the design space, or the inclusive design environment, is unchanged from a to b (Figure 6). A change in the design view, or representation of the design, is achieved through the accumulation of active knowledge.
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Resistance based Modeling of Collaborative Design Rtask1
Table 2. Resistance interaction in violation of Kirchoff’s law.
I1 Rtask2
I2
I
a
R ( )
b
Figure 7. Tasks conducted in parallel.
Resistors in parallel, as shown in Figure 7, represent tasks that are conducted concurrently, such as synchronous design of various components of an assembly or simultaneous detailed design and finite element analysis of a component. Equation 7 gives the equivalent resistance. Req ¼
1 Rtask1
þ
RInfo
RSeries
RParallel
RActual
2.0
5.0
7.0
1.4
3.3
Note that resistance values given in this table are theoretical. These values should be determined experimentally.
Rtask3
I3
RDistr
1 Rtask2
þ
1 Rtask3
1 :
ð7Þ
With parallel resistors in an electrical circuit, the current flow along each path is reduced while the voltage drop is the same across each resistor. The charge will take the path of least resistance. Accordingly, a voltage drop in the model represents the level of passive knowledge required to overcome the resistive collaborative design factor and provide the change in view of the design artifact (accumulation of active knowledge). The rate of accumulation of active knowledge in each task is reduced by a factor of Req.
In order to describe feedback loops of information in the collaborative design process, switches may be needed. Switches may be voltage controlled, current controlled, or manually controlled. These components may be used to direct current through particular junctions and along particular paths. Similarly, switches may direct the information flow to certain tasks in the design process. That is, the passive knowledge, flow of active knowledge, or an outside source (manager or customer) can alter the path of information flow. Voltage or current sources may also be useful in describing a collaborative design process. Voltage sources may be used to represent distinct bodies of passive knowledge introduced to the process. The source, such as network resources or designer training, would provide a prescribed level of passive knowledge, irrespective of the information flow in the process. The rate of accumulation of active knowledge would still be dependent on the circuit components, such as resistors, in the process. A current source provides a prescribed current to any circuit connected to it. This may be used to represent the introduction of elements of active knowledge not directly developed via the collaborative design process being modeled. 3.4
Areas Requiring Further Investigation
3.3 Additional Concepts While the components of voltage, current, and resistance may be sufficient to describe most collaborative design processes, other concepts from electrical circuits may also be useful. Capacitance is a mechanism for energy storage in electrical circuits. By storing electric potential energy, a capacitor can compensate for irregularities in voltage. A capacitor quickly loses its charge, though, so it can supply voltage for only a limited time. This component may be applied to the collaborative design model in cases where the level of passive knowledge varies with time but an interruption in flow is undesirable. A charged capacitor may compensate for these irregularities for a short time without interrupting the accumulation of active knowledge. For example, network resources may be unreliable. A possible application of a capacitor in the model may be a backup of information saved to the network. The analogous capacitance of this capacitor is determined by the frequency of archival.
In future work, the behavior of interacting resistances should be studied to verify that the assumptions made in the behavior of the model are correct. Consider the interaction of resistances within a single task. It may not be accurate to represent these factors (e.g., personnel distribution and incomplete information) as separate resistances, combined in parallel. Instead the mixture of these resistances may possess a new resistivity not clearly defined by Kirchoff’s laws. Table 2 summarizes a possible combination of resistances. In this example, the actual resistance is not a simple combination of resistances in series or parallel. Instead, the resistances combine in parallel with an additional component of resistance based on the interaction as shown in Equation 8). Ractual ¼
RDist: RInfo þ RInteraction : RDist: þ RInfo
ð8Þ
This may be clarified by considering the electrical resistivity of alloys. Table 3 describes the resistive
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Table 3. Resistivity combination of brass. Resistivity : Cu Zn Brass
1.70E–6 6.24E–6 3.92E–6
Weighted Weighted parallel series Parallel 1.83E–6
53.2
2.15E–6
1.34E–6
Percent error (%) 45.1 65.9
Series
Tasks design
7.94E–6
1 1.1 1.2 1.3 2 3 3.1 3.2 4 5 5.1 5.2 6 7 8 8.1 8.2 8.3 9 10 11 12 13
102.6
Resistivity (W-m)
5
Cu Zn Composition
J. D. SUMMERS
Table 4. Tasks in the collaborative design of a ceiling mounted retractable bed.
10
100% 0%
AND
0% 100%
Figure 8. Relationship between material composition and resistivity.
properties of brass and its components (90% copper, 10% zinc) [37]. The resistivity of brass is not equal to a parallel or series combination of the resistivity of copper and zinc. Figure 8 shows a possible relationship between material composition and resistivity given the resistivity of copper, zinc, and brass. Based on Equation 8, the resistivity of brass may be described by Equation 9. Experimental investigations may show that interaction of some resistances in the taxonomy cannot be characterized by the linear properties and related laws of ideal electrical elements used in this model.
Problem definition Presentation of problem by customer Product design specification Customer approval of problem Peer review Benchmarking of technologies Patent search Available technologies Peer review Market investigation Interview loft owners Interview real estate agents Peer review Loft frame build Prototype build Component sizing and design Bill of materials (prototype) Fabrication Peer review Preliminary design review Manufacturability analysis Testing and refinement Submission of design
Rtask
Rank
0.0351 0.0349 0.0305 0.0377
10 11 16 2
0.0330 0.0308 0.0364
12 14 7
0.0373 0.0366 0.0357 0.0279
3 5 8 17
0.0279 0.0278 0.0308 0.0368 0.0420 0.0309 0.0352 0.0365
17 19 14 4 1 13 9 6
in a class project based on changes in the collaborative design structure for different tasks. This theoretical model is compared qualitatively and post-hoc to the actual experience that the students had. This application illustrates how the model generally may be used to predict some resistance in information flow while also highlighting the current limitations of this modeling approach. This application is admittedly limited in scope with additional validation in industry oriented scenarios required. 4.1 Theoretical Resistances
actual
0:9Cu 0:1Zn ¼ þ interaction : 0:9Cu þ 0:1Zn
ð9Þ
Future work should include experimental studies to determine resistance values for each factor in the collaborative design taxonomy. With these values known, the proposed model may be applied to realworld design cases in order to test the model. The model may be applied to design cases without these values, though, using estimated resistances. An example of such an application with estimated resistances is presented in the following section.
4. Application of Model This model is used to highlight where there should be resistance to the product development process found
The design goal is to develop further a preliminary concept of a ceiling mounted retractable bed that has been patented by the sponsoring customer. In this project, several tasks are identified and their resulting ‘resistances’ are speculated. It is important to note that the exact values for these resistances are not known and can only be derived through extensive experimentation of the interactions between the identified collaborative design taxons. The tasks for this project are listed in Table 4. The tasks are associated with a set of theoretical resistances. These resistances are determined based on the different collaborative design scenarios for each task. For example, in task 1.1, ‘presentation of problem by customer’, the complete team was present, increasing the resistance according to this element in the collaborative design taxonomy, while the stage of design (problem definition) has a lower resistance as there is more
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Resistance based Modeling of Collaborative Design
freedom for the collaborative design team to make changes and gain understanding of the design problem. Table 5 illustrates a comprehensive break down of several different tasks’ resistances. In order to determine the combined resistance for each collaborative scenario, each taxon is analyzed with respect to the scenario and a judgment is made based on literature and experience with respect to what level of resistance that taxon value might contribute to the resistance against the accumulation of active knowledge during the examined task. Each taxon is assigned a qualitative resistance value
(1 ¼ low; 2 ¼ medium; 3 ¼ high). For example, in the peer reviews (tasks 4 and 9), the group size is 14 (six students on this design project, six students from a second design project, one graduate design coach, and one faculty member). When compared with interviewing the loft owners where only two students are involved, the peer reviews, based on size, should provide a larger resistance to the design process. The highest calculated resistances are for the preliminary design review (task 10), the first peer review (task 2), and interviewing loft owners (task 5.1). The lowest rated design tasks
Table 5. Resistances for each collaborative design taxon for different tasks.
Team composition
Group Individual
Communication
Distribution
Design approach
Information
Nature of problem
Size Culture Personality Expertise
Team relations Leadership Mode Verbal Written Graphic Gestures Quantity Frequency Duration Syntax Common language Proficiency Techniques Technology Dependability Reliability Availability Intent Inform Request Decide Propose Record People Co-located Geo. distributed Information Co-located Tools Not applied Evaluation Self-assessment Structure Company policy Process Variant Stage Clarification of task Form Design artifact Management Ownership Change permission Security Change propagation Criticality Medium Dependability Reliability Completeness Type Routine Concurrency Parallel Coupling Loose Abstraction Concrete Scope Multi-domain Complexity Low
Presentation of problem by customer (1.1)
Peer review (4)
Interview loft owners (5.1)
Component sizing and design (8.1)
Preliminary design review (10)
1 1 1 1 1 1 3 3 1 3 3 1 3 1 1 3 3 1 1 3 3 3 2 3 2 3 1 3 1 3 3 3 3 1 3 3 1 3 1 3 1 1 1 2 0.0351
3 3 3 3 3 2 3 2 3 1 2 1 3 3 1 2 2 2 2 1 2 3 1 1 1 2 2 3 2 2 2 1 1 1 1 3 1 1 1 3 1 3 1 3 0.0364
1 1 2 3 2 2 3 1 2 1 1 2 3 3 1 3 3 2 1 3 1 2 3 3 3 3 2 3 3 1 1 1 1 1 1 2 2 2 3 1 2 3 2 3 0.0373
2 2 2 1 1 1 2 1 2 1 3 3 1 1 1 1 1 3 3 3 3 2 1 1 1 1 1 3 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 0.0279
3 3 3 3 2 2 3 2 3 2 3 2 3 2 1 2 2 2 2 1 1 3 2 3 3 2 2 3 2 1 2 1 1 1 1 3 2 2 2 3 1 3 2 3 0.0420
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include the bill of materials task (task 8.2), the loft frame build (task 7), and component sizing and design (task 8.1). In comparing the differences between these tasks, one can see that there are many contributing factors, from size to leadership, to stage of design process. These generic tasks are similar to those high level tasks found in common systematic design approaches, such as [38–40]. It is important to recognize that these resistances are qualitatively determined based on literature and experience. Thus, this model should be compared with the actual results from this project to see how the tasks compared. The following section discusses the actual resistances observed in the project. 4.2 Actual Resistances This project was undertaken by a team of six students. The team was composed of two graduate students, four undergraduate; two international, four domestic; one part-time student, five full time students; six mechanical engineering students, one with a fine arts minor, one with a business minor. The class met every day for approximately ten hours to work on the project. By the second day of the twelve day course, the students broke into sub-teams to tackle the different tasks. Evenings were reserved for advisor/coach review and peer review by another team in the same class who was working on a different sponsored project. In this project, the student worked with the original patent holders who were available only by e-mail and phone throughout the majority of the project duration. Several of the tasks were accomplished in parallel. For example, the market analysis and the benchmarking of technologies were done by two sub-teams of students. Other tasks were done in series, such as the loft build and then the prototype build. The team worked well when decoupled, progressing well when part of the team focused on market research (interviewing potential loft owners and real estate agents) and a second portion of the team focused on building the loft frame on which to assemble the bed prototype. The team encountered the greatest resistance to making progress on the design project during the peer design reviews. It is believed that a major factor in this limitation was the difficulty in communicating with the industry sponsor as they were two time zones away. Further, the peer review included both student design teams, one of which was not as familiar with the project. This increased the size of the group and reduced the shared understanding. The students reported that the most difficult tasks for them were preparing for the preliminary design review, interviewing the loft owners and realtors, and finally defining the problem, especially the sponsor approval. These reported tasks and the predicted high resistance tasks agree fairly well. The amount of information found in the individual design team members’ design
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journals tend to support these observations. This approach to evaluating the rate of accumulation of active design knowledge is currently being refined to provide a better view of the design process, but it is felt that it is sufficient to demonstrate the general applicability of the collaborative design model based on electric analogy.
5. Evaluation In evaluating the model, one may consider three general requirements: expressiveness, usability, and functionality. A collaborative design model should be expressive enough to include as many factors as possible that have a marked impact on the collaborative design process. The collaborative design taxonomy developed by Ostergaard is a clear guide in evaluating this requirement. With respect to usability, a collaborative design model should be easily used, where the acceptable level of complexity is dependent on whether the intended end-user is a person or a computer. Computer programs are capable of handling more complex models and can serve as navigation and visualization tools for human users. The level of detail, navigability, and scalability may be used to assess the level of usability of a collaborative design model in the absence of extrinsic experimentation. Finally, a collaborative design model ought to support basic functionality, such as management, analysis, and prediction. Many existing collaborative design tools serve to aid a particular factor in the design process, often communication. An effective model of the collaborative design process, though, should enable a more widespread application to the effective management of collaboration. The first requirement, expressiveness, is met by basing the model on the collaborative design taxonomy to ensure that each influential design factor is accounted for in the model. The second requirement, usability, is achieved by using an analogy to the familiar system of electrical circuits in the design of the model. Significant inferential effectiveness, both for humans and computers, is gained with this approach. The third requirement, facilitating effective analysis and planning of collaborative engineering design processes, was addressed in the model application. When design cases are modeled with this system, areas that introduce the highest resistance to the accumulation of active knowledge are highlighted. Using the model as a planning tool, resistance or flow may be controlled to achieve desired knowledge accumulation. Some aspects of this collaborative design model require further investigation. The assumption that ideal electrical elements are sufficient should be confirmed, and the behavior of interacting resistors should be explored. Additionally, the proposed model
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should be applied to real-world design cases to test the model once empirically derived values for resistance are developed.
6. Discussion and Future Work The opportunities and limitations afforded by collaborative design may be explained via collaborative design models. A functional collaborative design model should serve to explain the process and facilitate a more efficient application of collaborative design. Recent collaborative design research has produced a number of collaborative design models. These models are agent-based, decision/activity centered, or information-based. Requirements of expressiveness, usability, and facilitation of effective analysis and planning of collaborative design have been recommended for evaluating model effectiveness. Based on the requirements and a collaborative design taxonomy proposed in an earlier work, a new collaborative design model has been presented. The proposed model is based on information flow and resistance to that flow in the collaborative design process. It is constructed from an analogy to electrical circuits to clearly highlight areas of highest resistance, where the greatest need for collaborative design tools exists. This proposed model is currently being evaluated in several case studies. The values of resistance for the taxonomy factors are not yet identified in this research, but should be determined in order to gain full benefits of the proposed model. In future works, the behavior of interacting resistances should be studied to verify that the assumptions made in the behavior of the model are correct. Further investigation may show that some resistances cannot be characterized by the linear properties and related laws of ideal electrical elements. With these issues resolved, the proposed model may be applied to real-world design cases to provide a view of which resistance elements have the greatest impact on collaborative design, thus directing future development of collaborative design aids.
References 1. Giampa, F., Luchi, M. and Muzzupappa, R.S. (2001). A Web Portal for E-Design: A First Model for Cooperation among SMES Sited in Underdeveloped Regions, International Conference on Engineering Design, Design Society, ICED2001, Glasgow, Scotland. 2. Macgregor, S., Thomson, A. and Juster, N. (2001). Information Sharing within a Distributed Collaborative Design Process: A Case Study, Design Engineering Technical Conferences, ASME, DETC-2001, Pittsburgh, PA, CIE21230.
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3. Chiu, M. and Lan, J. (2002). DECADE: A Distributed Environment for Collaborative Architectural Design and Data Mining, International Conference on Computing in Civil and Building Engineering, 9, Taipei, Taiwan. 4. Szykman, S. and Sriram, R. (1998). Internet-Based Delivery of CAD/CAM Capability: An Industry Case Study, Design Engineering Technical Conferences, ASME, DETC-1998, Atlanta, GA, CIE-5520. 5. Lim, S., Lee, I., Lim, L. and Ngoi, B. (1995). ComputerAided Concurrent Design of Product and Assembly Processes: A Literature Review, Journal of Design and Manufacturing, 5: 67–88. 6. Noori, H. and Lee, W.B. (2004). Collaborative Design in a Networked Enterprise: The Case of the Telecommunications Industry, International Journal of Production Research, 42(15): 3041–3054. 7. Gerwin, D. and Barrowman, N.J. (2002). An Evaluation of Research on Integrated Product Development, Management Science, 48(7): 938–953. 8. Mitchell, W. (1990). Creative Design in the Computer Era, In: McCullough, M., Mitchell, J.M. and Purcell, P. (eds), The Electronic Design Studio: Architectural Knowledge and Media in the Computer Age, Cambridge, MA: The MIT Press. 9. Ostergaard, K.J. (2002). Investigation of Resistance to Information Flow in the Collaborative Design Process, Masters, Clemson, SC: Mechanical Engineering, Clemson Univeristy. 10. Ostergaard, K.J. and Summers, J.D. (2003). A Taxonomy for Collaborative Design, Design Engineering Technical Conferences, ASME, DETC-2003, Chicago, IL, DAC48781. 11. Ostergaard, K.J. and Summers, J.D. (2003). A Taxonomic Classification for Collaborative Design, International Conference on Engineering Design, The Design Society, ICED03, Stockholm, Sweden, No. 1349. 12. Klein, M. (2000). Towards a Systematic Repository of Knowledge about Managing Collaborative Design Conflicts, 6th International Conference on Artificial Intelligence in Design, Worcester, MA. 13. Ullman, D. and D’ambrosio, B. (1995). Taxonomy for Classifying Engineering Decision Problems and Support Systems, Artificial Intelligence for Engineering Design, Analysis, and Manufacturing, 9: 427–438. 14. Dixon, J., Duffey, M., Irani, R., Meunier, K. and Orelup, M. (1988). A Proposed Taxonomy of Mechanical Design Problems, Computers in Engineering Conference, pp. 41–46, San Francisco, CA. 15. Shah, J.J. (1998). Experimental Investigation of Collaborative Techniques for Progressive Idea Generation, Design Engineering Technical Conferences, ASME, DETC-1998, Atlanta, GA, DTM-5676. 16. Gershenson, J. and Stauffer, L. (1999). Taxonomy for Design Requirements from Corporate Customers, Research in Engineering Design, 11(2): 103–105. 17. Gerhard, J., Rosen, D., Allen, J. and Mistree, F. (2000). A Distributed Product Realization Environment for Design and Manufacturing, Design Engineering Technical Conferences, ASME, DETC-2000, Baltimore, MD, CIE-14624. 18. Brazier, F., Moshkina, L. and Wijngaards, N. (2001). Knowledge Level Model of an Individual Designer as an Agent in Collaborative Distributed Design, Artificial Intelligence in Engineering, 15(2): 137–152.
Downloaded from http://cer.sagepub.com at PENNSYLVANIA STATE UNIV on April 16, 2008 © 2007 SAGE Publications. All rights reserved. Not for commercial use or unauthorized distribution.
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19. Zha, X. (2002). A Knowledge Intensive Multi-Agent Framework for Cooperative/Collaborative Design Modeling and Decision Support of Assemblies, Knowledge-Based Systems, 15(8): 493–506. 20. Case, M. and Lu, S. (1996). Discourse Model for Collaborative Design, Computer-Aided Design, 28(5): 333–345. 21. Mcgreavy, C., Wang, X., Lu, M., Zhang, M. and Yang, S. (1996). Objects, Agents, and Workflow Modeling for Concurrent Engineering Process Design, Computers & Chemical Engineering, 20(S2): S1167–S1172. 22. Summers, J.D. and Shah, J.J. (2001). Leadership Style Selection Rules for Agent Architecture in Engineering Design, In: Cuginin, U. and Wozny, M. (eds), From Knowledge Intensive CAD to Knowledge Intensive Engineering, The Netherlands: Kluwer Academic Press. 23. Lu, S. and Cai, J. (1999). Modeling Collaborative Design Process with a Socio-Technical Framework, 6th ISPE International Conference on Concurrent Engineering, Bath, UK. 24. Zhao, L. and Jin, Y. (2000). Modeling Collaborative Engineering Design Process Using Petri-Net, Design Engineering Technical Conferences (DETC2000), Computers in Engineering, Baltimore, MD, CIE-14609. 25. Chen, L. and Li, S. (2001). Concurrent Parametric Design Using a Multifunctional Team Approach, Design Engineering Technical Conferences, ASME, DETC-2001, Pittsburgh, PA, DAC-21038. 26. Chiu, M. (1998). The Design Guidance of CSCW: Learning from Collaborative Design Studios, CAADRIA’98: Proceedings of the Third Conference on Computer Aided Architectural Design Research in Asia, pp. 261–270, Osaka, Japan. 27. Zhou, W. and Jin, Y. (2000). Modeling Knowledge Structure for Collaborative Engineering, Design Engineering Technical Conferences (DETC2000), Computers in Engineering, Baltimore, MD, CIE-14610. 28. Boston, O., Culley, S. and Mcmahon, C. (1997). Modelling the Information Flows in Engineering Design: A New Paradigm, Design Engineering Technical Conferences, ASME, DETC-1997, Sacramento, CA, DTM-3874.
AND
J. D. SUMMERS
29. Christian, A., Grasso, K. and Seering, W. (1996). Validation Studies of an Information-Flow Model of Design, Design Engineering Technical Conferences, ASME, DETC-1996, Irvine, CA, DTM-1306. 30. Rezgui, Y., Brown, A., Cooper, G., Yip, J., Brandon, P. and Kirkham, J. (1996), An Information Management Model for Concurrent Construction Engineering, Automation in Construction, 5(4): 343–355. 31. Cicognani, A. and Maher, M. (1997). Models of Collaboration for Designers in a Computer Mediated Environment, 3rd International IFIP WG5.2 Workshop on Formal Aspects of Collaborative CAD, pp. 99–105, Sydney, Australia. 32. Eykhoff, P. (1974). System Identification: Parameter and State Estimation, New York, NY: John Wiley and Sons. 33. Zachary, W., Campbell, G., Laughery, R. and Glenn, F. (1998). Application of Human Modeling Technology to the Design, Operation and Evaluation of Complex Systems, In: ONR (eds), CHI Systems Technical Memo: 980727.9705. 34. Liang, J., Shah, J.J., D’souza, R., Urban, S., Ayyaswamy, K., Harter, E. and Bluhm, T. (1999). Synthesis of Consolidated Data Schema for Engineering Analysis from Multiple STEP Application Models, ComputerAided Design, 31(7): 429–447. 35. Davis, J., Subrahmanuan, E., Konda, S., Granger, H., Collins, M. and Westerberg, A. (2000). Creating Shared Information Spaces to Support Collaborative Design Work, Institute for Complex Engineered Systems Research Report, Carnegie Mellon University, Pittsburgh. 36. Rizzoni, G. (1993). Principles and Applications of Electrical Engineering, R. D. Irwin, Inc., Burr Ridge, IL. 37. (2006). MatWeb: The Online Materials Information Resource, url, http://www.matweb.com, accessed: April 6, 2006. 38. Pahl, G. and Beitz, W. (1996). Engineering Design: A Systematic Approach, New York, NY: Springer-Verlag. 39. Ullman, D. (2003). The Mechanical Design Process, New York, NY: McGraw-Hill, Inc. 40. Dym, C. and Little, P. (2000). Engineering Design: A Project-Based Introduction, New York, NY: John Wiley & Sons, Inc.
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