| Presenter |
Title |
Affiliation |
Abstract |
|
Bonson Lam
Gabriel Leung
|
Cargo Demand Forecasting
|
Industry specialist and mentor |
Air cargo demand forecasting has historically received less analytical attention than passenger demand forecasting within aviation economics. However, structural changes in global supply chains—including the increased adoption of just‑in‑time logistics and rising demand for time‑ and temperature‑critical goods—have heightened the strategic importance of air freight. This study examines air cargo demand using both top‑down (macroeconomic and trade‑based) and bottom‑up (microeconomic and network‑based) forecasting approaches. Where appropriate, established passenger demand modelling techniques—such as Quality of Service Index analysis, market share allocation, and route back‑tracking—are adapted to assess their applicability to cargo markets. The study also investigates the continued transport of low‑value cargoes by air and examines the apparent divergence between relatively low cargo load factors and the sustained operation of dedicated freighter services. |
|
Houman Goudarzi
|
Leveraging exogenous signals as leading indicators for unconstrained flight demand prediction |
ZYTLYN Technologies |
Traditional flight demand models rely heavily on booking data to forecast demand. However, in an era of rapid market shifts and evolving consumer behavior, true "unconstrained" demand—unfolds the latent interest that exists before capacity limits or pricing hurdles are applied, making unconstrained demand signals powerful, e.g. for price elasticity models. Houman Goudarzi will explore various aspects related to unconstrained demand signals, and furthermore the use of exogenous signals will be expanded on—such as events, weather, exchange rates, as second degree leading indicators used as features in predictive models of unconstrained demand. Houman will present a case study focused on the methodology of denoising unconstrained flight demand through cleaning but also through calibration against exogenous signals that have a causational relationship with demand. Concluding with an example of joint analytics of unconstrained vs. constrained flight signals. |
|
Muge Tekin
Kalyan Talluri
|
Estimation using marginal competitor sales information |
Rotterdam School of Management |
An abiding challenge for firms is understanding how customers value their product relative to competitors. This is hard to quantify because, while prices are public, rival sales are not. In industries like aviation and hospitality, aggregated competitor sales can be obtained from third-party brokers, yet this data is rarely used in revenue management due to a lack of suitable models. We develop a constrained maximum likelihood method to address key challenges: (i) competitor data is aggregated across multiple lengths-of-stay; (ii) the no-purchase segment is unobservable; (iii) private group sales reduce competitor capacity and affect prices; and (iv) the partial-information likelihood is intractable. Monte Carlo simulations show our method recovers true parameters and outperforms existing approaches on real booking data. |
|
Joanna Kuras
Liudmila Gorkun-Voevoda
|
From Invention to Innovation: Leading Large‑Scale Methodological Change in Revenue Management Forecasting |
Swiss International Air Lines (Lufthansa Group) |
Building on Jonas Rauch’s theoretical case for disentangling ('A Practical Perspective on “Disentangling Capacity Control from Price Optimization"', AGIFORS 2025), this presentation shows how Lufthansa Group moved from concept to operational reality based on the example of transforming its demand forecasting method. Such a fundamental shift in methodology cannot succeed through technical implementation alone: it requires bold, out‑of‑the‑box transformation leadership to challenge entrenched mindsets, reshape processes, and guide users into a new, disentangled RM logic. We share how this enabled the step from invention to innovation - and the key learnings it offers for any future major change in Revenue Management. |
|
Kalyan Talluri
Dmitrii Tikhonenko
|
What price did the competitior charge? The peculiar airline cargo RM estimation problem |
Imperial College Business School |
In airline cargo RM the firm has to price or respond to a bid without knowing what the competitor is pricing. It becomes very difficult to estimate customer price-sensitivity as we not only do not observe no-purchasers or the prices they paid, but even for those who purchased our product or service, we do not know to what they compared our price to. Potentially one can eventually learn the true price-sensitivity and other parameters, but it would just require too many samples and becomes infeasible in volatile fast-changing markets such as airline cargo. In this talk we leverage existing data (schedules, own bids, wins, worldACD data) to device an econometric method to estimate this, and recover the expected price they paid at the competition. |
|
Ron Karo
|
Feasibility of Expanding Ryanair Routes to Tel Aviv: Demand Forecasting, Fare Regression, and Cost-Benefit Analysis in a Deregulated Market
|
Ryanair |
This study evaluates the commercial viability of expanding Ryanair's low-cost operations to Tel Aviv, Israel, leveraging new capacity at Terminal 3A amid market deregulation and LCC growth. It analyzes Israel's air travel market, Ryanair's strategy via Porter's Five Forces and Ansoff Matrix, and shortlists 15 routes from 91 bases using exclusion criteria. Employing linear fare regression for pricing insights, gravity modeling for demand forecasting (projecting 864,864 passengers), and driver-based cost simulations with fuel and load factor sensitivities, the study assesses profitability across scenarios. Results show profits from €30.2M (best-case) to losses of €9.8M (worst-case), with 44 weekly flights generating 4,900 jobs. Findings highlight RM opportunities in competitive route optimization and hedging strategies. |
|
Jakub Figura
Stanislaw Robak
Maria Browarska
Maciej Pawelczyk
Hadar Sharvit
|
Quantifying External Shocks in Airline Demand: A Multi-Agent LLM Approach to Automated Event Intelligence |
Fetcherr |
Incorporating rare external events into airline demand forecasting remains a challenge. Manual curation and third-party feeds fail to quantify events at scale, lacking the responsiveness to address emerging shocks. We propose a multi-agent LLM-based pipeline to automate event discovery and quantification. Our framework extracts normalized attributes, including location, time, and severity, via web-grounded retrieval and evidence-backed validation. By converting complex global events into structured records, the system maintains model alignment with real-world conditions. Validated across historical scenarios, it demonstrates significant accuracy gains over baselines reliant on endogenous signals. Automating discovery improves disruption performance, scales efficiently, and supports robust forecasting, revenue management, and network planning under planned and unexpected shocks. |