DataEcoSys - Data EcoSystem in Information Systems
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Paper Type
Complete
Paper Number
1527
Description
Additive Manufacturing (AM) promises to redefine production by enabling unforeseen product designs, new degrees of customization, or transformative approaches to spare parts management. However, the radical degree of innovation of AM also incurs unprecedented changes to production planning and control with a plethora of new technical and managerial decisions to be made. As a new bridge between the physical and digital world, the technology is inherently suited to address these issues in a data-driven, analytics-based manner. We argue that it is advisable to look at this subject with a business ecosystem lens. Based on a series of interviews and workshops, we derive a set of analytics services for AM that can be embedded into such ecosystems. For each service, we define the benefit, the analytics potential, the involved roles, the data provision, and the information generation. Our results suggest that an analytics-driven ecosystem approach helps unlock the true AM potential.
Recommended Citation
Pfähler, Kathrin; Baars, Henning; Hiller, Simon; Morar, Dominik; and Petrik, Dimitri, "Data Analytics Services for Additive Manufacturing Ecosystems" (2022). AMCIS 2022 Proceedings. 2.
https://aisel.aisnet.org/amcis2022/DataEcoSys/DataEcoSys/2
Data Analytics Services for Additive Manufacturing Ecosystems
Additive Manufacturing (AM) promises to redefine production by enabling unforeseen product designs, new degrees of customization, or transformative approaches to spare parts management. However, the radical degree of innovation of AM also incurs unprecedented changes to production planning and control with a plethora of new technical and managerial decisions to be made. As a new bridge between the physical and digital world, the technology is inherently suited to address these issues in a data-driven, analytics-based manner. We argue that it is advisable to look at this subject with a business ecosystem lens. Based on a series of interviews and workshops, we derive a set of analytics services for AM that can be embedded into such ecosystems. For each service, we define the benefit, the analytics potential, the involved roles, the data provision, and the information generation. Our results suggest that an analytics-driven ecosystem approach helps unlock the true AM potential.
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DataEcoSys