Analysis Model of the Value of Information Flow for Decision Making and Performance Improvement in Shop Floor of Developing Countries Using Machine Learning Algorithms

Abstract Performance improvement is a daily activity for Small and Medium Size companies in the manufacturing sector, which always depends on the good management of information flow for a better decision making to facilitate shop floor operations that will have a major impact on quality and timely product delivery to customers. The management of information flow is also conditioned by the characterization of the information flow. In this paper we used the characteristics of information flow to determine and predict an analysis model of the value of information flow that will facilitate decision making in shop floor operations by operators (machine, humans and computers ) through the means of machine learning. The outcome of our work proposes the Decision Trees Model as the better one to predict the value of information flow as long as the characteristics are binary data or scale data, it shows that a digital information can always has a good value of information flow if there is no disruptions and finally we can still have a good value of information flow if using papers, visual, electronic real-time information which are accessible, timely, none volatile, and that has a major concern which the shop floor operations.