UPS is exploring artificial intelligence to help identify alternative aircraft components, improve maintenance operations, and address workforce shortages affecting the aviation industry. The initiative reflects growing interest in using AI to streamline aircraft maintenance, repair, and overhaul (MRO) processes as operators face rising costs and increasing pressure on their supply chains.
Lauren Purcell, aircraft engineering manager at UPS, discussed the company’s efforts at IFS Unleashed 2026. Representatives from Southwest Airlines and Japan Airlines also shared how their organisations are exploring AI to improve maintenance troubleshooting, technician training, and operational decision-making.
The discussions highlighted both the potential benefits of AI in aviation and the challenges organisations face when applying the technology to complex maintenance environments.
Aircraft Parts Shortages Drive AI Exploration
UPS has spent the past year exploring how AI could help identify alternative sources for aircraft parts. Finding suitable replacement components could give maintenance teams greater flexibility when traditional supply channels face delays or shortages.
The wider aviation industry continues to experience pressure from rising maintenance costs, ageing aircraft fleets, and supply chain disruptions.
According to Oliver Wyman’s 2026 MRO survey, the global aviation maintenance, repair, and overhaul market exceeded US$136 billion in 2025, representing an 8% increase from the previous year. The report attributed the growth partly to additional maintenance requirements for ageing aircraft and reliability challenges affecting newer models.
Material cost inflation also exceeded expectations by 100 to 200 basis points across different maintenance segments. Parts shortages, geopolitical instability, and increased spare parts inventories contributed to the pressure.
Tariffs have added another challenge, with approximately 90% of survey respondents reporting tariff-related impacts, particularly involving component purchases, repairs, and engine overhauls.
However, sourcing alternative aircraft parts involves more than finding a component that fits. In the United States, replacement parts must meet applicable regulatory requirements. The Federal Aviation Administration’s Parts Manufacturer Approval (PMA) programme allows manufacturers to produce approved replacement or modification parts for specified aircraft, engines, and propellers.
Aircraft leasing agreements can introduce additional restrictions, as some lessors require operators to replace PMA components before returning leased aircraft.
UPS has not disclosed whether its AI initiative involves identifying PMA-approved components or other alternatives. The company has also not identified the AI systems being evaluated or reported measurable results from the project.
AI Could Help Train Aircraft Maintenance Technicians
Beyond parts sourcing, AI could help address a growing shortage of experienced aircraft maintenance professionals.
As senior technicians retire, aviation operators face the challenge of training replacements while preserving the technical knowledge needed to diagnose and resolve complex aircraft faults.
UPS is exploring how automation and AI could accelerate training for younger employees. The objective is to improve training processes as experienced workers leave the industry.
Oliver Wyman’s survey found that approximately two-thirds of respondents considered recruiting aircraft technicians and mechanics moderately to very challenging. Labour rate inflation also remained elevated, at approximately 5.5% to 6% across most maintenance categories, compared with around 3% before the pandemic.
Southwest Airlines is investigating similar applications. DJ Rounsoville, the airline’s managing director of strategy and digital solutions, highlighted the time required for new mechanics to develop troubleshooting expertise. New technicians may need up to two years to become fully efficient at diagnosing problems.
AI assistants could help shorten this learning curve by giving technicians faster access to maintenance manuals, service bulletins, historical repair records, and technical knowledge accumulated by experienced colleagues.
These tools could also help identify recurring faults and previous repair attempts, potentially improving troubleshooting and reducing maintenance work carried over between shifts. Such applications have been described as potential industry use cases, rather than confirmed deployments by UPS or Southwest Airlines.
Rounsoville also stressed that workforce planning must remain connected to aircraft maintenance scheduling. Optimising one part of the operation without accounting for staffing requirements could create problems elsewhere in the maintenance process.
Japan Airlines Focuses on Data-Driven Maintenance
Japan Airlines is also exploring how improved access to operational information can support maintenance decisions.
Sugimoto Kei, the airline’s vice president of IT and digital promotion, said the company wants to equip employees with better information so they can make faster, more informed decisions.
One obstacle is that operational data remains distributed across different systems. Information may not be readily available within enterprise datasets, limiting its usefulness for AI applications.
In June 2025, Japan Airlines announced plans to implement IFS Cloud for Aviation Maintenance, replacing an aircraft maintenance management system that had been in operation for more than 15 years.
The planned platform combines aircraft, engine, and component maintenance management with spare parts inventory and supply chain operations. Japan Airlines also intends to transition from its existing on-premises infrastructure to a cloud-based environment.
According to IFS, the platform is designed to support predictive maintenance, real-time analytics, and workflow automation. However, the availability of these capabilities does not confirm that Japan Airlines has deployed them.
The airline is also examining how better data access and AI could improve preventive maintenance and planning amid supply chain disruptions.
Data Quality Remains a Major Barrier
Despite growing interest in AI, aviation companies continue to face challenges when moving from experimentation to operational deployment.
Oliver Wyman’s 2026 MRO survey found that 58% of respondents remained at the experimental stage of AI development, unchanged from the previous year. At the same time, approximately two-thirds said their AI initiatives were delivering value that met or exceeded expectations.
Poor data quality and limited data availability remain significant obstacles to wider adoption.
Aircraft maintenance systems contain extensive information about service histories, component performance, previous repairs, and recurring faults. When this information is accurate, accessible, and properly structured, it can provide a foundation for AI tools that support maintenance planning and troubleshooting.
However, fragmented records and inconsistent processes can undermine these applications. AI systems cannot reliably deliver useful recommendations when the underlying operational data is incomplete or poorly maintained.
Purcell emphasised the importance of establishing processes that ensure information is collected and used consistently. Without those processes, maintaining data integrity becomes difficult.
Aviation AI Moves Toward Practical Applications
The initiatives being explored by UPS, Southwest Airlines, and Japan Airlines illustrate how aviation operators are considering AI for practical operational challenges rather than experimentation alone.
Potential applications include identifying alternative aircraft components, helping technicians diagnose faults, accelerating employee training, and improving preventive maintenance planning.
Realising these benefits will depend on more than the capabilities of individual AI systems. Organisations must also address regulatory requirements, data quality, workforce needs, and integration with existing maintenance infrastructure.
For UPS, the immediate focus includes finding more flexible sources for aircraft parts and improving technician training. Whether AI can deliver measurable improvements in these areas remains to be seen, but continued investment across the industry suggests that data-driven maintenance will remain an important area of aviation technology development.
Source: https://www.artificialintelligence-news.com/news/ups-ai-aircraft-maintenance-parts/
