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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.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

A stroke is a serious, life-threatening condition that occurs when the blood supply to a part of the brain is cut off. The earlier a stroke is treated, the less damage is likely to occur. One of the methods that can lead to faster treatment is timely and accurate prediction and diagnosis. This paper aims to compare the binary integer programming-data envelopment analysis (BIP-DEA) model and the logistic regression (LR) model for diagnosing and predicting the occurrence of stroke in Iran.

In this study, two algorithms of the BIP-DEA and LR methods were introduced and key risk factors leading to stroke were extracted.

The study population consisted of 2,100 samples (patients) divided into six subsamples of different sizes. The classification table of each algorithm showed that the BIP-DEA model had more reliable results than the LR for the small data size. After running each algorithm, the BIP-DEA and LR algorithms identified eight and five factors as more effective risk factors and causes of stroke, respectively. Finally, predictive models using the important risk factors were proposed.

The main objective of this study is to provide the integrated BIP-DEA algorithm as a fast, easy and suitable tool for evaluation and prediction. In fact, the BIP-DEA algorithm can be used as an alternative tool to the LR model when the sample size is small. These algorithms can be used in various fields, including the health-care industry, to predict and prevent various diseases before the patient’s condition becomes more dangerous.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

This study aims to examine the technical efficiency of the chemical-free farming system in India using a hybrid combination of data envelopment analysis (DEA) and machine learning (ML) approaches.

The study used a two-stage approach. In the first stage, the efficiency scores of decision-making units’ efficiency (DMUs) are obtained using an input-oriented DEA model under the assumption of a variable return to scale. Based on these scores, the DMUs are classified into efficient and inefficient categories. The 2nd stage of analysis involves the identification of the most important predictors of efficiency using a random forest model and a generalized logistic regression model.

The results show that by using their resources efficiently, growers can reduce their inputs by 34 percent without affecting the output. Orchard's size, the proportion of land, grower's age, orchard's age and family labor are the most important determinants of efficiency. Besides, growers' main occupation and footfall of intermediaries at the farm gate also demonstrate significant influence on efficiency.

The study used only one output and a limited set of input variables. Incorporating additional variables or dimensions like fertility of the land, climatic conditions, altitude of the land, output quality (size/taste/appearance) and per acre profitability could yield more robust results. Although pineapple is cultivated in all eight northeastern states, the data for the study has been collected from only two states. The production and marketing practices followed by the growers in the remaining six northeastern states and other parts of the country might be different. As the growers do not maintain farm records, their data might suffer from selective retrieval bias.

Given the rising demand for organic food, improving the efficiency of chemical-free growers will be a win-win situation for both growers and consumers. The results will aid policymakers in bringing necessary interventions to make chemical-free farming more remunerative for the growers. The business managers can act as a bridge to connect these remote growers with the market by sharing customer feedback and global best practices.

Although many developments have happened to the DEA technique, the present study used a traditional form of DEA. Therefore, future research should combine ML techniques with more advanced versions like bootstrap and fuzzy DEA. Upcoming research should include more input and output variables to predict the efficiency of the chemical-free farming system. For instance, environmental variables, like climatic conditions, degree of competition, government support and consumers' attitude towards chemical-free food, can be examined along with farm and grower-specific variables. Future studies should also incorporate chemical-free growers from a wider geographic area. Lastly, future studies can also undertake a longitudinal estimation of efficiency and its determinants for the chemical-free farming system.

No prior study has used a hybrid framework to examine the performance of a chemical-free farming system.

This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

Because of the importance of the wheat industry in the economy, a real-featured performance measurement approach is essential for the wheat production process. The purpose of this paper is to develop a data envelopment analysis (DEA) model that is fully compatible with the wheat production process so that managers and farmers can use it to evaluate the efficiency of wheat farms for strategic decisions.

A dynamic multi-stage network DEA model is developed to evaluate the efficiency of wheat production farms in short-term (two-year) and long-term (eight-year) periods.

The results of this study show that because of the lack of long-term planning and excessive reliance on rain, most of the investigated regions have no stability in efficiency, and the efficiency of the regions changes in a zigzag manner over time. Among studied regions, only the Hashtrood region has high and stable efficiency, and other regions can follow the example of this region's cultivation method.

To the best of the authors’ knowledge, this study is the first one that uses the dynamic multi-stage network DEA considering every other year cultivation method and direct–indirect inputs in the agricultural section.