A proposed DEA window analysis for assessing efficiency from asymmetry dynamic data

Abstract

Nowadays, one of the main challenges facing production management is how to enhance the performance of manufacturing processes by utilizing asymmetry input and output data. This research, therefore, developed a framework for window analysis in data envelopment analysis (DEA) for evaluating the overall technical efficiencies from asymmetry dynamic input and output data. The framework was applied to assess the technical (TE), managerial (PTE), and scale (SE) efficiencies of a blowing machine under three fuzzy input variables (planned production quantity, number of defectives, and idle time) and a fuzzy output variable (actual or target production quantity). The efficiency measures were then evaluated for all DMUs at low (L), middle (M), and high (H) data levels. The obtained optimal fuzzy efficiencies were then transformed into a single crisp optimal efficiency. The results showed that all seven DMUs of the blowing machine were technically inefficient. The input and output slacks were estimated and utilized to determine the necessary improvement actions. Improvement results revealed that the optimal TE, PTE, and SE were significantly improved, which may result in significant savings in production and quality costs. In conclusion, the proposed framework is effective in improving the efficiency of the blowing process and can be utilized for efficiency assessment in a wide range of applications.