Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques
- DOI
- 10.1016/j.sca.2024.100087
- Published
- 12
- Container
- Supply Chain Analytics
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1016/j.sca.2024.100087,
title = {Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques},
author = {Ognjen Radišić-Aberger and Peter Burggräf and Fabian Steinberg and Alexander Becher and Tim Weißer},
year = {2024},
journal = {Supply Chain Analytics},
doi = {10.1016/j.sca.2024.100087},
url = {https://doi.org/10.1016/j.sca.2024.100087}
}RIS
TY - JOUR TI - Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques AU - Ognjen Radišić-Aberger AU - Peter Burggräf AU - Fabian Steinberg AU - Alexander Becher AU - Tim Weißer PY - 2024 JO - Supply Chain Analytics DO - 10.1016/j.sca.2024.100087 UR - https://doi.org/10.1016/j.sca.2024.100087 ER -
APA
Radišić-Aberger, O., Burggräf, P., Steinberg, F., Becher, A., & Weißer, T. (2024). Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques. Supply Chain Analytics. https://doi.org/10.1016/j.sca.2024.100087
Source records
- doaj · retrieved 2026-09-25T10:35:14.856Z