| dc.date.accessioned | 2023-07-14T08:40:01Z | |
| dc.date.issued | 2023-06 | |
| dc.identifier | doi:10.17170/kobra-202306308317 | |
| dc.identifier.uri | http://hdl.handle.net/123456789/14888 | |
| dc.description.sponsorship | This dissertation has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Innovative Training Networks (H2020-MSCA-ITN-2018) scheme, grant agreement number 814247 (ReTraCE project). | |
| dc.language.iso | eng | |
| dc.subject.ddc | 330 | |
| dc.title | Industry 4.0-driven additive manufacturing for operations in the circular economy | eng |
| dc.type | Dissertation | |
| dcterms.abstract | This dissertation focuses on exploring how key elements of Industry 4.0 (I4.0), such as additive manufacturing (AM) and quantitative techniques/simulation, instigate the operations of an organisation towards the circular economy (CE). AM represents the technological foundation of future manufacturing which efficiently utilises materials avoiding unnecessary waste. Further, it could positively impact the transition towards CE by addressing issues related to the operations and supply chains while positively influencing product lifecycle management. Therefore, it is essential to understand the challenges and opportunities that arise when integrating AM and CE since the scholarly discussion on this is still emerging. Hence, this dissertation answers the following overarching research questions.
ORQ (1): What are the factors of AM influencing CE?
ORQ (2): How do AM-driven operations facilitate the transition towards CE?
To comprehend the main focused areas of this dissertation and answer ORQ (1), the link between AM and CE was conceptualised through a systematic literature review while highlighting the role of AM as a digital enabler of CE. Moreover, it exemplified how the inherent characteristics of AM could support sustainable manufacturing using biobased and non-virgin materials while extending the discussion beyond recycling. Further extending the discussion, the impact of supply chain actors and key AM decisions towards CE implementation was discussed while illustrating the vital role of customers, external parties, material suppliers and organisational structure in promoting the transition towards CE. Elaborative guidance highlighted that managerial decisions must be made after careful analysis of how the supply chain actors and CE implementation strategies influence operational practices while analysing the impact of drivers and key AM decisions.
ORQ (2) of this dissertation was answered by mainly focusing on the AM’s operational perspectives. AM enables distributed manufacturing by involving fewer stakeholders in the supply chains. Hence, this reduces the logistics activities and environmental footprint. From the CE perspective, it was revealed that these characteristics of AM could facilitate overcoming the inherent limits of CE by focusing on governance and management and system boundaries. AM has a highly energy-intensive manufacturing process. Hence, selecting the most suitable AM process needs careful investigation. Through a systematic literature review, this dissertation analysed the merits and drawbacks of each AM process while focusing on the production planning viewpoint. Integration of AI methods was highlighted to maintain high efficiency and accuracy in the AM operations while enabling a dynamic planning horizon.
Despite the comprehensiveness, this dissertation highlights several future directions. The findings of this dissertation mainly focus on the scholarly perspective. Hence, empirical studies such as mathematical modelling and simulations incorporating real-world data could further validate and strengthen the findings. Moreover, the high energy intensiveness and transfer of intellectual property remain the main factors hindering the adoption of AM, which needs to be thoroughly addressed in future studies to thrive this state-of-the-art technology moving forward. | eng |
| dcterms.accessRights | restricted access | |
| dcterms.creator | Hettiarachchi, Biman Darshana | |
| dcterms.dateAccepted | 2023-06-29 | |
| dcterms.extent | xvii, 167 Seiten | |
| dc.contributor.corporatename | Kassel, Universität Kassel, Fachbereich Wirtschaftswissenschaften | |
| dc.contributor.referee | Seuring, Stefan (Prof. Dr.) | |
| dc.contributor.referee | Brandenburg, Marcus (Prof. Dr.) | |
| dc.contributor.referee | Genovese, Andrea (Prof. Dr.) | |
| dc.relation.projectid | H2020-MSCA-ITN-2018 | |
| dc.relation.projectid | 814247 (ReTraCE project) | |
| dc.subject.swd | Industrie 4.0 | ger |
| dc.subject.swd | Fertigung | ger |
| dc.subject.swd | Kreislaufwirtschaft | ger |
| dc.subject.swd | Qualifikation | ger |
| dc.type.version | publishedVersion | |
| ubks.embargo.terms | 2025-06-29 | |
| ubks.embargo.end | 2025-06-29 | |
| kup.iskup | false | |
| ubks.epflicht | true | |
| ubks.kumDiss | true | |
