Results-driven Decision-making for Supply Chain Since 1971

Solutions

Inventory Optimization

Reduce inventory investment & minimize operating costs
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
  • 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