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Article
Publication date: 11 October 2021

Dyanne Brendalyn Mirasol-Cavero and Lanndon Ocampo

University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in…

Abstract

Purpose

University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation.

Design/methodology/approach

This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs.

Findings

Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score.

Originality/value

This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

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Article
Publication date: 20 August 2021

Mohamed El-Sayed Mousa and Mahmoud Abdelrahman Kamel

This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial…

Abstract

Purpose

This study aims to develop and test a framework for integration between data envelopment analysis (DEA) and artificial neural networks (ANN) to predict the best financial performance concerning return on assets and return on equity for banks listed on the Egyptian Exchange, to help managers generate what-if scenarios? For performance improvement and benchmarking.

Design/methodology/approach

The study empirically tested the three-stage DEA-ANN framework. First, DEA was used as a preprocessor of the banks’ efficiency scores. Second, a back-propagation neural network as a multi-layer perceptron-ANN’s model was designed using expected data sets from DEA to learn optimal performance patterns. Third, the superior performance of banks was forecasted.

Findings

The results indicated that banks are not operating under their most productive operations, and there is room for potential improvements to reach outperformance. Moreover, the neural networks’ empirical test results showed high correlations between the actual and expected values, with low prediction errors in both the test and prediction phases.

Practical implications

Based on best performance prediction, banks can generate alternative scenarios for future performance improvement and enabling managers to develop effective strategies for performance control under uncertainty and limited data. Besides, supporting the decision-making process and proactive management of performance.

Originality/value

Despite the growing research stream supporting DEA-ANN integration applications, these are still limited and scarce, especially in the Middle East and North Africa region. Therefore, the study trying to fill this gap to help bank managers predict the best financial performance.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

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Book part
Publication date: 31 May 2016

Chunyan Yu

This chapter provides a survey of alternative methodologies for measuring and comparing productivity and efficiency of airlines, and reviews representative empirical…

Abstract

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.

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Book part
Publication date: 12 April 2012

Daniel G. Shimshak and Janet M. Wagner

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…

Abstract

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.

Details

Applications of Management Science
Type: Book
ISBN: 978-1-78052-100-8

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Book part
Publication date: 3 February 2015

D. K. Malhotra, Rashmi Malhotra and Kathleen T. Campbell

As cable and satellite industry undergoes transformation in the 21st century with the onslaught of innovation-driven changes, it is important to know which company is…

Abstract

As cable and satellite industry undergoes transformation in the 21st century with the onslaught of innovation-driven changes, it is important to know which company is doing better and which company is falling behind. This study compares the relative performance of eight cable companies using three factors: operating expense for every dollar of operating revenue, earnings before interest, taxes, depreciation, and amortization, and return on assets. We also evaluate the performance of each firm against itself for the period 2010–2013 to see if they show improvement or deterioration in operating efficiency.

Details

Applications of Management Science
Type: Book
ISBN: 978-1-78441-211-1

Keywords

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Book part
Publication date: 15 August 2006

N.K. Kwak, Yong Soo Chun and Seongho Kim

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations…

Abstract

Data Envelopment Analysis (DEA) is a nonparametric mathematical programming technique used to measure the relative efficiency of the production organization's operations. This paper presents the theoretical measures of the railway systems, along with the bootstrap DEA analysis. A DEA model is applied to evaluate the relative efficiency of railway operations of 29 UIC (Union Internationale des Chemins de fer) countries, based on the data obtained from the International UIC publications. The bootstrap DEA analysis provides information (bias estimates) on the sensitivity of the DEA efficiency index to the sampling variations. The model results are analyzed and evaluated in terms of their relative operational performance efficiency. The model results facilitate an organization's decision-making by providing valuable information.

Details

Applications of Management Science: In Productivity, Finance, and Operations
Type: Book
ISBN: 978-0-85724-999-9

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Book part
Publication date: 12 April 2012

Rashmi Malhotra, Raymond R. Poteau and D.K. Malhotra

This study develops a multidimensional framework using data envelopment analysis (DEA) as a benchmarking tool to assess the performance of the commercial banks in India…

Abstract

This study develops a multidimensional framework using data envelopment analysis (DEA) as a benchmarking tool to assess the performance of the commercial banks in India. Using the DEA approach, this study compares the relative performance of 35 banks against one another with 8 variables as the benchmark parameters. This study finds that most of the banks are consistently performing well over a period from 2005 to 2009. The study also shows the areas in which inefficient banks are lagging behind and how they can improve their performance to bring them at par with the efficient commercial banks.

Details

Applications of Management Science
Type: Book
ISBN: 978-1-78052-100-8

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Article
Publication date: 8 March 2021

Jafar Pourmahmoud and Maedeh Gholam Azad

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by…

Abstract

Purpose

The purpose of this paper is to propose the data envelopment analysis (DEA) model that can be used as binary-valued data. Often the basic DEA models were developed by assuming that all of the data are non-negative. However, there are situations where all data are binary. As an example, the information on many diseases in health care is binary data. The existence of binary data in traditional DEA models may change the behavior of the production possibility set (PPS). This study defines a binary summation operator, expresses the modified principles and introduces the extracted PPS of axioms. Furthermore, this study proposes a binary integer programming of DEA (BIP-DEA) for assessing the efficiency scores to use as an alternate tool in prediction.

Design/methodology/approach

In this study, the extracted PPS of modified axioms and the BIP-DEA model for assessing the efficiency score is proposed.

Findings

The binary integer model was proposed to eliminate the challenges of the binary-value data in DEA.

Originality/value

The importance of the proposed model for many fields including the health-care industry is that it can predict the occurrence or non-occurrence of the events, using binary data. This model has been applied to evaluate the most important risk factors for stroke disease and mechanical disorders. The targets set by this model can help to diagnose earlier the disease and increase the patients’ chances of recovery.

Details

Journal of Modelling in Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1746-5664

Keywords

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Book part
Publication date: 31 May 2016

Mark R. Greer

This chapter examines the impact of recent airline consolidations in the United States on the technical efficiencies of the airlines involved. Data envelopment analysis …

Abstract

This chapter examines the impact of recent airline consolidations in the United States on the technical efficiencies of the airlines involved. Data envelopment analysis (DEA) is used to assess the efficiencies, and the consolidations examined are those that occurred among major network carriers between 2005 and 2013. The airline production process is conceptualized as the transformation of labor, fuel, and fleet-wide seating capacity into available seat-miles, or, under an alternative model specification, into user value, as measured by the airline’s operating revenue. Efficiency is conceptualized in terms of minimizing the airline’s usage of the three inputs, given its output level. The analysis seeks to determine whether the airlines that consolidated were more efficient, post-consolidation, than they were prior to consolidation, compared to airlines that did not enter into consolidations. Although there are limitations owing to the small number of airlines in the dataset, the chapter finds no evidence that the consolidations enhanced the efficiencies of the airlines involved, relative to the efficiencies of the airlines that did not enter into consolidations.

Details

Airline Efficiency
Type: Book
ISBN: 978-1-78560-940-4

Keywords

Content available
Article
Publication date: 11 January 2021

Liu-Liu Li, Young-Joon Seo and Min-Ho Ha

Seaports are a signifier for the world economy and international trade. Notwithstanding the considerable role of Chinese ports in global trade, only few studies have…

Abstract

Purpose

Seaports are a signifier for the world economy and international trade. Notwithstanding the considerable role of Chinese ports in global trade, only few studies have explored the efficiency of Chinese container terminals. Furthermore, studies on Chinese port efficiency has typically centered on port-level analysis, not terminal level. Therefore, this study aims to examine the operation efficiency of Chinese container terminals.

Design/methodology/approach

This study uses super-efficiency data envelopment analysis (SE-DEA) approach. SE-DEA is superior than basic DEA model because it is feasible for categorizing and ranking the efficiency of container terminals more accurately and comprehensively. In the basic model, if the several decision-making units (DMUs) are efficient, the efficiency value of them is “1.” However, in the SE-DEA model, the most efficient DMU is over “1.” Based on the level of container throughput in 2018, the top 20 Chinese container terminal companies were selected. Various production quotas were selected as inputs, while the container throughput was considered output.

Findings

The findings show that Terminal Shanghai Mingdong Container Terminal Co., Ltd. was ranked 1, followed by Shanghai Shengdong International Container Terminal Co., Ltd., Shanghai International Port (Group) Co., Ltd. and Yidong Container Terminal Branch.

Originality/value

This study contributes to providing some insights into Chinese container terminal industry to augment the efficiency. This study also provides practical and policy implications (e.g. better terminal operations) for container terminals.

Details

Maritime Business Review, vol. 6 no. 2
Type: Research Article
ISSN: 2397-3757

Keywords

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