Common set of weights in data envelopment analysis: a linear programming problem

Davoodi, Alireza; Rezai, H. Zhiani

doi:10.1007/s10100-011-0195-6

Cited by 27 publications

(13 citation statements)

References 15 publications

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“…Moreover, the use of USW for a supplier evaluation may cause a TCO-based DEA to overestimate the performance of suppliers. This approach labels suppliers as "efficient" if they perform significantly strong (weak) on some (most) of the inputs by assigning very high (low) weights to the inputs [38] .…”

Section: Sp I T Comentioning

confidence: 99%

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

Shabani

Visani

Barbieri

et al. 2019

Omega

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Total cost of ownership (TCO) is a management accounting technique that evaluates the total cost of a business partnership using a time-consuming activity-based costing procedure. Studies have suggested that TCO-based data envelopment analysis (DEA) can effectively estimate the results of TCO with substantially less effort and time; however, its adoption in practice is limited due to certain shortcomings. First, managers struggle to understand and accept the uncommon weighting schemes of existing TCObased DEA models because traditional TCO analyses require a common set of weights. Second, both the traditional TCO approach and TCO-based DEA models are designed to handle precise data, whereas TCO analyses often involve imprecise data from conflicting data sources and estimations. To address the managerial and technical issues of handling weighting schemes and imprecise data, this paper proposes a novel TCO-based model: common set of weights imprecise DEA (CSW-IDEA). We validate the proposed methodology using real-life datasets from 175 suppliers that serve five key components to two multinational mechanical manufacturers. For both precise data and imprecise data, the proposed CSW-IDEA reliably approximates traditional TCO calculations significantly better than existing TCO-based DEA. The cost savings that can be theoretically generated by applying the CSW-IDEA approach are similar to the cost savings estimated by the traditional TCO approach.

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Section: Sp I T Comentioning

confidence: 99%

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

Shabani

Visani

Barbieri

et al. 2019

Omega

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“…One of them is goal programming (GP). Based on the GP method, model ( 6 ) can be transformed to the following model for attaining a set of common weights [ 23 ]:

Here A j is the goal of the j th objective function and n j , p j represent the positive deviation and negative deviation of the j th goal, respectively. On the other hand, while in the conventional DEA models, every individual DMU tends to maximize its efficiency, so the amounts of A j ( j = 1,…, n ) in model ( 7 ) would be one.…”

Section: Common Set Of Weights Modelmentioning

confidence: 99%

A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

Barzegarinegad

Jahanshahloo

Rostamy-Malkhalifeh

2014

The Scientific World Journal

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We propose a procedure for ranking decision making units in data envelopment analysis, based on ideal and anti-ideal points in the production possibility set. Moreover, a model has been introduced to compute the performance of a decision making unit for these two points through using common set of weights. One of the best privileges of this method is that we can make ranking for all decision making units by solving only three programs, and also solving these programs is not related to numbers of decision making units. One of the other advantages of this procedure is to rank all the extreme and nonextreme efficient decision making units. In other words, the suggested ranking method tends to seek a set of common weights for all units to make them fully ranked. Finally, it was applied for different sets holding real data, and then it can be compared with other procedures.

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“…Amin and Toloo (2007) presented a model capable of finding the most efficient DMUs using CSWs. Davoodi and Zhiani Rezai (2011) suggested a CSWs method for solving a linear programming problem and calculating the efficiency scores and rankings of all the DMUs.…”

Section: Introductionmentioning

confidence: 99%

Robust efficiency measurement with common set of weights under varying degrees of conservatism and data uncertainty

Aghayi

Tavana

Raayatpanah

2016

EJIE

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The conventional paradigm in data envelopment analysis (DEA) is to develop an efficiency measurement model that assumes the input and output data are precise and equal to some nominal values. However, this paradigm does not take into consideration the inherent uncertainties in real-life performance measurement problems. As a result of these uncertainties, the input and output data may take non-nominal values and violate the basic assumptions in DEA. This phenomenon has motivated us to design a DEA model that is 'robust' and immune to uncertain data. We present a robust DEA model with a common set of weights (CSWs) under varying degrees of conservatism and data uncertainty. We use goal programming (GP) and compute the relative efficiencies of the decision making units (DMUs) by producing CSWs in one run. The proposed model uses a confidence criterion to produce a ranking of the DMUs and determine a set of efficient DMUs. We present a numerical example and a case study to exhibit the efficacy of the procedures and to demonstrate the applicability of the proposed method to a performance measurement problem in the banking industry. [

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Common set of weights in data envelopment analysis: a linear programming problem

Cited by 27 publications

References 15 publications

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

Reliable estimation of suppliers’ total cost of ownership: An imprecise data envelopment analysis model with common weights

A Full Ranking for Decision Making Units Using Ideal and Anti-Ideal Points in DEA

Robust efficiency measurement with common set of weights under varying degrees of conservatism and data uncertainty

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