Funded by the National Science Foundation
Award number CMMI-1436658.
Multidimensional Network Analysis for Analyzing and Predicting Complex Customer-Product Relations in Engineering Design
This research is intended to develop a multidimensional network analysis (MNA) approach, rooted in social network analysis for analyzing and predicting complex customer-product relations in support of engineering design decisions. As shown in Fig. 1, using vehicles as an example, customer-product interactions form a complex socio-technical system, not only because there are complex relations between the customers (e.g., social interactions) and amongst the products (e.g., market segmentation or product family), but also because there exist multiple types of relations between customers and products (e.g., “consideration” versus “purchase”).
The research premise is that, similar to other complex systems exhibiting dynamic, uncertain, and emerging behaviors, customer-product relations should be viewed as a complex socio-technical system and analyzed using social network theory and techniques. The structure and topological characteristics identified in customer-product networks can reveal emerging patterns of customer-product relations and the interacting effects of product and customer attributes by taking into account the heterogeneities among customers and products.
The MNA approach has many advantages over traditional statistical analysis and utility- based preference modeling, because it considers dependency among complex customer-produce relations and analyzes an entire network as one entity. While the methodology is widely applicable for analyzing any type of relational data, this research will be focused on analysis of customer preferences (consideration and choice behaviors) among a set of competing products as well as on examination of how the results can be used to assist the design of sustainable engineering products. The ultimate goal is to develop rigorous methods to bridge the gap between market research and engineering design decision-making.