Euiyoung Kim

Design Roadmapping: A Framework and Case Study on Planning Development of High-Tech Products in Silicon Valley

© 2016 by ASME. We propose a framework for design roadmapping that parallels existing product roadmapping and technology roadmapping processes. It leverages three needs we have observed in organizations as they use existing roadmapping processes: (1) to focus on development of customer and user experiences (UX), not just on features; (2) to increase engagement of designers early in the planning process; and (3) to provide a means for rapidly responding to changes in the environment. Design roadmapping is an attempt to reconcile differences that arise when customer/user needs are not considered simultaneously with technology choices. The proposed design roadmapping process assists project prioritization and selection. The process aggregates design experience elements along a timeline that associates key user needs with the products, services, and/or systems the organization wishes to deliver. To illustrate the design roadmapping process, we conducted a case study in which we applied the design roadmapping process to projects undertaken by a large corporations innovation lab located in research centers in San Francisco and Mountain View, CA, in partnership with corporate stakeholders located in Asia. The five-step design roadmapping procedure is provided along with detailed information. The decisions from the design roadmapping process have been incorporated into the companys commercial plans. Key findings in this corporate case study bolster the positive impact of design roadmapping in moving strategic thinking from a technology/ feature-driven process to one that is design/experience-driven. It shows how firms might weigh choices between user needs, design principles, and technological innovation.

Design Roadmapping in an Uncertain World: Implementing a Customer-Experience-Focused Strategy

Technology is rapidly changing, and customers are seeking not just products and services but experiences. Traditional approaches to strategic planning using product and technology roadmaps are no longer sufficient. This article describes the emergence of what the authors call “design roadmapping” approaches that are grounded in the creation of customer experiences, and which drive the choices of features, functionality, and technology. Design roadmapping provides stability for the future, even as technologies are changing, and keeps organizations more focused on the customers they are serving.

Scenario-Based Conjoint Analysis: Measuring Preferences for User Experiences in Early Stage Design

Conjoint analysis has proven to be a useful method for decomposing and estimating consumer preference for each attribute of a product or service through evaluations of sets of different versions of the product with varying attribute levels. The predictive value of conjoint analysis is confounded, however, by increasing market uncertainties and changes in user expectations. We explore the use of scenario-based conjoint analysis in order to complement qualitative design research methods in the early stages of concept development. The proposed methodology focuses on quantitatively assessing user experiences rather than product features to create experience-driven products, especially in cases in which the technology is advancing beyond consumer familiarity. Rather than replace conventional conjoint analysis for feature selection near the end of the product development cycle, our method broadens the scope of conjoint analysis so that this powerful measurement technique can be applied in the early stage of design to complement qualitative research and drive strategic directions for developing product experiences. We illustrate on a new product development case study of a flexible wearable for parent-child communication and tracking as an example of scenario-based conjoint analysis implementation. The results, limitations, and findings are discussed in more depth followed by future research directions.

Deep Learning for Design in Concept Clustering

Concept clustering is an important element of the product development process. The process of reviewing multiple concepts provides a means of communicating concepts developed by individual team members and by the team as a whole. Clustering, however, can also require arduous iterations and the resulting clusters may not always be useful to the team. In this paper, we present a machine learning approach on natural language descriptions of concepts that enables an automatic means of clustering. Using data from over 1,000 concepts generated by student teams in a graduate new product development class, we provide a comparison between the concept clustering performed manually by the student teams and the work automated by a machine learning algorithm. The goal of our machine learning tool is to support design teams in identifying possible areas of “over-clustering” and/or “underclustering” in order to enhance divergent concept generation processes.