A framework to guide the selection and configuration of machine-learning-based data analytics solutions in manufacturing
Open accessAbstract
Users in manufacturing willing to apply machine-learning-based (ML-based) data analytics face challenges related to data quality or to the selection and configuration of proper ML algorithms. Current approaches are either purely empirical or reliant on technical data. This makes understanding and comparing candidate solutions difficult, and also ignores the way it impacts the real application problem. In this paper, we propose a framework to generate analytics solutions based on a systematic profiling of all aspects involved. With it, users can visually and systematically explore relevant alternatives for their specific scenario, and obtain recommendations in terms of costs, productivity, results quality, or execution time.
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Nine of these papers (i.e., [2,15,21,29,34,40,[49][50][51]) propose a data analytic solution as part of a CPS or IoT system.
“Nine of these papers (i.e., [2,15,21,29,34,40,[49][50][51]) propose a data analytic solution as part of a CPS or IoT system.”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [50]
The Conclusion describes a framework that recommends and compares machine-learning-based analytics solutions for manufacturing, with a prototype applied to two use cases. It does not describe the framework or those solutions as part of a CPS or IoT system (searched: CPS, cyber-physical system, IoT). (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe most frequently mentioned reasons for data quality issues in the reviewed literature are heterogeneous multiple data sources, sensor malfunctions, network problems, high sampling frequency, and cyber attacks for data tampering.
“The most mentioned reasons are heterogeneous multiple data sources [4,12,30,44,50], sensor malfunctions [15,31,35], network problems (connection failures, communication delays) [16,37,40], high sampling frequency [18], and cyber attacks for data tampering [7,31].”
From A Systematic Review of Data Quality in CPS and IoT for Industry 4.0 (2023) · cites this paper as [50]
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A Systematic Review of Data Quality in CPS and IoT for Industry 4.0
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- Authors
- Villanueva Zacarias, A. G., Reimann, P., & Mitschang, B.
- Affiliations
- University of Stuttgart (DE)
- Venue
- Procedia CIRP
- Publisher
- Elsevier BV
- Published
- 2018
- Volume
- 72
- Pages
- 153-158
- ISSN
- 2212-8271 (ISSN-L)
- OpenAlex
- W2810477621
- Cited by
- 26
- References
- 17
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 4 · 2022: 7 · 2021: 2 · 2020: 3 · 2019: 2 · 2018: 1
- FWCI
- 1.65
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Procedia CIRP (publishedVersion, cc-by-nc-nd)
- Funders
- Deutsche Forschungsgemeinschaft
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Digital Transformation in Industry
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- machine learning, data analytics, manufacturing industry, solution selection, algorithm configuration, solution recommendation, data quality, decision support framework
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