COMBINING DATA FARMING AND DATA ENVELOPMENT ANALYSIS FOR MEASURING PRODUCTIVE EFFICIENCY IN MANUFACTURING SIMULATIONS

Discrete event simulation is an established methodology for investigating the dynamic behavior of complex manufacturing and logistics systems. In addition to traditional simulation studies, the concept of data farming and knowledge discovery in simulation data is a current research topic that consist of broad scale experimentation and data mining assisted analysis of massive simulation output data. While most of the current research aims to investigate key drivers of production performance, in this paper we propose a methodology for investigating productive efficiency. We therefore developed a concept of combining our existing approach of data farming and visual analytics with data envelopment analysis (DEA), which is used to investigate efficiency in operations research and economics. With this combination of concepts, we are not only able to determine key factors and interactions that drive productive efficiency in the modeled manufacturing system, but also to identify the most productive settings.

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