Compară metode

	Tehnologia de Producție cu Subproduse DEA	Analiza Envelopării Datelor (model CCR) pentru clasificare bazată pe eficiență	Analiza de rețea a anvelopei de eficiență (Network DEA)
Domeniu	Luarea deciziilor	Luarea deciziilor	Analiza eficienței
Familie	MCDM	MCDM	Regression model
Anul apariției	2005	1978	2000
Autorul original	Färe, Grosskopf, Noh et al.	Charnes, A., Cooper, W. W., Rhodes, E.	Färe & Grosskopf
Tip	Non-parametric efficiency with undesirable outputs and by-products	Non-parametric efficiency frontier (CCR model)	Multi-stage nonparametric efficiency model
Sursa seminală	Scheel, H. (2001). Undesirable outputs in efficiency valuations. European Journal of Operational Research, 132(2), 400-410.	Charnes, A., Cooper, W. W., Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research	Färe, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.
Denumiri alternative	By-Production DEA, Joint Production DEA	—	Network Data Envelopment Analysis, Network Efficiency Analysis, Multi-Stage DEA, Ağ Veri Zarflama Analizi
Înrudite	2	0	2
Rezumat	By-Production Technology DEA is a variant of Data Envelopment Analysis designed for production systems that generate both desirable outputs and undesirable by-products or emissions. Rather than ignoring or arbitrarily penalizing undesirable outputs, this method explicitly models them as joint products of the production process. It evaluates efficiency while accounting for the trade-off between desired production and environmental impact.	DEA (Data Envelopment Analysis (CCR model) for efficiency-based ranking) is a dea multi-criteria decision-making (MCDM) method introduced by Charnes, A., Cooper, W. W., Rhodes, E. in 1978. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.	Network Data Envelopment Analysis (Network DEA) is a nonparametric efficiency measurement framework introduced by Färe and Grosskopf (2000) that extends classical DEA to multi-stage or multi-division production processes. Rather than treating a decision-making unit as a black box, it explicitly models the internal structure — the divisions and the intermediate products that flow between them — enabling stage-level and overall efficiency scores to be estimated simultaneously within a single coherent model.