Сравнение методов

	Теория ограничений (ТО)	Закон Литтла (L = λW)	Шесть сигм DMAIC
Область	Управление качеством	Исследование операций	Управление качеством
Семейство	Process / pipeline	Regression model	Process / pipeline
Год появления	1990	1961	2014
Автор метода	Eliyahu Goldratt	John D. C. Little	Motorola; Pyzdek & Keller
Тип	Continuous improvement framework	Exact queueing identity	Structured process improvement methodology
Основополагающий источник	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
Другие названия	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
Связанные	3	3	3
Сводка	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.