ABC-XYZ Classifier
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
1---2name: abc-xyz-classifier3description: Multi-dimensional inventory classification skill combining value (ABC) and demand variability (XYZ) analysis for differentiated policies4---5
6# ABC-XYZ Classifier
7
8## Overview
9
10The 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.
11
12## Capabilities
13
14- **Pareto Analysis Automation**: Automatically classify inventory into A, B, C categories based on value contribution using Pareto principles
15- **Demand Pattern Classification**: Analyze demand variability to classify items as X (stable), Y (variable), or Z (erratic)
16- **Inventory Policy Recommendation**: Recommend appropriate inventory policies based on combined ABC-XYZ classification
17- **Service Level Differentiation**: Suggest differentiated service level targets based on item classification and business importance
18- **Review Frequency Optimization**: Determine optimal inventory review frequencies for each classification
19- **Stocking Strategy Suggestions**: Recommend make-to-stock, make-to-order, or hybrid strategies based on classification
20- **Cross-Docking Candidacy Identification**: Identify items suitable for cross-docking based on velocity and predictability
21
22## Tools and Libraries
23
24- Statistical Analysis Libraries (pandas, numpy)
25- Inventory Optimization Models
26- Data Visualization Libraries
27- Classification Algorithms
28
29## Used By Processes
30
31- ABC-XYZ Analysis
32- Reorder Point Calculation
33- Dead Stock and Excess Inventory Management
34
35## Usage
36
37```yaml
38skill: abc-xyz-classifier
39inputs:
40 inventory_data:
41 - sku: "SKU001"
42 annual_value: 150000
43 monthly_demand: [100, 98, 102, 99, 101, 100, 98, 103, 99, 100, 101, 99]
44 unit_cost: 125
45 - sku: "SKU002"
46 annual_value: 45000
47 monthly_demand: [50, 75, 30, 60, 45, 80, 35, 55, 70, 40, 65, 50]
48 unit_cost: 75
49 classification_parameters:
50 abc_thresholds:
51 A: 80 # Top 80% of value
52 B: 95 # Next 15% of value
53 xyz_thresholds:
54 X: 20 # CV < 20%
55 Y: 50 # CV 20-50%
56outputs:
57 classifications:
58 - sku: "SKU001"
59 abc_class: "A"
60 xyz_class: "X"
61 combined_class: "AX"
62 annual_value: 150000
63 value_rank: 1
64 cv_percent: 1.8
65 recommendation:
66 service_level: 99.5
67 review_frequency: "daily"
68 stocking_strategy: "make_to_stock"
69 safety_stock_method: "statistical"
70 - sku: "SKU002"
71 abc_class: "B"
72 xyz_class: "Y"
73 combined_class: "BY"
74 annual_value: 45000
75 value_rank: 15
76 cv_percent: 32.5
77 recommendation:
78 service_level: 97.0
79 review_frequency: "weekly"
80 stocking_strategy: "make_to_stock"
81 safety_stock_method: "buffer"
82 summary:
83 AX_count: 45
84 AY_count: 30
85 AZ_count: 25
86 BX_count: 150
87 BY_count: 200
88 BZ_count: 150
89```
90
91## Integration Points
92
93- Enterprise Resource Planning (ERP) Systems
94- Inventory Management Systems
95- Demand Planning Systems
96- Warehouse Management Systems (WMS)
97- Financial Systems
98
99## Performance Metrics
100
101- Classification accuracy
102- Policy compliance rate
103- Service level achievement by class
104- Inventory investment by class
105- Turn rate by class
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