Dashboard application model in supplier evaluation by using artificial immune system and data mining methods

Abstract

Globalization and rapid developments in science and technology lead to an increase in competition and diffraction in the objectives in the production methods. In order to meet the rapidly changing and differentiated needs, manufacturing businesses are left against technological renewal. Especially usage of the data that is collected in electronic media and the ease of access to information forces businesses to review computer systems on point of production management. Visualization of the data analyzed in the databases is a suitable solution in the decision-making processes of the manufacturing companies. In this context, the dashboard is seen as a good support tool especially for the manufacturing businesses, at a fast and accurate decision-making point. This article represents a new model approach to accumulated analysis and its sharing for the manufacturing businesses by using the artificial immune system and data mining techniques under the title of the dashboard. In the model, data is increased and handled with clonal selection algorithm. In the analysis stage, the data is clustered with kmeans algorithm. The data are visualized by calculating the weighted average and the performance indicators. The visuals that have been obtained will be shared with an app which supports the decision makers with the dashboard rules. Our approach provides a new approaching model to unite, analyze and visualize the collections of data.