Traditional product development efforts are primarily based on well-structured and hierarchical product development processes. The products are systematically decomposed into subsystems that are designed by dedicated teams with well-defined information flows. Over the last 2 decades, a new product development approach called mass-collaborative product development (MCPD) has emerged. The fundamental difference between a traditional product development process and a MCPD process is that the former is based on top-down decomposition while the latter is based on evolution and self-organization. The paradigm of MCPD has resulted in highly successful products such as Wikipedia, Linux, and Apache. Despite the success of various projects using MCPD, it is not well understood how the product architecture affects the evolution of products developed using such processes. Toward addressing this gap, we present an agent-based model to study the effect of product architectures in MCPD processes. The model is executed for different architectures ranging from slot architecture to bus architecture and the rates of product evolution are determined. The agent-based modeling approach allows us to study how (a) the degree of modularity of products and (b) the sequence of decoupling affect the evolution time of individual modules and overall products developed through MCPD processes. The approach is presented using the architecture of mobile phones as an illustrative example. This approach provides a simple and intuitive way to study the effects of product architecture on the MCPD processes. It is helpful in determining suitable strategies for product decomposition and module decoupling, and in identifying the product architectures that are suitable for MCPD processes.
Skip Nav Destination
e-mail: panchal@wsu.edu
Article navigation
March 2011
Research Papers
Modeling the Effect of Product Architecture on Mass-Collaborative Processes
Qize Le,
Qize Le
School of Mechanical and Materials Engineering,
Washington State University
, Pullman, WA 99164
Search for other works by this author on:
Jitesh H. Panchal
Jitesh H. Panchal
Mem. ASME
School of Mechanical and Materials Engineering,
e-mail: panchal@wsu.edu
Washington State University
, Pullman, WA 99164
Search for other works by this author on:
Qize Le
School of Mechanical and Materials Engineering,
Washington State University
, Pullman, WA 99164
Jitesh H. Panchal
Mem. ASME
School of Mechanical and Materials Engineering,
Washington State University
, Pullman, WA 99164e-mail: panchal@wsu.edu
J. Comput. Inf. Sci. Eng. Mar 2011, 11(1): 011003 (12 pages)
Published Online: March 30, 2011
Article history
Received:
January 4, 2010
Revised:
November 24, 2010
Online:
March 30, 2011
Published:
March 30, 2011
Citation
Le, Q., and Panchal, J. H. (March 30, 2011). "Modeling the Effect of Product Architecture on Mass-Collaborative Processes." ASME. J. Comput. Inf. Sci. Eng. March 2011; 11(1): 011003. https://doi.org/10.1115/1.3563054
Download citation file:
Get Email Alerts
Manufacturing Feature Recognition with a Sparse Voxel-based Convolutional Neural Network
J. Comput. Inf. Sci. Eng
Ontology-Guided Data Sharing and Federated Quality Control With Differential Privacy in Additive Manufacturing
J. Comput. Inf. Sci. Eng (January 2025)
Related Articles
Architecture, Performance, and Investment in Product Development Networks
J. Mech. Des (January,2017)
The Management of Product Data in an Integrated Aircraft Analysis Environment
J. Comput. Inf. Sci. Eng (December,2004)
A Fuzzy Method for Propagating Functional Architecture Constraints to Physical Architecture
J. Mech. Des (June,2009)
From Engineering Information Management (EIM) to Product Lifecycle Management (PLM)
J. Comput. Inf. Sci. Eng (December,2004)
Related Proceedings Papers
Related Chapters
Digital Human in Engineering and Bioengineering Applications
Advances in Computers and Information in Engineering Research, Volume 1
Product Development and Decision Production Systems
Decision Making in Engineering Design
Methods to Select and Compound Noise Factors
Taguchi Methods: Benefits, Impacts, Mathematics, Statistics and Applications