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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.

The purpose of this paper is to discuss the use of data envelopment analysis (DEA) to benchmark store performance for the purpose of rationalising retail distribution network.

As an illustration of the approach, DEA is applied to a sample of front stores of a major retailer in Australia to compare their relative efficiency in distribution. Together with other techniques such as customer segmentation and spatial distribution of demand, this paper shows that DEA can provide an objective basis for distribution network rationalisation and be a suitable analytical tool to facilitate continuous improvement.

Based on the DEA results, it is concluded that overall distribution efficiency of the part of the retail network under study can be improved by either closing the less efficient stores or merging them with the others in the same service areas to streamline the network. Such rationalisation will help aggregate demand and improve vehicle utilisation for distribution with minor impact on current level of customer service.

This study lends insight into the use of DEA, together with other analyses, for distribution network rationalisation. This approach is less data hungry and relatively easy to implement than full‐fledged optimisation through integer programming. To serve mainly as a proof of concept and an illustration of the approach, the scope of the study is limited to six stores in the retail network with relative performance in distribution evaluated on a single input and a single output variables.

Managers can use DEA to benchmark the distribution performance of their stores against the best performers in the retail network so as to identify areas for improvement. The approach can also assist in the adoption of best practice and facilitate more effective allocation of resources across the entire retail network.

Retail network rationalisation through benchmarking with DEA can facilitate continuous improvement in distribution efficiency. This will help reduce fuel consumption, carbon emission, as well as other pollutions such as noise and traffic congestion.

Research in retail network performance using DEA to date is mainly on comparative performance of supermarkets within or between chains. The focus is mainly placed on the relationship between floor area, workforce, and sales. This paper fills the gap in the literature by applying DEA in distribution network rationalisation instead of mere performance comparison of individual stores. It focuses on distribution costs rather than store attributes and supplements DEA with other techniques to obtain a fuller picture of the overall network efficiency in terms of distribution. It also contributes to a better understanding of how demand management can affect distribution efficiency of the retail network.

The purpose of this paper is to provide a theoretical conceptualization of how data envelopment analysis (DEA) can be applied to rail freight rolling stock in order to develop a tariff for track access charges which is functionally dependent upon the derived relative benchmark values of performance.

It is posited that track access charges should be differentiated to reflect differences in the performance of rolling stock and that this can be achieved purely on the basis of technical and other characteristics. The performance benchmarking of rolling stock is proposed as the basis for formulating and justifying a performance-based tariff structure. Using DEA, relative index measures of rolling stock performance can be derived, benchmark performance can be identified and a tariff structure can be developed.

A workable approach to implementing the concept, utilizing existing in-house databases, is found to be feasible and a template for tariff setting is established.

In the absence of access to in-house technical data on rolling stock, which is commercially sensitive, no empirical application of the concept is possible.

There are many ways to improve the efficiency of a railway system. Many are inherently long term and involve significant investment. Using Sweden as an example, this paper proposes the more immediate, simpler and cheaper approach of incentivising the use of better rolling stock through appropriate track access charging. Such an approach should reduce the number of problems arising on the rail network and the costs imposed on other rail users, the infrastructure providers and society. Ultimately, the implementation of this approach would support the objective of increasing long-term robustness and reducing disruptions to railways.

This paper aims to evaluate the financial performance of companies listed on Tehran Stock Exchange by using negative data envelopment analysis (DEA) approach.

First, the financial metrics for performance evaluation were extracted and then filtered based on the experts’ opinions. Upon choosing the appropriate financial measures, the financial information of 72 companies selected from four automotive, pharmaceutical, petrochemical and cement industries were collected, and the criteria values were also measured. The financial performance of selected companies was assessed using negative data bounded adjusted measure in the DEA, and efficient and inefficient companies were identified. Finally, the efficient companies were ranked using Andersen and Petersen model.

The required analysis was conducted, and the financial performance of selected companies listed on Tehran Stock Exchange was evaluated. There were 58 efficient companies with a performance value of 1; 14 companies became inefficient because the efficiency size was less than 1; therefore, reference units were also introduced to the managers for efficiency of inefficient companies.

The aim of this study was to identify the required financial criteria and to determine an appropriate model for performance evaluation based on negative DEA. The findings can help shareholders to identify efficient companies and make the optimal portfolio accordingly; the managers of inefficient companies can also take the proper reforming actions to improve efficiency.

Restaurants are characterised by predictable, seasonal factors and unpredictable, individual customer demand, which make it difficult for restaurateurs to attain efficiency. A combination of these two factors, macro-predictability and micro-uncertainty, produces economic risks, which make it difficult for restaurants to attain operational efficiency. The purpose of this study is to identify factors impacting restaurant efficiency in South Africa.

By using primary and secondary sources, data were collected from 16 different types of restaurants in South Africa, for the period 2012-2016, on a variety of parameters. A two-stage empirical analysis was carried out, which involved the estimation of operational efficiencies during the first stage by using data envelopment analysis (DEA) and determination of factors impacting restaurant performance in South Africa during the second stage by using two-way random-effects generalised least squares and Tobit regression models.

The results clearly show that the ability of restaurants to succeed will not be determined by their size but by their type, location and revenue per available seat. While the study finds various factors impacting on operational efficiency, the survival of restaurants in South Africa seem to be determined by cost efficiency, which brings in better market performance through lowering cost of sales.

The results have implications for restaurant managers in that if they want to improve cost efficiency, they must manage restaurant capacity and customer demand in a way that maximises revenue. To stimulate demand during periods of low demand, management could consider strategies that attract more customers or encourage upselling, whereas during periods of high demand, management may consider raising prices or reducing meal durations. The results indicate that DEA is a useful tool to identify factors impacting restaurant efficiency and could enhance the service data and revenue management with regards to restaurant efficiency in South Africa.

To the best of the author’s knowledge, this paper is the first that attempts to identify factors impacting restaurant efficiency in South Africa by using DEA. The findings could enhance the service data and revenue management with regards to restaurant efficiency in South Africa.

This study aims to provide a meaningful comparison of airports’ performance and better understand the differences observed in the analysed airport performance by presenting a model to analyse the relationship between operational and financial performance and airport characteristics.

This study uses a quantitative analysis approach. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and entropy weight were utilised to analyse 17 airports in three Airports Council International regions: Asia, Europe and North America. Through operational and financial factors, these sample airports identified the most efficiently operated airports from 2016 to 2019.

Overall, Asian airports were superior in operational and financial efficiency. Unlike operating performance, the sample airport’s financial and total performance results show a similar trend. There were no noticeable changes in operational factors. Therefore, differences in financial variables for each airport may affect the total performance.

This study provides insightful implications for airport policymakers to establish a standardised information disclosure foundation for consistent analysis and encourage airports to provide this information.

The adoption of Earnings Before Interest, Taxes, Depreciation, and Amortisation (EBITDA) to debt ratio and EBITDA per passenger, which had previously been underutilised in the previous study as financial factors, demonstrated differences between airports for airport stakeholders. In addition, the study presented a model that facilitates producing more intuitive results using TOPSIS, which was relatively underutilised compared to other methodologies such as date envelopment analysis.

The purpose of this paper is introducing an alternative model to measure the relative efficiency of observations with undesirable products. Describing the reference set and benchmarking.

In this paper, an alternative definition of weak disposability assumption is introduced to handle undesirable outputs. Actually, two types of undesirable outputs are addressed and a substitute definition of weak disposability is presented.

Using this assumption a linear production technology set along with a performance analysis model is constructed to assess the relative efficiency of the decision-making units. To illustrate the radial application of the proposed approach, a real case on transportation system of USA during 1992-2009 is given.

To date, data envelopment analysis studies have investigated undesirable outputs by the assumption of weak disposability, defined as the proportional contraction of good and bad products, which leads to the null-joint assumption between good and bad outputs. Therefore, the only way to produce no undesirable outputs is producing zero desirable outputs. So the production process should be stopped while it is not economically cost-effective. However, in some processes there are some undesirable outputs, which are decreased with non-same percentages. So these undesirable outputs can be stopped while the good outputs have a strictly positive value. In this situation, the good outputs are not null-joint with this type of bad outputs. In the current paper, a new definition of the weak disposability of outputs was represented while two groups of undesirable outputs were considered. Hence, desirable outputs and the first kind of undesirable outputs were decreased proportionally. However, the reduction value was different for the second kind of undesirable outputs. Hence, the null-joint assumption is removed from the production technology. Then, a new technology was proposed based on five postulates as inclusion of observations, free disposability of desirable outputs and inputs, new weak disposability, convexity and minimum extrapolation.

The airline industry is structurally challenged by its very nature, because of high overhead and capital costs. This is further exacerbated by macro-predictability and micro-uncertainty, thereby making it difficult for airlines in South Africa to attain operational efficiency. The purpose of this study is to identify drivers of operational efficiency and their impacts on airline performances in South Africa.

An extensive data collection using primary and secondary sources enabled the researchers to gather data on all the airlines operating in South Africa, for the period of 2012-2016, on a variety of parameters. A two-stage empirical analysis was carried out, which involved estimation of operational efficiencies during the first stage by using data envelopment analysis (DEA) and determination of performance drivers during the second stage by using a two-way random-effects generalised least squares regression and also a Tobit model.

From the study, it is clear that two structural drivers, namely, “aircraft size” and “seat load factor”, and two executional drivers, namely, “low cost business model” and “revenue hours per aircraft”, significantly impacted (p < 0.05) positively on airline efficiencies in South Africa. To improve efficiency, management should first concentrate on the drivers that can be changed in the short-term (executional drivers) and later focus on the drivers that require long-term planning (structural drivers). However, among the structural drivers, only “aircraft families” had a negative impact on airline efficiencies, whilst among executional drivers, only “block hours” negatively impacted on airline efficiencies.

Despite the importance of this study, it is not free of limitations. Firstly, because of the small size of the industry, fewer airlines and lack of detailed data, the study could not consider other important factors such as optimal routing and network structure. Secondly, although non-aeronautical revenues have become increasingly important in airline management, they were not included in this study. Further studies may investigate the impact of these factors on airline efficiency.

The results have potential policy implications. Firstly, as the domestic airline market in South Africa is too small to operate with a smaller aircraft efficiently, airlines that intend to make use of smaller aircraft should first identify niche markets where they can have a route monopoly, such as SA Airlink. Secondly, as block time negatively affected airline efficiency, airlines can undertake schedule adjustments to reduce block time and thus improve technical efficiency.

This paper is a first attempt to identify drivers of operational efficiency in the airline industry in South Africa. The results indicate that DEA is a useful tool to identify factors impacting airline efficiency and could improve airline performances in South Africa.

The purpose of this paper is to measure and evaluate the efficiency of materials management in the European Union countries (EU-28) during the period of 2008–2017.

The study was conducted using the method of data envelopment analysis (DEA) and variables applied to determine the resource productivity indicator. Therefore, the components of domestic material consumption constituted inputs in the DEA method, while gross domestic product (GDP) was applied as an output.

The results of the analysis showed that the Netherlands, Luxembourg, Latvia and the UK are the efficiency leaders among all the member states of the European Union. One of the least efficient countries is Poland, which uses too much natural resources in the process of generating goods and services. However, this consumption is growing at a slower rate than the value of GDP, which is beneficial from the point of view of sustainable development. Poland, like other inefficient countries, should reduce its consumption of natural resources in line with the best international practices.

The obtained research results can be a valuable source of information for decision-makers, and contribute to the adoption of more effective policies in order to improve the relationship between materials consumption and economic growth.

The application of the DEA method for calculating the efficiency of materials management represents a new approach, and it is the first attempt of its kind in the European Union countries.

Supply chain (SC) environment is surrounded by risk variables. This issue is regarded as an emerging and strategic problem which must be resolved by SC executives. The ability to measuring green supplier’s performance and affecting risk variables to demonstrating effective suppliers list has a potential contribution to be investigated. This paper aims to develop a decision-making model to assess green suppliers under legislation and risk factors. This leads to fewer disruptions in managing the SC and its impact to further improvement. It also presents research concepts forming a new approach for identification, prediction and understating relationship of supply risk.

At primal stage, different risk factors that influence green suppliers’ performance are indicated and their relationship is analyzed using decision-making trial and evaluation laboratory (DEMATEL) method. At the same time, failure mode and effect analysis is used to determine risk rating of each supplier. Finally, the evaluation based on distance from average solution (EDAS) method ranks suppliers and several comparisons and analysis are performed to test the stability of the results. The approaches include comparison to technique for order performance by similarity to ideal solution, multi-attributive border approximation area comparison, Vlse Kriterijumska Optimizacija I Kompromisno Resenje and complex proportional assessment methods, followed by analysis of rank reversal, weight sensitivity analysis and effect of dynamic metrics.

A real-time case study on green supplier selection (GSS) problem of a reputed construction company of Spain has been presented to demonstrate the practical aspects of the proposed method. In practice, though organizations are aware of various risks from local and global suppliers, it is difficult to incorporate these risk factors for ranking the suppliers. This real-case application shows the evaluation and incorporation of risk factors into the supplier selection model.

The proposed multi-criteria decision model quantitatively aids managers in selecting green suppliers considering risk factors.

A new model has been developed to present a sound mathematical model for solving GSS problems which considers the interaction between the supplier selection risk factors by proposing an integrated analytical approach for selecting green suppliers strategically consisting of DEMATEL, FMEA and EDAS methods.