A Study on the Improvement Targets of Data Envelopment Analysis Models
- Resource Type
- Conference
- Authors
- Wang, Xu; Iwamoto, Hiroki; Hasuike, Takashi
- Source
- 2023 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) Industrial Engineering and Engineering Management (IEEM), 2023 IEEE International Conference on. :0607-0611 Dec, 2023
- Subject
- Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Transportation
Analytical models
Industrial engineering
Data models
Mathematical models
Numerical models
Data envelopment analysis
Mathematical programming
DEA
Improvement Target
ADD Model
Least-distance Model
- Language
In this paper, we focus on the characteristics of improvement targets generated by two distinct types of Data Envelopment Analysis (DEA) models: the conventional additive (ADD) model and the least-distance DEA model. DEA is a mathematical programming technique employed to evaluate the relative efficiency of decision making units (DMUs) that have multiple inputs and outputs. One of the notable aspects of DEA is its ability to generate improvement targets for each inefficient DMU to achieve efficiency. Thus, the concept of the least-distance DEA model has been introduced to generate the closest improvement target that closely resembles the evaluated DMU and can be easily achieved. To evaluate the effectiveness of the different improvement targets, we compare the improvement targets generated by the ADD and the least-distance DEA models. This analysis is performed using a time-series dataset comprising 86 retail companies in Japan. The results of the numerical experiments indicate that the improvement targets generated by the least-distance DEA model exhibit superiority in achieving efficiency for the inefficient DMUs. These findings shed light on the potential advantages and effectiveness of the least-distance DEA model in improving the efficiency of DMUs.