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Output Directory Structure

The system generates all outputs in the output/ directory: 
output/
├── reporte_inventario.xlsx
└── graficos/
    ├── estado_inventario.png
    └── clasificacion_abc.png
The output/ directory and graficos/ subdirectory are created automatically if they don’t exist (see src/reportes_graficos.py:7).

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Excel Report Structure

The main report file output/reporte_inventario.xlsx contains multiple sheets with different views of the inventory data.

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Sheet 1: Inventario_Completo

Contains the complete inventory with all analysis columns:
ColumnDescriptionSource
Original DataProduct info, stock levels, salesInput file
porcentaje_acumuladoCumulative percentage for ABC classificationsrc/analisis.py:17
categoria_abcABC category (A, B, or C)src/analisis.py:28
estado_stockStock status (OK, RIESGO, CRITICO)src/decisiones.py:19
reponer_cantidadRecommended replenishment quantitysrc/decisiones.py:27
This sheet provides a comprehensive view of the entire inventory with all calculated metrics.

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Sheet 2: Productos_Criticos

Filtered view showing only products in CRITICO status (stock at or below minimum). Filter Logic (src/reportes.py:11-12): 
criticos = df[df["estado_stock"] == "CRITICO"]
criticos.to_excel(writer, sheet_name="Productos_Criticos", index=False)
Products in this sheet require immediate attention and replenishment to avoid stockouts.

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Sheet 3: Productos_En_Riesgo

Filtered view showing only products in RIESGO status (stock slightly above minimum but approaching critical levels). Filter Logic (src/reportes.py:14-15): 
riesgo = df[df["estado_stock"] == "RIESGO"]
riesgo.to_excel(writer, sheet_name="Productos_En_Riesgo", index=False)
Products in this sheet should be monitored closely and replenished soon to prevent reaching critical levels.

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Excel Formatting

The Excel report includes automatic formatting (from src/reportes.py:17-30):
1

Column Width Adjustment

Columns are automatically sized based on content length:
max_length = max(max_length, len(str(cell.value)))
ws.column_dimensions[column].width = max_length + 2
2

Cell Alignment

All cells are centered horizontally and vertically:
cell.alignment = Alignment(horizontal="center", vertical="center")

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Stock Status Classification

Understanding the estado_stock values (from src/decisiones.py:11-17):

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CRITICO Status

Condition: stock_actual <= stock_minimo 
if row["stock_actual"] <= row["stock_minimo"]:
    return "CRITICO"
  • Stock is at or below minimum threshold
  • Action Required: Immediate replenishment needed
  • Risk: High risk of stockout

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RIESGO Status

Condition: stock_actual <= stock_minimo * 1.2 
elif row["stock_actual"] <= row["stock_minimo"] * 1.2:
    return "RIESGO"
  • Stock is within 20% above minimum threshold
  • Action Required: Plan replenishment soon
  • Risk: Moderate risk, approaching critical level

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OK Status

Condition: stock_actual > stock_minimo * 1.2 
else:
    return "OK"
  • Stock is sufficiently above minimum threshold
  • Action Required: Monitor normally
  • Risk: Low risk, adequate inventory

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ABC Classification

Products are classified using the Pareto principle (from src/analisis.py:4-31):

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Category A

Criteria: Top products contributing to 70% of total sales 
if p <= 0.7:
    return "A"
  • Priority: High
  • Management: Tight inventory control, frequent review
  • Typical Percentage: ~20% of products, 70% of sales value

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Category B

Criteria: Products contributing to next 20% of sales (70-90% cumulative) 
elif p <= 0.9:
    return "B"
  • Priority: Medium
  • Management: Moderate control, periodic review
  • Typical Percentage: ~30% of products, 20% of sales value

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Category C

Criteria: Remaining products (90-100% cumulative sales) 
else:
    return "C"
  • Priority: Low
  • Management: Basic control, occasional review
  • Typical Percentage: ~50% of products, 10% of sales value
Focus your attention on Category A products as they represent the majority of your sales value.

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Replenishment Recommendations

The reponer_cantidad column suggests how much to reorder (from src/decisiones.py:22-27): 
def recomendar_reposicion(row):
    if row["estado_stock"] in ["CRITICO", "RIESGO"]:
        return max(row["ventas_mensuales"] - row["stock_actual"], 0)
    return 0
Logic:
  • Only calculated for CRITICO and RIESGO products
  • Recommendation = Monthly Sales - Current Stock
  • Minimum recommendation is 0 (never negative)
  • OK products show 0 (no replenishment needed)
The recommendation aims to bring stock up to at least one month’s worth of sales.

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Generated Charts

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Chart 1: estado_inventario.png

Type: Bar Chart Location: output/graficos/estado_inventario.png Generated by: src/reportes_graficos.py:9-19 
estado_counts = df["estado_stock"].value_counts()

plt.figure()
estado_counts.plot(kind="bar")
plt.title("Estado del Inventario")
plt.xlabel("Estado")
plt.ylabel("Cantidad de Productos")
plt.tight_layout()
plt.savefig(os.path.join(carpeta_salida, "estado_inventario.png"))
Displays:
  • Number of products in each status (OK, RIESGO, CRITICO)
  • Quick visual assessment of overall inventory health
A healthy inventory should have most products in “OK” status with minimal CRITICO items.

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Chart 2: clasificacion_abc.png

Type: Pie Chart Location: output/graficos/clasificacion_abc.png Generated by: src/reportes_graficos.py:21-30 
abc_counts = df["categoria_abc"].value_counts()

plt.figure()
abc_counts.plot(kind="pie", autopct="%1.1f%%")
plt.title("Clasificación ABC del Inventario")
plt.ylabel("")
plt.tight_layout()
plt.savefig(os.path.join(carpeta_salida, "clasificacion_abc.png"))
Displays:
  • Distribution of products across A, B, and C categories
  • Percentage breakdown of each category
The pie chart shows product count distribution, not sales value distribution.

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Interpreting Results

Indication: Inventory management issues or supply chain problemsActions:
  • Review the Productos_Criticos sheet
  • Prioritize replenishment for Category A products first
  • Check reponer_cantidad for recommended order quantities
  • Investigate if minimum stock levels are set too low
Indication: Products approaching critical levelsActions:
  • Review the Productos_En_Riesgo sheet
  • Plan replenishment orders proactively
  • Monitor Category A and B products more closely
  • Adjust reorder points if this happens frequently
Indication: Normal ABC distributionActions:
  • Consider reducing inventory of slow-moving C items
  • Evaluate if Category C products are still needed
  • Focus management attention on A and B categories
Indication: Stock levels are adequateActions:
  • No immediate action needed
  • Continue normal monitoring
  • Product should show “OK” status

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Using the Reports for Decision Making

1

Review Critical Products

Open the Productos_Criticos sheet and focus on Category A products first
2

Check Replenishment Quantities

Review the reponer_cantidad column to determine order sizes
3

Analyze Trends

Compare current report with previous reports to identify patterns
4

Adjust Inventory Policies

Use ABC classification to optimize inventory management strategies
5

Monitor Visual Indicators

Check the charts for quick health assessment of overall inventory

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Report Frequency Recommendations

Inventory TypeRecommended Frequency
High-velocity productsDaily
Standard inventoryWeekly
Slow-moving inventoryMonthly
Seasonal productsAs needed before season

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Next Steps

Configuration

Learn how to configure file paths and system settings

Email Setup

Set up automatic email delivery of reports

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