Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning

To facilitate the continuous improvement of performance and the management of information flow (MIF) for production and manufacturing purposes on the shop floor of developing countries, there is a need to characterize information flow that will be shared during the process. MIF provides a key perfor...

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Autores principales:	André Marie Mbakop, Florent Biyeme, Joseph Voufo, Jean Raymond Lucien Meva'a
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Deep learning Characteristics of information flow Developing countries Performance PSO-ANN Value of information flow Science (General) Q1-390 Social sciences (General) H1-99
Acceso en línea:	https://doaj.org/article/48a699ad2c6c4f468762cbd76e9f233b
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spelling	oai:doaj.org-article:48a699ad2c6c4f468762cbd76e9f233b2021-12-02T05:02:35ZPredictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning2405-844010.1016/j.heliyon.2021.e08315https://doaj.org/article/48a699ad2c6c4f468762cbd76e9f233b2021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S240584402102418Xhttps://doaj.org/toc/2405-8440To facilitate the continuous improvement of performance and the management of information flow (MIF) for production and manufacturing purposes on the shop floor of developing countries, there is a need to characterize information flow that will be shared during the process. MIF provides a key performance shop floor metric called the value of information flow (VIF). Previous methods have been used to analyze VIF in developed countries. However, these methods are sometimes limited when applied to developing countries where the shop floor is disorganized. It then renders the MIF with the imported software inefficient because of the gap between the user environments. Taking Cameroon as a case study, this study proposes a new method of modeling and analyzing the information flow and its value based on the characteristics of information flow (CIF) for developing countries. In addition, a predictive analysis of the VIF based on CIF using an artificial neural network (ANN) on one hand and optimized ANN with particle swarm optimizer (PSO) and genetic algorithms (GA) on the other is performed. The ANN model of regression developed has the following performance: coefficient of determination: 0.99 and mean squared error (MSE): 0.00043. For the PSO-ANN, the MSE decreased to 0.00011, and this model result was similar to that of the deep learning model used for regression. The GA-ANN model results were not as satisfactory as those of the PSO-ANN model. A predictive system to analyze VIF is proposed for managers of companies in developing countries.André Marie MbakopFlorent BiyemeJoseph VoufoJean Raymond Lucien Meva'aElsevierarticleDeep learningCharacteristics of information flowDeveloping countriesPerformancePSO-ANNValue of information flowScience (General)Q1-390Social sciences (General)H1-99ENHeliyon, Vol 7, Iss 11, Pp e08315- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Deep learning Characteristics of information flow Developing countries Performance PSO-ANN Value of information flow Science (General) Q1-390 Social sciences (General) H1-99
spellingShingle	Deep learning Characteristics of information flow Developing countries Performance PSO-ANN Value of information flow Science (General) Q1-390 Social sciences (General) H1-99 André Marie Mbakop Florent Biyeme Joseph Voufo Jean Raymond Lucien Meva'a Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
description	To facilitate the continuous improvement of performance and the management of information flow (MIF) for production and manufacturing purposes on the shop floor of developing countries, there is a need to characterize information flow that will be shared during the process. MIF provides a key performance shop floor metric called the value of information flow (VIF). Previous methods have been used to analyze VIF in developed countries. However, these methods are sometimes limited when applied to developing countries where the shop floor is disorganized. It then renders the MIF with the imported software inefficient because of the gap between the user environments. Taking Cameroon as a case study, this study proposes a new method of modeling and analyzing the information flow and its value based on the characteristics of information flow (CIF) for developing countries. In addition, a predictive analysis of the VIF based on CIF using an artificial neural network (ANN) on one hand and optimized ANN with particle swarm optimizer (PSO) and genetic algorithms (GA) on the other is performed. The ANN model of regression developed has the following performance: coefficient of determination: 0.99 and mean squared error (MSE): 0.00043. For the PSO-ANN, the MSE decreased to 0.00011, and this model result was similar to that of the deep learning model used for regression. The GA-ANN model results were not as satisfactory as those of the PSO-ANN model. A predictive system to analyze VIF is proposed for managers of companies in developing countries.
format	article
author	André Marie Mbakop Florent Biyeme Joseph Voufo Jean Raymond Lucien Meva'a
author_facet	André Marie Mbakop Florent Biyeme Joseph Voufo Jean Raymond Lucien Meva'a
author_sort	André Marie Mbakop
title	Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
title_short	Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
title_full	Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
title_fullStr	Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
title_full_unstemmed	Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
title_sort	predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/48a699ad2c6c4f468762cbd76e9f233b
work_keys_str_mv	AT andremariembakop predictiveanalysisofthevalueofinformationflowontheshopfloorofdevelopingcountriesusingartificialneuralnetworkbaseddeeplearning AT florentbiyeme predictiveanalysisofthevalueofinformationflowontheshopfloorofdevelopingcountriesusingartificialneuralnetworkbaseddeeplearning AT josephvoufo predictiveanalysisofthevalueofinformationflowontheshopfloorofdevelopingcountriesusingartificialneuralnetworkbaseddeeplearning AT jeanraymondlucienmevaa predictiveanalysisofthevalueofinformationflowontheshopfloorofdevelopingcountriesusingartificialneuralnetworkbaseddeeplearning
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Predictive analysis of the value of information flow on the shop floor of developing countries using artificial neural network based deep learning

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