Data envelopment analysis (DEA) is a non-parametric technique for measuring and analyzing the relative efficiency of Decision-Making Unit (DMU) having multiple homogeneous types inputs outputs. Traditional DEA treats DMU as black boxes calculates their efficiencies by considering initial final But overall directly depended upon performances intermediate processes operations. In many situations,...

Stepwise benchmark target selection in data envelopment analysis (DEA) is a realistic and effective method by which inefficient decision-making units (DMUs) can choose benchmarks in a stepwise manner. We propose, for the construction of a benchmarking network (i.e., a network structure consisting of an alternative sequence of benchmark targets), an approach that integrates the cross-efficiency ...

Assembly activities represent a crucial phase of new product development processes. The identification of potential assembly issues early in the design stage can thus allow companies to save time and cost of successive manufacturing phases. In such context, this article analyses the potential of digital mock-up (DMU) to enhance assembly feasibility and efficiency through specific design analysi...

Data envelopment analysis (DEA) is a non-parametric method for evaluating the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. The context-dependent DEA is introduced to measure the relative attractiveness of a particular DMU when compared to others. In real-world situation, because of incomplete or non-obtainable information, the data (Input and ...

Classic data envelopment analysis (DEA) models determine the efficiency of productive units, called decision making units (DMUs). DEA uses as its methodology the equiproportional reduction of inputs or increase of outputs and the finding of a single target for each DMU. This target does not incorporate the preference of the decision maker. Later works propose obtaining alternative targets based...

In data envelopment analysis (DEA) efficient decision making units (DMUs) are of primary importance as they define the efficient frontier. By means of modified CCR model, in which the test DMU is excluded from the reference set, we are able to determine what perturbations of data can be tolerated before frontier DMUs become nonfrontier. In this paper we discuss simultaneous data perturbations i...

This paper studies the inverse data envelopment analysis using the nonradial enhanced Russell model. Necessary and sufficient conditions for inputs/outputs determination are introduced based on Pareto solutions of multiple-objective linear programming. In addition, an approach is investigated to identify extra input/lack output in each of input/output components (maximum/minimum reduction/incre...

DEA model selection is problematic. The estimated efficiency for any DMU depends on the inputs and outputs included in the model. It also depends on the number of outputs plus inputs. It is clearly important to select parsimonious specifications and to avoid as far as possible models that assign full high efficiency ratings to DMUs that operate in unusual ways (mavericks). A new method for mode...

Data Envelopment Analysis (DEA) is the use of non-parametric mathematical programming that useful for measuring efficiency Decision Making Unit (DMU) an organization. This study uses Cooper and Rhodes (CCR) method known as DEA-CCR multiplier which aims to determine weight value each input output variable DMU being evaluated, but it not sufficient measure optimization. To get efficient a referen...

Data envelopment analysis (DEA) is a widely used mathematical programming approach for evaluating the relative efficiency of decision making units (DMUs) in organizations. Crisp input and output data are fundamentally indispensable in traditional DEA evaluation process. However, the input and output data in real-world problems are often imprecise or ambiguous. In this study, we present a four-p...