ReddRadar
Agent skill
inventory-optimizer
Multi-echelon inventory optimization skill with ABC/XYZ segmentation and service level targeting
Install this agent skill to your Project
npx add-skill https://github.com/a5c-ai/babysitter/tree/main/library/specializations/domains/business/supply-chain/skills/inventory-optimizer
Metadata
Additional technical details for this skill
- domain
- business
- category
- inventory
- priority
- high
- specialization
- supply-chain
SKILL.md
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
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
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
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
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
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
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