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In this paper, we introduce a novel multi-period data envelopment analysis (MDEA) model that attempts to circumvent the limitations of the existing MDEA models. The proposed global MDEA model is essentially based on major modifications of fundamental DEA axioms to enable a decision making unit (DMU), defined with inputs and outputs of period t, to be evaluated within the production possibility set (PPS) of another period l, t ≠ l. Building on the properties of the global MDEA model, we also introduce a global productivity index, identified as Global Progress and Regress index (GPRI), that render possible the evaluation of a DMU's extent of progress or regress over multi-period time horizons under variable returns to scale (VRS) production technologies. This lifts the restrictions to two successive periods and constant returns to scale (CRS) of existing productivity indices. The most salient features of the new MDEA model as well as the GPRI are highlighted using an application that involves a real-life sample of 25 bank branches considered over 4 years. [ABSTRACT FROM AUTHOR]

In the real world, there are processes whose structures are like a parallel-series mixed network. Network data envelopment analysis (NDEA) is one of the appropriate methods for assessing the performance of processes with these structures. In the paper, mixed processes with two parallel and series components are considered, in which the first component or parallel section consists of the shared inputs, and the second component or series section consists of undesirable factors. By considering the weak disposability assumption for undesirable factors, a DEA approach as based on network slackbased measure (NSBM) is introduced to evaluate the performance of processes with mixed structures. The proposed model is illustrated with a real case study. Then, the model is developed to discriminate efficient units. [ABSTRACT FROM AUTHOR]

In this paper, while data envelopment analysis (DEA) technique has been used, four-stage Malmquist Productivity Index has been mooted in order to investigate the evaluation of balanced scorecards in commercial banks. With this index the progress and regress of each sub-process, exists within each decision-making units, can be examined. Finally, with an application in commercial banks validity of the proposed models has been demonstrated. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) models can calculate the Malmquist Productivity Index (MPI). Classic Malmquist Productivity Index shows regress and progress of a DMU in different periods with efficiency and technology variations without considering the present value of money. This issue is of major importance since while a currency of in previous year is not equal to that of now this would yield bias results which can affect the correct interpretation. The index developed here is defined in terms of Modified Malmquist Productivity Index model, which can calculate progress and regress by using the factor of present time value of money. The incorporation of present time value of money is also calculated within the framework of data envelopment analysis. This factor is fundamental and should be considered in DEA Malmquist Productivity Index. Moreover, here, differences between presented models are compared to those of previous ones indeed, biased results will be shown in the case study in banks, and problem and solution have been investigated in the literature. [ABSTRACT FROM AUTHOR]

In most production activities, desirable and undesirable outputs are produced together, and they are interdependent. As a result, reducing undesirable outputs can also decrease desirable ones. This study introduces concepts and models and applies them to a practical example for the first time, considering the dependency of production between desirable and undesirable outputs in an SBM network system. The aim is to assess the sustainability of supply chains. To achieve this goal, a new network data envelopment analysis (NDEA) model is developed based on the Slack Based Model (SBM). A case study is presented to evaluate the sustainability of wheat supply chains, which demonstrates various aspects of the proposed model, which considers both desirable and undesirable outputs produced together. • A new SBM network model is introduced to handle the jointly produced unwanted (bad) outputs and desirable outputs. • The possible changes of desirable and unwanted outputs are combined into the NDEA model. • Managerial strategies for altering the desirable and unwanted (bad) outputs are provided. [ABSTRACT FROM AUTHOR]