INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Data Envelopment Analysis (DEA) is a non-parametric method of measuring the efficiency of a decision–making unit (DMU) such as a firm or a public-sector agency. In contrast to the statistical approach, DEA evaluates DMUs relative to an average DMU. It compares each unit with only the best performing one. In DEA, there are a number of DMUs. These make decisions based on the available set of inputs which produce a set of outputs. Each unit has a varying level of inputs and gives a varying level of outputs. This technique attempts to determine which of the units are most efficient, and to point out specific inefficiencies of the other units thus assisting them to make realistic improvement targets and allocate resources more efficiently and effectively. Farrel [1957] suggested the concept of Frontier Analysis, which forms the basis of DEA. Four basic DEA models can be used to calculate the DEA efficiency score for the decision making units. The optimal objective function values of models, when solved, represent the efficiency score of the DMU. DEA has been used to evaluate efficiency of financial institutions such as banks, manufacturing companies, service organizations, pharmaceutical industries. DEA has also been used to evaluate efficiency of mutual funds, impact of strategies and policies adopted by governments, and for evaluating value of brand names. In the following article we review some of the prominent literatures in the field of DEA. Recent researches in the given field entails the work carried out by Saranga (2009) and Ramanathan (2009). Saranga (2009) carried out the performance analysis of the Indian auto component industry from the perspectives of an original equipment manufacturer and a component supplier. Ramanathan (2009) proposed (DEA) to generate local weights of alternatives from pair-wise comparison judgment matrices used in the analytic hierarchy process (AHP).
Keywords:
Keywords: Data Envelopment Analysis, Decision Making Units, Constant Return to Scale, Variable Return to Scale, Operational Inefficiencies
Cite Article:
"DATA ENVELOPMENT ANALYSIS & ITS APPLICATIONS:AN OVERVIEW", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.d188-d200, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305327.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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