Assembly Time Standard Setting Based on Kernel Estimators

Time standards belong to vital indicators of the production process that facilitate making decisions related to product and process improvement. The presented issues concern the determination of the assembly time standard using kernel estimators. The development of neural networks offers the possibi...

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Main Author: Izabela Kutschenreiter-Praszkiewicz
Format: Article
Language:English
Published: Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT 2025-03-01
Series:Journal of Machine Engineering
Subjects:
Online Access:http://jmacheng.not.pl/Assembly-Time-Standard-Setting-Based-on-Kernel-Estimators,203100,0,2.html
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author Izabela Kutschenreiter-Praszkiewicz
author_facet Izabela Kutschenreiter-Praszkiewicz
author_sort Izabela Kutschenreiter-Praszkiewicz
collection DOAJ
description Time standards belong to vital indicators of the production process that facilitate making decisions related to product and process improvement. The presented issues concern the determination of the assembly time standard using kernel estimators. The development of neural networks offers the possibility to identify begin-end points in the assembly process that can provide big data related to the assembly time standard. The problem addressed in this paper is development of a method of big data analysis, on the basis of which the assembly time standard can be determined. In the presented approach adequate formulas are developed together with some examples. The paper presents an application of the theory of kernel estimators as well as results of the proposed approach.
format Article
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institution DOAJ
issn 1895-7595
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language English
publishDate 2025-03-01
publisher Publishing House of Wrocław Board of Scientific Technical Societies Federation NOT
record_format Article
series Journal of Machine Engineering
spelling doaj-art-b2021446fe014d62bbec9db5f2cbbddd2025-08-20T03:07:34ZengPublishing House of Wrocław Board of Scientific Technical Societies Federation NOTJournal of Machine Engineering1895-75952391-80712025-03-0125110211210.36897/jme/203100203100Assembly Time Standard Setting Based on Kernel EstimatorsIzabela Kutschenreiter-Praszkiewicz0Industrial Engineering, University of Bielsko-Biala Willowa 2, 43-300 Bielsko-Biała, Poland, PolandTime standards belong to vital indicators of the production process that facilitate making decisions related to product and process improvement. The presented issues concern the determination of the assembly time standard using kernel estimators. The development of neural networks offers the possibility to identify begin-end points in the assembly process that can provide big data related to the assembly time standard. The problem addressed in this paper is development of a method of big data analysis, on the basis of which the assembly time standard can be determined. In the presented approach adequate formulas are developed together with some examples. The paper presents an application of the theory of kernel estimators as well as results of the proposed approach.http://jmacheng.not.pl/Assembly-Time-Standard-Setting-Based-on-Kernel-Estimators,203100,0,2.htmlassemblytime standardkernel estimators
spellingShingle Izabela Kutschenreiter-Praszkiewicz
Assembly Time Standard Setting Based on Kernel Estimators
Journal of Machine Engineering
assembly
time standard
kernel estimators
title Assembly Time Standard Setting Based on Kernel Estimators
title_full Assembly Time Standard Setting Based on Kernel Estimators
title_fullStr Assembly Time Standard Setting Based on Kernel Estimators
title_full_unstemmed Assembly Time Standard Setting Based on Kernel Estimators
title_short Assembly Time Standard Setting Based on Kernel Estimators
title_sort assembly time standard setting based on kernel estimators
topic assembly
time standard
kernel estimators
url http://jmacheng.not.pl/Assembly-Time-Standard-Setting-Based-on-Kernel-Estimators,203100,0,2.html
work_keys_str_mv AT izabelakutschenreiterpraszkiewicz assemblytimestandardsettingbasedonkernelestimators