The post-pandemic recovery of the aviation industry has triggered a sharp rebound in passenger demand, placing commercial airlines under immense pressure to expand their fleets strategically. Selecting the right aircraft is a high-stakes, multi-billion dollar decision that must balance technical performance, economic volatility, and passenger experience. While these decisions have traditionally relied on the deep intuition of human experts, the rise of generative AI introduces a new frontier in strategic planning. This insight, based on a recent peer-reviewed study published in the journal AVITEC (August 2025), explores whether AI can truly match—or enhance—the nuanced judgment of industry veterans.

To navigate this complexity, SEMS Lab researchers implemented a hybrid Multi-Criteria Decision-Making (MCDM) framework. The study compared human expert evaluations with AI-generated recommendations (specifically using GPT-4) through the integration of the Analytic Hierarchy Process (AHP) and TOPSIS. By structuring the decision into technical, economic, and operational criteria, the research successfully quantified the priorities of both human and artificial intelligence, providing a rigorous mathematical basis for comparing subjective judgment against algorithmic logic in the selection of narrow-body aircraft like the Airbus A320neo and Boeing 737 MAX 8.

Key Highlights

  • Ranking Convergence: Both human experts and AI identified the Airbus A320neo as the top-performing aircraft for the post-pandemic Indonesian market, indicating that AI can effectively mirror human logic in identifying high-level technical and economic winners.

  • The Nuance Gap: While the final rankings were identical, their reasoning differed significantly; human experts prioritized customer experience and fleet commonality, whereas the AI placed much higher weight on raw technical specifications and acquisition costs.

  • Structural Consistency: The AI demonstrated a remarkably low “Consistency Ratio” within the AHP model, suggesting that Large Language Models can provide highly logical and structured decision frameworks, even when they lack the “grounded” contextual intuition of a human professional.

  • Systemic Collaboration: The findings suggest that the most resilient industrial decisions emerge from a “human-in-the-loop” system—using AI to handle data-heavy structural modeling while relying on humans to define the strategic weights that reflect long-term market realities.

The SEMS Perspective

At SEMS Lab, our mission is to move From Complexity to Clarity. This research demonstrates that the future of industrial engineering lies not in choosing between human intuition and artificial intelligence, but in harmonizing them. By Making Complexity Navigable, we empower decision-makers to use tools like hybrid MCDM to bridge the gap between Big Data and human expertise. This approach is vital for building resilient transportation systems that can adapt to the unpredictable shifts of a globalized economy, ensuring that every strategic choice leads to a measurable, positive impact.

Read the full paper here: Aircraft Acquisition Post-Pandemic: Human vs. AI Perspectives using Multi-Criteria Decision Methods

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