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Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

This paper aims to present two-stage network models in the presence of stochastic ratio data.

Black-box, free-link and fix-link techniques are used to apply the internal relations of the two-stage network. A deterministic linear programming model is derived from a stochastic two-stage network data envelopment analysis (DEA) model by assuming that some basic stochastic elements are related to the inputs, outputs and intermediate products. The linkages between the overall process and the two subprocesses are proposed. The authors obtain the relation between the efficiency scores obtained from the stochastic two stage network DEA-ratio considering three different strategies involving black box, free-link and fix-link. The authors applied their proposed approach to 11 airlines in Iran.

In most of the scenarios, when alpha in particular takes any value between 0.1 and 0.4, three models from Charnes, Cooper, and Rhodes (1978), free-link and fix-link generate similar efficiency scores for the decision-making units (DMUs), While a relatively higher degree of variations in efficiency scores among the DMUs is generated when the alpha takes the value of 0.5. Comparing the results when the alpha takes the value of 0.1–0.4, the DMUs have the same ranking in terms of their efficiency scores.

The authors innovatively propose a deterministic linear programming model, and to the best of the authors’ knowledge, for the first time, the internal relationships of a two-stage network are analyzed by different techniques. The comparison of the results would be able to provide insights from both the policy perspective as well as the methodological perspective.

It is always of great importance for managers in organizations to evaluate their staff members and create incentive systems, using instruments such as Data Envelopment Analysis (DEA) and DEA-R (DEA models based on ratio analysis). The purpose of this paper is to propose a two-stage network incentives system for commercial banks.

Centralized Resource Allocation (CRA) models make it possible to project all decision-making units (DMUs) onto the efficient frontier by solving a single linear programming model. In this paper, we use our proposed DEA-R-based CRA models to evaluate commercial banks in a two-stage case when the only ratios available are the assets-to-costs and income-to-assets vectors.

Thirteen commercial banks modeled as two-stage networks were evaluated by the models proposed in two different cases of ratio data. Results suggest that the proposed methodology yields more accurate efficiency scores, thus allowing better discrimination among DMUs. Furthermore, evaluating the DMUs when they are structured as two-stage (or even three-stage) networks makes it possible to examine the incentives system in more detail. Therefore, the use of incentive systems by managers would allow a better focus on the priority activities of commercial banks and a faster movement toward the frontier of best practices.

The super-efficiency scores of a number of commercial banks are evaluated based on the CRA model, as a cornerstone criterion for the two-stage evaluation in DEA-R, thus allowing the rank of each commercial bank in terms of the incentives system rather on the performance of the productive process.

This chapter analyses the efficiency of African airlines using a two-stage network DEA (Data Envelopment Analysis) model. Network DEA models usually take into account the production process with intermediate inputs derived from the first stage and a second stage that departs from it. This fundamental feature enables one to view the airline production process as a carry-over activity. The analysis covers the 2010–2013 period. The relative efficiency ranks are presented and policy implications are derived.

The purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.

In this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.

The advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

The recent COVID-19 outbreak and severe natural disasters make the design of the humanitarian supply chain network (HSCN) a crucial strategic issue in a pre-disaster scenario. The HSCN design problem deals with the location/allocation of emergency response facilities (ERFs). This paper aims to propose and demonstrate how to design an efficient HSCN configuration under the risk of ERF disruptions.

This paper considers four performance measures simultaneously for the HSCN design by formulating a weighted goal programming (WGP) model. Solving the WGP model with different weight values assigned to each performance measure generates various HSCN configurations. This paper transforms a single-stage network into a general two-stage network, treating each HSCN configuration as a decision-making unit with two inputs and two outputs. Then a two-stage network data envelopment analysis (DEA) approach is applied to evaluate the HSCN schemes for consistently identifying the most efficient network configurations.

Among various network configurations generated by the WGP, the single-stage DEA model does not consistently identify the top-ranked HSCN schemes. In contrast, the proposed transformation approach identifies efficient HSCN configurations more consistently than the single-stage DEA model. A case study demonstrates that the proposed transformation method could provide a more robust and consistent evaluation for designing efficient HSCN systems. The proposed approach can be an essential tool for federal and local disaster response officials to plan a strategic design of HSCN.

This study presents how to transform a single-stage process into a two-stage network process to apply the general two-stage network DEA model for evaluating various HSCN configurations. The proposed transformation procedure could be extended for designing some supply chain systems with conflicting performance metrics more effectively and efficiently.

This paper aims to present a study which uses an innovative two‐stage data envelopment analysis (DEA) model that separates efficiency and effectiveness to evaluate the performance of 28 online stockbrokers in Taiwan from 2003 to 2005.

The approach is based on two‐stage DEA.

The results show that seven companies are CCR‐efficient in their operating efficiency; five companies are CCR‐efficient operating effectiveness and only two companies are CCR‐efficient both in operating efficiency and effectiveness. There is no apparent correlation between efficiency and effectiveness.

This paper presents a two‐stage DEA study to investigate the efficiency and effectiveness in the online stockbroking sector. The online stockbroking business is a development from the integration of the internet and the stock trading. As the stock brokerage industry is undergoing a rapid change due to the proliferation of the internet, analyzing the relative efficiency and effectiveness of online stockbrokers is important for management to understand, monitor and sustain performance.

The originality of this paper is in the use of a new conceptual framework to assess the performance of online stockbrokers in Taiwan. This study uses the two‐stage DEA in conjunction with return on assets ratio, which is widely used in financial analysis, to define and assess performance in the framework.

The purpose of this paper is to measure profitability and marketability efficiencies as well as examine the efficiencies’ determinants of listed manufacturing firms in Vietnam.

This study employs a bootstrap two-stage data envelopment analysis (DEA) approach to investigate the profitability and marketability efficiencies of 102 listed manufacturing firms on Vietnam stock market from 2007 to 2018. The study also applies fractional regression models (FRM) to identify the determinants of Vietnam manufacturing firms’ efficiencies.

The results reveal that Vietnam manufacturing firms obtain higher average profitability efficiency scores (0.888) than marketability efficiency scores (0.527) from 2007 to 2018. The high-tech firms achieve better profitability and marketability efficiencies than the traditional (resource-intensive and labour-intensive) Vietnam manufacturing firms in recent years (2016–2018). Further, the financial and non-financial factors have heterogeneous impacts on Vietnam manufacturing enterprises’ profit and market valuation efficiencies.

Due to the nature of DEA technique that requires every decision-making unit to have available data of all inputs and outputs, the listed Vietnam manufacturing firms that have incomplete data or go public after 2007 are not included in the data set.

This study provides a reference for Vietnam manufacturing managers to position their firms competitively in the market as well as make wise operating, financing and management decisions.

This is the first study that attempts to combine bootstrap two-stage DEA and FRM, which are considered advantageous methods for DEA scores’ measurements and determinant evaluations in the current literature.

A Nash bargaining game data envelopment analysis (NBG-DEA) model is proposed to measure the efficiency of dynamic multi-period network structures. This paper aims to propose NBG-DEA model to measure the performance of decision-making units with complicated network structures.

As the proposed NBG-DEA model is a non-linear mathematical programming, finding its global optimum solution is hard. Therefore, meta-heuristic algorithms are used to solve non-linear optimization problems. Fortunately, the NBG-DEA model optimizes the well-formed problem, so that it can be solved by different non-linear methods including meta-heuristic algorithms. Hence, a meta-heuristic algorithm, called particle swarm optimization (PSO) is proposed to solve the NBG-DEA model in this paper. The case study is Industrial Management Institute (IMI), which is a leading organization in providing consulting management, publication and educational services in Iran. The sub-processes of IMI are considered as players where their pay-off is defined as the efficiency of sub-processes. The network structure of IMI is studied during multiple periods.

The proposed NBG-DEA model is applied to measure the efficiency scores in the IMI case study. The solution found by the PSO algorithm, which is implemented in MATLAB software, is compared with that generated by a classic non-linear method called gradient descent implemented in LINGO software.

The experiments proved that suitable and feasible solutions could be found by solving the NBG-DEA model and shows that PSO algorithm solves this model in reasonable central process unit time.

The power industry is the pillar industry of the Chinese economy, and also a major carbon emitter. The performances of both the production and operation of the power industry are crucial for a harmonious development of society. This study proposes an improved data envelopment analysis (DEA) model to analyze the sustainable performance of China's power supply chain (PSC).

To analyze the sustainable performance of PSC systems in China's provincial regions, this study proposes a two-stage directional distance function (DDF) model. The proposed model not only considers the leader–follower game relationship between the power-generation system and the retail system, but also considers the factors that measure the sustainability level of the PSC.

The proposed model is applied to assess the sustainable performance of the PSCs of China's provincial regions. The findings are valuable and mainly include the following aspects: First, compared with other models, this study regards the intermediate variable of the power system as a freely disposable variable; therefore, the efficiency of the proposed model is more realistic. Second, the average efficiency of China's power retailing system is generally lower than the average efficiency of its power-generation system. Third, significant regional differences affect the power-generation efficiency, while the regional differences in power retail efficiency are not significant. The power-generation performances of PSCs in East China and Northeast China are generally higher than in other regions.

This study introduces the convex technique into a DEA model and thus proposes an improved two-stage DDF DEA model. In response to the game-theoretic inherent in power systems, this study also introduces the leader–follower game into the two-stage model. In addition to the theoretic novelty, all PSCs can be classified with this model. Moreover, specific recommendations for each type of PSCs are proposed based on the efficiency results, thus providing vital guidance for the practice.