An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production

The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air shipment and production line down at other entities wi...

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Main Author:	Ahmad Afif, Ahmarofi
Format:	Thesis
Language:	eng eng eng
Published:	2019
Subjects:	HD61 Risk Management
Online Access:	https://etd.uum.edu.my/8605/1/s96234_01.pdf https://etd.uum.edu.my/8605/2/s96234_02.pdf https://etd.uum.edu.my/8605/3/s96234_references.docx
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id	my-uum-etd.8605
record_format	uketd_dc
spelling	my-uum-etd.86052022-02-16T01:42:16Z An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production 2019 Ahmad Afif, Ahmarofi Ramli, Razamin Zainal Abidin, Norhaslinda Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences HD61 Risk Management The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air shipment and production line down at other entities within the supply chain. The uncertainty of completion time has created a big problem for manufacturers of audio speakers which involved semiautomatic production lines. Therefore, the main objective of this research is to develop an integrated model that enhances the artificial neural networks (ANN) and system dynamics (SD) methods in estimating completion time focusing on the cycle time. Three ANN models based on multilayer perceptron (MLP) were developed with different network architectures to estimate cycle time. Furthermore, a proposed momentum rate equation was formulated for each model to improve learning process, where the 3-2-1 network emerged as the best network with the smallest mean square error. Subsequently, the estimated cycle time of the 3-2-1 network was simulated through the development of an SD model to evaluate the performance of completion time in terms of product quantity, manpower fatigue and production workload scores. The success of the proposed integrated ANNSD model also relied on a proposed coefficient correlation of causal loop diagram (CLD) to identify the most influential factor of completion time. As a result, the proposed integrated ANNSD model provided a beneficial guide to the company in determining the most influential factor on completion time so that the time to complete a new audio product can be estimated accurately. Consequently, product delivery was smooth for on-time shipment while successfully fulfilling customers’ demand. 2019 Thesis https://etd.uum.edu.my/8605/ https://etd.uum.edu.my/8605/1/s96234_01.pdf text eng public https://etd.uum.edu.my/8605/2/s96234_02.pdf text eng public https://etd.uum.edu.my/8605/3/s96234_references.docx text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Ramli, Razamin Zainal Abidin, Norhaslinda
topic	HD61 Risk Management
spellingShingle	HD61 Risk Management Ahmad Afif, Ahmarofi An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
description	The determination of completion time to produce a new product is one of the most important indicators for manufacturers in delivering goods to customers. Failure to fulfil delivery on-time or known as tardiness contributes to a high cost of air shipment and production line down at other entities within the supply chain. The uncertainty of completion time has created a big problem for manufacturers of audio speakers which involved semiautomatic production lines. Therefore, the main objective of this research is to develop an integrated model that enhances the artificial neural networks (ANN) and system dynamics (SD) methods in estimating completion time focusing on the cycle time. Three ANN models based on multilayer perceptron (MLP) were developed with different network architectures to estimate cycle time. Furthermore, a proposed momentum rate equation was formulated for each model to improve learning process, where the 3-2-1 network emerged as the best network with the smallest mean square error. Subsequently, the estimated cycle time of the 3-2-1 network was simulated through the development of an SD model to evaluate the performance of completion time in terms of product quantity, manpower fatigue and production workload scores. The success of the proposed integrated ANNSD model also relied on a proposed coefficient correlation of causal loop diagram (CLD) to identify the most influential factor of completion time. As a result, the proposed integrated ANNSD model provided a beneficial guide to the company in determining the most influential factor on completion time so that the time to complete a new audio product can be estimated accurately. Consequently, product delivery was smooth for on-time shipment while successfully fulfilling customers’ demand.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Ahmad Afif, Ahmarofi
author_facet	Ahmad Afif, Ahmarofi
author_sort	Ahmad Afif, Ahmarofi
title	An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
title_short	An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
title_full	An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
title_fullStr	An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
title_full_unstemmed	An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
title_sort	integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2019
url	https://etd.uum.edu.my/8605/1/s96234_01.pdf https://etd.uum.edu.my/8605/2/s96234_02.pdf https://etd.uum.edu.my/8605/3/s96234_references.docx
_version_	1747828421815173120

An integrated approach of artificial neural networks and system dynamics for estimating product completion time in a semiautomatic production

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