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Research Papers

A Knowledge-Driven, Network-Based Computational Framework for Product Development Systems

[+] Author and Article Information
Ali A. Yassine

Engineering Management Program,
American University of Beirut,
Beirut 1107-2020, Lebanon
e-mail: ali.yassine@aub.edu.lb

Joe A. Bradley

Software Engineering Group,
Applied Research Associates,
Champaign, IL 61802
e-mail: jbradley@ara.com

Our scope is focused primarily on detailed design where an existing team and process decomposition exist.

A summary of all input matrices, output matrices, and intermediate calculations is found in the Appendix.

In matrix [Y], a connection between a person and itself implies that the individual is a subject matter expert and makes unilateral decisions in the product development process as a result of this expertise.

In this framework, impedance only applies to the individual not to the databases. We do not consider the databases to be intelligent artifacts that can impede the formation of information.

Of course, it can be argued to extend the unit of analysis to quads or larger sub-network. The justification for this approach is that as the path length between the network nodes increases, the network search and the transfer of information become more expensive and complex [21,51]. Additionally, the use of triadic relationships provides an opportunity to draw upon a vast body of the literature that has studied various properties of triads [51,57]. Furthermore, if we accept the argument of bounded rationality, then the search space of human capacity has some limitation [1].

The clustering algorithm of Yu et al. [44] is used to identify the knowledge architecture and resultant knowledge modules.

Robocode is an educational robot design game (http://robocode.sourceforge.net).

1Corresponding author.

Contributed by the Computers and Information Division of ASME for publication in the JOURNAL OF COMPUTING AND INFORMATION SCIENCE IN ENGINEERING. Manuscript received October 5, 2011; final manuscript received November 5, 2012; published online March 14, 2013. Assoc. Editor: Xiaoping Qian.

J. Comput. Inf. Sci. Eng 13(1), 011005 (Mar 15, 2013) (15 pages) Paper No: JCISE-11-1440; doi: 10.1115/1.4023166 History: Received October 05, 2011; Revised November 05, 2012

Today's fast-paced product development (PD) environment brings many new challenges to the PD community. These challenges are mainly due to a drastic increase in the scale and complexity of engineered systems, which require the collaboration of functionally and geographically distributed resources within and outside a firm's boundary. To address these new challenges, this paper proposes a novel theoretical and computational framework for an enterprise-wide PD management system. The proposed framework considers an integrative view of the various dependencies that co-exist in three PD domains (i.e., people, products, and processes). Additionally, it provides a computational tool that links them together in a succinct and tractable way and provides an analysis method for assessing their influence on shaping the product development process. Using this framework, we suggest that the characteristics of how an organization acquire data, interpret information, and apply knowledge will impact the final architecture of a product. We demonstrate this framework by analyzing the development efforts for a software project called robocode.

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References

Figures

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Fig. 1

Model of product development

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Fig. 2

Network and matrix representation (a) five node network (b) corresponding five node binary matrix

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Fig. 3

Data → Information → Knowledge → Product Framework (a) robocode Development Team (social matrix) [TM] (b) robocode database matrix [DB] (c) robocode expert matrix [EP] (d) robocode query matrix [QR]

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Fig. 4

Input data for the software-development example

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Fig. 5

Dyads in information layer

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Fig. 6

Information matrix (Y), calculated using Eq. (2)

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Fig. 7

Person 2 ego network

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Fig. 8

robocode knowledge matrix [Z], knowledge impact [KI], and knowledge weight [KW] (computed using Eqs. (6)(8), respectively)

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Fig. 9

Representative knowledge node network

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Fig. 11

Partial knowledge network representation

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Fig. 12

robocode knowledge network

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Fig. 13

Theoretical mapping

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Fig. 14

Summary input–output flow chart

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