scholarly journals Efficiency of Vegetables Produced in Glasshouses: The Impact of Data Envelopment Analysis (DEA) in Land Management Decision Making

Land ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 17
Author(s):  
Thomas Bournaris ◽  
George Vlontzos ◽  
Christina Moulogianni
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Land Management ◽  
Crop Protection ◽  
Efficiency Score ◽  
Management Decision ◽  
Data Envelopment ◽  
Climate Conditions ◽  
Management Decision Making ◽  
The Impact

Glasshouse farming is one of the most intensive types of production of agricultural products. Via this process, consumers have the ability to consume mainly off-season vegetables and farmers are able to reduce operational risks, due to their ability to control micro-climate conditions. This type of farming is quite competitive worldwide, this being the main reason for formulating and implementing assessment models measuring operational performance. The methodology used in this study is Data Envelopment Analysis (DEA), which has wide acceptance in agriculture, among other sectors of the economy. The production protocols of four different vegetables—cucumber, eggplant, pepper, and tomato—were evaluated. Acreage (m2), crop protection costs (€), fertilizers (€), labor (Hr/year), energy (€), and other costs (€) were used as inputs. The turnover of every production unit (€) was used as the output. Ninety-eight agricultural holdings participated in this survey. The dataset was obtained by face-to-face interviews. The main findings verify the existence of significant relative deficiencies (including a mean efficiency score of 0.87) as regards inputs usage, as well as considerably different efficiency scores among the different cultivations. The most efficient of these was the eggplant production protocol and the least efficient was that used for the tomato. The implementation of DEA verified its utility, providing incentives for continuing to use this methodology for improving land management decision making.

Fuzzy C-means based data envelopment analysis for mitigating the impact of units’ heterogeneity

Kybernetes ◽  
2016 ◽  
Vol 45 (3) ◽  
pp. 536-551 ◽  
Author(s):  
Seyed Hossein Razavi Hajiagha ◽  
Shide Sadat Hashemi ◽  
Hannan Amoozad Mahdiraji
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Negative Impact ◽  
Positive Impact ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Content Type ◽  
Fuzzy C Means ◽  
The Impact ◽  
Decision Making Units

Purpose – Data envelopment analysis (DEA) is a non-parametric model that is developed for evaluating the relative efficiency of a set of homogeneous decision-making units that each unit transforms multiple inputs into multiple outputs. However, usually the decision-making units are not completely similar. The purpose of this paper is to propose an algorithm for DEA applications when considered DMUs are non-homogeneous. Design/methodology/approach – To reach this aim, an algorithm is designed to mitigate the impact of heterogeneity on efficiency evaluation. Using fuzzy C-means algorithm, a fuzzy clustering is obtained for DMUs based on their inputs and outputs. Then, the fuzzy C-means based DEA approach is used for finding the efficiency of DMUs in different clusters. Finally, the different efficiencies of each DMU are aggregated based on the membership values of DMUs in clusters. Findings – Heterogeneity causes some positive impact on some DMUs while it has negative impact on other ones. The proposed method mitigates this undesirable impact and a different distribution of efficiency score is obtained that neglects this unintended impacts. Research limitations/implications – The proposed method can be applied in DEA applications with a large number of DMUs in different situations, where some of them enjoyed the good environmental conditions, while others suffered from bad conditions. Therefore, a better assessment of real performance can be obtained. Originality/value – The paper proposed a hybrid algorithm combination of fuzzy C-means clustering method with classic DEA models for the first time.

Loyalty card adoption in SME retailers: the impact upon marketing management

2015 ◽  
Vol 49 (3/4) ◽  
pp. 467-490 ◽  
Author(s):  
Karise Hutchinson ◽  
Lisa Victoria Donnell ◽  
Audrey Gilmore ◽  
Andrea Reid
Keyword(s):  
Decision Making ◽  
Current Knowledge ◽  
Marketing Management ◽  
Management Decision ◽  
Adoption Process ◽  
Longitudinal Case Study ◽  
Content Type ◽  
Management Decision Making ◽  
The Impact ◽  
Loyalty Card

Purpose – The purpose of this paper is to understand how small to medium-sized enterprise (SME) retailers adopt and implement a loyalty card programme as a marketing management decision-making tool. Design/methodology/approach – A qualitative and longitudinal case study research design is adopted. Data were collected from multiple sources, incorporating semi-structured interviews and analysis of company documents and observation within a retail SME. Findings – The findings presented focus on the loyalty card adoption process to reflect both the organisational issues and impact upon marketing management decision-making. Research limitations/implications – This research is restricted to one region within the UK, investigating loyalty card adoption within a specific industry sector. Practical implications – SME retailers operate in an industry environment whereby there is a competitive demand for loyalty card programmes. SME retailers need to carefully consider how to match the firm’s characteristics with customer relationship management (CRM) operational requirements as highlighted in this case. Originality/value – The evidence presented extends current knowledge of retail loyalty card programmes beyond the context of large organisations to encompass SMEs. The study also illustrates the value of a structured, formal CRM system to help SME retailers compete in a complex, competitive and omni-channel marketplace, adding new insights into the retail literature.

scholarly journals Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis

2021 ◽  
Vol 40 (1) ◽  
pp. 813-832
Author(s):  
Sajad Kazemi ◽  
Reza Kiani Mavi ◽  
Ali Emrouznejad ◽  
Neda Kiani Mavi
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Ideal Point ◽  
Weight Vector ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Complex Organizations ◽  
Measuring Efficiency ◽  
Common Set Of Weights ◽  
Decision Making Units

Data Envelopment Analysis (DEA) is the most popular mathematical approach to assess efficiency of decision-making units (DMUs). In complex organizations, DMUs face a heterogeneous condition regarding environmental factors which affect their efficiencies. When there are a large number of objects, non-homogeneity of DMUs significantly influences their efficiency scores that leads to unfair ranking of DMUs. The aim of this study is to deal with non-homogeneous DMUs by implementing a clustering technique for further efficiency analysis. This paper proposes a common set of weights (CSW) model with ideal point method to develop an identical weight vector for all DMUs. This study proposes a framework to measuring efficiency of complex organizations, such as banks, that have several operational styles or various objectives. The proposed framework helps managers and decision makers (1) to identify environmental components influencing the efficiency of DMUs, (2) to use a fuzzy equivalence relation approach proposed here to cluster the DMUs to homogenized groups, (3) to produce a common set of weights (CSWs) for all DMUs with the model developed here that considers fuzzy data within each cluster, and finally (4) to calculate the efficiency score and overall ranking of DMUs within each cluster.

RELAXING THE IMPACT OF EXTREME UNITS IN DATA ENVELOPMENT ANALYSIS

2012 ◽  
Vol 11 (05) ◽  
pp. 893-907 ◽  
Author(s):  
YIANNIS G. SMIRLIS ◽  
DIMITRIS K. DESPOTIS
Keyword(s):  
Data Envelopment Analysis ◽  
Original Data ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Programming Technique ◽  
Data Set ◽  
Usual Practice ◽  
Dea Models ◽  
Production Possibility Set ◽  
The Impact

Data envelopment analysis (DEA) is a nonparametric linear programming technique for measuring the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. DEA assessments, however, are proved to be sensitive to extreme units that deviate substantially in their input/output patterns. In this paper we introduce an approach for handling extreme observations in DEA, i.e., observations that exhibit irregularly high values in some outputs and/or low values in some inputs. Unlike the usual practice of removing such observations, we retain them in the production possibility set reducing their impact on the other units. Our modeling approach is based on the concept of diminishing returns, assuming that the contribution of an output (input) to the efficiency score diminishes as the output increases beyond a pre-specified level, i.e., the level beyond which a value is characterized as extreme. According to our approach the original data set is transformed to an augmented data set, where standard DEA models can then be applied, remaining thus in the grounds of the standard DEA methodology. We illustrate our approach with a numerical example.

Integrated data envelopment analysis and multicriteria decision-making ranking approach based on peer-evaluations and subjective preferences: case study in banking sector

2020 ◽  
Vol 54 (4) ◽  
pp. 551-582
Author(s):  
Jolly Puri ◽  
Meenu Verma
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Decision Maker ◽  
Multicriteria Decision Making ◽  
Peer Evaluation ◽  
Data Envelopment ◽  
Content Type ◽  
Multicriteria Decision ◽  
Subjective Preference ◽  
The Impact

PurposeThis paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.Design/methodology/approachSelf-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.FindingsThe proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.Research limitations/implicationsThe choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.Practical implicationsTo prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.Originality/valueTo the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

Monitoring thermal springs to improve land management decision-making, Sierra Nevada, California

2017 ◽  
pp. 1078-7275.EEG-1937
Author(s):  
Jerome De Graff ◽  
Christopher J Pluhar ◽  
Alan Gallegos ◽  
Kellen Takenaka ◽  
Bryant Platt
Keyword(s):  
Decision Making ◽  
Sierra Nevada ◽  
Land Management ◽  
Thermal Springs ◽  
Management Decision ◽  
Management Decision Making

scholarly journals Selection and Analysis of Input-Output Variables using Data Envelopment Analysis of Decision Making Units - Indian Private Sector Banks

2021 ◽  
Vol 10 (5) ◽  
pp. 119-127
Author(s):  
B. Vittal ◽  
Raju Nellutla ◽  
M. Krishna Reddy
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Banking System ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Input Output ◽  
Key Factor ◽  
And Performance ◽  
Using Data ◽  
Decision Making Units

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

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