Hosseinzadeh Lotfi, F.Allahviranloo, T.Shafiee, M.Saleh, H.2024-05-192024-05-1920232197-6503https://doi.org/10.1007/978-3-031-28247-8_6https://hdl.handle.net/20.500.12713/4449Throughout 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.eninfo:eu-repo/semantics/closedAccessDecision MakingEfficiencySupply ChainsAnalysis DefinitionAnalysis ModelsCritical TasksData Envelopment Analysis ModelsEfficiency MeasurementLearn+Nonparametric MethodsPerformancePerformances EvaluationScientific MethodData Envelopment AnalysisBook Chapter1221792412-s2.0-8515809341010.1007/978-3-031-28247-8_6N/A