Volkswagen will halve its model range, but stays silent on the 100,000 job cuts

Introduction

In response to the automotive industry's transformation driven by electric vehicle adoption and changing consumer demands, major automakers like Volkswagen are undergoing significant restructuring. This tutorial will teach you how to analyze and model automotive product line strategies using Python and data visualization techniques. You'll learn to create a comprehensive dashboard that visualizes model portfolio changes, production capacity trends, and strategic implications for automotive companies.

Prerequisites

• Python 3.7+ installed on your system
• Basic understanding of pandas, matplotlib, and seaborn libraries
• Knowledge of data analysis and visualization concepts
• Optional: Jupyter Notebook or any Python IDE

Step-by-Step Instructions

1. Install Required Libraries

First, we need to install the necessary Python libraries for data analysis and visualization. The libraries we'll use include pandas for data manipulation, matplotlib and seaborn for visualization, and plotly for interactive charts.

pip install pandas matplotlib seaborn plotly

Why this step? These libraries provide the foundation for handling automotive data, creating static visualizations, and building interactive dashboards that can help analyze strategic decisions like Volkswagen's model lineup reduction.

2. Create Sample Automotive Data

Let's create a sample dataset that represents an automotive company's model portfolio and production capacity over time. This simulates the kind of data Volkswagen might analyze when making decisions about model reduction.

import pandas as pd
import numpy as np

# Create sample automotive data
np.random.seed(42)
models = ['Golf', 'Passat', 'Tiguan', 'Polo', 'Jetta', 'Atlas', 'Golf GTI', 'Touareg', 'Arteon', 'ID.3', 'ID.4', 'ID.5']
years = list(range(2015, 2026))

# Generate production data
production_data = []
for year in years:
    for model in models:
        # Simulate declining production for older models
        if year < 2020:
            base_production = np.random.randint(50000, 200000)
        else:
            base_production = np.random.randint(20000, 100000)
        
        # Apply model age factor (older models have lower production)
        age_factor = max(0.1, (2025 - int(model.split()[-1]) if model.split()[-1].isdigit() else 2025 - 2015) / 10)
        production = int(base_production * age_factor)
        
        production_data.append({
            'year': year,
            'model': model,
            'production': production,
            'capacity': np.random.randint(100000, 300000)
        })

# Create DataFrame
df = pd.DataFrame(production_data)
print(df.head())

Why this step? Creating realistic sample data helps us understand how to structure automotive portfolio data that could inform strategic decisions like Volkswagen's model reduction strategy.

3. Analyze Model Portfolio Changes

Now let's analyze how the model portfolio has changed over time, focusing on the decline of older models and emergence of new electric vehicle models.

# Group by year and model to analyze trends
model_trends = df.groupby(['year', 'model'])['production'].sum().reset_index()

# Calculate production changes over time
model_trends['production_change'] = model_trends.groupby('model')['production'].diff()
model_trends['production_change_pct'] = model_trends.groupby('model')['production'].pct_change() * 100

# Identify models with significant decline
declining_models = model_trends[model_trends['production_change_pct'] < -50]
print("Models with significant production decline:")
print(declining_models[['model', 'year', 'production_change_pct']].head())

Why this step? This analysis mirrors how Volkswagen might evaluate which models to discontinue based on declining production and market performance, similar to their announced strategy to halve the model range.

4. Visualize Production Capacity Trends

Let's create visualizations showing how production capacity has changed over time, including the planned reduction to 9 million vehicles annually.

import matplotlib.pyplot as plt
import seaborn as sns

# Set up the plotting style
plt.style.use('seaborn-v0_8')
fig, ax = plt.subplots(figsize=(12, 8))

# Plot total production by year
yearly_production = df.groupby('year')['production'].sum().reset_index()

# Plot the production capacity
ax.plot(yearly_production['year'], yearly_production['production'], marker='o', linewidth=2, markersize=6)

# Add horizontal line for planned capacity (9 million)
ax.axhline(y=9000000, color='red', linestyle='--', linewidth=2, label='Planned Capacity (9M)')

ax.set_xlabel('Year')
ax.set_ylabel('Production Volume')
ax.set_title('Automotive Production Capacity Over Time')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

Why this step? Visualizing production trends helps stakeholders understand the scale of capacity reduction, similar to Volkswagen's announced 25% reduction from 12 million to 9 million vehicles annually.

5. Create Interactive Dashboard with Plotly

Now let's build an interactive dashboard that allows users to explore model performance and capacity utilization over time.

import plotly.express as px
import plotly.graph_objects as go

# Create interactive time series plot
fig = px.line(model_trends, x='year', y='production', color='model', 
              title='Model Production Trends Over Time', 
              labels={'production': 'Production Volume', 'year': 'Year'})

fig.update_layout(
    xaxis_title="Year",
    yaxis_title="Production Volume",
    hovermode='x unified'
)

fig.show()

Why this step? Interactive dashboards enable business stakeholders to explore automotive data dynamically, helping them make informed decisions about product line strategies, similar to what Volkswagen would need when planning their model reduction.

6. Analyze Strategic Implications

Finally, let's create a summary analysis that shows the strategic implications of model reduction, including potential impacts on workforce planning and market positioning.

# Calculate summary statistics
summary_stats = df.groupby('year').agg({
    'production': ['sum', 'mean', 'count'],
    'capacity': 'mean'
}).round(0)

print("Summary Statistics by Year:")
print(summary_stats)

# Calculate capacity utilization
df['capacity_utilization'] = (df['production'] / df['capacity']) * 100

# Identify years with highest capacity utilization
high_utilization = df.groupby('year')['capacity_utilization'].mean().sort_values(ascending=False).head(3)
print("\nYears with Highest Capacity Utilization:")
print(high_utilization)

# Model portfolio reduction analysis
portfolio_reduction = df.groupby('year')['production'].sum().pct_change().mean() * 100
print(f"\nAverage Annual Production Change: {portfolio_reduction:.2f}%")

Why this step? This analysis provides insights into the strategic implications of Volkswagen's decisions, including the impact on capacity utilization and workforce planning, which could relate to their silence on the 100,000 job cuts mentioned in the news.

Summary

In this tutorial, you've learned how to analyze automotive product line strategies using Python data analysis techniques. You created sample automotive data representing model portfolios and production capacity trends, analyzed model decline patterns, visualized production capacity changes, and built interactive dashboards. These skills are directly applicable to understanding how major automakers like Volkswagen make strategic decisions about model reduction and capacity management. The techniques demonstrated here can help analyze the impact of industry-wide shifts toward electric vehicles and the strategic implications of reducing product lines, similar to Volkswagen's announced 50% model reduction and 25% capacity cut.