Symbolic data as measures for logistical performances

Abstract

In this paper we are analysing logistical surveys. The surveys assess the level of best practices application within manufacturing companies and are processed in our prototype of a machine-learning system combining categorical data and numerical financial data building value factors. We extended the meta-data phase measuring dissimilarities between different modal variables. The main objective is to find patterns in the distribution of the results that can be translated in general rules.