Mass Collaboration Project Recommendation within Open-Innovation Design Networks

[+] Author and Article Information
Zachary Ball

ASME Student Member, Graduate Research Assistant, Mechanical and Aerospace Engineering, University at Buffalo - SUNY, Buffalo, New York, 14260

Kemper Lewis

ASME Fellow, Professor, Mechanical and Aerospace Engineering, University at Buffalo - SUNY, Buffalo, New York, 14260

1Corresponding author.

ASME doi:10.1115/1.4041858 History: Received July 05, 2018; Revised October 19, 2018


Mass collaboration within the design engineering process supports the inclusion of unique perspectives when working on complex problems. Increasing the number of individuals providing input and support into these perplexing challenges can increase innovation, decrease product development times and provide solutions that truly encompass the needs of the market. One of the greatest challenges within mass collaboration engineering projects is the organization of individuals within these large design efforts. Understanding which projects would most effectively benefit from additional designers or contributors is paramount to supporting mass collaboration design networks. Within such networks, there exists a large number of contributors, as well as, a large pool of potential challenges. Matching individuals with the challenges that they can provide the greatest benefit to, or building a team of individuals for newly developed challenges requires the consideration of previous performance and an understanding of individual competencies and design abilities. This work presents a framework which recommends individual project placement based on individual abilities and the project requirements. With this work a pool of individuals and potential projects are simulated and the application of a hybrid recommender system is explored. To complement the simulation, an additional case study with empirical data is performed to study the potential applicability of the proposed framework. Overall it was found that recommended team compositions greatly outperform the baseline team development, most notably as greater consideration is placed on collaborative recommendations.

Copyright (c) 2018 by ASME
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