Data Envelopment Analysis

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dc.contributor.authorHosseinzadeh Lotfi, F.
dc.contributor.authorAllahviranloo, T.
dc.contributor.authorShafiee, M.
dc.contributor.authorSaleh, H.
dc.date.accessioned2024-05-19T14:34:16Z
dc.date.available2024-05-19T14:34:16Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThroughout history, considering the limitations, humanity has tried to make the most of the available facilities and resources. In this regard, performance evaluation is considered one of the managers' most vital issues. In fact, for a manager, knowing the performance of supervised units is the most critical task in making a decision and adopting a suitable strategy. The complexity of information, a lot of data, and the influence of various other factors make managers unable to learn about the performance of the units under their supervision without a scientific approach. One of the essential concepts in performance evaluation is calculating the efficiency of the units under the assessment. Therefore, more scientific methods are needed to calculate efficiency than in the past. One of the appropriate and efficient tools in the field of efficiency measurement is data envelopment analysis (DEA), which is used as a non-parametric method to calculate the efficiency of decision-making units. DEA models, in addition to determining the relative efficiency, the weak points of the organization in various indicators, also the resources affecting the inefficiency of organizations, are selected by DEA models, and finally, presenting an efficient projection defines the organization's policy toward improving efficiency and productivity. These reasons have caused this technique to grow increasingly from the theoretical and practical aspects and become one of the essential branches in the science of operations research. In recent years, many theoretical and practical developments have happened in DEA models, making it indispensable to know its various aspects for a more precise application of DEA models for the performance evaluation of a supply chain. Thus, in the rest of this chapter, we will explain the DEA definitions and models needed in the following chapters. Thus, in the rest of this chapter, we will explain the DEA definitions and models required for the following chapters. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.identifier.doi10.1007/978-3-031-28247-8_6
dc.identifier.endpage241en_US
dc.identifier.issn2197-6503
dc.identifier.scopus2-s2.0-85158093410en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage179en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-031-28247-8_6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4449
dc.identifier.volume122en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofStudies in Big Dataen_US
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectDecision Makingen_US
dc.subjectEfficiencyen_US
dc.subjectSupply Chainsen_US
dc.subjectAnalysis Definitionen_US
dc.subjectAnalysis Modelsen_US
dc.subjectCritical Tasksen_US
dc.subjectData Envelopment Analysis Modelsen_US
dc.subjectEfficiency Measurementen_US
dc.subjectLearn+en_US
dc.subjectNonparametric Methodsen_US
dc.subjectPerformanceen_US
dc.subjectPerformances Evaluationen_US
dc.subjectScientific Methoden_US
dc.subjectData Envelopment Analysisen_US
dc.titleData Envelopment Analysisen_US
dc.typeBook Chapteren_US

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