A performance management on automobile dealers with applying data envelopment analysis

Author(s):  
T. T. Lin ◽  
C.C. Lee ◽  
F.T. Chang
2020 ◽  
Vol 39 (5) ◽  
pp. 7705-7722
Author(s):  
Mohammad Kachouei ◽  
Ali Ebrahimnejad ◽  
Hadi Bagherzadeh-Valami

Data Envelopment Analysis (DEA) is a non-parametric approach based on linear programming for evaluating the performance of decision making units (DMUs) with multiple inputs and multiple outputs. The lack of the ability to generate the actual weights, not considering the impact of undesirable outputs in the evaluation process and the measuring of efficiencies of DMUs based upon precise observations are three main drawbacks of the conventional DEA models. This paper proposes a novel approach for finding the common set of weights (CSW) to compute efficiencies in DEA model with undesirable outputs when the data are represented by fuzzy numbers. The proposed approach is based on fuzzy arithmetic which formulates the fuzzy additive DEA model as a linear programing problem and gives fuzzy efficiencies of all DMUs based on resulting CSW. We demonstrate the applicability of the proposed model with a simple numerical example. Finally, in the context of performance management, an application of banking industry in Iran is presented for analyzing the influence of fuzzy data and depicting the impact of undesirable outputs over the efficiency results.


2020 ◽  
Vol 15 (7) ◽  
pp. 133
Author(s):  
Claudio Pinto

Performance management is a central point for both public and private organizations. In the data envelopment analysis (DEA) method, performance management takes the form of measuring relative efficiency. Furthermore, considering each organization and or production process as a black box,  inputs are transformed into outputs. In reality, production organizations or processes are composed of different parts that carry out different related activities. For this reason, modeling the internal structure of a production process like a system of interconnected parts makes it possible to measure its performance at the sub-process level. In this paper, we hypothesized a production process, made up of three interconnected parts. It is a new strategy to acquire relative efficiency consisting of building a block inside the system with at least two sub-processes. This step refers to a basic model of relational Network Data Envelopment Analysis (NDEA). Also, we used the additive decomposition formula to measure the efficiency of the whole process. We highlighted the differences in the measurement, between the direct application of the relational NDEA model and the measurement with the block approach model.We compared the cumulative empirical distribution functions of the efficiency scores of a sub-process with the decomposition formula multiplicative and our  approach. In conclusion, the paper proposes, a new strategy to measure the relative performances of a production process model as a network system of three subprocesses, which combines the NDEA and the DEA. This allows us to reevaluate, the indications of policy at the individual sub-process level (block). Moreover, it is a versatile approach which allows aggregation of the sub-processes in blocks, according to the particular policy requirements, legislative technological constraints, etc.


2018 ◽  
Vol 9 (6) ◽  
pp. 109-121
Author(s):  
Stephen Migiro ◽  
Patricia Shewell

 The practice of measuring performance of the finance function as a business support unit is not widespread. This study assessed the importance of measuring finance function performance, by ascertaining whether such measurement facilitates identification of the relative efficiency of business finance functions, and by establishing its impact, if any, on overall company performance. Focussing on a sample of companies in the South African Freight Forwarding industry, a performance metric was developed and implemented to measure finance function performance. Relative finance function efficiency was then evaluated using inputorientated data envelopment analysis (DEA) to identify ‘best in class’ performance and to benchmark participants’ performance. Further, value chain DEA (VC-DEA) was applied to evaluate finance function efficiency simultaneously with overall company efficiency. Results show that implementation of the performance metric together with DEA facilitated the benchmarking of the finance functions of the sample group and the establishment of improvement targets for the finance functions determined as inefficient. In addition, a link between overall company performance and finance function performance in terms of inputs was confirmed; however, this link was not conclusively established as regards finance function performance in terms of outputs. The contribution of the study includes confirmation that implementation of the performance metric together with DEA facilitates the critical evaluation of finance function performance, thus establishing the importance of measuring the performance of the finance functions. In addition, incorporating the use of DEA in a performance framework for the finance function as a business support unit has extended the range of applications of DEA.  


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