Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques
By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned imp...
Saved in:
| Main Authors: | Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weißer |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-12-01
|
| Series: | Supply Chain Analytics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S294986352400030X |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Hybrid genetic algorithm to minimize scheduling cost with unequal and job dependent earliness tardiness cost
by: Prasad Bari, et al.
Published: (2023-11-01) -
Turbofan engine health status prediction with artificial neural network
by: Slawomir Szrama, et al.
Published: (2024-12-01) -
Early Engineers and Architects Born on the Territory of Present North Macedonia
by: Vladimir B. Ladinski
Published: (2023-10-01) -
Engine Health Status Prediction Based on Oil Analysis With Augmented Machine Learning Algorithms
by: Slawomir Szrama
Published: (2024-12-01) -
Research on resource monitoring and billing mechanisms of application engine in cloud computing environment
by: Yi REN, et al.
Published: (2012-09-01)