Abstract
Predictive models are an important success factor for smart manufacturing. Accordingly, purely data-driven models as well as hybrid models are increasingly deployed within manufacturing environments for optimal control of plants. However, long-term monitoring and adaptation of predictive models has not been a focus of studies so far but will likely become increasingly more important as more and more predictive models are deployed. We give a number of recommendations for effectively managing predictive models in smart manufacturing environments.
Original language | English |
---|---|
Pages (from-to) | 528-531 |
Number of pages | 4 |
Journal | Procedia Manufacturing |
Volume | 42 |
DOIs | |
Publication status | Published - 2020 |
Event | 1st International Conference on Industry 4.0 and Smart Manufacturing, ISM 2019 - Rende (CS), Italy Duration: 20 Nov 2019 → 22 Nov 2019 |
Keywords
- Artificial Intelligence
- Concept Drift
- Predictive Models
- Smart Manufacturing