Digital transformation company Capgemini is developing artificial intelligence and digital twin tools to improve aircraft parts traceability and give low-cost and regional carriers real-time visibility into Tier 2 and Tier 3 supplier networks.
Speaking to Low-Cost and Regional Airline Business at the Farnborough International Airshow, Mike Dwyer, Head of Intelligent Industry at Capgemini UK, said aircraft-on-ground (AOG) delays remain a persistent drain on operator revenue because traditional reactive maintenance identifies faults too late in the operational cycle.
"A lot of the time AOG is too late," Dwyer noted, emphasising that the key is shifting towards predictive maintenance powered by onboard prognostic and diagnostic systems that flag developing issues mid-flight.
This real-time telemetry allows carriers to make critical maintenance decisions while the aircraft is still airborne—whether to swap a line replaceable unit (LRU) or pre-position a repair kit at the destination airport to ensure a rapid turnaround.
Turning to the expanding Used Serviceable Material (USM) market, Dwyer highlighted how extended aircraft lifespans and supply chain bottlenecks have amplified the risk of counterfeit components and fraudulent certification entering the supply chain.
"We have to make sure that we really can identify our parts," he warned, pointing out that fake documentation in circulation remains a serious industry concern.
To combat this, Capgemini is advocating a digital passport framework for every component. This links physical identification features—ranging from QR codes and RFID tags to chemical markings and unique material pattern scans—to a secure, cyber-protected digital record detailing the part’s complete lifecycle history from point of origin.
Dwyer compared the approach to anti-counterfeiting methods in the luxury goods sector, where an asset's unique surface or material structure can be authenticated instantly via a smartphone camera, eliminating sole reliance on physical nameplates or barcodes.
On supply chain integration, Dwyer revealed Capgemini is working with OEMs to extend data visibility down to Tier 2 and Tier 3 suppliers. This enables lower-tier manufacturers to anticipate demand fluctuations and feed component-level reliability data back into the broader operational ecosystem.
He stressed that data sharing must be bi-directional: "It can't just be pulled; it has to be bi-directional. We're sharing demand data, we're sharing learnings and knowledge, but we're making sure we have that safe, secure system."
Dwyer noted that emerging advanced air mobility and heavy drone supply chains are equally sensitive to weight and battery density parameters, meaning raw manufacturing data must feed directly into live performance and mission-planning models.
To capture field knowledge, Capgemini is also implementing a knowledge-graph system where line engineers talk through unscheduled repairs using body cameras. The audio is transcribed, indexed alongside repair schemes and NOTAMs, and fed into an accessible knowledge bank to assist other technicians encountering similar faults. Capgemini's Maestro platform then connects on-site engineers with remote experts for real-time, "over-the-shoulder" technical guidance.
To prevent transcription errors or false data from corrupting the knowledge base, Capgemini runs an ensemble of two or three distinct AI models or large language models (LLMs) against the ingested input to establish consensus before committing any entry to the permanent record.
Following initial fault identification, the workflow typically applies a 24-hour containment window, after which engineering and supply chain teams determine whether a fleet-wide modification or an updated local working procedure is required.
Mike Dwyer of Capgemini gives his impressions of FIA 2026
