Abstract
Developing a complex product in a concurrent engineering environment requires managing information flow among ten or even hundreds of people of different specialties organized in a large number of product development (PD) teams. Managing these teams effectively requires understanding the level of information dependencies among them which are often vague and cannot be precisely predicted. Taking into account the limitations of relevant previous studies, this article proposes a fuzzy-social network analysis approach for modeling and analyzing the information flow among PD teams. The approach involves four major steps: mapping of dependencies, measuring the level of information dependencies based on the fuzzy set theory, visualizing the network, and performing quantitative analysis using three measures (network density, in-degree centrality, and out-degree centrality). To validate its practicality, the approach is used to model and analyze the dependencies among 22 PD teams in a real project adapted from the literature and involved developing an automobile engine. An advantage of the approach is that in addition to providing a holistic view of the interactions among PD teams, it permits PD project managers to identity the most important PD teams with respect to information flow control using a proposed new classification system that classifies PD teams under four categories: autonomous, receivers, transmitters, and transceivers.
Highlights
Developing innovative products rapidly at a minimum cost is essential to maintain high competitiveness in increasingly global markets [1,2,3]
Taking into account the limitations of relevant previous studies, this current paper proposes using a fuzzy-Social Network Analysis (SNA) approach for modeling and analyzing information dependencies among product development (PD) teams
The in-degree centrality of PD team j can be computed by adding all the entries in column j of the fuzzy adjacency matrix, whereas the outdegree centrality of PD team i can be computed by adding all the entries row i of the fuzzy adjacency matrix
Summary
Developing innovative products rapidly at a minimum cost is essential to maintain high competitiveness in increasingly global markets [1,2,3]. PD teams can be described by an information structure created by unidirectional and bidirectional information transmission among PD teams This structure can be complex due to performing a large number of coupled activities, which are essential for achieving high efficiency and optimal performance in concurrent product development [8]. Creating effective information flow among PD teams is a critical success factor in PD [10,11,12,13,14] The concern of this current paper is on modeling and Engineering and Applied Sciences 2021; 6(1): 18-25 analyzing information flow among PD teams, which is an important aspect for creating effective communication and coordination strategies, creating an improved PD process [14]. A brief review of the studies that have addressed this issue is given below
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