Free SKILL.md scraped from GitHub. Clone the repo or copy the file directly into your Claude Code skills directory.
npx versuz@latest install a5c-ai-babysitter-library-specializations-domains-business-supply-chain-skills-invgit clone https://github.com/a5c-ai/babysitter.gitcp babysitter/SKILL.MD ~/.claude/skills/a5c-ai-babysitter-library-specializations-domains-business-supply-chain-skills-inv/SKILL.md---
name: inventory-optimizer
description: Multi-echelon inventory optimization skill with ABC/XYZ segmentation and service level targeting
allowed-tools:
- Read
- Write
- Glob
- Grep
- Bash
metadata:
specialization: supply-chain
domain: business
category: inventory
priority: high
---
# 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
```
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
1. Refresh segmentation quarterly
2. Validate demand variability calculations
3. Consider service differentiation by customer segment
4. Monitor fill rate vs. inventory investment tradeoffs
5. Establish SLOB review cadence
6. Document policy rationale for auditing