Módszerek összehasonlítása

	A korlátozottság elmélete (TOC)	Little-törvény (L = λW)	Six Sigma DMAIC
Tudományterület	Minőségmenedzsment	Operációkutatás	Minőségmenedzsment
Módszercsalád	Process / pipeline	Regression model	Process / pipeline
Keletkezés éve	1990	1961	2014
Megalkotó	Eliyahu Goldratt	John D. C. Little	Motorola; Pyzdek & Keller
Típus	Continuous improvement framework	Exact queueing identity	Structured process improvement methodology
Alapmű	Goldratt, E. M. (1990). Theory of Constraints. North River Press. ISBN: 978-0-88427-166-6	Little, J. D. C. (1961). A proof for the queuing formula: L = λW. Operations Research, 9(3), 383–387.	Pyzdek, T., & Keller, P. (2014). The Six Sigma Handbook (4th ed.). McGraw-Hill. ISBN: 978-0-07-184053-9
Alternatív nevek	TOC, Constraint Management, Bottleneck Theory, Kısıtlar Teorisi	L = λW Theorem, Little's Theorem, Little's Result, Little Yasası	DMAIC Framework, Six Sigma Process Improvement Cycle, Define-Measure-Analyze-Improve-Control, Altı Sigma DMAIC
Kapcsolódó	3	3	3
Összefoglaló	The Theory of Constraints (TOC) is a management philosophy and continuous improvement framework introduced by Eliyahu Goldratt in his 1984 novel The Goal and formalized in his 1990 book. TOC holds that every system has at least one constraint — a bottleneck that limits the system's overall throughput — and that systematically identifying and addressing that constraint is the most effective lever for improving performance. It is widely applied in manufacturing, project management, supply chains, and service operations.	Little's Law is a fundamental theorem in queueing theory that relates the long-run average number of items in a stable system (L) to the long-run average arrival rate (λ) and the long-run average time an item spends in the system (W), expressed as L = λW. Introduced and rigorously proved by John D. C. Little in 1961, the law holds for virtually any stable stochastic system, requiring no assumptions about arrival distributions, service distributions, or queue disciplines.	Six Sigma DMAIC is a data-driven, five-phase process improvement methodology — Define, Measure, Analyze, Improve, and Control — used to reduce defects and process variation to fewer than 3.4 defects per million opportunities. Originating at Motorola in the 1980s and systematized by practitioners including Pyzdek and Keller, it is widely adopted in manufacturing, healthcare, finance, and service industries seeking sustained quality gains.