650 Management und unterstützende Tätigkeiten
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Additive Manufacturing (AM), also known as rapid prototyping or 3D printing, is widely used across various industries, including medical products and automotive spare parts. The COVID-19 pandemic has further accelerated its adoption to address supply chain disruptions caused by shortages in production resources and logistics constraints. However, as AM integrates into supply chains, structural changes in nodes and data flows create new challenges in information sharing and data standardization. Ontologies have proven effective in enhancing data interoperability and improving information quality through semantic modeling. Despite this, a comprehensive approach that combines AM and logistics ontologies to address cross-domain challenges remains underexplored. This study develops an ontology-based supply chain model for AM by integrating existing AM and logistics ontologies. Using the Design Science Research Methodology (DSRM), the proposed ontology is constructed and instantiated with a sample dataset for validation. The results provide a foundational framework for improving data management and coordination in AM supply chains.
Additive manufacturing (AM) revolutionises traditional manufacturing by enabling localised, on-demand production, reducing waste, and enhancing design flexibility. The adoption of the AM method also transforms supply chains (SCs) in several perspectives due to, removing and adding some nodes and arcs. While this transformation offers numerous benefits, it also presents significant challenges in configuring an optimal network for AM SCs, especially when a decentralization network is preferable. In this regard, this study investigates using the network optimisation modelling (NOM) method to optimise decentralised AM SCs. Utilising AnyLogistix software, the study models an AM SC to determine the optimal network configuration that minimises costs while ensuring timely deliveries. It explores the advantages of decentralised production, such as reduced lead times and costs. This study contributes to the growing body of literature by addressing gaps related to NOM in AM contexts, providing valuable insights for practical applications in SC management.
Although the management of customer relationships has become a key priority in firms in a broad range of industries, its effective implementation often creates major challenges. Couched in configuration theory and using a configurational approach, this article investigates how factors associated with firms’ customer management, market approach, and business environment interact and fall into patterns to predict profitability. The results of a fuzzy-set qualitative comparative analysis reveal different configurations to a profitable implementation of customer relationship management. Insight into these configurations contributes to a deeper understanding of digital ecodynamics and advances the understanding of the interplay among important strategic, technological, and environmental factors. The findings of this inquiry can guide managers in designing effective customer relationship management approaches.
(Abstract vom Preprint - siehe Bemerkungen - übernommen gemäß CC BY 4.0: http://creativecommons.org/licenses/by/4.0/)
