Performance Improvement in Manufacturing Shop Floor Operations of Developing Countries Based on Three Characteristics of Information Flow
Open Access
- 1 January 2022
- journal article
- research article
- Published by Scientific Research Publishing, Inc. in Journal of Computer and Communications
- Vol. 10 (03), 46-65
- https://doi.org/10.4236/jcc.2022.103004
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.Keywords
This publication has 20 references indexed in Scilit:
- Supervised Machine Learning Algorithms: Classification and ComparisonInternational Journal of Computer Trends and Technology, 2017
- Random Forest: A ReviewInternational Journal of Advanced Research in Computer Science and Software Engineering, 2017
- Management von WertschöpfungskettenPublished by Verlag C.H.Beck oHG ,2016
- Modelling information flow for organisations: A review of approaches and future challengesInternational Journal of Information Management, 2013
- Resource Structuring or Capability Building? An Empirical Study of the Business Value of Information TechnologyJournal of Management Information Systems, 2012
- Fault diagnosis of ball bearings using machine learning methodsExpert Systems with Applications, 2011
- Fault classification technique for series compensated transmission line using support vector machineInternational Journal of Electrical Power & Energy Systems, 2010
- Information flows and business process integrationBusiness Process Management Journal, 2009
- Random ForestsMachine Learning, 2001
- Bayesian Network ClassifiersMachine Learning, 1997