期刊
OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE
卷 102, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.omega.2020.102355
关键词
Data envelopment analysis; Negative data; Directional distance function; Flexible measures; Productivity; Supplier selection; Automotive industry
资金
- Czech Science Foundation [GACR19-13946S]
This study introduces a new extended non-radial directional distance model to address negative data and uses flexible measures to handle the unknown status of performance measures. The efficacy of the proposed models is demonstrated through a case study in the automotive industry.
Data envelopment analysis (DEA) is a mathematical approach for evaluating the efficiency of decision making units that convert multiple inputs into multiple outputs. Traditional DEA models measure technical (radial) efficiencies by assuming the input and output status of each performance measure is known, and the data associated with the performance measures are non-negative. These assumptions are restrictive and limit the applications of DEA to real-world problems. We propose a new extended non-radial directional distance model, which is a variant of the weighted additive model, to cope with negative data. We then extend our model and use flexible measures, which play the role of both inputs and outputs, to cope with the unknown status of the performance measures. We also present a case study in the automotive industry to exhibit the efficacy of the models proposed in this study. (c) 2020 Elsevier Ltd. All rights reserved.
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