IIoT-enabled and Data-driven Sustainability Evaluation Framework for Textile Supply Chain

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IIoT-enabled and Data-driven Sustainability Evaluation Framework for Textile Supply Chain
Title:
IIoT-enabled and Data-driven Sustainability Evaluation Framework for Textile Supply Chain
Journal Title:
IEEE Conference on Industrial Electronics and Applications
Keywords:
Publication Date:
30 August 2021
Citation:
T. W. Chit, L. Ning, N. A. Paliath, Y. M. Long, H. Akhtar and Y. Shanshan, "IIoT-enabled and Data-driven Sustainability Evaluation Framework for Textile Supply Chain," 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), Chengdu, China, 2021, pp. 297-304, doi: 10.1109/ICIEA51954.2021.9516314.
Abstract:
Global ecolabelling scheme for consumer products could be beneficial to the world by increasing the public awareness about the environmental impact that might be caused by the manufacturing production along the supply chain activities, i.e., carbon footprint emission. By putting the conventional life cycle assessment (LCA) in tandem with the latest Industrial Internet-of-Things (IIoT) technologies, heterogeneous input and output data sources can be accumulated in real-time manner, towards a data-driven sustainability metric evaluation to produce the ecolabels to promote the eco-friendly consumer products in supply chain. In this paper, we present a high-level technical overview architecture to showcase how we utilize the IIoT technologies to enhance the sustainability evaluation metric along textile supply chain. The input and output data collection along all stages in the textile supply chain thus can be qualified and quantified to yield a guideline for LCA practitioners.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - RIE2020 INDUSTRY ALIGNMENT FUND – INDUSTRY COLLABORATION PROJECTS (IAF-ICP)
Grant Reference no. : I2001E0061
Description:
© 2021 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISBN:
978-1-6654-2248-2
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