Multidimensional Network Analysis for Analyzing and Predicting Complex Customer-Product Relations in Engineering Design

了解客户的偏好和需求对于开发成功的产品至关重要。该奖项支持跨学科研究，以开发数据驱动的数学方法，用于分析和预测可持续工程产品设计中的消费者偏好，例如替代燃料汽车和智能家电。 将顾客与产品之间的关系概念化为一个复杂网络，并运用网络理论和技术进行分析。这项研究将有助于工业界在更短的时间内生产出更具竞争力的产品。研究结果将有助于开发分析大型复杂网络的新技术。 将组织关于分析作为网络的客户和产品的讲习班和小组会议，以便向更广泛的社区传播。 研究将通过跨学科的本科设计证书和研究生设计集群计划与教育相结合。产品设计中的客户偏好分析建模本质上是困难的，因为它面临着建模异构人类行为和产品供应的挑战。该研究的新奇在于采用了多维客户-产品网络（MCPN）框架，同时对独立的“客户”和“产品”网络进行建模，并考虑与购买、产品关联和客户关系等多种类型的关系。社交网络。该研究将扩展指数随机图模型（ERGM）作为一个统一的统计推理框架，分析多维客户-产品关系，并预测未知的客户偏好（考虑或选择）下的新的设计方案。在同一框架下分析了采用“绿色”技术的社会影响。该方法通过考虑产品选择之间的依赖性和社会影响导致的顾客行为“非理性”，克服了传统统计分析和基于效用偏好建模的局限性。 我们还将探索在社交媒体中使用客户生成数据的文本分析，从而创建众包“虚拟实验室”，以推进产品设计中的数据分析和计算社会科学。

Understanding customer preferences and needs is critically important in developing successful products. This award supports an interdisciplinary research to develop a data-driven mathematical approach for analyzing and predicting consumer preferences in design of sustainable engineering products, such as alternative fuel vehicles and smart appliances. The relations among customers and products are conceptualized as a complex network and analyzed using network theory and techniques. This study will help industry produce more competitive products in shorter time to market. The findings will contribute to the development of new techniques for analyzing large complex networks. Workshop and panel sessions on analyzing customers and products as networks will be organized for dissemination to a broader community. Research will be integrated with education through interdisciplinary undergraduate Design Certificate and graduate Design Cluster programs. Analytical modeling of customer preferences in product design is inherently difficult as it faces challenges in modeling heterogeneous human behavior and product offerings. The novelty of the research lies in the employment of a Multidimensional Customer-Product Network (MCPN) framework, where separate networks of "customers" and "products" are simultaneously modeled, and multiple types of relations, such as consideration and purchase, product associations, and customer social networks are considered. The research will extend the Exponential Random Graph Model (ERGM) as a unified statistical inference framework for analyzing multidimensional customer-product relations and predicting unknown customer preferences (consideration or choice) under new design scenarios. Social influences on adopting "green" technology are analyzed in the same framework. Our approach overcomes the limitations of the traditional statistical analysis and utility-based preference modeling by considering the dependency among product choices and the social influence induced "irrationality" of customer behavior. We will also explore the use of text analysis of customer-generated data in social media thereby creating crowdsourced "virtual labs" for advancing data analytics and computational social science in product design.