TY - JOUR
T1 - Smart manufacturing and continuous improvement and adaptation of predictive models
AU - Kronberger, Gabriel
AU - Bachinger, Florian
AU - Affenzeller, Michael
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - Artificial Intelligence
KW - Concept Drift
KW - Predictive Models
KW - Smart Manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85084235380&partnerID=8YFLogxK
U2 - 10.1016/j.promfg.2020.02.037
DO - 10.1016/j.promfg.2020.02.037
M3 - Conference article
AN - SCOPUS:85084235380
SN - 2351-9789
VL - 42
SP - 528
EP - 531
JO - Procedia Manufacturing
JF - Procedia Manufacturing
T2 - 1st International Conference on Industry 4.0 and Smart Manufacturing, ISM 2019
Y2 - 20 November 2019 through 22 November 2019
ER -