방법 비교

	VMI (Vendor-Managed Inventory)	총괄 생산 계획	재고 경로 문제 (Inventory Routing Problem, IRP)
분야	운영관리	운영관리	운영관리
계열	Machine learning	Machine learning	Machine learning
기원 연도	2006	1992	2014
창시자	Disney, S. M., & Towill, D. R.	Wallace, T. F.	Coelho, L. C., Cordeau, J. F., & Laporte, G.
유형	Business and inventory model	Demand-supply planning framework	Optimization problem
원전	Disney, S. M., & Towill, D. R. (2006). Vendor-managed inventory: A taxonomy of approaches and implications. International Journal of Production Economics, 106(2), 440-456.	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.
별칭	VMI, supplier-managed inventory	sales and operations planning, production planning	IRP, vendor-managed logistics
관련	5	5	5
요약	Vendor-Managed Inventory (VMI) is a supply chain arrangement in which the supplier (vendor) has visibility into the customer's inventory levels and assumes responsibility for replenishing inventory to pre-agreed levels. Rather than customers placing orders based on internal forecasts, the supplier monitors actual consumption and triggers replenishment shipments automatically. VMI reduces administrative burden, minimizes stock-outs, improves cash flow (by reducing inventory in the supply chain), and fosters collaboration between supplier and customer.	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.