Prioritization of Identified Data Science Use Cases in Industrial Manufacturing via C-EDIF Scoring

While data science and artificial intelligence (AI) can be highly beneficial for industrial manufacturers, it is not yet readily usable. Therefore, putting it to good use requires to understand the domain challenges and identify opportunities for deploying AI. Our work aims at solving this task by p...

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Bibliographic Details
Published in2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA) pp. 1 - 4
Main Authors Fischer, Raphael, Pauly, Andreas, Wilking, Rahel, Kini, Anoop, Graurock, David
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.10.2023
Subjects
application
Artificial intelligence
Business
Companies
Data science
exploration
Industries
industry transfer
Manufacturing
prioritization
Task analysis
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Summary:While data science and artificial intelligence (AI) can be highly beneficial for industrial manufacturers, it is not yet readily usable. Therefore, putting it to good use requires to understand the domain challenges and identify opportunities for deploying AI. Our work aims at solving this task by proposing a generalized framework for ❨1❩ exploring companies for use cases and (2) prioritizing them via C-EDIF scoring. This novel approach allows to determine the business importance of any use case by considering the underlying evaluability, data situation, impact and infeasibility. Besides the theoretical framework, our work also provides real-world insights from applying C-EDIF scoring in an extensive use case exploration phase. These results stem from a strategic partnership between data scientists and Wilo SE, a renowned pump manufacturing company, where we successfully identified and rated opportunities for AI.
DOI:10.1109/DSAA60987.2023.10302632