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Benjamin van Giffen
Title
Prof. Dr.
Last Name
van Giffen
First name
Benjamin
Email
benjamin.vangiffen@unisg.ch
Phone
+41 71 224 3635
Now showing
1 - 3 of 3
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PublicationEngineering AI-Enabled Computer Vision Systems: Lessons From Manufacturing( 2022)
;Johannes SchniertshauerThis article shares our results on challenges in engineering artificial intelligence (AI)-enabled computer vision systems for manufacturing and highlights critical success factors that have proven their worth. We provide AI engineers and development teams with timely and engaging inputs from the field.Type: journal articleJournal: IEEE SoftwareVolume: 39Issue: 6Scopus© Citations 2 -
PublicationBecoming Certain About the Uncertain: How AI Changes Proof-of-Concept Activities in Manufacturing - Insights from a Global Automotive Leader( 2022)
;Johannes SchniertshauerIn this paper, we examine Proof-of-Concept activities in the presence of Artificial Intelligence (AI). To this end, we conducted an exploratory, revelatory case study at a leading automotive OEM that constantly explores new technologies to improve its manufacturing processes. We highlight how AI properties affect specifics in project execution and how they are addressed within the focal company. We carved out four key areas affecting underlying activities, i.e., data assessment, process alignment, value orientation, and AI empowerment. With our findings, we provide practical insights into AI-related challenges and corresponding pathways for action. Drawn upon, we develop novel, timely, and actionable recommendations for AI project leaders planning to implement this novel technology in manufacturing. This shall provide empirically grounded and conceptually sound guidance for researchers and practitioners alike, and ultimately foster the success of AI in manufacturing.Type: conference paper -
PublicationActualizing Affordances: A Socio-Technical Perspective on Big Data Analytics in the Automotive Sector( 2017)Big data analytics affects organization in multiple ways, among others in regard to (1) new task possibilities for companies (i.e., affordances), (2) required technology implementations, (3) the development of peoples’ capabilities as well as a data-driven culture, and (4) the implementation of organizational structures that ensure collaboration across departmental boundaries. Drawing on a multiple-case study approach, we use affordance theory in conjunction with socio-technical systems theory to elaborate ex-ante required organizational changes as well as the subsequent actualization of big data analytics affordances. First results indicate that to actualize big data analytics’ affordances the successful adaption of components within the socio-technical system (i.e., actors, structures, and technology) is necessary. In this article, we report on our research design as well as preliminary results of an initial case analysis discussing organizational antecedents for the actualization of big data analytics affordances.Type: conference paper