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The aim of this paper is to evaluate the efficiency of 22 cargo seaports situated in the regions of East Africa and Middle East.

The data envelopment analysis (DEA) with window analysis model evaluates the efficiency score in terms of normal efficiency and super efficiency. The analysis is based on the panel data for the period of 2000‐2005.

The number of efficient decision making units (DMUs) under super efficiency is more than the number under normal efficiency.

Using panel data, this paper is the first study to use super efficiency with window analysis that compares the efficiency estimated with the normal efficiency and with super efficiency.

The traditional data envelopment analysis (DEA) model as a non-parametric technique can measure the relative efficiencies of a decision-making units (DMUs) set with exact values of inputs and outputs, but it cannot handle the imprecise data. The purpose of this paper is to establish a super efficiency interval data envelopment analysis (IDEA) model, an IDEA model based on cross-evaluation and a cross evaluation-based measure of super efficiency IDEA model. And the authors apply the proposed approach to data on the 29 public secondary schools in Greece, and further demonstrate the feasibility of the proposed approach.

In this paper, based on the IDEA model, the authors propose an improved version of establishing a super efficiency IDEA model, an IDEA model based on cross-evaluation, and then present a cross evaluation-based measure of super efficiency IDEA model by combining the super efficiency method with cross-evaluation. The proposed model cannot only discriminate the performance of efficient DMUs from inefficient ones, but also can distinguish between the efficient DMUs. By using the proposed approach, the overall performance of all DMUs with interval data can be fully ranked.

A numerical example is presented to illustrate the application of the proposed methodology. The result shows that the proposed approach is an effective and practical method to measure the efficiency of the DMUs with imprecise data.

The proposed model can avoid the fact that the original DEA model can only distinguish the performance of efficient DMUs from inefficient ones, but cannot discriminate between the efficient DMUs.

This paper introduces the effective method to obtain the complete rank of all DMUs with interval data.

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.

Waste management is one of the vital objectives for the EU since it has a substantial effect on the environment. European Commission expects annual waste creation on Earth to increase by 70% by 2050. European Commission also estimates that efficient waste management might boost the EU economy's gross domestic product (GDP) by 0.5% by 2030. Hence, it is essential to conduct research including both efficiency and influencing factors analysis for effective waste management. First, we employ both slack-based measure (SBM) and super-SBM data envelopment analysis approaches to investigate the waste management efficiency of the EU region and distinguish between efficient countries. The countries with small areas such as Luxembourg and Ireland have demonstrated super efficiency. Second, we maintain our empirical research with ordinary least square analysis to explore the determinants of waste management. We also conclude that population density, GDP per capita, and tourism rise the amount of waste generated in the EU region.

Zakat is an important Islamic economic instrument that plays significant role in Sustainable Development Goals. Accordingly, Zakat Institutions must manage zakat in a proper and efficient manner. This study aims to examine the efficiency of Zakat Institutions based on their clusters which are government, business and social organizations.

This study uses three quantitative methods: data envelopment analysis (DEA), free disposal hull and super-efficiency DEA. The analytical method is based on production approach, variable return to scale assumption and output orientation. The sample consists of 14 Zakat Institutions from three clusters: Zakat Institutions managed by government, Zakat Institutions managed by corporation and Zakat Institution managed by social organizations.

The results revealed that all of three techniques culminate the same ranking order of efficiency. Zakat Institution managed by the government is the most efficient Zakat Institution, with the average value of 0.87 by using three approaches combined. Meanwhile, Zakat Institutions owned by company and social institutions cluster are in second and third position, with the average value of 0.65 and 0.4, respectively, based on the results of the three approaches. This study contends that the level of efficiency of Zakat Institutions may be supported by clusters (affiliations) in their management.

This study’s limitation is the inadequacy of the required data. Nonetheless, this study provides insights to improve the efficiency of Zakat Institutions based on their clusters. Zakat Institutions in each cluster can improve their efficiency by optimizing inputs to produce multiple outputs.

This study enhances research on the efficiency of Zakat Institutions using three methods to assess the consistency and strength of Zakat Institutions’ efficiency values. In addition, this study examines the efficiency level of Zakat Institutions based on their clusters.

We adopt a super slack-based measurement (SBM) data envelopment analysis (DEA) model to estimate the efficiency of five biggest freight rail operators in Russia, which are included in the top 30 freight rail operators in terms of two dimensions – financial and operational efficiency during 2013–2017. The result shows that the private companies characterized by high financial and operational efficiency, while the Rossiiskye Zheleznye Dorogi (RZD) subsidiaries characterized by sufficiently low financial and operational efficiency scores. And the result also presents that operational efficiency score of operators handling universal rolling stock is higher than financial efficiency scores. In contrast, financial efficiency scores of operators handling special rolling stock is higher than operational efficiency scores. rail freight operators in addition to a special rolling stock park should have a universal rolling stock park for higher profitability. State-owned companies and its subsidiary operate inefficiently in the midst of a market economy in Russia. Rail freight operators for a higher level of financial efficiency should be transferred to the private sector.

The purpose of this study is to propose a comprehensive benchmarking approach to help subsidiaries of a company to implement continuous improvement (CI).

The proposed approach classifies subsidiaries of a company into the three stages of CI. After that, the proposed approach employs data envelopment analysis (DEA)-super slacks-based measure (SBM) model and Malmquist Productivity Index (MPI) to evaluate the operational efficiency of the subsidiaries and selects benchmarking targets and sets benchmarking goals based on the operational efficiency for benchmarking of input and output criteria. Then, the proposed approach suggests a four-step benchmarking process for benchmarking of detailed efficiency. Finally, the proposed approach makes the subsidiaries achieve CI by implementing the input and output benchmarking and the efficiency benchmarking.

The results show that the proposed approach can help subsidiaries of a company to implement a staged benchmarking which considers input and output criteria and Malmquist productivity and efficiency comprehensively.

Generally, benchmarking is implemented in many aspects. However, due to the restriction of a company’s resource level, a practical and staged benchmarking is preferred. This study proposes a comprehensive approach to benchmark systematically and gradually, and provides a more reasonable benchmarking process for implementing CI.

The purpose of this paper is to propose a performance evaluation framework using an integrated approach of stochastic frontier analysis (SFA) and technique of order preference with similarity to ideal solution (TOPSIS) called efficiency ranking method using SFA and TOPSIS (ERM-ST) specifically in the banking sector where service excellence is of prime importance for business growth.

The proposed approach ERM-ST measures the performance of a DMU in the SFA framework by considering multiple outputs and multiple inputs. It is a non-parametric tool which does not need any prior model assumptions which enhances its applicability in real-life business scenarios. Moreover, the efficiency score obtained using the proposed model ERM-ST lies between 0 and 1, unlike in case of super efficiency data envelopment analysis (DEA) which may go well above 1.

The proposed framework is evaluated for its applicability using two various data sets and is further used to evaluate the performance of a group of 26 public sector banks in India. The results obtained by the proposed method ERM-ST are compared with those obtained by super efficiency DEA using Friedman’s test.

The proposed approach ERM-ST is developed to evaluate the performance of a service unit with multiple outputs and multiple inputs in the SFA framework.

The evolution of six sigma has morphed from a method or set of techniques to a movement focused on business‐process improvement. Business processes are transformed through the successful selection and implementation of competing six sigma projects. However, the efforts to implement a six sigma process improvement initiative alone do not guarantee success. To meet aggressive schedules and tight budget constraints, a successful six sigma project needs to follow the proven define, measure, analyze, improve, and control methodology. Any slip in schedule or cost overrun is likely to offset the potential benefits achieved by implementing six sigma projects. The purpose of this paper is to focus on six sigma projects targeted at improving the overall customer satisfaction called Big Q projects. The aim is to develop a mathematical model to select one or more six sigma projects that result in the maximum benefit to the organization.

This research provides the identification of important inputs and outputs for six sigma projects that are then analyzed using data envelopment analysis (DEA) to identify projects, which result in maximum benefit. Maximum benefit here provides a Pareto optimal solution based on inputs and outputs directly related to the efficiency of the six sigma projects under study. A sensitivity analysis of efficiency measurement is also carried out to study the impact of variation in projects' inputs and outputs on project performance and to identify the critical inputs and outputs.

DEA, often used for relative efficiency analysis and productivity analysis, is now successfully constructed for six sigma project selection.

Provides a practical approach to guide the selection of six sigma projects for implementation, especially for companies with limited resources. The sensitivity analysis discussed in the paper helps to understand the uncertainties in project inputs and outputs.

This paper introduces DEA as a tool for six sigma project selection.

The purpose of this paper is to analyze the performance of Takaful and conventional insurance companies in Saudi Arabia during a period of 2010-2015 by using the data envelopment analysis (DEA) technique for the whole population of insurance companies.

Given its objectives, the present study adopts the most prevalent DEA approach, by using the DEA Solver-Pro (Version 13). The DEA has emerged as a valuable analytical research technique. It measures the relative efficiency of firms in the presence of multiple inputs and outputs, based on a linear programming technique, and attempts to find out the firms that determine an envelopment frontier, are super efficient and with a higher productivity index.

It stems from the analysis that on a yearly basis, the average efficiency scores of firms have soared up overtime since 2010 till 2014 reflecting that most of the companies did well on the efficiency front. It is notable to mention here that the top slots for super efficiency are taken over by smaller firms, while the bigger firms are laggards here rather than the leaders. This reflects that the larger insurance firms need to augment their efficiency levels through more efficient utilization of inputs. The results of the study reveal that both Takaful and the larger conventional insurance firms in the country need to strengthen their operations more efficiently in order to take advantage of the economies of scale and scope. Market share and profitability are important determinants of efficiency.

The larger insurance firms in the country need to a possible solution to the issue of inefficient market dynamics might lie in consolidation of the market through mergers and acquisitions. However, this needs a direct involvement of regulators in the Kingdom so that the market becomes healthy. Even though the Saudi insurance sector appears to have benefited from the compulsory insurance regulations for the expatriates and their families, however, there is still a need for efficiency and productivity improvement in the industry. The Takaful firms need to adopt such measures that would help them to take advantage of their specialized products toward efficiency vis-à-vis productivity drives. Finally, the insurance firms in Saudi Arabia need to adopt the use of threshold practices in order to compare their relative performance to improve on their efficiency and productivity levels by catching-up with the frontiers of best practices.

Based on the available literature, an exclusive study on the insurance sector of Saudi Arabia is so far non-existent. The study stands as pioneer to provide a starting point on overall performance evaluation of insurance firms in Saudi Arabia in various contexts in addition to the current and future trends of the insurance sector in the Kingdom.