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Research Papers: D3 and Lifecycle

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
e-mail: devr@mit.edu

William Z. Bernstein

Systems Integration Division,
National Institute of Standards & Technology,
Gaithersburg, MD 20988
e-mail: wzb@nist.gov

Senthil K. Chandrasegaran

College of Information Studies,
University of Maryland,
College Park, MD 20742
e-mail: senthilc@umd.edu

Karthik Ramani

Donald W. Feddersen Professor
of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907;
Professor
Department of Electrical and Computer Engineering,
Purdue University,
West Lafayette, IN 47907;
Professor
Department of Educational Studies,
College of Education,
Purdue University,
West Lafayette, IN 47907
e-mail: ramani@purdue.edu

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 28, 2017; final manuscript received July 14, 2017; published online October 2, 2017. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 139(11), 111415 (Oct 02, 2017) (19 pages) Paper No: MD-17-1184; 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 sensemaking 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 and supply chain, use-phase, end-of-life (EoL), as well as life cycle assessment (LCA). 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.

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References

Figures

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Fig. 1

Creating VA tools for SLD requires research in (1) SLD, (2) data-driven approaches for lifecycle data collection/analysis, and (3) computer-supported, interactive, visual interfaces

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Fig. 2

Decision-making approach for filtering papers obtained from the keyword search

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Fig. 3

Histograms illustrating publication years of 164 papers (categorized by lifecycle stage) left after the filtering process

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Fig. 4

Graphical view showing the organization of Sec. 4. As shown, the review of VA tools for SLD is split into subsections that follow the stages in the product lifecycle—from design to end of life. VA tools for LCA are also reviewed. Each lifecycle stage is further divided into subthemes based on the type and number of tools that we found in our literature survey. Each subsection ends with a discussion of relevant current challenges and future opportunities.

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Fig. 5

While a product’s environmental impact is a consequence of parameters chosen in early design, explicitly relating the two is a challenge. Sustainability-focused exploration of design spaces requires approaches that can dynamically link complex, multimodal, multiply related data.

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Fig. 6

Overcoming the variety in data formats, contexts, and scales is a challenge for developing VA tools in sustainable manufacturing. A manufacturing environment generates information related to both the operations and build levels in a wide variety of forms, such asnatural language maintenance issues and near-continuous data streams [93]. The wide adoption and convergence of manufacturing standards [59,60,75,9598] present promising opportunities.

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Fig. 7

Supply chains can exhibit unique structures that span multiple geographic regions. This particular example represents the supply chain for farm equipment [112]. As shown, in such systems, distribution subnetworks could have different attributes (i.e., energy mix, demand models). Similarly, the characteristics of assembly clusters can depend on their location. These complexities make it difficult to assess how a change in a supply chain’s structure affects its environmental impact.

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Fig. 8

Challenges in monitoring sustainable behavior in the use phase include limited framing of user behavior in persuasive computing support [131], and the need for user models to consider a wider range of user behaviors [132]. Solutions could lie in the direction of addressing scalability using single, central smart sensors for monitoring resource consumption, coupled with machine learning to identify consumption at the appliance-level [126,127].

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Fig. 9

A key challenge in decision-making for sustainable EoL is the need to gather information from a wide variety of stakeholders across the product lifecycle. Potential research directions toward addressing this challenge are also listed.

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Fig. 10

Current LCA software and tools for analyzing/visualizing LCA results are not sufficiently integrated with enterprise knowledge management systems. Consequently, there is a lack of information flow back to PLM/PDM databases, denoted by the break in the dotted red arrows in the figure. Addressing this challenge could significantly aid sustainability-focused decision-making throughout the product lifecycle by helping organizations archive and make better use of previous LCA studies. Potential research directions toward achieving better integration include open-source software and LCI databases [169171], nonproprietary data sharing standards [172], and cloud-based and web-based LCA platforms [165167].

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Fig. 11

Product lifecycle data from downstream stages and results from environmental assessments, e.g., LCAs, need to be projected back through integrated VA tools for holistic decision-making in SLD. Such tools enable designers to perform data-driven analyses of the implications of design changes on environmental performance.

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