Geely will purge excess factory capacity and focus on becoming a global competitor to BYD

Introduction

In the automotive industry, strategic restructuring and capacity optimization are critical for maintaining competitiveness in a rapidly evolving global market. This tutorial will guide you through creating a Capacity Optimization Dashboard using Python and Pandas to analyze factory capacity data, similar to what Geely might use to assess excess production facilities. You'll learn how to process manufacturing data, identify underutilized assets, and visualize capacity utilization trends.

Prerequisites

• Basic Python knowledge (variables, loops, functions)
• Installed Python libraries: pandas, matplotlib, seaborn
• Sample manufacturing capacity data (provided in the tutorial)

Step-by-Step Instructions

1. Install Required Libraries

Before starting, ensure you have the necessary Python packages installed. Run the following command in your terminal:

pip install pandas matplotlib seaborn

Why: These libraries provide data manipulation capabilities (pandas), plotting functionality (matplotlib), and enhanced visualization (seaborn) for analyzing capacity data.

2. Prepare Sample Data

Create a Python script and define sample manufacturing data that simulates Geely's factory capacity information:

import pandas as pd

data = {
    'factory_id': ['F001', 'F002', 'F003', 'F004', 'F005'],
    'location': ['Chongqing', 'Hangzhou', 'Shanghai', 'Guangzhou', 'Beijing'],
    'capacity_tons': [50000, 75000, 60000, 45000, 80000],
    'actual_production_tons': [30000, 70000, 40000, 42000, 65000],
    'product_line': ['SUV', 'Sedan', 'Electric', 'Commercial', 'Luxury']
}

df = pd.DataFrame(data)
print(df)

Why: This creates a realistic dataset with key metrics like factory capacity and actual production, enabling analysis of utilization rates.

3. Calculate Capacity Utilization

Add a new column to calculate the percentage of capacity being used:

df['utilization_rate'] = (df['actual_production_tons'] / df['capacity_tons']) * 100
print(df)

Why: The utilization rate helps identify which factories are operating efficiently versus those with excess capacity.

4. Identify Excess Capacity Factories

Filter factories with utilization below a threshold (e.g., 50%) to flag potential candidates for restructuring:

excess_capacity = df[df['utilization_rate'] < 50]
print("Factories with excess capacity:")
print(excess_capacity)

Why: This step mimics Geely's strategic assessment process to identify underperforming facilities that may need closure or reconfiguration.

5. Visualize Capacity Trends

Create a bar chart to visualize utilization rates across factories:

import matplotlib.pyplot as plt
import seaborn as sns

plt.figure(figsize=(10, 6))
sns.barplot(data=df, x='factory_id', y='utilization_rate', palette='viridis')
plt.title('Factory Capacity Utilization Rates')
plt.xlabel('Factory ID')
plt.ylabel('Utilization Rate (%)')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

Why: Visual representation helps stakeholders quickly identify patterns and prioritize decision-making for capacity optimization.

6. Export Analysis Results

Save the analysis to a CSV file for further reporting:

df.to_csv('factory_capacity_analysis.csv', index=False)
excess_capacity.to_csv('excess_capacity_factories.csv', index=False)
print("Analysis saved to CSV files")

Why: Exporting data allows for sharing results with management and integrating findings into broader strategic planning processes.

Summary

This tutorial demonstrated how to build a basic capacity optimization dashboard using Python and Pandas, mimicking the strategic decision-making process that companies like Geely might use to assess and restructure manufacturing assets. You learned to calculate utilization rates, identify underperforming factories, visualize trends, and export findings for strategic planning. This framework can be extended with real-time data feeds, advanced analytics, or integration with enterprise systems for production planning and capacity management.