Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain
Author(s)
Tozanli Yilmaz, Ozden; Kongar, Elif; Gupta, Surendra M.![Thumbnail](/bitstream/handle/1721.1/126874/sustainability-12-05416-v2.pdf.jpg?sequence=5&isAllowed=y)
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Manufacturing and supply chain operations are on the cusp of an era with the emergence of groundbreaking technologies. Among these, the digital twin technology is characterized as a paradigm shift in managing production and supply networks since it facilitates a high degree of surveillance and a communication platform between humans, machines, and parts. Digital twins can play a critical role in facilitating faster decision making in product trade-ins by nearly eliminating the uncertainty in the conditions of returned end-of-life products. This paper demonstrates the potential effects of digital twins in trade-in policymaking through a simulated product-recovery system through blockchain technology. A discrete event simulation model is developed from the manufacturer’s viewpoint to obtain a data-driven trade-in pricing policy in a fully transparent platform. The model maps and mimics the behavior of the product-recovery activities based on predictive indicators. Following this, Taguchi’s Orthogonal Array design is implemented as a design-of-experiment study to test the system’s behavior under varying experimental conditions. A logistics regression model is applied to the simulated data to acquire optimal trade-in acquisition prices for returned end-of-life products based on the insights gained from the system.
Date issued
2020-07Department
Massachusetts Institute of Technology. Center for Transportation & LogisticsJournal
Sustainability
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Özden Tozanlı et al. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain." Sustainability 12, 13 (July 2020): 5416 ©2020 Author(s)
Version: Final published version
ISSN
2071-1050