A linear relational DEA model to evaluate two-stage processes with shared inputs

Two-stage data envelopment analysis (DEA) efficiency models identify the efficient frontier of a two-stage production process. In some two-stage processes, the inputs to the first stage are shared by the second stage, known as shared inputs. This paper proposes a new relational linear DEA model for dealing with measuring the efficiency score of two-stage processes with shared inputs under constant returns-to-scale assumption. Two case studies of banking industry and university operations are taken as two examples to illustrate the potential applications of the proposed approach

Performance evaluation of Taiwanese international tourist hotels: evidence from a modified NDEA model with ICA technique

The motivation for this study is to assess the managerial performance in Taiwanese international tourist hotels based on the two-stage NDEA performance mechanism with ICA technique for enhancing the discriminatory power of performance evaluation model. The two-stage managerial performance structure is applied, incorporating the service production and service operation stages, as a reduced form to introduce the relatively complex business environment of modern enterprise. However, we have need to be considerable of dimensionality curse problem in NDEA performance model. A modified NDEA-based evaluation model, therefore, is proposed to integrate the network slacks-based measure (NSBM) with a dimensional reduction technique, the independent component analysis (ICA). The results indicate that the performance of the profit dimension significantly hampers operational performance, and that both regulators and managers must adjust their market orientation business strategy. Moreover, compared with the NSBM model, this modified ICA-NSBM performance model has a high discriminatory ability to measure the relative performance of the selected hotels

Bootstrapping in Network Data Envelopment Analysis

Data Envelopment Analysis (DEA) is a linear-programming method used to measure the relative efficiency of firms. The objective of this thesis is (i) to study the efficiency of the railway transport process in Europe considering its inner structure and the impact of railway noise on humans and (ii) to study the performance of bootstrapping approaches in obtaining DEA efficiency estimates when the production process has a network structure and the relation between the different stages is considered. First, the railway transport process is divided into two stages, related to assets and service provision, respectively. The negative impact of railways on people is measured as the number of people that are exposed to high levels of railway noise. The number of rail wagons in each country that is retrofitted with more silent braking technology is used as a proxy to measure the effort to reduce railway noise pollution. Data is extracted from Eurostat (2016), ERA 006REC1072 Impact Assessment (2018), and EEA (2020) and the additive efficiency decomposition approach is used. Based on the results, asset-efficient countries are usually service-efficient, but the inverse does not hold. Sensitivity analysis revealed that efficiency rankings are robust to alterations in the decomposition weight restrictions. Subsampling bootstrap was chosen as the most appropriate as it does not require any restrictive assumptions. The performance of subsampling is examined through Monte Carlo simulations for various sample and subsample sizes for general two-stage series structures. Results indicate great sensitivity both to the sample and subsample size, as well as to the data generating process-higher than in one-stage structures. A practical approach is suggested to overcome some result inconsistencies that are due to the peculiarities of the additive decomposition algorithm. The method is applied to obtain confidence interval estimates for the overall and stage efficiency scores of European railways

Application of DEA in benchmarking: a systematic literature review from 2003–2020

Benchmarking is an effective method for organizations to increase their productivity, quality of products, reliability of processes or services. The organization may make a comparison between its performance and that of the peers from benchmarking, and recognize their advantages as well as disadvantages. The main objective of the present systematic literature review has been the study of DEA benchmarking process. Therefore, it examined and gave a summary of various DEA models applied worldwide to improve benchmarking. Accordingly, a list of published academic papers that appeared in high-ranking journals between 2003 and February 2020 was collected for a systematic review of the DEA benchmarking application. Consequently, the papers selected have been classified according to year of publication, purpose of research, outcomes and results. This study has identified eight major applications including: transportation, service sector, product planning, maintenance, hotel industry, education, distribution and environmental factors. They take up a total of 82% of all application-embedded papers. Among all the applications, the highest recent development has been in both the transportation and service sectors. Results showed higher potential of DEA as a suitable evaluation method for the further benchmarking researches, wherein the production feature between outputs and inputs has been practically lacked or very hard to obtain. First published online 4 January 202

Sustainability and Related Factors of High Speed Railways

High-Speed Railways (HSR), which represent a safe and sustainable mode of transportation, provide access and mobility for the society, and support the growth of the economy in addition to creating new jobs, supporting welfare, and promoting local business activities. This research addresses the shortage of knowledge in evaluating the performance of selected HSR systems and in distinguishing the factors that contribute to the sustainable performance of HSRs. The aim of this study is to evaluate the sustainability of selected HSRs and identify factors that affect such sustainability. The objectives of this research are to evaluate productivity, technical and technological efficiency of the selected HSRs, define the factors that can affect productivity and efficiency scores and make suggestions for improving the sustainability of HSRs. The secondary data methodology has been used, supported by empirical evidence. Most of the data was gathered from the Internet, research in depth of the high-speed railways in the selected countries, and International Union of Railway’s websites in addition to analysing railway statistics and data from European and institutional publications. This includes the use of a multi-stage approach of applying three specialised software packages, namely, NVivo, DEA, and ISM SPSS. The main findings show that HSRs in Asia has higher productivity and higher efficiency scores than that of HSRs in Europe. The research found that the key factors among all the identified factors that affected the productivity and efficiency of HSRs are; density of population, average traction power of HSR trains, average time that passengers spend on trains and average distance that passengers travel on the HSR. The findings of this research can help develop strategic guidelines to improve the performance and, by the result, the sustainability of HSRs. The recommendations are drawn for more research expansion, including the consideration of other HSRs, particularly their best practices

Operational research and artificial intelligence methods in banking

Supplementary materials are available online at https://www.sciencedirect.com/science/article/pii/S037722172200337X?via%3Dihub#sec0031 .Copyright © 2022 The Authors. Banking is a popular topic for empirical and methodological research that applies operational research (OR) and artificial intelligence (AI) methods. This article provides a comprehensive and structured bibliographic survey of OR- and AI-based research devoted to the banking industry over the last decade. The article reviews the main topics of this research, including bank efficiency, risk assessment, bank performance, mergers and acquisitions, banking regulation, customer-related studies, and fintech in the banking industry. The survey results provide comprehensive insights into the contributions of OR and AI methods to banking. Finally, we propose several research directions for future studies that include emerging topics and methods based on the survey results

Strategy Tripod Perspective on the Determinants of Airline Efficiency in A Global Context: An Application of DEA and Tobit Analysis

The airline industry is vital to contemporary civilization since it is a key player in the globalization process: linking regions, fostering global commerce, promoting tourism and aiding economic and social progress. However, there has been little study on the link between the operational environment and airline efficiency. Investigating the amalgamation of institutions, organisations and strategic decisions is critical to understanding how airlines operate efficiently. This research aims to employ the strategy tripod perspective to investigate the efficiency of a global airline sample using a non-parametric linear programming method (data envelopment analysis [DEA]). Using a Tobit regression, the bootstrapped DEA efficiency change scores are further regressed to determine the drivers of efficiency. The strategy tripod is employed to assess the impact of institutions, industry and resources on airline efficiency. Institutions are measured by global indices of destination attractiveness; industry, including competition, jet fuel and business model; and finally, resources, such as the number of full-time employees, alliances, ownership and connectivity. The first part of the study uses panel data from 35 major airlines, collected from their annual reports for the period 2011 to 2018, and country attractiveness indices from global indicators. The second part of the research involves a qualitative data collection approach and semi-structured interviews with experts in the field to evaluate the impact of COVID-19 on the first part’s significant findings. The main findings reveal that airlines operate at a highly competitive level regardless of their competition intensity or origin. Furthermore, the unpredictability of the environment complicates airline operations. The efficiency drivers of an airline are partially determined by its type of business model, its degree of cooperation and how fuel cost is managed. Trade openness has a negative influence on airline efficiency. COVID-19 has toppled the airline industry, forcing airlines to reconsider their business model and continuously increase cooperation. Human resources, sustainability and alternative fuel sources are critical to airline survival. Finally, this study provides some evidence for the practicality of the strategy tripod and hints at the need for a broader approach in the study of international strategies