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Article

Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain

1
Center for Transportation and Logistics, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
2
Departments of Mechanical Engineering and Technology Management, School of Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
3
Department of Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(13), 5416; https://doi.org/10.3390/su12135416
Received: 9 June 2020 / Revised: 1 July 2020 / Accepted: 3 July 2020 / Published: 4 July 2020
(This article belongs to the Section Economic and Business Aspects of Sustainability)
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. View Full-Text
Keywords: disassembly; smart remanufacturing; trade-in; digital twins; blockchain; IoT; discrete-event simulation; logistic regression disassembly; smart remanufacturing; trade-in; digital twins; blockchain; IoT; discrete-event simulation; logistic regression
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MDPI and ACS Style

Tozanlı, Ö.; Kongar, E.; Gupta, S.M. Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain. Sustainability 2020, 12, 5416. https://doi.org/10.3390/su12135416

AMA Style

Tozanlı Ö, Kongar E, Gupta SM. Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain. Sustainability. 2020; 12(13):5416. https://doi.org/10.3390/su12135416

Chicago/Turabian Style

Tozanlı, Özden, Elif Kongar, and Surendra M. Gupta. 2020. "Evaluation of Waste Electronic Product Trade-in Strategies in Predictive Twin Disassembly Systems in the Era of Blockchain" Sustainability 12, no. 13: 5416. https://doi.org/10.3390/su12135416

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