Operations Research · 2001 · 182 citations · 33 references
BusinessBusiness EconomicsOperations ManagementBusiness StrategyDecision AnalysisOperations ResearchData AnalysisEfficiency AnalysisInput-output AnalysisProductivity AnalysisProductivity MeasurementTechnical EfficiencyAdministrative Efficiency MeasurementData Envelopment AnalysisEconomic Efficiency Analysis
Data Envelopment Analysis typically assumes exact input and output data, yet many real‑world applications involve imprecise information such as ordinal rankings or bounds. This study demonstrates the application of IDEA to a Korean mobile telecommunication company. The authors employ the Imprecise Data Envelopment Analysis framework, incorporating weight restrictions and Assurance Region bounds, and extend it to an AR‑IDEA model that transforms managerial bounds into data adjustments. The extended AR‑IDEA model is applied to assess the efficiencies of the company’s branch offices, illustrating its practical utility.
Data Envelopment Analysis (DEA) models, as ordinarily employed, assume that the data for all inputs and outputs are known exactly. In some applications, however, a number of factors may involve imprecise data, which take forms such as ordinal rankings and knowledge only of bounds. Here we provide an example involving a Korean mobile telecommunication company. The Imprecise Data Envelopment Analysis (IDEA) method we use permits us to deal not only with imprecise data and exact data but also with weight restrictions as in the (now) widely used “Assurance Region” (AR) and “cone-ratio envelopment” approaches to DEA. We also show how to transform AR bounds on the variables, obtained from managerial assessments, for instance, into data adjustments. This involves an extended IDEA model, which we refer to as AR-IDEA. All these uses are illustrated by an example application directed to evaluate efficiencies of branch offices of a telecommunication company in Korea.
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