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Implementation of machine learning to improve the decision-making process of end-of-usage products in the circular economy

  • Rising consumption due to a growing world population and increasing prosperity, combined with a linear economic system have led to a sharp increase in garbage collection, general pollution of the environment and the threat of resource scarcity. At the same time, the perception of environmental protection becomes more sensitive as the consequences of neglecting sustainable business and eco-efficiency become more visible. The Circular Economy (CE) could reduce waste production and is able to decouple economic growth from resource consumption, but most of the products currently in use are not designed for the reuse-forms of the CE. In addition, the decision-making process of the End of-Usage (EoU) products regarding the following steps has further weaknesses in terms of economic attractiveness for the participants, which leads to low return rates and thus the disposal is often the only alternative. This paper proposes a model of the decision-making process, which uses machine learning. For this purpose, a Machine Learning (ML) classification is created, by applying the waterfall model. An artificial neural network (ANN) uses information about the model, use phase and the obvious symptoms of the product to predict the condition of individual components. The resulting information can be used in a downstream economic and ecological evaluation to assess the possible next steps. To test this process comprehensive training data is simulated to train the ANN. The decentralized implementation, cost savings and the possibility of an incentive system for the return of an end-of-usage product could lead to increased return rates. Since electronic devices in particular are attractive for the CE, laptops are the reference object of this work. However, the obtained findings are easily applicable to other electronic devices.

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Author of HS ReutlingenDiem, Michael; Braun, Anja
Erschienen in:Proceedings of the 1st Conference on Production Systems and Logistics (CPSL 2020) : Stellenbosch, South Africa, 17. – 20. March 2020
Publisher:Leibniz-Universität Hannover
Place of publication:Hannover
Editor:Peter Nyhuis
Document Type:Conference proceeding
Publication year:2020
Tag:artificial neural network; circular economy; classification; decision-making process; machine learning; remanufacturing; reuse; sustainability
Page Number:10
First Page:188
Last Page:197
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Open access?:Ja
Licence (German):License Logo  Creative Commons - CC BY - Namensnennung 4.0 International