The Application of neural networks to modular arrangements of predetermined time standards

Basitere, Emmanuel (author), Daniyan, Ilesanmi (author), Mpofu, Khumbulani (author), Adeodu, Adefemi (author)

Modular arrangements of predetermined time standards (MODAPTS) is an effective and efficient method to measure work and the activities associated with it. The time standard is used all over the world in different industries, but the method is old, slow, and difficult for first-time users to work with. This study applied neural networks (NN) to MODAPTS. Primary training data in the form of MODAPTS keywords were employed. The training data were acquired as raw data in the form of MODAPTS time studies. These data however was then broken and processed to extract the keywords for the training of the NN. The NN was also trained with the data collected using the TensorFlow algorithm assisted by the Keras library. This was achieved by first learning the fundamentals of creating a NN. Thereafter, consolidating several tools, such as the Python programming language and the Keras library, were used to implement the artificial neural network (ANN). The results obtained indicated that 94.7 % of successful predictions were made by the NN while only 5.3 % were manually entered codes to correct the ANN chatbot. The mean difference between the two methods is 0.25 minutes; the t-test was calculated at 95 % confidence level (0.05) and produced a P-value of 0.9663. The computed P-value was greater than 0.05, showing that there is no significant difference between the two generated studies. The MODAPTS-ANN technique demonstrated in this study possesses great potential to improve and refine work measurement.

• Language: English
• Keywords: artificial neural network, modular arrangement, predetermined time standards, TensorFlow algorithm
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FS - Faculty of Mechanical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2023
• Number of pages: Str. 61-72
• Numbering: Vol. 69, no. 1/2
• PID: 20.500.12556/RUL-144951
• UDC: 681.5:004.021
• ISSN on article: 0039-2480
• DOI: 10.5545/sv-jme.2022.238
• COBISS.SI-ID: 146543619
• Publication date in RUL: 24.03.2023

Record is a part of a journal

• Title: Strojniški vestnik
• Shortened title: Stroj. vestn.
• Publisher: Zveza strojnih inženirjev in tehnikov Slovenije [etc.], = Association of Mechanical Engineers and Technicians of Slovenia [etc.
• ISSN: 0039-2480
• COBISS.SI-ID: 762116

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

• Language: Slovenian
• Title: Uporaba nevronske mreže pri modularni ureditvi vnaprej določenih časovnih normativov
• Keywords: umetna nevronska mreža, modularna ureditev, vnaprej določeni časovni normativi, algoritem TensorFlow