[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127720-en":3,"doc-seo-127720-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127720,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Development of a Product Return Process in the Context of the Circular Economy with the Help of Machine Learning - Research overview","The Circular Economy seeks to return product value to the economy at the same value-chain level, yet industry practices for handling returned products remain unclear. This study designs a process for treating returned items within this framework by using a machine learning-based algorithm to classify life-cycle data and extract relevant information. The research scope focuses on land transportation systems in the Sharing Economy sector.","[Available online at](Available online at www.sciencedirect.com)[ www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia CIRP 120 (2023) 1498–1503  \n56th CIRP Conference on Manufacturing Systems, CIRP CMS '23, South Africa  \nDevelopment of a product return process in the context of the Circular Economy with the help of Machine Learning  \nSelina Waltera,b, *, Anja Brauna, Louis Louwb  \na,*ESB Business School, Alteburgstraße 150, 72762 Reutlingen, Germany  \nb University of Stellenbosch, Stellenbosch Central, 7602 Stellenbosch, South Africa  \n* Corresponding author. Tel.: +491728636507. E-mail address: [selina.walter@student.reutlingen-university.de](selina.walter@student.reutlingen-university.de)  \nAbstract  \nThe Circular Economy aims to reintroduce the value of products back into the economic cycle at the same value chain level. While the activities of the Circular Economy are already well-defined, there exists a gap in how returned products are treated by the industry. This study aims to examine how a process should be designed to handle returned products in the context of the Circular Economy. To achieve this, a machine learning-based algorithm is used to classify data and extract relevant information throughout the product life cycle. The focus of this research is limited to land transportation systems within the Sharing Economy sector.  \n© 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the 56th CIRP International Conference on Manufacturing Systems 2023  \nKeywords: Circular Economy; Machine Learning; Sharing Economy; Data Analysis; Classification  \n1. Introduction  \nAccording to the Circular Economy (CE) Report of the Ellen MacArthur Foundation, only 8.6 % of the global economy implemented circularity for their products and materials [1] . One reason for this small number is the embedded nature of the linear system in the economy [1] . The problem of the Linear Economy is traced back to the high level of material consumption [2], which ends up in substantial environmental impacts, such as significant energy-related emissions, air pollution, climate change, and lots more [3] . Supporting the implementation of the CE is an integral approach to reducing these unfavourable impacts on the environment since this solution aims to maintain the products, equipment, and infrastructure to improve resource productivity [4] .  \nA vital enabler of the CE is digitisation. Digital solutions support the CE digital platforms with technologies such as Machine Learning (ML) [5] . The use of ML for implementing the CE has become increasingly popular in the last few years.  \nML enables an automatic recommendation of what to do with a product at the end of its life based on the CE's capabilities. However, the ML approach includes lifetime data and datasharing challenges. Therefore, this research paper develops a design for a return process based on the CE recommendations of a ML algorithm. The ML algorithm is created for a sharing economy (SE) that tracks and publishes lifetime data to address the challenges of lifetime data and data sharing.  \n2. Theoretical background  \n2.1. Digital Technologies in the CE  \nBased on the results of Antikainen et al. [5], digitalisation is a massive enabler for the realisation of CE. The different technologies offer opportunities for successful implementation [5] .  \nAccording to Pagoropoulos et al. [6], three different application fields of Digital Technologies exist in the CE. The  \n2212-8271 © 2023 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the s","cbCais7c6vUsIGut","https://ap.wps.com/l/cbCais7c6vUsIGut","pdf",638239,2,1,6,"English","en",105,"# Abstract\n# Introduction\n## Circular economy and linear system challenge\n## Role of digitisation and machine learning\n# Theoretical background\n## Digital technologies in the Circular Economy\n## Machine Learning in the Circular Economy","[{\"question\":\"What problem does the study address in the Circular Economy?\",\"answer\":\"It addresses the gap in how industries treat returned products, despite circular economy activities being broadly defined.\"},{\"question\":\"How does the research use machine learning?\",\"answer\":\"It applies a machine learning-based algorithm to classify life-cycle data and extract relevant information to support return-process design.\"},{\"question\":\"What is the study’s scope within the Sharing Economy?\",\"answer\":\"The focus is limited to land transportation systems in the Sharing Economy sector.\"}]","Development of a Product Return Process in the Context of the Circular Economy with the Help of Machine Learning - 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