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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.

We outline the standards for “good” performance measures and propose the Data Envelopment Analysis (DEA) as a method for performance measurement on individual level.

Using the example of a German cooperative bank with a cohort of 40 employees, we apply a multi-stage DEA approach to measure employee performance and report on the results. Based on those results a DEA-based approach for a strategic performance appraisal process is introduced.

We illustrate that DEA provides clear feedback information on an individual level, which enables management to accurately identify fields of specific improvement.

The proposed approach for a strategic performance appraisal process is yet of theoretical nature. Consequently, the practical implementation of this approach is a purpose of further research.

This chapter develops a productivity analysis of the US pharmaceutical industry via Data Envelopment Analysis (DEA). This study concerns itself with 16 US pharmaceutical companies. The output variables are profit margin, operating margin, return on assets, and return on equity. The input variables are corporate workers and market capital. Since negative data appear in DEA, a directional distance approach was applied.

The chapter examines a comprehensive review of cross-disciplinary literature in the domain of supply chain forecasting during research period 1991–2017, with the primary aim of exploring the growth of literature from operational to demand centric forecasting and decision making in service supply chain systems. A noted list of 15,000 articles from journals and search results are used from academic databases (viz. Science Direct, Web of Sciences). Out of various content analysis techniques (Seuring & Gold, 2012), latent sementic analysis (LSA) is used as a content analysis tool (Wei, Yang, & Lin, 2008; Kundu et al., 2015). The reason for adoption of LSA over existing bibliometric techniques is to use the combination of text analysis and mining method to formulate latent factors. LSA creates the scientific grounding to understand the trends. Using LSA, Understanding future research trends will assist researchers in the area of service supply chain forecasting. The study will be beneficial for practitioners of the strategic and operational aspects of service supply chain decision making. The chapter incorporates four sections. The first section describes the introduction to service supply chain management and research development in this domain. The second section describes usage of LSA for current study. The third section describes the finding and results. The fourth and final sections conclude the chapter with a brief discussion on research findings, its limitations, and the implications for future research. The outcomes of analysis presented in this chapter also provide opportunities for researchers/professionals to position their future service supply chain research and/or implementation strategies.

Healthcare industry, the largest sector of the US economy, is going through a dramatic transformation as the US economy recovers out of the current recession. In this chapter, we use data envelopment analysis, an operations research technique, to benchmark the performance of 12 publicly managed care organizations against one another for the period 2009–2011. We find that only 6 companies out of 12 are 100% efficient. We also identify the areas in which inefficient companies are lagging behind their efficient peers.

Globalization speeds up competition among nations in various sectors. In terms of multinational and transnational phenomena, countries are seen as inescapable from competition, thus the linking of the term global with “competitiveness.” The research described here explores the relationship between the competitiveness of a country and its implications for human development. For this purpose, using data envelopment analysis (DEA) and cluster analysis, 44 selected countries were evaluated. An output-oriented super-efficiency model where global competitiveness indicators are taken as input variables with human development indicators as output variables is utilized. Then cluster analysis depending on the competitiveness and human development indicators is conducted by using self-organizing maps to specify the development levels of the countries. Both analyses are repeated for years between 2005 and 2007. Finally, the relationship between the super efficiency scores and the development levels is analyzed.

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical studies. The survey shows the apparent shift from index procedures and traditional OLS estimation of production and cost functions to stochastic frontier methods and Data Envelopment Analysis (DEA) methods over the past three decades. Most of the airline productivity and efficiency studies over the last decade adopt some variant of DEA methods. Researchers in the 1980s and 1990s were mostly interested in the effects of deregulation and liberalization on airline productivity and efficiency as well as the effects of ownership and governance structure. Since the 2000s, however, studies tend to focus on how business models and management strategies affect the performance of airlines. Environmental efficiency now becomes an important area of airline productivity and efficiency studies, focusing on CO₂ emission as a negative or undesirable output. Despite the fact that quality of service is an important aspect of airline business, limited attempts have been made to incorporate quality of service in productivity and efficiency analysis.

Proper performance measurement is an important issue in library operational management. A data envelopment analysis (DEA) model is applied to evaluate the relative operational efficiency of 25 U.S. private research-university library members of the Association of Research Libraries (ARL). Operations of each library decision-making unit are considered as a production process using four resource input and four service output variables. The model results are analyzed and compared with the efficient group and a peer group by using a t-test. The model provides decision-makers with more accurate information to implement better library services with appropriate resource allocation.

As state funding for public higher education has declined, there is a rising demand for accountability. Past studies have relied on indicator ratios to look at the relationship between funding and performance measures. This approach has some inherent problems that make it difficult to identify inefficiencies. This chapter will study efficiency in state systems of higher education by applying data envelopment analysis (DEA). DEA methodology converts multiple variables into a single comprehensive measure of performance efficiency and has the ability to perform benchmarking for the purpose of establishing performance goals. The advantages of DEA modeling will be shown by comparing results with those from a recent study of higher education finance based on publicly available data. DEA is shown to be feasible and implementable for studying state systems of higher education, and provides useful information in identifying “best practice” state systems and guidance for improvement. The value of DEA modeling to state policy makers and education researchers is discussed.

This chapter empirically investigates the main drivers of the circular economy (CE) and sustainable development (SD) of European countries. The European Union (EU) legislation imposes equal rules for the members who should be followed to achieve CE and SD. This chapter gives a critical overview of the related literature on this topic. The second part focuses on measuring the efficiency of EU countries in achieving CE and SD via a nonparametric approach. Furthermore, the results from the efficiency evaluation are used as a dependent variable in determining which economic, social, institutional, and other factors have the greatest influence on CE and SD achievements. The nonparametric approach consists of selected models of data envelopment analysis (DEA), as this is a methodology useful in constructing a ranking system based on selected criteria. The results indicate that on average, the most efficient countries were (besides Malta and Luxembourg) the Netherlands, Poland, Germany, Sweden, Denmark, France, and the United Kingdom. The worst performing ones were Cyprus, Spain, Greece, Belgium, Portugal, and Croatia. The second part of the research indicates that the resource production and corruption perception index has the greatest effect on the efficiency scores, followed by education attainment. The research and development (R&D) variable is not significant in the observed sample. Based on these results, specific policy recommendations are given at the end of this chapter.