Inventory Optimizer
Overview
The Inventory Optimizer provides comprehensive inventory optimization capabilities including segmentation, service level targeting, and multi-echelon optimization. It balances inventory investment against service levels to maximize supply chain performance.
Capabilities
- •ABC/XYZ Inventory Classification: Segment by value and demand variability
- •Service Level to Inventory Tradeoff: Model cost-service curves
- •Multi-Echelon Inventory Optimization: Optimize across network tiers
- •Safety Stock Calculation: Demand and lead time variability-based
- •Reorder Point and EOQ Optimization: Economic order quantity analysis
- •Slow-Moving/Obsolete Identification: SLOB analysis and disposition
- •Inventory Investment Optimization: Working capital optimization
- •Network Inventory Rebalancing: Cross-location optimization
Input Schema
yaml
inventory_optimization_request:
items: array
- sku_id: string
annual_usage_value: float
demand_history: array
lead_time: integer
unit_cost: float
current_stock: integer
service_level_targets: object
network_locations: array
cost_parameters:
carrying_cost_rate: float
ordering_cost: float
stockout_cost: float
optimization_objectives: array
Output Schema
yaml
inventory_optimization_output:
segmentation:
abc_classification: object
xyz_classification: object
abc_xyz_matrix: object
optimal_parameters: array
- sku_id: string
safety_stock: integer
reorder_point: integer
order_quantity: integer
service_level: float
investment_analysis:
current_investment: float
optimal_investment: float
reduction_potential: float
slob_analysis:
slow_moving: array
obsolete: array
disposition_recommendations: array
network_rebalancing: object
Usage
ABC/XYZ Segmentation
code
Input: SKU master with annual usage and demand history Process: Calculate value classification (ABC) and variability (XYZ) Output: Nine-box segmentation with policy recommendations
Safety Stock Optimization
code
Input: Demand variability, lead time variability, service targets Process: Calculate optimal safety stock by segment Output: Safety stock quantities with investment impact
Network Inventory Balance
code
Input: Multi-location inventory positions, demand by location Process: Identify imbalances and rebalancing opportunities Output: Transfer recommendations with cost savings
Integration Points
- •ERP Systems: Inventory data, transactions, master data
- •Planning Systems: Demand forecasts, supply plans
- •Optimization Solvers: scipy, CPLEX, Gurobi
- •Tools/Libraries: scipy optimization, inventory algorithms
Process Dependencies
- •Inventory Optimization and Segmentation
- •Safety Stock Calculation and Optimization
- •Demand-Driven Material Requirements Planning (DDMRP)
Best Practices
- •Refresh segmentation quarterly
- •Validate demand variability calculations
- •Consider service differentiation by customer segment
- •Monitor fill rate vs. inventory investment tradeoffs
- •Establish SLOB review cadence
- •Document policy rationale for auditing