How to exploit risk/uncertainty in forecasting and optimization. Incorporating Risk explicitly in commercial and operating decisions.

1. Profiting from Uncertainty
Exploiting Volatility with Big Data
TOM BACON, AIRLINE STRATEGIST
MAY 2015

2. Profiting from Uncertainty
 Financial markets recognize uncertainty and capitalize on expected variances
 “Risk” justifies higher expected returns in equities and fixed income securities
 Investment counselors encourage diversification to reduce overall portfolio risk
 Exotic derivatives (puts, calls, collars, floors) are designed specifically to manage, or exploit, risk
 Risk is managed more crudely in “real” markets and daily business decisions
 Investment decisions, where the financial markets are best represented, typically have 1 hurdle rate based on the whole firm’s
risk profile (D/E ratio, cost of capital); risk differences among projects are often assessed more subjectively
 Marketing, pricing, product, and distribution decisions, typically, do not include a sophisticated risk metric
 At airlines, however, risk is incorporated explicitly in many commercial and operational decisions
 Optimal aircraft sizes and fleet structures incorporate the expected variance in demand across days-of-week and seasons
 Pricing – or more particularly revenue management – is based on both expected demand of each fare level on each flight and
on the uncertainty of the forecast for that specific fare level
 There is an opportunity for more industries to take a more disciplined approach to Risk

3. Risk-based Decision Making
 Airlines have incorporated risk into key commercial/operational decisions
 Sophisticated models using Big Data Analytics and linear programming optimization
 Exploiting Risk for increased profits is well developed in:
• Supply / Capacity Planning:
• Aircraft and scheduling decisions factor both average daily demand and projected variance in demand
• Pricing:
• Pricing is dynamic, based on forecast demand by flight by fare level
• Forecast uncertainty for each fare level within each flight is explicitly built into optimization models
• Lost Sales:
• Overbooking recognizes high variance in no-shows across flights and days
• Purchasing:
• Sophisticated fuel hedging is designed around specific goals and operating models

4. Capacity Planning: Fleet Decisions
 The optimal size of an aircraft on a route cannot solely be based on “average” demand.
 For example, average demand of 100 can come from multiple alternative profiles, ranging from a
constant, predictable 100 passengers to random fluctuations between zero and 200+
 The aircraft sizing algorithm assesses the Demand Distribution (passengers & fares) and
compares to the operating cost of different aircraft types
 Expected marginal revenue versus marginal cost for each incremental seat
 The profit maximizing aircraft rarely accommodates average demand
 In fact, due to high volatility in demand, 100 passengers on “average” may justify a 110 seat aircraft but this aircraft may only
capture 80 “observed” passengers (a 72% “observed” load factor) with 20 passengers on average unaccommodated
 Every flight has its own unique distribution of demand.
 High variance, with higher fares, will justify larger aircraft to meet more peak demand.
 Lower variance will allow more “perfect” aircraft sizing and higher observed load factors
 Optimization models predict “observed” loads and “spilled” (unmet) demand for each flight

5. Pricing Decisions
 A typical flight may have 100+ different fares
 Airline objective is for each passenger to pay his Maximum Willingness to Pay
 Discrete market segments are defined based on different elasticities and behavioral proxies
 Lower fares are only offered when demand for higher fares is forecast to be low
 Inventory allocations for each fare incorporate demand variance
 High variance combined with high upsell opportunity justifies setting aside more inventory for the higher fare demand
 Variance is measured, and applied in linear program optimization techniques, for each fare level on each flight
 Forecasts and optimized allocation recommendations are updated each night – over a million
distinct forecasts for a 50 aircraft fleet
 Such revenue management adds 5-7% revenue to the airline
 More discrete pricing based on distributions of demand at different price points – or
between market segments - can improve revenue results in a multitude of industries

6. Lost Sales: Overbooking Decisions
 In addition to the demand forecast, additional uncertainty for airlines occurs with no-shows
 Passengers who change their travel plans or discard non-refundable tickets
 No-shows can be 10-20% of bookings on certain flights on certain days
 Without overbooking, no shows result in empty seats and foregone revenue
 However, how many or which passengers will no show on a given flight isn’t known
 Airlines measure the average and variance of no-show behavior by flight
 The expected revenue from selling an extra seat is compared against the probability and cost of oversales
 Efficient management of oversales can drive very high overbooking rates when the variance is high
 Statistically-based overbooking can add 2% to total airline revenue

7. Other Risk-related Decisions
 Fuel hedging became common beginning in 2008 when Southwest earned more from its
hedges than from operating its fleet
 Fuel hedging, like financial options, is highly efficient and offers exotic alternatives including collars,
floors, etc. …for a price
 However, fuel hedging needs to be tied into operations to avoid being merely speculative
 The consolidated industry is now better positioned to pass on higher fuel prices to customers
 American Airlines believes any fuel hedging is speculative
 Different fleet strategies drive differences in exposure to fluctuating fuel prices
 Allegiant airlines operates older aircraft which it grounds when fuel prices are higher or demand lower
 Delta manages a fleet of both old and new aircraft, allowing it, too, to manage capacity in response to market changes
 Overall risk (operational and financial) needs to be measured and included to meet overall corporate risk/return objectives
 Fuel hedges are designed to reduce volatility; not to “make money”

8. Risk-oriented Culture
 A firm that relies on heavy statistical modeling and that includes calculated risk in
commercial and operating decisions must adopt different organizational processes
 Big Data-based statistical models require special skills and oversight
 The “Wall Street” trader mentality within a commercial organization
 Specific features of a successful organization built around “Risk” include:
 Recruiting and training of skilled analysts
 Model transparency and ease-of-use
 Checks and balances on analyst decisions and model interventions
 Standard metrics for both model and analyst performance; accountability
 Within a commercial organization, the Risk group cannot act as a silo
 Cross-functional collaboration insures model inputs & outputs (decisions) are “real world”
 Learning between the “quants” and the operators is continuous

9. Risk-oriented Decision Process
Point
Forecast
Plan / Act
Forecast
Average
Plan / Act
Type of
Distribution
Economic Assessment of all Outcomes
Overforecast vs. Underforecast
Optimization
Forecast
Volatility
Identification
of Outliers
Goals
Scenario Planning
Traditional
Forecast Process
Risk-oriented Forecast and Decision Process
Operational
constraints/
flexibility

10. Does your Firm Exploit Risk?
 Does your firm incorporate forecast uncertainty into its operational decision making?
 Analysis of, and metrics for, demand volatility
 Economic assessment of under- vs. over-forecasting
 Mathematical optimization across distribution of potential outcomes
 Capitalizing on high pay-off outcomes, even when they aren’t the most probable
 Do you prepare forecasts, along with associated uncertainty, at a sufficiently granular level?
 Discrete segmentation of customer market segments based on differing elasticities
 Millions of SKU’s including, for services, date- and time-of-day-specific demand
 Updated algorithms, coefficients, and the forecasts themselves with real-time orders
 Are Operations and Analytics aligned around risk-taking?

11. Can you further exploit Risk?
 Tom Bacon has applied Big Data Analytics and Risk Management to support commercial
decision-making at numerous airlines
 Led Capacity Planning, Pricing, and other commercial functions as an executive at 5 carriers
 Restructured carriers in changing markets or facing new competition
 All sectors: global legacy carriers, LCC’s, regionals, and niche carriers
 Global: North America, Asia, Middle East, Europe
 Assessed analytical systems & modules; achieved track record of success in exploiting risk
 Oversaw delivery and deployment of over 150 regional jets, transforming a turboprop carrier into a >$1 B airline
 Developed new processes for Pricing Analytics for a bankrupt airline during world recession
 Integrated analytical systems for merger of two major airlines
 Launched travel start-up designed to manage customers’ risk of fare increases
 Persistent advocate for cross-functional collaboration between Analytics and Operations
 Thought leader in Big Data Analytics; regular contributor to travel publication and speaker at
industry events
 To implement commercial and operational risk management in your organization please contact
Tom at tom.bacon@yahoo.com