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What is ABC Classification?

ABC classification is an inventory categorization method based on the Pareto Principle (80/20 rule). It divides products into three categories based on their contribution to total sales revenue:

Category A

Top 70% of revenueHigh-value items requiring close monitoring and tight inventory control

Category B

Next 20% of revenue (70-90%)Moderate-value items with standard control procedures

Category C

Bottom 10% of revenue (90-100%)Low-value items with minimal control requirements

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Implementation Algorithm

The ABC classification is implemented in analisis.py using the following algorithm:

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Step-by-Step Process

1

Sort by Sales Volume

Products are sorted in descending order by ventas_mensuales (monthly sales)
2

Calculate Cumulative Percentage

For each product, calculate the cumulative percentage of total sales revenue
3

Assign Category

Apply threshold rules:
  • A: Cumulative ≤ 70%
  • B: Cumulative ≤ 90%
  • C: Cumulative > 90%

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Source Code

Here’s the actual implementation from analisis.py: 
import pandas as pd

def clasificacion_abc(df: pd.DataFrame) -> pd.DataFrame:
    """
    Clasifica productos según el método ABC usando ventas mensuales.
    """
    df = df.copy()

    # Ordenar por ventas
    df = df.sort_values(by="ventas_mensuales", ascending=False)

    # Total de ventas
    total_ventas = df["ventas_mensuales"].sum()

    # Porcentaje acumulado
    df["porcentaje_acumulado"] = df["ventas_mensuales"].cumsum() / total_ventas

    # Clasificación ABC
    def asignar_categoria(p):
        if p <= 0.7:
            return "A"
        elif p <= 0.9:
            return "B"
        else:
            return "C"

    df["categoria_abc"] = df["porcentaje_acumulado"].apply(asignar_categoria)

    print("✅ Clasificación ABC aplicada")
    return df

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Thresholds and Parameters

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Category A Threshold
float
default:"0.7"
Products contributing to the top 70% of cumulative revenueThese are your most valuable items — typically 10-20% of total SKUs
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Category B Threshold
float
default:"0.9"
Products contributing between 70% and 90% of cumulative revenueModerate-value items — typically 20-30% of total SKUs
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Category C Range
float
default:"> 0.9"
Products contributing to the final 10% of cumulative revenueLow-value items — typically 50-70% of total SKUs

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Business Logic

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Why ABC Classification Matters

Focus resources on high-value items (Category A) that drive the majority of revenue, while minimizing effort on low-value items (Category C)
  • Category A: Daily monitoring, precise forecasting, small safety stock
  • Category B: Weekly monitoring, moderate safety stock
  • Category C: Monthly monitoring, higher safety stock acceptable
Prioritize replenishment orders for Category A items to prevent stockouts of high-revenue products

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Use Cases

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Example 1: Identifying Critical Products

from loader import cargar_inventario
from analisis import clasificacion_abc

# Load and classify
df = cargar_inventario("data/inventario.xlsx")
df = clasificacion_abc(df)

# Filter Category A products
critical_products = df[df["categoria_abc"] == "A"]
print(f"High-value products: {len(critical_products)}")

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Example 2: Resource Allocation

# Allocate monitoring frequency based on category
def get_monitoring_frequency(category):
    return {
        "A": "Daily",
        "B": "Weekly",
        "C": "Monthly"
    }[category]

df["monitoring"] = df["categoria_abc"].apply(get_monitoring_frequency)

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Real Code Example

Here’s how the classification is used in the main pipeline (main.py:17): 
def main():
    # Load raw inventory data
    df = cargar_inventario(RUTA_INVENTARIO)
    
    # Apply ABC classification
    df = clasificacion_abc(df)
    
    # Now df contains 'categoria_abc' and 'porcentaje_acumulado' columns
    # These are used in reports and decision-making
    df = evaluar_riesgo_y_reposicion(df)
    generar_reporte_excel(df, RUTA_REPORTE)

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Output Columns

The clasificacion_abc() function adds two new columns to the DataFrame:
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porcentaje_acumulado
float
Cumulative percentage of total sales (0.0 to 1.0)Example: 0.45 means this product and all higher-selling products account for 45% of total revenue
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categoria_abc
string
Assigned category: “A”, “B”, or “C”Based on cumulative percentage thresholds

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Performance Considerations

  • Time Complexity: O(n log n) due to sorting
  • Space Complexity: O(n) for cumulative sum calculation
  • Suitable for datasets with up to 100,000+ SKUs

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