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Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem
Thesis (MEng)--Stellenbosch University, 2023.
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| Format: | Thesis |
| Language: | en_ZA en_ZA |
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Stellenbosch : Stellenbosch University
2023
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| _version_ | 1867613764058087424 |
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| access_status_str | Open Access |
| author | Walter, Selina |
| author2 | Louw, Louis |
| author_browse | Louw, Louis Walter, Selina |
| author_facet | Louw, Louis Walter, Selina |
| author_sort | Walter, Selina |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Thesis (MEng)--Stellenbosch University, 2023. |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/128878 |
| institution | Stellenbosch University (South Africa) |
| language | en_ZA en_ZA |
| last_indexed | 2026-06-10T12:41:19.685Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/128878 Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem Walter, Selina Louw, Louis Braun, Anja Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Engineering Management (MEM). Decision support systems Machine learning Circular economy Sustainable engineering Environmental protection Thesis (MEng)--Stellenbosch University, 2023. ENGLISH ABSTRACT: The transition from a linear to a circular economy currently only succeeded for 8.6% of companies in various sectors worldwide. This research project aims to tackle this challenge by developing a tool that facilitates the implementation of a circular economy within companies. The tool serves as a guide and directly supports implementing circular economy practices. The study includes a systematic literature review on the circular economy, digital ecosystem and the sharing economy. Quantitative methods, including machine learning algorithms, are used to analyse product usage data and generate recommendations for the circular economy. The results highlight the importance of implementing the whole circular economy and considering the specific characteristics of its activities. The decision support tool developed demonstrates the critical link between the sharing economy and the circular economy by using data and machine learning techniques to improve circular economy practices. By providing insights on monetising circular economy practices and offering financial benefits while promoting sustainability, the tool enables companies to make informed decisions. The tool's applicability has been validated for utility products in the sharing economy, with precise data tracking ensuring accurate recommendations. The adaptive machine learning algorithms used in the tool are universally applicable across industries, making it a versatile solution for companies looking to implement the principles of the circular economy. The developed decision support tool provides essential insights for the practical implementation of circular economy principles in companies. It serves as a valuable research framework that enables the construction of similar decision support systems for other sustainability-related fields. Rigorous verification and validation processes ensure the efficiency and effectiveness of the tool and enhance its reliability and usability. In summary, this research project represents a comprehensive approach to managing the transition to a circular economy. The decision support tool developed provides practical insights and highlights the importance of data-driven decision-making and practical implementation in advancing circular economy practices. The results contribute to the scientific field and provide opportunities for further research and applications that promote sustainable development and environmental protection. AFRIKAANSE OPSOMMING: Die oorgang van 'n lineere na 'n sirkulere ekonomie is tans slegs suksesvol vir 8.6% van maatskappye in verskeie sektore wereldwyd. Hierdie navorsingsprojek het ten doel om hierdie uitdaging aan te spreek deur 'n instrument te ontwikkel wat die implementering van 'n sirkulere ekonomie binne maatskappye fasiliteer. Die instrument dien as 'n leidraad en ondersteun direk die implementering van sirkulere ekonomie praktyke. Die studie sluit 'n sistematiese literatuuroorsig in oor die sirkulere ekonomie, digitale ekosisteem, en die deelekonomie. Kwantitatiewe metodes, insluitend masjienleer algoritmes, word gebruik om produkgebruiksdata te analiseer en aanbevelings vir die sirkulere ekonomie te genereer. Die resultate beklemtoon die belangrikheid van die implementering van die volledige sirkulere ekonomie en die oorweging van die spesifieke kenmerke van sy aktiwiteite. Die ontwikkelde besluitnemingsinstrument toon die kritiese skakel tussen die deelekonomie en die sirkulere ekonomie deur gebruik te maak van data en masjienleer tegnieke om sirkulere ekonomie praktyke te verbeter. Deur insig te bied oor die monetisering van sirkulere ekonomie praktyke en finansiele voordele te bied terwyl dit volhoubaarheid bevorder, stel die instrument maatskappye in staat om ingeligte besluite te neem. Die toepaslikheid van die instrument is geverifieer vir nutsprodukte in die deelekonomie, met noukeurige datavolging wat akkurate aanbevelings verseker. Die aanpasbare masjienleer algoritmes wat in die instrument gebruik word, is universeel toepaslik oor verskeie bedrywe, wat dit 'n veelsydige oplossing maak vir maatskappye wat die beginsels van die sirkulere ekonomie wil implementeer. Die ontwikkelde besluitnemingsinstrument bied noodsaaklike insigte vir die praktiese implementering van sirkulere ekonomie beginsels in maatskappye. Dit dien as 'n waardevolle navorsingsraamwerk wat die konstruksie van soortgelyke besluitnemingsstelsels vir ander volhoubaarheidverwante velde moontlik maak. Strenge verifikasie- en valideringsprosesse verseker die doeltreffendheid en doeltreffendheid van die instrument en verbeter sy betroubaarheid en bruikbaarheid. In opsomming verteenwoordig hierdie navorsingsprojek 'n omvattende benadering tot die bestuur van die oorgang na 'n sirkulere ekonomie. Die ontwikkelde besluitnemingsinstrument bied praktiese insigte en beklemtoon die belangrikheid van data-gedrewe besluitneming en praktiese implementering in die bevordering van sirkulere ekonomie praktyke. Die resultate dra by tot die wetenskaplike veld en bied geleenthede vir verdere navorsing en toepassings wat volhoubare ontwikkeling en omgewingsbeskerming bevorder. Masters 2023-11-10T09:55:28Z 2024-01-08T14:19:27Z 2023-11-10T09:55:28Z 2024-01-08T14:19:27Z 2023-11 Thesis https://scholar.sun.ac.za/handle/10019.1/128878 en_ZA en_ZA Stellenbosch University xii, 166 pages : illustrations application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Decision support systems Machine learning Circular economy Sustainable engineering Environmental protection Walter, Selina Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title | Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title_full | Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title_fullStr | Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title_full_unstemmed | Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title_short | Development of a decision-support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| title_sort | development of a decision support tool for implementing circular value creation structures using machine learning and a digital ecosystem |
| topic | Decision support systems Machine learning Circular economy Sustainable engineering Environmental protection |
| url | https://scholar.sun.ac.za/handle/10019.1/128878 |
| work_keys_str_mv | AT walterselina developmentofadecisionsupporttoolforimplementingcircularvaluecreationstructuresusingmachinelearningandadigitalecosystem |