Bounded and discrete data in data envelopment analysis with assurance regions

2020 ◽  
Vol 15 (3) ◽  
pp. 1017-1036
Author(s):  
Bao Zhang ◽  
Chenpeng Feng ◽  
Min Yang ◽  
Jianhui Xie ◽  
Ya Chen
Keyword(s):  
Data Envelopment Analysis ◽  
Efficient Frontier ◽  
Discrete Data ◽  
Data Envelopment ◽  
Content Type ◽  
Dea Model ◽  
Decision Making Unit ◽  
Assurance Region ◽  
Context Dependent ◽  
Gear Shaping

Purpose The purpose of this paper is to evaluate design performance of 51 gear shaping machines by using data envelopment analysis (DEA). Design/methodology/approach Existing studies extend traditional DEA by handling bounded and discrete data based on envelopment models. However, value judgment is usually neglected and fail to be incorporated in these envelopment models. In many cases, there is a need for prior preferences. Using existing DEA approaches as a backdrop, the current paper presents a methodology for incorporating assurance region (AR) restrictions into DEA with bounded and discrete data, i.e. the assurance region bounded discrete (AR-BD) DEA model. Then, the AR-BD DEA model is combined with a context-dependent DEA to obtain an efficiency stratification. Findings The authors examine different AR restrictions and calculate efficiency scores of five scenarios of AR restrictions by using the proposed AR-BD DEA model. It shows that AR restrictions have a great impact on the efficiency scores. The authors also identify nine efficient frontiers in total. For each decision-making unit, it could set benchmarks and improve its performance based on each higher efficient frontier. Originality/value This paper first evaluates efficiency of gear shaping machines by considering different (bounded and discrete) variable types of data and including AR restrictions. The AR-BD DEA model and context-dependent AR-BD DEA model proposed in this paper further enrich the DEA theory. The findings in this paper certainly provide useful information for both producers and consumers to make smart decisions.

Evaluating the comparative efficiency of the postal services in OECD countries using context-dependent and measure-specific data envelopment analysis

2015 ◽  
Vol 22 (5) ◽  
pp. 839-856 ◽  
Author(s):  
Süleyman Çakır ◽  
Selçuk Perçin ◽  
Hokey Min
Keyword(s):  
Data Envelopment Analysis ◽  
Oecd Countries ◽  
Data Envelopment ◽  
Postal Service ◽  
Public Enterprises ◽  
Policy Makers ◽  
Content Type ◽  
Comparative Efficiency ◽  
Postal Services ◽  
Context Dependent

Purpose – In an effort to help policy makers develop competitive postal service strategies, the purpose of this paper is to evaluate the comparative operating efficiencies of postal services across the Organization for Economic Cooperation and Development (OECD) nations and then identify room for service improvement. Design/methodology/approach – As a better alternative to the conventional data envelopment analysis (DEA) which requires the proportional improvements of inputs and outputs simultaneously, the authors propose the combined use of both context-dependent and measure-specific DEAs to measure the relative attractiveness and progress of the national postal operators of OECD countries. Findings – Defying the conventional notion that public enterprises operate less efficiently than private enterprises, the author discovered that some state-owned public enterprises such as postal service operators could still be efficient if managed properly. Even inefficient postal services operators could significantly improve their service performances, once they identified the root causes of their service failures. Through a series of model experiments and testing, the authors found that proposed context-dependent and measure-specific DEA models were more useful for finding such causes than the conventional DEA model. Practical implications – For public officials and policy makers, the proposed DEAs can pinpoint what it takes to become more efficient and what steps need to be taken to improve postal service operations gradually. Originality/value – This paper is the first to combine the context-dependent DEA with measure-specific DEA to evaluate the comparative efficiency (or progress) and inefficiency (or regress) of the national postal operators of 25 OECD countries.

Data envelopment analysis with interactive variables

2015 ◽  
Vol 53 (10) ◽  
pp. 2390-2406 ◽  
Author(s):  
Aibing Ji ◽  
Hui Liu ◽  
Hong-jie Qiu ◽  
Haobo Lin
Keyword(s):  
Data Envelopment Analysis ◽  
Special Form ◽  
Design Methodology ◽  
Data Envelopment ◽  
Content Type ◽  
Dea Model ◽  
Proposed Model ◽  
Choquet Integrals ◽  
Practical Implications ◽  
Decision Making Units

Purpose – The purpose of this paper is to build a novel data envelopment analysis (DEA) model to evaluate the efficiencies of decision making units (DMUs). Design/methodology/approach – Using the Choquet integrals as aggregating tool, the authors give a novel DEA model to evaluate the efficiencies of DMUs. Findings – It extends DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form. At last, the authors use the numerical examples to illustrate the performance of the proposed model. Practical implications – The proposed DEA model can be used to evaluate the efficiency of the DMUs with multiple interactive inputs and outputs. Originality/value – This paper introduce a new DEA model to evaluate the DMU with interactive variables (inputs or outputs), the classical DEA model is a special form.

A data envelopment analysis approach by partial impacts between inputs and desirable-undesirable outputs for sustainable supplier selection problem

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Nemati ◽  
Reza Farzipoor Saen ◽  
Reza Kazemi Matin
Keyword(s):  
Data Envelopment Analysis ◽  
Supplier Selection ◽  
Selection Problem ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Content Type ◽  
Dea Model ◽  
Sustainable Supplier Selection ◽  
Overall Efficiency ◽  
Supplier Selection Problem

PurposeThe objective of this paper is to propose a new data envelopment analysis (DEA) model for assessing sustainability of suppliers with partial impacts between inputs, desirable outputs and undesirable outputs.Design/methodology/approachThis paper examines partial impacts of inputs on desirable and undesirable outputs and applies weak disposability assumption to propose a novel DEA model to determine the sustainability of suppliers.FindingsThis paper shows the type of resource sharing in DEA models and takes into account sustainable development and sustainability assessment concepts for sustainable supplier selection problem and develops a DEA model for selecting the most sustainable suppliers with partial sharing of resources. To select the most sustainable suppliers, this model helps managers to consider aggregate efficiency, overall efficiency and bundle efficiency. The paper introduces the supplier which is efficient at all levels as the most sustainable supplier.Originality/valueFor the first time, this paper suggests a new DEA model by partial impact between inputs and good outputs/bad outputs for selecting sustainable supplier and deals with the situations in which each supplier has several subunits. The new model calculates aggregate efficiency, overall efficiency and subunit efficiency of supplier. paper introduces the supplier which is efficient in all levels including aggregate efficiency, overall efficiency and subunit efficiency as the best supplier.

Generalized Fuzzy Sample DEA Model and its Application in the Evaluation of Projects

2011 ◽  
Vol 63-64 ◽  
pp. 407-411
Author(s):  
Ren Mu ◽  
Zhan Xin Ma ◽  
Wei Cui ◽  
Yun Morigen Wu
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Input Output ◽  
Output Data ◽  
Dea Model ◽  
Decision Making Unit ◽  
Fuzzy Input ◽  
Fuzzy Dea

Evaluating the performance of activities or organizations by traditional data envelopment analysis model requires crisp input/output data. However, in real-world problems inputs and outputs are often with some fuzziness. To evaluate DMU with fuzzy input/output data, researchers provided fuzzy data envelopment analysis (FDEA) model and proposed related evaluating method. But up to now, we still cannot evaluate a fuzzy sample decision making unit (SDMU) for FDEA model. So this paper proposes a generalized fuzzy DEA model which can evaluate a sample decision making unit and a numerical experiment is used to illustrate this model.

Detection of cost leaders using non-central principal component analysis and data envelopment analysis

2019 ◽  
Vol 26 (2) ◽  
pp. 548-566 ◽  
Author(s):  
Subhadip Sarkar
Keyword(s):  
Data Envelopment Analysis ◽  
Primary School ◽  
Primary Schools ◽  
Data Envelopment ◽  
Content Type ◽  
Cost Leadership ◽  
True Cost ◽  
Proposed Model ◽  
Decision Making Unit ◽  
The Cost

Purpose The purpose of this paper is to express the strategic positioning of a firm among its rivals based on an overall analysis. The proposed model uses data envelopment analysis (DEA) to determine the indexes due to cost leadership and differentiation. The model can be useful to identify the true cost leaders and those who are stuck in the middle. This work suggests the way how the strategic position can be explored from the consumption of resources (unlike the prevalent models like Banker et al., 2014). Design/methodology/approach Depending on the previous surveys, two inputs (spending per student and percentage of non-poor income group) and two outputs (average scores attained by students in science group and in language group in six private schools, located within the outskirt of Durgapur) were analyzed. Findings The classification made on the basis of the result of the proposed model reveals that out of the six schools (A, B, C, D, E and F), A, E and F occupy a strong position in this context, whereas B can be an example of stuck in the middle scenario. It not only has to reduce cost by 30 percent but also improve the differentiation index by 140 percent. C and D are lagging behind as they do not have enough differentiating qualities. Research limitations/implications Only six schools were taken for the analysis. Second, the input and output vectors had to be non-negative. In case of a negative input (output) set, separate treatment must be applied to them before the application of non-central PCA. Any decision-making unit producing an output of 0 will prohibit the use of the non-central PCA. Practical implications The extant study provides the indices to measure cost leadership and differentiation strategies for the classification as per the generic strategies. A firm which is lagging behind can adjust its consumption to remain successful. Social implications According to Hillman and Jenkner (2002), the developing countries lack the willingness of a primary school to impart education to children. The current study is used to explore whether any private primary school has the same goal or not. They also pointed out the possible future consequences while stating that the cost of educating children from the poorer section might be outweighed by the cost of not educating them and adults lacking basic skills had greater difficulty in finding well-paying jobs to escape poverty. So it is important to understand the role of a private primary school to offer seats to underprivileged students for educating them. The intention of six private primary schools toward educating the population of the small area within Durgapur is analyzed in this study, The study revealed that few schools spend more to serve the students belonging to upper classes to remain successful, whereas few schools as a differentiator make conscious attempts for providing services to poorer sections in an economical manner like a cost leader. Originality/value The extant research aims to formulate the determining methods of identifying strategic groups (proposed by Hunt, 1983) to make a parity between business definition view and strategic type concepts. The model can assess the rivals within an industry to explore the true cost leaders and those who are stuck in the middle using DEA. There are not enough kinds of literature which could effectively measure them.

scholarly journals The efficiency of major container terminals in China: super-efficiency data envelopment analysis approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Liu-Liu Li ◽  
Young-Joon Seo ◽  
Min-Ho Ha
Keyword(s):  
Data Envelopment Analysis ◽  
Container Terminal ◽  
Container Terminals ◽  
Policy Implications ◽  
Terminal Branch ◽  
Data Envelopment ◽  
Content Type ◽  
Super Efficiency ◽  
Dea Model ◽  
Efficiency Data

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.

A data envelopment analysis (DEA) model for building energy benchmarking

2019 ◽  
Vol 17 (4) ◽  
pp. 747-768 ◽  
Author(s):  
Baabak Ashuri ◽  
Jun Wang ◽  
Mohsen Shahandashti ◽  
Minsoo Baek
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Building Energy ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Dea Model ◽  
Energy Benchmarking

Purpose Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking. Design/methodology/approach Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency. Findings The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers. Originality/value The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

Establishing the DEA energy management system for individual departments within universities

Facilities ◽  
2015 ◽  
Vol 33 (11/12) ◽  
pp. 716-735 ◽  
Author(s):  
Kung-Jen Tu
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Energy Management ◽  
Management System ◽  
Scale Efficiency ◽  
Energy Management System ◽  
Data Envelopment ◽  
Content Type ◽  
Decision Making Unit ◽  
Actual Energy Consumption

Purpose – The purpose of this study is to present the theoretical framework of the “data envelopment analysis (DEA) Energy Management System (DEMS)” proposed to assist individual departments occupying the same buildings on university campus in assessing the energy efficiencies of their facilities, as well as to demonstrate the implementation results of the DEMS applied in the case of the Department of Architecture of NTUST in Taiwan. Design/methodology/approach – The proposed DEMS considers each “space” within a department in a given “time” (such as a month) as a decision-making unit (DMU). Then, regression analysis is performed on data of “existing environment”, “occupancy” factors and “actual energy consumption EUI (energy usage intensity)” related variables. The regression equation derived is then used to calculate the “predicted EUI” for all DMUs. The “actual EUI” is further considered as the input data and the “predicted EUI” as the output data of the DEMS, on which data envelopment analysis is conducted to produce three types of energy-efficiency scores (overall efficiency, scale efficiency, pure technical efficiency) to indicate the energy efficiencies of all DMUs. Findings – The DEMS was developed and further implemented in the Department of Architecture of NTUST in Taiwan to illustrate how it can be used to assist individual departments within universities in assessing the energy management effectiveness of their spaces. Research limitations/implications – The accuracy of the energy-efficiency scores depends greatly on the accuracy of the predicted EUIs of spaces, and, therefore, it is critical to identify a better regression model with higher predictability (R2). The relatively low actual EUIs of certain student spaces during winter and summer breaks may greatly affect the resulting energy-efficiency scores. Practical implications – The DEMS allows facility managers to assess and compare the energy-efficiency scores “among different spaces”, to further review the energy efficiency of a space “over time” and to recognize the benchmark cases and pursue actions for energy improvement. Originality/value – This study explores the research concepts of “space type” and “internal benchmark” with an analytical method “data envelopment analysis” to assess the energy efficiency of an individual department which may only occupy certain floors of a building.

scholarly journals Airports economic efficient frontier

2018 ◽  
Vol 11 (1) ◽  
pp. 26
Author(s):  
Decio Yoshimoto ◽  
Cláudio Jorge Pinto Alves ◽  
Mauro Caetano
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Data Envelopment Analysis ◽  
Production Function ◽  
Programming Model ◽  
Efficient Frontier ◽  
Data Envelopment ◽  
International Airport ◽  
Decision Making Unit ◽  
Terminal Area

<p>Studies about airport operational efficiency models generally disregard the correlation between operational efficiencies and economic drivers. The goal of this study is, firstly, to isolate and detail the key economic drivers and then find their efficient frontier. The methodology employed was Data Envelopment Analysis (DEA) as a non-parametric and linear programming model. It provides relative measures of efficiency using multiple inputs and outputs for a given Decision-Making Unit (DMU) without requiring a prior production function. The number of variables in this study was limited in function of the DMUs analyzed, which consisted of the following Brazilian airports: Congonhas Airport (CGH), Guarulhos International Airport (GRU) and Viracopos International Airport (VCP). Two of the airports, GRU and VCP, were found to be efficient considering this study’s combination of very limited variables, meaning that theses airports, from this isolated standpoint, are maximizing their commercial, passenger parking and marketing revenues, given their terminal area and the number of yearly passengers.</p>

Evaluating after-sales service units by developing inverse network data envelopment analysis model

2019 ◽  
Vol 27 (2) ◽  
pp. 695-707 ◽  
Author(s):  
Reza Farzipoor Saen ◽  
Seyed Shahrooz Seyedi Hosseini Nia
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Efficiency Score ◽  
Data Envelopment Analysis Model ◽  
Network Data ◽  
Data Envelopment ◽  
Analysis Model ◽  
Content Type ◽  
Network Data Envelopment Analysis ◽  
Decision Making Unit

Purpose The purpose of this paper is to develop an inverse network data envelopment analysis (INDEA) model to solve resource allocation problems. Design/methodology/approach The authors estimate inputs’ variations based on outputs so that the efficiencies of decision-making unit under evaluation (DMUo) and other decision-making units (DMUs) are constant. Findings The new INDEA model is developed to allocate resources such that inputs are not increased while efficiency scores of all DMUs remain constant. Furthermore, the authors obtain new combinations of inputs and outputs, together with a growth in efficiency score of DMUo such that efficiency scores of other DMUs are not changed. A case study is provided. Originality/value This paper proposes INDEA model to estimate inputs (outputs) without changing efficiency scores of DMUs.

Sign in / Sign up

Export Citation Format

Share Document