Airlines Operations: Optimizing Occupancy and Profitability With Python

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Business Problem, Challenges, and Objectives:

The airline industry is currently facing profitability challenges due to several external factors:

  • Stricter environmental regulations increasing operational costs.

  • Higher flight taxes reducing demand and raising expenses.

  • Rising fuel prices impacting cost structures.

  • Increased interest rates affecting financing costs.

  • Tight labor market leading to higher labor expenses.

To address these challenges, airlines aim to increase occupancy rates to boost the average profit earned per seat. By analyzing operational data, they seek to develop strategies to enhance load factors on low-performing flights, optimize pricing, and ultimately improve overall profitability.

Main Challenges

The airline industry currently faces several key challenges impacting profitability and operations:

  1. Stricter Environmental Regulations

    • Impact: Increased compliance costs due to the need for fuel-efficient aircraft and sustainable practices.

    • Consequences: Elevated operational expenses and limited ability to expand fleets without significant investment.

  2. Higher Flight Taxes

    • Impact: Elevated taxes on air travel raise ticket prices.

    • Consequences: Reduced demand, particularly among price-sensitive customers, leading to decreased revenue.

  3. Tight Labor Market and Rising Labor Costs

    • Impact: Shortages of skilled personnel drive up wages and benefits.

    • Consequences: Higher operational costs and potential service disruptions due to increased turnover rates.

Business Objectives

To address profitability challenges, the airline aims to achieve the following:

  1. Increase Occupancy Rates

  2. Optimize Pricing Strategies

  3. Enhance Customer Experience

Overall Goal: Leverage data analysis to increase occupancy rates, optimize pricing, and enhance customer experience, thereby improving overall profitability.

Analysis:

The analysis provides key insights into fleet capacity, booking trends, revenue generation, fare structures, and occupancy rates, essential for developing strategies to enhance profitability.

1. Fleet Capacity

    • Overview: Identified aircraft with more than 100 seats.

    • Findings:

      • Figure 0: Lists aircraft codes and seat counts, highlighting fleet size and large-capacity planes for targeted occupancy optimization.

2. Ticket Bookings and Revenue Trends

  • Analysis: Utilized line charts to track ticket bookings and revenue over time.

  • Findings:

    • Booking Trends: Steady increase in ticket bookings from June 22nd to July 7th, followed by stabilization until August, with a peak on a single day.

    • Revenue Correlation: Revenue trends mirrored ticket bookings, indicating a direct relationship between bookings and revenue.

    • Implications: Understanding peak periods can inform targeted marketing and pricing adjustments to sustain revenue growth.

Figure 0

Figure 1

3. Fare Condition Analysis

  • Analysis: Compared average fares across business, economy, and comfort classes using bar graphs.

  • Findings:

    • Fare Structures: Business class fares consistently higher than economy across all aircraft.

    • Class Availability: Only aircraft 773 offers a comfort class, while CN1 and CR2 exclusively provide economy class.

    • Implications: Variations in fare structures influence passenger choices and revenue generation, informing pricing strategy adjustments.

Figure 2

Figure 3

4. Occupancy Rate Analysis

  • Metrics: Calculated total revenue, total tickets, and average revenue per ticket for each aircraft.

  • Findings:

    • Top Performer: SU9 aircraft generates the highest total revenue, attributed to lower fares for business and economy classes, leading to higher ticket sales.

    • Low Performer: CN1 aircraft has the lowest revenue, likely due to offering only economy class at very low prices and potentially limited facilities.

    • Occupancy Metrics: Higher occupancy rates correlate with increased revenue, emphasizing the importance of maximizing seat utilization.

Figure 4

Average Occupancy Rate:

  • Calculation: Booked seats divided by total seats.

  • Findings: Higher occupancy rates indicate better seat utilization, leading to increased profitability.

  • Strategic Insight: Enhancing occupancy rates on underperforming flights can significantly boost revenue without additional operational costs.

5. Impact of Increased Occupancy Rate

  • Scenario: Projected a 10% increase in occupancy rates across all aircraft.

  • Findings:

    • Revenue Growth: Total revenue shows a gradual increase with higher occupancy rates.

    • Financial Implications: A 10% increase in occupancy can substantially enhance annual turnover, demonstrating the financial benefits of occupancy optimization.

    • Strategic Recommendations: Focus on pricing strategies and operational adjustments to achieve higher occupancy rates, thereby improving overall profitability.

Figure 5

Conclusion:

Analyzing key revenue metrics—such as total annual revenue, average revenue per ticket, and occupancy rates per aircraft—is essential for maximizing profitability in the airline industry. This analysis highlights critical areas for improvement, enabling the optimization of pricing strategies and route planning.

Increasing occupancy rates directly enhances revenue while minimizing costs associated with vacant seats. Adjusting pricing based on aircraft conditions and facilities ensures fares align with customer expectations, preventing both underpricing and overpricing that can deter ticket purchases.

Moreover, enhancing occupancy should not compromise customer satisfaction or safety. Balancing profitability with high-quality service and adherence to safety regulations is crucial for sustainable success. By adopting a data-driven approach to revenue analysis and optimization, airlines can achieve long-term profitability and maintain a competitive edge in the industry.

Figure 6

Resources:

  • Dataset:

    • Airlines Dataset.

    • https://www.kaggle.com/datasets/saadharoon27/airlines-dataset

  • Tools & Platforms:

    • Google Colab: Used for data storage, cleaning, and analysis.

    • SQLite: Utilized for SQL-based data management and querying.

    • Python: Employed for data manipulation and visualization using libraries such as Pandas, Matplotlib, and Seaborn.

  • Curious about the work? Visit my GitHub page.