ABC-XYZ Classifier
SkillDev toolsMulti-dimensional inventory classification skill combining value (ABC) and demand variability (XYZ) analysis for differentiated policies
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the ABC-XYZ Classifier skill
What this skill tells your AI
The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/domains/business/logistics/skills/abc-xyz-classifier/SKILL.md and read by ahel’s review.
Overview
The ABC-XYZ Classifier is a multi-dimensional inventory classification skill that combines value-based (ABC) and demand variability (XYZ) analysis to enable differentiated inventory policies. It automates Pareto analysis and demand pattern classification to recommend optimal stocking strategies, service levels, and review frequencies.
Capabilities
- Pareto Analysis Automation: Automatically classify inventory into A, B, C categories based on value contribution using Pareto principles
- Demand Pattern Classification: Analyze demand variability to classify items as X (stable), Y (variable), or Z (erratic)
- Inventory Policy Recommendation: Recommend appropriate inventory policies based on combined ABC-XYZ classification
- Service Level Differentiation: Suggest differentiated service level targets based on item classification and business importance
- Review Frequency Optimization: Determine optimal inventory review frequencies for each classification
- Stocking Strategy Suggestions: Recommend make-to-stock, make-to-order, or hybrid strategies based on classification
- Cross-Docking Candidacy Identification: Identify items suitable for cross-docking based on velocity and predictability
Tools and Libraries
- Statistical Analysis Libraries (pandas, numpy)
- Inventory Optimization Models
- Data Visualization Libraries
- Classification Algorithms
Used By Processes
- ABC-XYZ Analysis
- Reorder Point Calculation
- Dead Stock and Excess Inventory Management
Usage
skill: abc-xyz-classifier
inputs:
inventory_data:
- sku: "SKU001"
annual_value: 150000
monthly_demand: [100, 98, 102, 99, 101, 100, 98, 103, 99, 100, 101, 99]
unit_cost: 125
- sku: "SKU002"
annual_value: 45000
monthly_demand: [50, 75, 30, 60, 45, 80, 35, 55, 70, 40, 65, 50]
unit_cost: 75
classification_parameters:
abc_thresholds:
A: 80 # Top 80% of value
B: 95 # Next 15% of value
xyz_thresholds:
X: 20 # CV < 20%
Y: 50 # CV 20-50%
outputs:
classifications:
- sku: "SKU001"
abc_class: "A"
xyz_class: "X"
combined_class: "AX"
annual_value: 150000
value_rank: 1
cv_percent: 1.8
recommendation:
service_level: 99.5
review_frequency: "daily"
stocking_strategy: "make_to_stock"
safety_stock_method: "statistical"
- sku: "SKU002"
abc_class: "B"
xyz_class: "Y"
combined_class: "BY"
annual_value: 45000
value_rank: 15
cv_percent: 32.5
recommendation:
service_level: 97.0
review_frequency: "weekly"
stocking_strategy: "make_to_stock"
safety_stock_method: "buffer"
summary:
AX_count: 45
AY_count: 30
AZ_count: 25
BX_count: 150
BY_count: 200
BZ_count: 150
Integration Points
- Enterprise Resource Planning (ERP) Systems
- Inventory Management Systems
- Demand Planning Systems
- Warehouse Management Systems (WMS)
- Financial Systems
Performance Metrics
- Classification accuracy
- Policy compliance rate
- Service level achievement by class
- Inventory investment by class
- Turn rate by class
Signals
- GitHub stars
- 2k
- Forks
- 112
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
abc-xyz-classifier- Source
- github.com/a5c-ai/babysitter
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