Big Data Needs and Challenges in Smart Manufacturing: An Industry-Academia Survey. Winkler, D., Korobeinykov, A., Novák, P., Lüder, A., & Biffl, S. In 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), pages 1–8, September, 2021. doi abstract bibtex The increasing availability of data in Smart Manufacturing opens new challenges and required capabilities in the area of big data in industry and academia. Various organizations have started initiatives to collect and analyse data in their individual contexts with specific goals, e.g., for monitoring, optimization, or decision support in order to reduce risks and costs in their manufacturing systems. However, the variety of available application areas require to focus on most promising activities. Therefore, we see the need for investigating common challenges and priorities in academia and industry from expert and management perspective to identify the state of the practice and promising application areas for driving future research directions. The goal of this paper is to report on an industry-academia survey to capture the current state of the art, required capabilities and priorities in the area of big data applications. Therefore, we conducted a survey in winter 2020/21 in industry and academia. We received 22 responses from different application domains highlighting the need for supporting (a) fault detection and (b) fault classification based on (c) historical and (d) real-time data analysis concepts. Therefore, the survey results reveals current and upcoming challenges in big data applications, such as defect handling based on historical and real-time data.
@inproceedings{winkler_big_2021,
title = {Big {Data} {Needs} and {Challenges} in {Smart} {Manufacturing}: {An} {Industry}-{Academia} {Survey}},
shorttitle = {Big {Data} {Needs} and {Challenges} in {Smart} {Manufacturing}},
doi = {10.1109/ETFA45728.2021.9613600},
abstract = {The increasing availability of data in Smart Manufacturing opens new challenges and required capabilities in the area of big data in industry and academia. Various organizations have started initiatives to collect and analyse data in their individual contexts with specific goals, e.g., for monitoring, optimization, or decision support in order to reduce risks and costs in their manufacturing systems. However, the variety of available application areas require to focus on most promising activities. Therefore, we see the need for investigating common challenges and priorities in academia and industry from expert and management perspective to identify the state of the practice and promising application areas for driving future research directions. The goal of this paper is to report on an industry-academia survey to capture the current state of the art, required capabilities and priorities in the area of big data applications. Therefore, we conducted a survey in winter 2020/21 in industry and academia. We received 22 responses from different application domains highlighting the need for supporting (a) fault detection and (b) fault classification based on (c) historical and (d) real-time data analysis concepts. Therefore, the survey results reveals current and upcoming challenges in big data applications, such as defect handling based on historical and real-time data.},
booktitle = {2021 26th {IEEE} {International} {Conference} on {Emerging} {Technologies} and {Factory} {Automation} ({ETFA} )},
author = {Winkler, Dietmar and Korobeinykov, Alexander and Novák, Petr and Lüder, Arndt and Biffl, Stefan},
month = sep,
year = {2021},
keywords = {Big Data Application, Big Data applications, Fault detection, Industries, Monitoring, Optimization, Organizations, Real-time systems, Required Capabilities, Smart Manufacturing, State of the Practice, Survey},
pages = {1--8},
}
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However, the variety of available application areas require to focus on most promising activities. Therefore, we see the need for investigating common challenges and priorities in academia and industry from expert and management perspective to identify the state of the practice and promising application areas for driving future research directions. The goal of this paper is to report on an industry-academia survey to capture the current state of the art, required capabilities and priorities in the area of big data applications. Therefore, we conducted a survey in winter 2020/21 in industry and academia. We received 22 responses from different application domains highlighting the need for supporting (a) fault detection and (b) fault classification based on (c) historical and (d) real-time data analysis concepts. 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