TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Diem, Michael
A1  - Braun, Anja
A1  - Louw, Louis
ED  - Nyhuis, Peter
T1  - Implementation of machine learning to improve the decision-making process of end-of-usage products in the circular economy
T2  - Proceedings of the 1st Conference on Production Systems and Logistics (CPSL 2020) : Stellenbosch, South Africa, 17. – 20. March 2020
N2  - 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.
KW  - artificial neural network
KW  - circular economy
KW  - classification
KW  - decision-making process
KW  - machine learning
KW  - remanufacturing
KW  - reuse
KW  - sustainability
Y1  - 2020
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:rt2-opus4-26949
SN  - 2701-6277
SS  - 2701-6277
U6  - https://doi.org/10.15488/9660
DO  - https://doi.org/10.15488/9660
SP  - 188
EP  - 197
S1  - 10
PB  - Leibniz-Universität Hannover
CY  - Hannover
ER  -