ABC-XYZ Classifier

SkillDev tools

Multi-dimensional inventory classification skill combining value (ABC) and demand variability (XYZ) analysis for differentiated policies

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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
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Forks
112
Last commit
Sep 2026
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Item type
skill
Key
abc-xyz-classifier
Source
github.com/a5c-ai/babysitter