Linganisha mbinu

	Stochastic Frontier Firm Efficiency Analysis	Data Envelopment Analysis of Firm Strategic Efficiency
Nyanja	Usimamizi wa Kimkakati	Usimamizi wa Kimkakati
Familia	Regression model	MCDM
Mwaka wa asili	1977	1978
Mwanzilishi	Dennis Aigner, C. A. Knox Lovell & Peter Schmidt; George Battese & Tim Coelli	Abraham Charnes, William W. Cooper & Edwardo Rhodes; Rajiv Banker, Charnes & Cooper
Aina	Parametric composed-error regression frontier for firm efficiency	Nonparametric linear-programming efficiency frontier for firm benchmarking
Chanzo asilia	Aigner, D., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21-37.	Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429-444.
Majina mbadala	SFA Firm Technical Inefficiency, Parametric Production Frontier Estimation, Composed-Error Efficiency Model, Stochastic Frontier Production Function for Firms	DEA Firm Efficiency Benchmarking, Strategic Efficiency Frontier Analysis, Firm-Level Data Envelopment Analysis, DEA Best-Practice Benchmarking
Zinazohusiana	3	3
Muhtasari	Stochastic frontier analysis (SFA) estimates how far a firm falls short of the best attainable output for its inputs while explicitly separating that shortfall from random noise. Aigner, Lovell and Schmidt's 1977 model introduced the defining idea: a production frontier whose error term is the sum of a symmetric, two-sided noise component and a one-sided, nonnegative inefficiency component. Because deviations below the frontier can come either from bad luck and measurement error or from genuine underperformance, SFA models both and recovers a firm-specific technical-efficiency estimate. Battese and Coelli's 1995 panel-data extension let the mean of the inefficiency term depend on firm characteristics, so analysts can simultaneously estimate the frontier and explain why some firms are more inefficient than others.	Data Envelopment Analysis (DEA) of firm strategic efficiency benchmarks each firm or strategic business unit against a best-practice frontier built directly from the data, with no need to assume prices, weights, or a functional form. Introduced by Charnes, Cooper and Rhodes in 1978 under constant returns to scale (the CCR model) and extended by Banker, Charnes and Cooper in 1984 to variable returns (the BCC model), DEA uses linear programming to envelop the observed firms with a piecewise-linear frontier and scores each one by its radial distance from it. In strategic management it answers a sharply practical question: given the resources a firm consumes, how much more output could it produce if it operated like the best comparable firms, and which efficient peers should it emulate.