Sustaining target levels of inventory and service simultaneously across complex multi-echelon
bills-of-materials and distribution networks is tough. That’s why Inventory Optimization with GAINS uses sophisticated, automated, proprietary algorithms that consider a comprehensive set of cost and source variabilities, including service-
level goals, demand plan error and lead-time for every SKU by location across the enterprise.
Maximizing Multi-Echelon Supply Chain Performance
By managing variability and uncertain demand, our customers improve service levels and reduce expediting while reducing inventory investment, minimizing operating costs and maximizing profit.
- Maximize Return-on-Assets (ROA)
- Cost-effectively manage and reduce risk
- Balance inventories with service levels and demand, holistically
- Find the optimal replenishment quantity and service stock parameters that minimize total annual costs
- Determine the optimal service level and stocking policy for each SKU by location
- Incorporate comprehensive error
- Provide Multi-echelon Optimization (MEO) setting the BOM level for stocking & postponement strategy
The GAINS Inventory Optimization proprietary algorithms include:
- Inventory Policy Optimization algorithms to precisely achieve targeted service levels by comprehensively managing sources of planning error. These include: demand plan/forecast error, lead-time variation, supply yield, and optimal ordering cycles
- Service Level Optimization algorithms that automatically determine service levels uniquely for each item to achieve an aggregate target while minimizing or maximizing a business objective, (e.g. minimized total cost, minimum inventory, specific inventory turns target, maximum profit, etc.)
- Sourcing Optimization algorithms that determine the supplier(s) that provide the lowest-total-cost supply, including solving for the trade-off of unit cost, purchase minimums, and lead time
- Network Flow Optimization algorithms that determine which supply route provides the lowest-total-cost supply, considering lead times, minimums (line-item and PO-level), handling, and transportation costs
- Multi-echelon/ Stocking Policy Inventory Optimization algorithms that determine whether or not to stock an item and at what service level. These solve for interdependencies among locations and/or within the bill-of-materiel (BOM) to devise postponement strategies and address customer expectations to minimize cost or maximize profit
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