Performance Measurement

2011 ◽  
pp. 709-715 ◽  
Author(s):  
João Carlos Namorado Clímaco ◽  
João Carlos Soares de Mello ◽  
Lidia Angulo Meza
Keyword(s):  
Raw Materials ◽  
Performance Measure ◽  
Point Of View ◽  
Decision Makers ◽  
The Other ◽  
Computational Technique ◽  
Data Envelopment ◽  
Multi Objective ◽  
Input And Output ◽  
Decision Making Units

Data envelopment analysis (DEA) is a non-parametric technique to measure the efficiency of productive units as they transform inputs into outputs. A productive unit has, in DEA terms, an all-encompassing definition. It may as well refer to a factory whose products were made from raw materials and labor or to a school that, from prior knowledge and lessons time, produces more knowledge. All these units are usually named decision making units (DMU). So, DEA is a technique enabling the calculation of a single performance measure to evaluate a system. Although some DEA techniques that cater for decision makers’ preferences or specialists’ opinions do exist, they do not allow for interactivity. Inversely, interactivity is one of the strongest points of many of the multi-criteria decision aid (MCDA) approaches, among which those involved with multi-objective linear programming (MOLP) are found. It has been found for several years that those methods and DEA have several points in common. So, many works have taken advantage of those common points to gain insight from a point of view as the other is being used. The idea of using MOLP, in a DEA context, appears with the Pareto efficiency concept that both approaches share. However, owing to the limitations of computational tools, interactivity is not always fully exploited. In this article we shall show how one, the more promising model in our opinion that uses both DEA and MOLP (Li & Reeves, 1999), can be better exploited with the use of TRIMAP (Climaco & Antunes, 1987, 1989). This computational technique, owing in part to its graphic interface, will allow the MCDEA method potentialities to be better used. MOLP and DEA share several concepts. To avoid naming confusion, the word weights will be used for the weighing coefficients of the objective functions in the multi-objective problem. For the input and output coefficients the word multiplier shall be used. Still in this context, the word efficient shall be used only in a DEA context and, for the MOLP problems, the optimal Pareto solutions will be called non-dominated solutions.

scholarly journals New inverse DEA models for budgeting and planning

2021 ◽  
Author(s):  
Tahere Sayar ◽  
Mojtaba Ghiyasi ◽  
Jafar Fathali
Keyword(s):  
Planning Process ◽  
Efficiency Score ◽  
Decision Makers ◽  
Data Envelopment ◽  
Input And Output ◽  
Proposed Model ◽  
Dea Models ◽  
Business Sectors ◽  
Input Level ◽  
Decision Making Units

Data envelopment analysis (DEA) measures the efficiency score of a set of homogeneous decision-making units (DMUs) based on observed input and output. Considering input-oriented, the inverse DEA models find the required input level for producing a given amount of production in the current efficiency level. This article proposes a new form of the inverse DEA model considering income (for planning) and budget (for finance and budgeting) constraints. In contrast with the classical inverse model, both input and output levels are variable in proposed models to meet income (or budget) constraints. Proposed models help decision-makers (DMs) to find the required value of each input and each output's income share to meet the income or budget constraint. We apply the proposed model in the efficiency analysis of 58 supermarkets belonging to the same chain. However, these methods are general and can be used in the budgeting and planning process of any production system, including business sectors and firms that provide services.

Classifying Inputs and Outputs in Data Envelopment Analysis Based on TOPSIS Method and a Voting Model

2014 ◽  
Vol 1 (2) ◽  
pp. 48-63 ◽  
Author(s):  
M. Soltanifar ◽  
S. Shahghobadi
Keyword(s):  
Data Envelopment Analysis ◽  
Programming Model ◽  
Performance Measure ◽  
Point Of View ◽  
Data Envelopment ◽  
Topsis Method ◽  
The Status ◽  
Decision Making Units ◽  
Inputs And Outputs ◽  
Voting Model

In conventional data envelopment analysis, it is assumed that the input versus output status of any particular performance measure is known. In some situations, finding the status of some variables from the input or output point of view is very difficult; these variables are treated as both inputs and outputs and are called flexible measures. In this paper, using the TOPSIS method, and also using a voting model, the status of such a variable will be determined, and the results obtained will be employed to evaluate the efficiency of homogeneous decision making units. Note that all the models used in this paper are linear programming models and there is no need to solve any integer programming model. The approach is illustrated by an example.

scholarly journals Evaluating Efficiency of Waqf Institutions: An Intermediation Approach Using Data Envelopment Analysis (DEA)

2019 ◽  
Vol 2 (2) ◽  
pp. 82-89
Author(s):  
Nor Tasik Misbahrudin
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Wealth Distribution ◽  
Scale Efficiency ◽  
Optimum Level ◽  
Data Envelopment ◽  
Input And Output ◽  
Distribution State ◽  
Using Data ◽  
Decision Making Units

Waqf is a voluntary charity that cannot be disposed of and the ownership cannot be transferred once it is declared as waqf assets. Waqf institutions play an important role in helping the development of Muslims ummah through wealth distribution. State Islamic Religious Councils (SIRCs) in Malaysia are the sole trustee that manage and develop waqf assets. Based on selected input and output, the intermediary approach assumes that cash waqf received as output while total expenditure of SIRCs as input. Under this approach SIRCs act as intermediary between waqif (giver) and beneficiaries. Thus, this paper attempts to analyze the efficiency of waqf institutions in Malaysia by using Data Envelopment Analysis (DEA) method under output-orientation using Variable Return to Scale (VRS) assumptions. Four SIRCs were selected as decision making units (DMU) for the period of 2011 to 2015. The result indicates that changes in average technical efficiency for every year is contributed by both pure technical and scale. However, inefficiency of Malaysian waqf institutions is mostly contributed by pure technical efficiency aspects rather than scale. 2012 showed the highest average technical efficiency with 73.9% as most of the institutions operated in optimum level of input to produce output. Thus, the result suggests that both technical and scale efficiency should be improved to achieve the most efficient and productive level of performance in order to fulfill objectives of the institutions as an intermediary between waqif and beneficiaries.

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

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
A. Barzegarinegad ◽  
G. Jahanshahloo ◽  
M. Rostamy-Malkhalifeh
Keyword(s):  
Decision Making ◽  
Real Data ◽  
The Other ◽  
Data Envelopment ◽  
Production Possibility ◽  
Decision Making Unit ◽  
Common Set Of Weights ◽  
Production Possibility Set ◽  
Ideal Points ◽  
Decision Making Units

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.

scholarly journals Forecasting the Confidence Interval of Efficiency in Fuzzy DEA

2021 ◽  
Vol 31 (1) ◽  
Author(s):  
Azarnoosh Kafi ◽  
Behrouz Daneshian ◽  
Mohsen Rostamy-Malkhalifeh
Keyword(s):  
Confidence Interval ◽  
Geometric Mean ◽  
Decision Makers ◽  
Data Sets ◽  
Data Envelopment ◽  
Future Time ◽  
Dea Models ◽  
Time Periods ◽  
Fuzzy Dea ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is a well-known method that based on inputs and outputs calculates the efficiency of decision-making units (DMUs). Comparing the efficiency and ranking of DMUs in different time periods lets the decision makers to prevent any loss in the productivity of units and improve the production planning. Despite the merits of DEA models, they are not able to forecast the efficiency of future time periods with known input/output records of the DMUs. With this end in view, this study aims at proposing a forecasting algorithm with a 95% confidence interval to generate fuzzy data sets for future time periods. Moreover, managers’ opinions are inserted in the proposed forecasting model. Equipped with the forecasted data sets and with respect to the data sets from previous periods, this model can rightly forecast the efficiency of the future time periods. The proposed procedure also employs the simple geometric mean to discriminate between efficient units. Examples from a real case including 20 automobile firms show the applicability of the proposed algorithm.

scholarly journals A full investigation of the directional congestion in data envelopment analysis

2019 ◽  
Author(s):  
somayeh khezri ◽  
Akram Dehnokhalaji ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Input Output ◽  
Input And Output ◽  
Dea Models ◽  
Systems Of Inequalities ◽  
Full Investigation ◽  
Decision Making Units ◽  
Inputs And Outputs

One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs re- sult in increases (decreases) in some outputs without worsening (im- proving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional de nition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or de- creases the inputs dis-proportionally. This means that, the traditional de nition of congestion in DEA may be unable to measure the con- gestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two di erent scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear pro- gramming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two nu- merical examples.

Comparing Data Envelopment Analysis and Human Decision Making Unit Rankings: A Survey Approach

2014 ◽  
Vol 29 (2) ◽  
Author(s):  
Stanko Dimitrov
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Makers ◽  
Data Envelopment ◽  
Rank Ordering ◽  
Decision Making Unit ◽  
Human Decision ◽  
Survey Approach ◽  
Decision Making Units ◽  
Better Than

AbstractIn this paper we compare the ordinal rankings generated through Data Envelopment Analysis (DEA) methods to ordinal rankings generated by human decision makers. Through eliciting the total rank ordering for approximately 100 individuals on all of the four different datasets of Decision Making Units (DMUs), we compare the rankings generated by individuals to those generated by ten DEA methods. We observe that depending on the characteristics of the dataset one of the DEA methods performs better than the others in matching human decision makers.

scholarly journals Dimension-Specific Efficiency Measurement Using Data Envelopment Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Hongjun Zhang ◽  
Youliang Zhang ◽  
Rui Zhang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
The Other ◽  
Data Envelopment ◽  
Input Output ◽  
Single Dimension ◽  
Measuring Efficiency ◽  
Using Data ◽  
The City ◽  
Decision Making Units

Data envelopment analysis (DEA) is a powerful tool for evaluating and improving the performance of a set of decision-making units (DMUs). Empirically, there are usually many DMUs exhibiting “efficient” status in multi-input multioutput situations. However, it is not appropriate to assert that all efficient DMUs have equivalent performances. Actually, a DMU can be evaluated to be efficient as long as it performs best in a single dimension. This paper argues that an efficient DMU of a particular input-output proportion has its own specialty and may also perform poorly in some dimensions. Two DEA-based approaches are proposed to measure the dimension-specific efficiency of DMUs. One is measuring efficiency in multiplier-form by further processing the original multiplier DEA model. The other is calculating efficiency in envelopment-form by comparing with an ideal DMU. The proposed approaches are applied to 26 supermarkets in the city of Nanjing, China, which have provided new insights on efficiency for the managers.

Slacks-Based Efficiency Measurements with Undesirable Outputs in Data Envelopment Analysis

2014 ◽  
Vol 16 (04) ◽  
pp. 1005-1021 ◽  
Author(s):  
Jie Wu ◽  
Xiang Lu ◽  
Dong Guo ◽  
Liang Liang
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Production Process ◽  
Environmental Performance ◽  
Decision Makers ◽  
European Countries ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Theoretical Justification ◽  
Decision Making Units

Data envelopment analysis (DEA) has recently gained great popularity in modeling environmental performance because it provides condensed information to decision makers when the production process includes undesirable outputs. In this paper, we develop a new slacks-based efficiency measurement for modeling environmental performance using the environmental DEA technology. The proposed index has more theoretical justification, and distinguishes among different decision making units (DMUs) better in practice. Then we further extend it to the nonoriented index with double aim of increasing desirable outputs and reducing undesirable outputs. Finally, we calculate the index for each of 25 OECD European countries in a model of CO2 emission performance from 2007 to 2009 and the results obtained are presented.

scholarly journals MULTI‐OBJECTIVE CONTRACTOR'S RANKING BY APPLYING THE MOORA METHOD

2008 ◽  
Vol 9 (4) ◽  
pp. 245-255 ◽  
Author(s):  
Willem Karel M. Brauers ◽  
Edmundas Kazimieras Zavadskas ◽  
Zenonas Turskis ◽  
Tatjana Vilutienė
Keyword(s):  
Point Of View ◽  
The Other ◽  
Facilities Management ◽  
External Costs ◽  
Ratio Analysis ◽  
Consumer Sovereignty ◽  
Multi Objective ◽  
Moora Method ◽  
Reasonable Price

Construction, taking off, maintenance and facilities management of a building is a typical example of consumer sovereignty: the new owner likes to have a reasonable price to pay, to have confidence in the contractor, to know about the duration of the works, the service after completion and the quality of the work. On the other side the contractor has his objectives too, like the satisfaction of the client, diminishing of external costs and annoyances and the management cost per employee as low as possible. In other words it concerns a problem of multi‐objectives. Therefore a final ranking will show the best performing contractor from the point of view of the clients but also from the point of view of the contractors themselves. The MOORA method based on ratio analysis and dimensionless measurement will accomplish the job of ranking the contractors in a non‐subjective way. As an application the largest maintenance contractors of dwellings in Vilnius, capital of Lithuania, were approached.

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