Shivi Agarwal

Bio: Shivi Agarwal is an academic researcher from Birla Institute of Technology and Science. The author has contributed to research in topics: Data envelopment analysis & Fuzzy logic. The author has an hindex of 6, co-authored 15 publications receiving 117 citations.

DEA based estimation of the technical efficiency of state transport undertakings in India

Abstract: This paper measures the technical efficiency of public transport sector in India. The study makes an attempt to provide an overview of the general status of the State Transport Undertakings (STUs) in terms of their productive efficiency. Data have been collected for 35 STUs for the year 2004-2005. Technical efficiency of the STUs is measured by applying Data Envelopment Analysis (DEA) technique with the use of four input and three output variables. Fleet size, Total staff, Fuel consumption and Accident per lakh kilometer are considered as inputs and Bus utilization, Passenger kilometers and Load factor as outputs. On the basis of the status of technical efficiency, it is concluded that the performance of the STUs are good but still very far from the optimal level. The mean overall technical efficiency (OTE) is 83.26% which indicates that an average STU has the scope of producing the same output with the inputs 16.74% lesser than their existing level. Significant variation in OTE across STUs is also observed.

Sensitivity analysis in data envelopment analysis

Abstract: Data envelopment analysis (DEA) is a non-parametric technique and therefore hypothesis testing is very difficult. So, to determine the robustness of the efficiency scores obtained by DEA, sensitivity analysis is applied. Sensitivity analysis is used to know how sensitive the solution values and efficiency scores of the DMUs are to the numerical observations. In this paper, we propose a new model of sensitivity analysis in data envelopment analysis (DEA). The proposed new model examines the robustness of DEA efficiency scores by changing the reference set of the decision making units (DMUs). The model is also used for ranking the efficient DMUs and to identify the outliers on the frontier. Super efficiency is also estimated by applying the model as omitting the DMU itself from its reference set. Applying the proposed sensitivity model, this article empirically examines the robustness of the efficiency scores of 15 regions of Uttar Pradesh State Road Transport Corporation (UPSRTC) in India obtained by new slack model of DEA. The results of empirical illustration of sensitivity analysis reveal that the efficiency scores of the regions are robust, i.e., they are not sensitive to the efficient regions.

A new slack DEA model to estimate the impact of slacks on the efficiencies

Abstract: The total potentials for improvement frequently remain unrevealed by calculating radial efficiency measure by basic data envelopment analysis (DEA) models. In this paper, we propose a new slack DEA model which extends the radial measure with the actual impact of slacks on efficiency scores. The new slack model (NSM) deals directly with input and output slacks. The model satisfies monotone decreasing property with respect to slacks. It also satisfies all other properties of radial DEA model, such as unit invariance and translation invariance, in outputs for the input-oriented model. The dual of this model reveals that all multipliers have become positive, i.e. all input and output variables are fully utilised in the performance assessment of the decision-making units. The study describes the characterisation of the NSM theoretically and empirically by numerical example. For this purpose, we measure the efficiency of the 15 regions of Uttar Pradesh State Road Transport Corporation for the year 2004?2005 through new slack DEA model.

A survey and analysis of the first 40 years of scholarly literature in DEA: 1978–2016

Abstract: In recent years there has been an exponential growth in the number of publications related to theory and applications of Data Envelopment Analysis (DEA). Charnes, Cooper, and Rhodes (1978) introduced DEA as a tool for measuring efficiency and productivity of decision making units. DEA has immediately been recognized as a modern tool for performance measurement. Since then, a large and considerable amount of articles has been appeared, including significant breakthroughs in theory and a great portion of works on DEA applications, both public and private sectors, to assess the efficiency and productivity of their activities. Although there have been several bibliographic collections reported, a comprehensive analysis and listing of DEA-related articles covering its first four decades of history is still missing. This paper, thus, aims to report an extensive listing of DEA-related articles including theory and methodology developments and "real" applications in diversified scenarios from 1978 to end of 2016. Some summary statistics of the publications' growth, the most utilized academic journals, authorship analysis, as well as keywords analysis are also provided.

The use of Data Envelopment Analysis (DEA) in healthcare with a focus on hospitals.

Abstract: The healthcare sector in general and hospitals in particular represent a main application area for Data Envelopment Analysis (DEA). This paper reviews 262 papers of DEA applications in healthcare with special focus on hospitals and therefore closes a gap of over ten years that were not covered by existing review articles. Apart from providing descriptive statistics of the papers, we are the first to examine the research purposes of the publications. These research goals can be grouped into four distinct clusters according to our proposed framework. The four clusters are (1) "Pure DEA efficiency analysis", i.e. performing a DEA on hospital data, (2) "Developments or applications of new methodologies", i.e. applying new DEAy approaches on hospital data, (3) "Specific management question", i.e. analyzing the effects of managerial specification, such as ownership, on hospital efficiency, and (4) "Surveys on the effects of reforms", i.e. researching the impact of policy making, such as reforms of health systems, on hospital efficiency. Furthermore, we analyze the methodological settings of the studies and describe the applied models. We analyze the chosen inputs and outputs as well as all relevant downstream techniques. A further contribution of this paper is its function as a roadmap to important methodological literature and publications, which provide crucial information on the setup of DEA studies. Thus, this paper should be of assistance to researchers planning to apply DEA in a hospital setting by providing information on a) what has been published between 2005 and 2016, b) possible pitfalls when setting up a DEA analysis, and c) possible ways to apply the DEA analysis in practice. Finally, we discuss what could be done to advance DEA from a scientific tool to an instrument that is actually utilized by managers and policymakers.

with Data Envelopment Analysis

Abstract: Data Envelopment Analysis (DEA) is an increasingly popular management tool. DEA is commonly used to evaluate the efficiency of a number of producers. A typical statistical approach is characterized as a central tendency approach and it evaluates producers relative to an average producer. In contrast, DEA compares each producer with only the "best" producers. By the way, in the DEA literature, a producer is usually referred to as a decision making unit or DMU. DEA is not always the right tool for a problem but is appropriate in certain cases.

Fuzzy Data Envelopment Analysis:An Adjustable Approach

Abstract: Possibilistic Data Envelopment Analysis (PDEA) is one of the most applicable and popular approaches in the literature to deal with imprecise and ambiguous data in DEA models. In this approach, with respect to tendency of decision maker (DM) in taking optimistic, pessimistic and compromise attitude, three measures including possibility, necessity and credibility measures are used to form the Fuzzy DEA (FDEA) models, respectively. However, decision makers may have different preference and so it is necessary to customize fuzzy DEA models according to properties of DMUs. This paper proposes a novel fuzzy DEA model based on general fuzzy measure in which the attitude of DMUs could be determined by the optimistic-pessimistic parameters. As a result, the proposed FDEA model is general, applicable, flexible, and adjustable based on each DMUs. A numerical example is used to explain the proposed approach while usefulness and applicability of this approach have been illustrated using a real data set to measure efficiency of 38 hospital in United States.

An integrated Multi-Criteria Decision Making Model for Sustainability Performance Assessment for Insurance Companies

Abstract: To stay competitive in a business environment, continuous performance evaluation based on the triple bottom line standard of sustainability is necessary. There is a gap in addressing the computational expense caused by increased decision units due to increasing the performance evaluation indices to more accuracy in the evaluation. We successfully addressed these two gaps through (1) using principal component analysis (PCA) to cut the number of evaluation indices, and (2) since PCA itself has the problem of merely using the data distribution without considering the domain-related knowledge, we utilized Analytic Hierarchy Process (AHP) to rank the indices through the expert’s domain-related knowledge. We propose an integrated approach for sustainability performance assessment in qualitative and quantitative perspectives. Fourteen insurance companies were evaluated using eight economic, three environmental, and four social indices. The indices were ranked by expert judgment though an analytical hierarchy process as subjective weighting, and then principal component analysis as objective weighting was used to reduce the number of indices. The obtained principal components were then used as variables in the data envelopment analysis model. So, subjective and objective evaluations were integrated. Finally, for validating the results, Spearman and Kendall’s Tau correlation tests were used. The results show that Dana, Razi, and Dey had the best sustainability performance.