Сравнение методов

	Эффект кнута	Агрегатное планирование	Маршрутизация запасов	Канбан	Планирование потребности в материалах
Область	Операционный менеджмент	Операционный менеджмент	Операционный менеджмент	Операционный менеджмент	Операционный менеджмент
Семейство	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Год появления	1961	1992	2014	1950	1975
Автор метода	Jay Forrester	Wallace, T. F.	Coelho, L. C., Cordeau, J. F., & Laporte, G.	Taiichi Ohno	Joseph Orlicky
Тип	Phenomenon and analysis framework	Demand-supply planning framework	Optimization problem	Production control system	Material planning algorithm
Основополагающий источник	Lee, H. L., Padmanabhan, V., & Whang, S. (1997). The bullwhip effect in supply chains. Sloan Management Review, 38(3), 93–102.	Wallace, T. F. (1992). Sales & Operations Planning: The how-to handbook. Cincinnati: APICS Publications.	Coelho, L. C., Cordeau, J. F., & Laporte, G. (2014). Thirty years of inventory routing. Transportation Research Part B: Methodological, 55, 28-67.	Ohno, T. (1988). Toyota production system: Beyond large-scale production. Cambridge, MA: Productivity Press.	Orlicky, J. (1975). Material requirements planning: The new way of life in production and inventory management. New York: McGraw-Hill.
Другие названия	demand amplification, Forrester effect	sales and operations planning, production planning	IRP, vendor-managed logistics	visual management, pull system	MRP, MRP I
Связанные	5	5	5	5	5
Сводка	The Bullwhip Effect is a phenomenon in supply chain management where small fluctuations in end-customer demand cause progressively larger fluctuations in orders as one moves upstream from retail to distributors to manufacturers to suppliers. First formally documented by Jay Forrester in his 1961 system dynamics work, and later popularized by Lee, Padmanabhan, and Whang in 1997, the effect reveals how information delays and ordering strategies amplify demand variability throughout supply chains, leading to excess inventory, inefficient production scheduling, and increased costs.	Aggregate Planning (or Sales & Operations Planning, S&OP) is a collaborative, iterative process that balances demand and supply at a high level—typically grouping products into families and planning over a 3–18 month horizon. Developed formally by Tom Wallace and popularized through APICS, aggregate planning helps organizations align sales forecasts, production capacity, inventory, and workforce to meet demand efficiently while managing costs. It serves as the bridge between strategic business plans and detailed operational execution.	The Inventory Routing Problem (IRP) is an optimization problem that jointly determines inventory levels at customer locations, delivery routes, and shipment quantities to minimize total logistics and inventory holding costs. Rather than treating inventory management and vehicle routing as separate decisions, IRP recognizes that they are interdependent: larger shipments reduce routing costs but increase inventory holding costs, and vice versa. IRP is solved using mixed-integer programming, heuristics, and metaheuristics, and is a cornerstone of vendor-managed inventory (VMI) programs.	Kanban is a pull-based production control system developed by Taiichi Ohno at Toyota in the 1950s that uses visual signals (traditionally cards or bins) to trigger production and movement of materials based on actual demand rather than forecasts. The Japanese word 'kanban' means 'visual card' or 'sign,' and the system operates on the principle that work should flow in response to downstream requirements. Kanban is a foundational element of the Toyota Production System and lean manufacturing, enabling just-in-time production, reduced inventory, and improved flow efficiency.	Material Requirements Planning (MRP) is a computerized system developed by Joseph Orlicky in the 1970s that calculates material requirements based on master production schedules and bill-of-materials data. MRP determines what materials to buy, how much to order, and when to order them to meet production demand while minimizing inventory carrying costs. It became a foundational technology for manufacturing planning and later evolved into manufacturing resource planning (MRP II) and enterprise resource planning (ERP) systems.