2022
Burduk, A.; Dybała, B.; MacHado, J.
Efficiency forecasting of electric bundle assembly with use of ANN model Proceedings Article
Em: A.D.L., Xavior M. A. Burduk A. Batako (Ed.): Institute of Physics, 2022, ISSN: 17426588, (cited By 0; Conference of 15th Global Congress on Manufacturing and Management, GCMM 2021 ; Conference Date: 25 November 2020 Through 27 November 2020; Conference Code:180965).
Resumo | Links | BibTeX | Etiquetas: Artificial neural network modeling; Assembly line; Assembly process; Decision-based; IT system; Production companies; Production system; Productline; Use-model; Workers', Assembly, Assembly machines; Information management; Manufacture; Neural networks; Production control; Production efficiency
@inproceedings{Burduk2022,
title = {Efficiency forecasting of electric bundle assembly with use of ANN model},
author = {A. Burduk and B. Dybała and J. MacHado},
editor = {Xavior M. A. Burduk A. Batako A.D.L.},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134739780&doi=10.1088%2f1742-6596%2f2198%2f1%2f012001&partnerID=40&md5=76ecb3dac2c40e4ab964e68e0041091a},
doi = {10.1088/1742-6596/2198/1/012001},
issn = {17426588},
year = {2022},
date = {2022-01-01},
journal = {Journal of Physics: Conference Series},
volume = {2198},
number = {1},
publisher = {Institute of Physics},
abstract = {Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles. © Published under licence by IOP Publishing Ltd.},
note = {cited By 0; Conference of 15th Global Congress on Manufacturing and Management, GCMM 2021 ; Conference Date: 25 November 2020 Through 27 November 2020; Conference Code:180965},
keywords = {Artificial neural network modeling; Assembly line; Assembly process; Decision-based; IT system; Production companies; Production system; Productline; Use-model; Workers', Assembly, Assembly machines; Information management; Manufacture; Neural networks; Production control; Production efficiency},
pubstate = {published},
tppubtype = {inproceedings}
}
Management of production systems requires making immediate decisions based on the data generated in bulk by IT systems. In this case, it can be helpful to use models of artificial neural networks (ANN) that, on the grounds of accessible data, will determine results of the made decision. One of the key problems in production companies is determination of execution time and cost of a production order. The problem is especially important in a company manufacturing a variable product line with a big part of manual operations. In the article, the way of building an ANN model for efficiency forecasting of the assembly process of electric bundles is presented. With regard to the very wide and variable product line, the products with different complexity degree are manufactured on three types of assembly lines. The assembly processes are performed on the assembly lines manually by groups of workers, so efficiency of the process is influenced mostly by skills and experience of these workers. Therefore, numbers of new assembly workers assigned to individual assembly lines and quantities of new products in the production schedule are selected as explanatory variables in the ANN model. The explained variable in the ANN model is production volume of the manufactured electric bundles. © Published under licence by IOP Publishing Ltd.