[Zurück]

D. Gyulai, A. Pfeiffer, V. Gallina, W. Sihn, L. Monostori:
"Lead time prediction in a flow-shop environment with analytical and machine learning approaches";
Vortrag: 16th IFAC Symposium on Information Control Problems in Manufacturing, Bergamo; 11.06.2018 - 13.06.2018; in: "IFAC PapersOnLine", Vol. 51, Nr. 11 (2018), ISSN: 2405-8963; S. 1029 - 1034.

Manufacturing lead time (LT) is often among the most important corporate
performance indicators that companies wish to minimize in order to meet the customer
expectations, by delivering the right products in the shortest possible time. Most production
planning and scheduling methods rely on LTs, therefore, the e_ciency of these methods is
crucially a_ected by the accuracy of LT prediction. However, achieving high accuracy is often
complicated, due to the complexity of the processes and high variety of products. In the paper,
analytical and machine learning prediction techniques are analyzed and compared, focusing on
a real ow-shop environment exposed to frequent changes and uncertainties resulted by the
changing customer order stream. The digital data twin of the processes is applied to accurately
predict the manufacturing LT of jobs, keeping the prediction models up-to-date via online
connection with the manufacturing execution system, and frequent retraining of the models.

Production control, Lead time, Manufacturing systems, Machine learning, Prediction methods, Statistical inference

http://dx.doi.org/10.1016/j.ifacol.2018.08.474

https://publik.tuwien.ac.at/files/publik_271172.pdf