Review Article

Visual Analytics Tools for Sustainable Lifecycle Design: Current Status, Challenges, and Future Opportunities

[+] Author and Article Information
Devarajan Ramanujan

Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139

William Bernstein

Systems Integration Division, National Institute of Standards & Technology, Gaithersburg, MD 20988

Senthil K. Chandrasegaran

College of Information Studies, University of Maryland, College Park, MD 20742

Karthik Ramani

School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907

1Corresponding author.

ASME doi:10.1115/1.4037479 History: Received February 28, 2017; Revised July 14, 2017


The rapid rise in technologies for data collection has created an unmatched opportunity to advance the use of data-rich tools for lifecycle decision-making. However, the usefulness of these technologies is limited by the ability to translate lifecycle data into actionable insights for human decision-makers. This is especially true in the case of sustainable lifecycle design (SLD), as the assessment of environmental impacts, and the feasibility of making corresponding design changes, often relies on human expertise and intuition. Supporting human sense-making in SLD requires the use of both data-driven and user-driven methods while exploring lifecycle data. A promising approach for combining the two is through the use of visual analytics (VA) tools. Such tools can leverage the ability of computer-based tools to gather, process, and summarize data along with the ability of human-experts to guide analyses through domain knowledge or data-driven insight. In this paper, we review previous research that has created VA tools in SLD. We also highlight existing challenges and future opportunities for such tools in different lifecycle stages---design, manufacturing, distribution & supply chain, use-phase, end-of-life, as well as life cycle assessment. Our review shows that while the number of VA tools in SLD is relatively small, researchers are increasingly focusing on the subject matter. Our review also suggests that VA tools can address existing challenges in SLD and that significant future opportunities exist.

Copyright (c) 2017 by ASME
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