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Civil helicopter chartering is growing across the APAC region, and Indonesia is no exception. Saladin Siregar, Key Account Management for Passenger Air Chartering, APAC at global charter experts Chapman Freeborn, provides in-depth insights on helicopter charters in Indonesia, explaining why they are a good fit for the country and which sectors are driving growth. A diverse and growing civil helicopter fleet Fleet growth in civil turbine helicopters in the Asia-Pacific (APAC) region has been consistent and steady for over a decade. Between 2014 and 2024, it expanded from 3,287 up to 4,131 with a CAGR of 2.3%, according to research by the Asian Sky Group.At 154, Indonesia’s civil helicopter fleet represents 3.7% of the region’s total and is the largest fleet in Southeast Asia. This should come as little surprise considering the country’s unique geography, Saladin Siregar explains. “Indonesia is vast, and its geography is complex and unique,” he comments. “It is a country of over 17,000 islands, and many locations are extremely remote and difficult to access due to challenging terrain.”Travel difficulties are exacerbated by challenging infrastructure in both urban and rural areas. “In the cities, congestion is a significant issue. And outside urban areas, there are many locations where road or sea connections are impractical. These factors combined create strong demand for point-to-point helicopter transport.” A range of civil helicopter mission types Roughly half of all helicopter flights in Indonesia last year were multi-mission, according to the Asian Sky Group’s Helicopter Fleet Report 2024. The remaining 50% of flights were made up a of combination of corporate or private hire, offshore flights, and charters, which accounted for over 15% of all missions. There were also a small number of Emergency Medical Services (EMS), Search and Rescue (SAR), law enforcement, and training flights. Sectors driving helicopter charter demand The expansion of helicopter charters in Indonesia is being driven by a combination of sectors and use cases.First of all, there has been an uptick in usage around major cities and tourist locations. “VIP and corporate transport via helicopter charter is growing, especially in Jakarta and Bali,” explains Saladin Siregar. “In congested cities, helicopters provide faster and more reliable travel, which makes passenger helicopter charters popular with both businesses and tourists. In terms of the business sector, executives and project teams are making use of Chapman Freeborn’s chartering services.” Another sector that makes extensive use of helicopter charters is heavy industry. “There were over 4,600 active mining permits in Indonesia in 2024. And there are thousands of energy facilities and power plants spread across the country, including many small diesel plants and multiple Oil & Gas facilities. Many industrial sectors are expanding. For example, there is an increase in nickel and copper smelters, and in refineries. As you would imagine, these facilities are often located in remote areas. Furthermore, the transportation of mission-critical crew and cargo to these sites is often time sensitive, making helicopter charters the most viable and convenient option,” says Saladin Siregar. “Whether it is tourism, corporate travel, or accessing remote industrial facilities, helicopters provide unmatched access and flexibility. They help executives avoid city congestion and enable teams to mobilize rapidly to remote industrial sites,” Saladin Siregard concludes. The future outlook The team at Chapman Freeborn APAC anticipates further development in Indonesia’s helicopter charter market in the near-term and will be on hand to support their customers over this period.“We expect industrial growth in the country to continue and infrastructure challenges to persist. Based on this scenario, we expect to see a further increase in passenger and cargo helicopter charter demand over the next 5 years,” comments Saladin Siregard. “We are also looking at opportunities to expand passenger air chartering services for the corporate and VIP vertical.” With Chapman Freeborn’s strong understanding of Indonesia’s unique aviation landscape and close partnerships with key operators and regulating bodies, the company is well-positioned to meet this growing demand. “We are ready to offer safe, reliable and convenient helicopter charters to serve these customers these segments as demand increases,” concludes Saladin Siregard. With more than 50 years of experience and extensive global coverage, Chapman Freeborn provides air cargo charter services for major corporations, governments, NGOs, relief agencies, and high net-worth individuals. The company is part of Avia Solutions Group, the world’s largest ACMI (Aircraft, Crew, Maintenance, and Insurance) provider, operating a fleet of 187 aircraft worldwide and the parent company of over 250 subsidiaries. The group offers a wide range of aviation solutions, including MRO (Maintenance, Repair, and Overhaul), pilot and crew training, ground handling, and other related aviation services. Supported by 14,000 highly skilled aviation professionals, the group operates across 6 continents. ( views expressed are personal)

At a modern airline operations control centre, predictive maintenance systems now aspire to flag early warnings based on real-time engine health data. When a forecast indicates that an engine is likely to exceed performance limits within a defined number of days, maintenance planners can proactively adjust flight schedules, arrange for spare parts, or schedule shop visits in advance. What once triggered emergency responses or unscheduled groundings is now managed through data-driven foresight. This transformation reflects how predictive AI modelling has turned potential disruptions into planned maintenance activities, improving operational reliability and reducing cost without compromising safety. By Sanjay Deshmukh,COO, Findability Sciences Before AI: maintenance by pattern, gut, and schedule Fifteen years ago, an engine showing subtle changes in exhaust gas temperature margins or vibration signatures would generate a paper trail: a technician’s note on a line maintenance card, a call into TechOps, and a conservative defensive response — ground the aircraft if in doubt, or ferry it to a maintenance base the next time it could be scheduled. Preventive maintenance relied heavily on flight-hour or cycle-based schedules, inspections driven by OEM service bulletins, and human judgment informed by experience. This approach protected safety but was inefficient: unnecessary shop visits, emergency AOG events, and expensive part shipments were common. The limitations were structural: data lived in silos, sensor streams were high-volume but noisy, and no operational system translated complex telemetry into a simple operational decision such as “this engine needs a shop visit within X days.” That translation — days to failure — is what airlines now prize, because calendar days map directly to planning windows for crews, aircraft rotations and spare logistics. The shift: combining data, twins and AI The first practical step was not a model but instrumentation and integration: richer telemetry (temperature, pressure, spectral vibration), consistent logging of flight context (thrust settings, profiles), and a link to maintenance history and environmental feeds. OEMs and MROs invested in digital twins — virtual replicas of engines that run physics-informed simulations alongside live telemetry — so that patterns in sensor drift could be interpreted against expected physical behavior rather than treated as raw anomalies. Rolls-Royce and others have championed digital-twin programs that let engineers explore “what if” scenarios without turning a wrench. Once the data plumbing was in place, airlines piloted models that forecast remaining useful life (RUL) and — crucially for operations — converted that into days to failure: calendarized RUL that factors in flight schedules and utilization. Early models were statistical or tree-based and offered explainability at the cost of flexibility. Today’s state-of-the-art prognostics blends physics constraints with deep sequence architectures — combinations of CNN-LSTM with attention or transformer-like encoders — that can capture long-range temporal patterns in noisy telemetry and fuse them with maintenance logs and environmental modes. Academic work in 2024–2025 has demonstrated improvements in RUL accuracy using CNN–LSTM–Attention hybrids and multiscale transfer learning tailored for aeroengines. A concrete example: Air France–KLM’s operational experiment Air France–KLM’s recent move to accelerate AI across its operations highlights how a large carrier approaches adoption. In late 2024 the group announced partnerships to deploy generative and predictive AI across its data estate — a strategic step that explicitly names predictive maintenance as a target area. For a carrier that operates hundreds of aircraft across global networks, moving from hours-long analyses to near-real-time probabilistic forecasting materially changes the cadence of maintenance decisions. In practice, the airline’s prototype architecture mirrored the emerging industry pattern: an edge tier on aircraft performs low-latency anomaly detection and compresses high-frequency data; a cloud tier runs ensemble prognostics and digital-twin simulations; and a frontline dashboard surfaces a clear days-to-failure forecast with confidence bands and top contributing factors (e.g., rising core temperature during climb cycles, elevated particle ingestion on specific sectors). The OCC used this information to make graded decisions — limit dispatch to short sectors, pre-order parts for the next planned shop visit, or approve a non-urgent ferry to a maintenance base — turning uncertain emergencies into scheduled, auditable actions. Best practices that made it work Successful deployments share practical patterns: • Align forecasts to decisions. Build the model output to answer operational questions: if the decision is “can this aircraft depart tomorrow?”, then present probability of failure within 24–72 hours, not a raw RUL number. • Quantify uncertainty. Probability distributions, prediction intervals, and cost-weighted decision thresholds prevent overreaction to a single point estimate. Operators need to know both the most likely days-to-failure and the worst-case window. • Human-in-the-loop workflows. Use the AI as an advisor. Maintenance planners validate and override forecasts early in rollout; their feedback then becomes training signals for model recalibration. • Hybrid models for explainability. Blend physics-informed constraints with deep ML so that failure drivers link to physically meaningful phenomena — vital for certification and trust. • Data governance and interoperability. Standardized data exchange (OEM, MRO, airline) and careful privacy controls make federated improvements possible without sharing raw operational data. Air France–KLM’s partnerships reflect this need to keep control of data while leveraging cloud AI capabilities. Challenges that persist Not all is solved. Data quality and left-censoring (engines observed only after installation) complicate life-history modeling; true failures are rare, so training sets are imbalanced; and operations evolve — flight profiles, new routings, or maintenance practices create covariate drift that must be detected and handled. Moreover, the certification and safety assurance of AI-infused prognostics remains a conservative, resource-intensive step: regulators and safety managers require rigorous demonstrations of conservatism under uncertainty and robust behavior in edge cases. Academic and industry studies stress the need for continuous validation pipelines and scenario stress tests to address these issues. Benefits realized — and measured When the system works, benefits cascade. Airlines report fewer aircraft on ground events, better shop utilization, and lower logistics costs from fewer urgent part shipments. Delta, Lufthansa and others have publicly discussed multi-year programs to digitize TechOps and apply ML for predictive maintenance; case studies point to measurable reductions in unscheduled removals and time-to-repair when analytics are tied to operations. For Air France–KLM, the

ModAir Aviation IFSC Pvt Ltd is spearheading a major shift in India’s aviation training ecosystem by enabling access to leased training aircraft through GIFT City, Gujarat. This forward-looking initiative not only strengthens the national aviation framework but also reflects the vision of the Prime Minister’s Viksit Bharat mission, emphasizing self-reliance, skill development, and global competitiveness. Headquartered in India’s only operational International Financial Services Centre, ModAir Aviation has emerged as one of the earliest and most dynamic adopters of GIFT City’s specialized aircraft leasing platform. The company has successfully pioneered leasing arrangements for flight training academies across the country, creating a new standard in operational efficiency and accessibility for aviation education. Through this model, ModAir has already inducted multiple modern trainer aircraft, which are now supporting active pilot training operations across established Flight Training Organizations (FTOs) in Jalgaon, Khajuraho, and Karnataka. By offering flight schools the ability to lease rather than purchase aircraft, the company has effectively bridged a long-standing financial and logistical gap in India’s pilot training infrastructure. Flight academies benefit from improved cash flow, expanded training capacities, and access to technologically advanced fleets—all of which contribute to more cost-effective and efficient training processes. ModAir’s approach redefines the economics of pilot education in India by lowering entry barriers for flight schools and ensuring students train on the latest generation of equipment. This aligns closely with national initiatives such as UDAN and AtmaNirbhar Bharat, both designed to foster connectivity, job creation, and self-sufficiency across the aviation value chain. The company’s mission directly supports the government’s strategic focus on building a globally recognized hub for aviation skill development, where Indian pilots are trained to international standards. Atul Jain, Director and Promoter of ModAir Aviation IFSC Pvt Ltd, expressed pride in the company’s contribution to national development. “We aim to empower India’s youth to become world-class pilots and innovators,” he said. “Through access to leased aircraft and helicopters, ModAir is nurturing the next generation of aviation professionals while strengthening India’s standing as a leader in flight training and leasing across Asia. Our presence in GIFT City underscores our belief in India’s potential as a global aviation hub.” Building upon this strong foundation, ModAir has set an ambitious goal to induct 20 additional trainer aircraft by the end of 2026. This will substantially enhance the availability of modern aircraft for pilot training across India’s growing network of academies. Through GIFT City’s internationally competitive ecosystem—offering tax efficiencies, streamlined regulations, and global financial access—ModAir continues to demonstrate how innovative leasing models can accelerate the modernization of Indian aviation. By making aircraft leasing accessible, transparent, and sustainable, ModAir Aviation IFSC Pvt Ltd is helping India achieve its goal of producing thousands of highly skilled pilots while ensuring the growth of ancillary aviation sectors. The company’s efforts mark a pivotal step toward realizing the dream of a Viksit Bharat—one that takes flight through education, innovation, and enterprise. ( Advertorial)