Factors impacting restaurant efficiency: a data envelopment analysis

2018 ◽  
Vol 73 (1) ◽  
pp. 82-93 ◽  
Author(s):  
Oswald Mhlanga
Keyword(s):  
South Africa ◽  
Data Envelopment Analysis ◽  
Revenue Management ◽  
Cost Efficiency ◽  
Demand Management ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Customer Demand ◽  
Content Type ◽  
Service Data

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

A comparative approach of stochastic frontier analysis and data envelopment analysis estimators: evidence from banking system

2020 ◽  
Vol 47 (7) ◽  
pp. 1787-1810
Author(s):  
Kekoura Sakouvogui
Keyword(s):  
Data Envelopment Analysis ◽  
Cost Efficiency ◽  
Stochastic Frontier Analysis ◽  
Stochastic Frontier ◽  
Data Envelopment ◽  
Content Type ◽  
Frontier Analysis ◽  
Efficiency Measures ◽  
Clustering Approach ◽  
Over Time

PurposeThe consistency of stochastic frontier analysis (SFA) and data envelopment analysis (DEA) cost efficiency measures using a sample of 650 commercial and domestic banks in the United States is investigated based on cluster analysis while accounting for the yearly variation in banks.Design/methodology/approachDue to the importance of efficiency measures for policy and managerial decision-making, the cost efficiency measures of SFA and DEA estimators are examined according to four criteria: levels, rankings, stability over time and stability over clustering groups. In this paper, we present two clustering methods, Gap Statistic and Dindex, that involve SFA and DEA cost efficiency measures. The clustering approach creates homogeneous groups of banks offering a similar mix of efficiency levels. Hence, each evaluated bank knows the cluster to which it belongs. Furthermore, this paper provides nonparametric statistical tests of SFA and DEA cost efficiency measures estimated with and without a clustering approach.FindingsThe results suggest that the clustering approach plays a considerable role in the rankings of US banks. Furthermore, the average SFA and DEA cost efficiency measures over time of the homogeneous US banks are substantially higher than those of the heterogeneous US banks.Originality/valueThis research is the first to provide comparative efficiency measures needed for desirable policy conclusions of heterogeneous and homogeneous US banks.

Measuring operational efficiency of isolation hospitals during COVID-19 pandemic using data envelopment analysis: a case of Egypt

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahmoud Abdelrahman Kamel ◽  
Mohamed El-Sayed Mousa
Keyword(s):  
Data Envelopment Analysis ◽  
Design Methodology ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Allocation Of Resources ◽  
Tobit Regression ◽  
Health Crisis ◽  
Content Type ◽  
Dea Analysis ◽  
Using Data

PurposeThis study used Data Envelopment Analysis (DEA) to measure and evaluate the operational efficiency of 26 isolation hospitals in Egypt during the COVID-19 pandemic, as well as identifying the most important inputs affecting their efficiency.Design/methodology/approachTo measure the operational efficiency of isolation hospitals, this paper combined three interrelated methodologies including DEA, sensitivity analysis and Tobit regression, as well as three inputs (number of physicians, number of nurses and number of beds) and three outputs (number of infections, number of recoveries and number of deaths). Available data were analyzed through R v.4.0.1 software to achieve the study purpose.FindingsBased on DEA analysis, out of 26 isolation hospitals, only 4 were found efficient according to CCR model and 12 out of 26 hospitals achieved efficiency under the BCC model, Tobit regression results confirmed that the number of nurses and the number of beds are common factors impacted the operational efficiency of isolation hospitals, while the number of physicians had no significant effect on efficiency.Research limitations/implicationsThe limits of this study related to measuring the operational efficiency of isolation hospitals in Egypt considering the available data for the period from February to August 2020. DEA analysis can also be an important benchmarking tool for measuring the operational efficiency of isolation hospitals, for identifying their ability to utilize and allocate their resources in an optimal manner (Demand vs Capacity Dilemma), which in turn, encountering this pandemic and protect citizens' health.Originality/valueDespite the intensity of studies that dealt with measuring hospital efficiency, this study to the best of our knowledge is one of the first attempts to measure the efficiency of hospitals in Egypt in times of health' crisis, especially, during the COVID-19 pandemic, to identify the best allocation of resources to achieve the highest level of efficiency during this pandemic.

Comprehensive efficiency analysis of logistics industry based on machine learning and self-service data envelopment analysis model

2020 ◽  
pp. 1-12
Author(s):  
Huang Man ◽  
Lian Jie
Keyword(s):  
Machine Learning ◽  
Data Envelopment Analysis ◽  
Efficiency Analysis ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Analysis Model ◽  
Logistics Industry ◽  
Logistics Enterprises ◽  
Self Service ◽  
Service Data

The research on operational efficiency focuses on the macro-level research. However, there are relatively few studies on the industry level. In particular, there are fewer studies on the logistics industry, which has a leading and fundamental significance in the national economic system and is regarded as the third important source of profit. Moreover, scholars are more focused on the research on the operational performance and profitability of logistics enterprises. In order to study the efficiency of the logistics industry, this paper uses machine learning technology as the foundation and self-service data envelopment analysis to construct a comprehensive efficiency analysis model for the logistics industry. Moreover, this paper adopts a combination of qualitative and quantitative analysis to conduct empirical research on the operational efficiency and influencing factors of the logistics industry to explore the factors that affect the operational efficiency of logistics enterprises. In addition, this article optimizes the model data through statistics, and compares the model analysis data with the actual situation. It can be seen from the research results that the model constructed in this paper has a certain effect.

Are the best African banks really the best? A Malmquist data envelopment analysis

2016 ◽  
Vol 24 (4) ◽  
pp. 588-610 ◽  
Author(s):  
Tesfatsion Sahlu Desta
Keyword(s):  
South Africa ◽  
Data Envelopment Analysis ◽  
Total Factor Productivity ◽  
Commercial Banks ◽  
Productivity Index ◽  
Data Envelopment ◽  
Factor Productivity ◽  
Global Finance ◽  
Content Type ◽  
Efficiency And Productivity

Purpose This paper aims to examine whether the African commercial banks selected as the best African banks by Global Finance Magazine really are the best. Design/methodology/approach Panel data envelopment analysis (DEA) was used, as well as the Malmquist total factor productivity index, to distinguish productive banks from unproductive banks. Nineteen commercial banks were selected from the 30 best African banks as identified by the Global Finance Magazine. Findings Of the 19 banks, five were found to be unproductive. Bank productivity was attributed mainly to technological change, and different methods marked different results, for example, the regional winner bank (Standard Bank of South Africa) selected by Global Finance Magazine ranked ninth in this study, whereas the Bank Windhoek Limited, Namibia, ranked first. Practical implications The study confirms the applicability of DEA for the banking industry. The model shows variability among the banks’ efficiency and productivity and provides different results to the Global Finance Magazine’s best bank selection. For example, the Standard Bank of South Africa, which is selected as the regional winner, is now ranked ninth under the DEA Malmquist’s total factor productivity. Originality/value The study shows that the DEA model can be applied not only for analysing the firm’s efficiency but also for objective rating, ranking and selecting best banks.

Fuzzy preference programming formulation in data envelopment analysis for university department evaluation

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dyanne Brendalyn Mirasol-Cavero ◽  
Lanndon Ocampo
Keyword(s):  
Data Envelopment Analysis ◽  
Real Life ◽  
The Philippines ◽  
Data Envelopment ◽  
Data Accessibility ◽  
Content Type ◽  
Proposed Model ◽  
University Department ◽  
Fuzzy Dea ◽  
Fuzzy Preference

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.

scholarly journals Operational Efficiency of Chinese Provincial Electricity Grid Enterprises: An Evaluation Employing a Three-Stage Data Envelopment Analysis (DEA) Model

2018 ◽  
Vol 10 (9) ◽  
pp. 3168 ◽  
Author(s):  
Haoran Zhao ◽  
Huiru Zhao ◽  
Sen Guo
Keyword(s):  
Data Envelopment Analysis ◽  
Environmental Variables ◽  
Market Competition ◽  
Environmental Values ◽  
Operational Efficiency ◽  
Data Envelopment ◽  
Electricity Grid ◽  
Frontier Analysis ◽  
Dea Model ◽  
Statistical Noise

With the implementation of new round electricity system reform in China, the provincial electricity grid enterprises (EGEs) of China should focus on improving their operational efficiency to adapt to the increasingly fierce market competition and satisfy the requirements of the electricity industry reform. Therefore, it is essential to conduct operational efficiency evaluation on provincial EGEs. While considering the influences of exterior environmental variables on the operational efficiency of provincial EGEs, a three-stage data envelopment analysis (DEA) methodology is first utilized to accurately assess the real operational efficiency of provincial EGEs excluding the exterior environmental values and statistical noise. The three-stage DEA model takes the amount of employees, the fixed assets investment, the 110 kV and below distribution line length, and the 110 kV and below transformer capacity as input variables and the electricity sales amount, the amount of consumers, and the line loss rate as output variables. The regression results of the stochastic frontier analysis model indicate that the operational efficiencies of provincial EGEs are significantly affected by exterior environmental variables. Results of the three-stage DEA model imply that the exterior environmental values and statistical noise result in the overestimation of operational efficiency of provincial EGEs, and the exclusion of exterior environmental values and statistical noise has provincial-EGE-specific influences. Furthermore, 26 provincial EGEs are divided into four categories to better understand the differences of operational efficiencies before and after the exclusion of exterior environmental values and statistical noise.

Efficiency evaluation of Indian oil and gas sector: data envelopment analysis

2019 ◽  
Vol 14 (2) ◽  
pp. 362-378 ◽  
Author(s):  
Vikas Vikas ◽  
Rohit Bansal
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Capital Investment ◽  
Oil And Gas ◽  
Stock Exchange ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Content Type ◽  
Indian Economy ◽  
Oil And Gas Sector

Purpose Data envelopment analysis (DEA), a non-parametric technique is used to assess the efficiency of decision-making units which are producing identical set of outputs using identical set of inputs. The purpose of this paper is to find the technical efficiency (TE), pure technical efficiency and scale efficiency (SE) levels of Indian oil and gas sector companies and to provide benchmark targets to the inefficient companies in order to achieve efficiency level. Design/methodology/approach In the present study, a group of 22 oil and gas companies which are listed on the National Stock Exchange for which the data were available for the period 2013–2017 has been considered. DEA has been performed to compare the efficiency levels of all companies. To measure efficiency, three input variables, namely, combined materials consumed and manufacturing expenses, employee benefit expenses and capital investment and two output variables – operating revenues and profit after tax (PAT) have been considered. On the basis of performance for the financial year ending 2017, benchmark targets based on DEA–CCR (Charnes, Cooper and Rhodes) model have been provided to the inefficient companies that should be focused upon by them to attain the efficiency level. The performance of the companies for the past five years has been examined to check the fluctuations in the various efficiency scores of the companies considered in the study over the years. Findings From the results obtained, it is observed that 59 percent, i.e. 13 out of 22 companies are technically efficient. By considering DEA BCC (Banker, Charnes and Cooper) model, 16 companies are observed to be pure technically efficient. In terms of SE, there are 14 such companies. The inefficient units need to improve in terms of input and output variables and for this motive, specified targets are assigned to them. Some of these companies need to upgrade significantly and the managers must take the concern earnestly. The study has also thrown light on the performance of the companies over last five years which shows Oil India Ltd, Gujarat State Petronet Ltd, Petronet LNG Ltd, IGL Ltd, Mahanagar Gas, Chennai Petroleum Corporation Ltd and BPCL Ltd as consistently efficient companies. Research limitations/implications The present study has made an attempt to evaluate the efficiency of Indian oil and gas sector. The results of the study have significant inferences for the policy makers and managers of the companies operating in the sector. The results of the study provide benchmark target level to the companies of Oil and Gas sector which can help the managers of the relatively less efficient companies to focus on the ways to improve efficiency. The improvement in efficiency of a company would not only benefit the shareholders, but also the investors and other stakeholders of the company. Originality/value In the context of Indian economy, very limited number of studies have focused to measure the efficiency of oil and gas sector in the context of Indian economy. The present study aims to provide the latest insight to the efficiency of the companies especially operating in the Indian oil and gas sector. Further, as per our knowledge, this study is distinctive in terms of analyzing the efficiency of Indian oil and gas sector for a period of five years. The longitudinal study of the sector efficiency provides a bird eye view of the average efficiency level and changes in the efficiency levels of the companies over the years.

Benchmarking the efficiencies of Indonesia’s municipal water utilities using Stackelberg data envelopment analysis

2015 ◽  
Vol 22 (4) ◽  
pp. 588-609 ◽  
Author(s):  
Andreas Wibowo ◽  
Hans Wilhelm Alfen
Keyword(s):  
Data Envelopment Analysis ◽  
The Body ◽  
Water Utilities ◽  
Data Envelopment ◽  
Two Stage ◽  
Restrictive Assumption ◽  
Content Type ◽  
Second Stage ◽  
Municipal Water ◽  
The Impact

Purpose – The purpose of this paper is to present a yardstick efficiency comparison of 269 Indonesian municipal water utilities (MWUs) and measures the impact of exogenous environmental variables on efficiency scores. Design/methodology/approach – Two-stage Stackelberg leader-follower data envelopment analysis (DEA) and artificial neural networks (ANN) were employed. Findings – Given that serviceability was treated as the leader and profitability as the follower, the first and second stage DEA scores were 55 and 32 percent (0 percent = totally inefficient, 100 percent = perfectly efficient), respectively. This indicates sizeable opportunities for improvement, with 39 percent of the total sample facing serious problems in both first- and second-stage efficiencies. When profitability instead leads serviceability, this results in more decreased efficiency. The size of the population served was the most important exogenous environmental variable affecting DEA efficiency scores in both the first and second stages. Research limitations/implications – The present study was limited by the overly restrictive assumption that all MWUs operate at a constant-return-to-scale. Practical implications – These research findings will enable better management of the MWUs in question, allowing their current level of performance to be objectively compared with that of their peers, both in terms of scale and area of operation. These findings will also help the government prioritize assistance measures for MWUs that are suffering from acute performance gaps, and to devise a strategic national plan to revitalize Indonesia’s water sector. Originality/value – This paper enriches the body of knowledge by filling in knowledge gaps relating to benchmarking in Indonesia’s water industry, as well as in the application of ensemble two-stage DEA and ANN, which are still rare in the literature.

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