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Mbakop, A.M., Biyeme, F., Voufo, J. and Meva’a, J.R.L. (2021) Predictive Analysis of the Value of Information Flow on the Shop Floor of Developing Countries Using Artificial Neural Network Based Deep Learning. Heliyon, 7, Article ID: E08315.
https://doi.org/10.1016/j.heliyon.2021.e08315

has been cited by the following article:

• TITLE: Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow

  AUTHORS: Joseph Voufo, André Marie Mbakop, Florent Biyeme, Rolland Djomi, Pélagie Flore Nanfack Temgoua, Jean Raymond Lucien Meva’a

  KEYWORDS: Developing Countries, Information Flow Characteristics, Machine Learning, Shop Floor Operations, Value of Information Flow

  JOURNAL NAME: Journal of Computer and Communications, Vol.10 No.3, March 17, 2022

  ABSTRACT: The management of information flow for production improvement has always been a target in the research. In this paper, the focus is on the analysis model of the characteristics of information flow in shop floor operations based on the influence that dimension (support or medium), direction and the quality information flow have on the value of information flow using machine learning classification algorithms. The obtained results of classification algorithms used to analyze the value of information flow are Decision Trees (DT) and Random Forest (RF) with a score of 0.99% and the mean absolute error of 0.005. The results also show that the management of information flow using DT or RF shows that, the dimension of information such as digital information has the greatest value of information flow in shop floor operations when the shop floor is totally digitalized. Direction of information flow does not have any great influence on shop floor operations processes when the operations processes are digitalized or done by operators as machines.