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[HTML][HTML] Data science for engineering design: State of the art and future directions
Engineering design (ED) is the process of solving technical problems within requirements
and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that …
and constraints to create new artifacts. Data science (DS) is the inter-disciplinary field that …
Design for the marketing mix: The past, present, and future of market-driven engineering design
JA Donndelinger, SM Ferguson - Journal of …, 2020 - asmedigitalcollection.asme.org
The four Ps of the marketing mix (Product, Price, Place, and Promotion) serve as a
framework for characterizing the marketing decisions made during the product development …
framework for characterizing the marketing decisions made during the product development …
Cyber-empathic design: A data-driven framework for product design
A critical task in product design is map** information from consumer to design space.
Currently, this process largely depends on designers identifying and map** psychological …
Currently, this process largely depends on designers identifying and map** psychological …
A Network‐Based Approach to Modeling and Predicting Product Coconsideration Relations
Understanding customer preferences in consideration decisions is critical to choice
modeling in engineering design. While existing literature has shown that the exogenous …
modeling in engineering design. While existing literature has shown that the exogenous …
Modeling multi-year customers' considerations and choices in China's auto market using two-stage bipartite network analysis
Choice modeling is important in transportation planning, marketing and engineering design,
as it can quantify the influence of product attributes and customer demographics on …
as it can quantify the influence of product attributes and customer demographics on …
Two-stage modeling of customer choice preferences in engineering design using bipartite network analysis
Customers' choice decisions often involve two stages during which customers first use
noncompensatory rules to form a consideration set and then make the final choice through …
noncompensatory rules to form a consideration set and then make the final choice through …
A graph neural network approach for product relationship prediction
Graph representation learning has revolutionized many artificial intelligence and machine
learning tasks in recent years, ranging from combinatorial optimization, drug discovery …
learning tasks in recent years, ranging from combinatorial optimization, drug discovery …
An analysis of modularity as a design rule using network theory
Increasing the modularity of system architectures is generally accepted as a good design
principle in engineering. In this paper, we explore whether modularity comes at the expense …
principle in engineering. In this paper, we explore whether modularity comes at the expense …
[HTML][HTML] D3 framework: An evidence-based data-driven design framework for new product service development
Despite growing interest in the use of data for product and service development, a
comprehensive understanding of how data is employed in the context of new product …
comprehensive understanding of how data is employed in the context of new product …
Data-driven dynamic network modeling for analyzing the evolution of product competitions
Understanding the impact of engineering design on product competitions is imperative for
product designers to better address customer needs and develop more competitive …
product designers to better address customer needs and develop more competitive …