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Planning and Operating United Airlines: Business Model and Optimization Enablers Gregory Taylor Senior Vice President – Planning United Airlines.

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Presentation on theme: "Planning and Operating United Airlines: Business Model and Optimization Enablers Gregory Taylor Senior Vice President – Planning United Airlines."— Presentation transcript:

1 Planning and Operating United Airlines: Business Model and Optimization Enablers Gregory Taylor Senior Vice President – Planning United Airlines

2 2 Uniteds Route Network Model Air travel is dominated by thousands of small markets where total travel demand does not justify point-to-point non-stop flights Western United States Las Vegas(LAS) Seattle(SEA) Portland(PDX) Eastern United States Boston(BOS) Albany(ALB) Buffalo(BUF) LAS SEA PDX BOS ALB BUF

3 3 Uniteds Route Network Model United has chosen a Hub-and-spoke model that maximizes number of markets served with given aircraft assets ORD LAS BOS SEA ALB PDX BUF This model provides several additional connecting options to the customers through Chicago (ORD) United is also able to carry local traffic between all six cities and ORD Hub-and-spoke

4 4 Uniteds Route Network Model In addition to the 59 passengers from the original three markets, 91 more passengers from six new markets were accommodated In addition, United was able to carry 1600 passengers each-way between the six cities and its hub, ORD

5 5 The Chicago Hub Chicago Operating Statistics (Daily) Number of cities served 125 Number of markets 7,800 Number of departures 1,015 Total passengers 42,300 Local passengers 22,000 (52%) Connecting passengers 20,300 (48%) United and United Express

6 6 The United System System Operating Statistics (Daily) Number of cities served 201 Number of markets 19,682 Number of departures 3,407 Total passengers 185,000 Aircraft 780 United and United Express

7 7 Overview of Uniteds Network Planning Automation Suite - Zeus

8 8 Uniteds Scheduling Strategy Marketing strategy Maintain market share Competitive response Provide travel day and time flexibility to passengers Uniteds scheduling strategy balances marketing goals and operating imperatives to meet financial goals Market selection –Where should we fly? Flight frequency/time –How often should we fly? –When should we depart/arrive? Fleet selection –Which aircraft type should we use? Maximize revenue Minimize cost Marketing goals Safety/maintenance requirements Aircraft availability Crew availability Other operating restrictions Operating imperatives Financial goals Profitability

9 9 Fine Tuning the Schedule United changes its schedule based on passenger travel patterns Weekdays – higher business demand Weekends – higher leisure demand Business destinations – more weekday flights Leisure destinations – more weekend flights Schedule changes based on season Uniteds flight schedule Higher leisure demand during school vacations/holidays Higher leisure demand during summer Higher business demand during spring/fall External factors e.g. Iraq war, SARS, etc.

10 10 The Zeus Suite AIRFLITE Schedule Database/Editor Slot Administrator Data Query & Analysis Profitability Forecast Fleet Assignment Through Assignment 1PLAN Web Portal Maintenance Routing Re-fleeting Models Level of Operations (LOOPS) Weekend Cancellation Airline Simulation International Flouting SIMON O&D Fleeting Neighborhood Search Dissemination - IDEAS

11 11 ZEUS Enables All Stages of Planning and Scheduling Operational Planning Mid Term Planning Long Term Planning Strategic Planning Process Activities Key Models Hub Planning Fleet Plan Acquisitions Schedule Structure Markets Frequencies Schedule Structure International Slots Fleeting Crew Interactions Reliability Maintenance Operability Aircraft Flows De-peaking Reliability Flight Number Integrity Weekends, Transition Profitability Forecast (PFM) Joint UA-UAX Fleet Planning Codeshare Optimizer PFM Joint UA-UAX Fleet Assignment UA Fleet Assignment Re-Fleeting Routing Through Assignment / Routing Flight Number Continuity Exception Scheduling De-peaking Suite Multi-year365-108 days108-80 days80-52 days Time* *Time = days from schedule start date Strategic Planning Schedule Optimization

12 12 Profitability Forecast Model (PFM) PFM employs advanced econometric techniques (Multinomial Logit (MNL) methodology) Passenger preference factors for itinerary attributes (# of stops, departure time, equipment, codeshare, etc.) are simultaneously estimated using MNL techniques Consistent with passenger utility-maximizing choice behavior Methodology and Key Capabilities air-carrier schedule (OAG) Industry Demands Industry fares PFM aids strategic decisions such as: Merger and acquisition scenarios Codeshare scenarios Equipment preference studies Hub location/buildup studies Cost model Passengers (total, local) Fares (local, OD) Revenue (local, OD) Profitability of future schedule Inputs Outputs Objective PFM is Uniteds strategic network-planning tool. PFM incorporates historical cost and fare data with itinerary-level passenger forecasts to determine schedule profitability MAPD – Mean Absolute Percent Deviation

13 13 Fleet Assignment Models The model uses advanced Operations Research techniques to solve the entire network to determine the optimal fleet assignment. Uses a Mixed Integer Linear Program. Maximizes UAs profitability subject to various operational and other constraints. Time Windows capability creates opportunity for further improve profitability by making small changes to departure/arrival times Methodology and Key Capabilities UA Schedule Itinerary Level demand and fare forecasts Aircraft Characteristics, Cost, Operational, other constraints Aircraft Inventory By Type Fully fleeted schedule Inputs Outputs Objective The O&D models are used to obtain the optimal fleet assignment for a flight schedule based on itinerary based demands and market share

14 14 Codeshare Optimizer Codeshare Optimizer uses a Dynamic Program-like approach to model incremental code share opportunities and PFMs itinerary building algorithms and LOGIT methodology The objective is to maximize incremental revenue while satisfying the flight number and other marketing constraints Methodology and Key Capabilities OAG Schedule Market List Marketing Constraints Ability to support several scenarios: Evaluate new codeshare or expand existing codeshare Optimize flight number usage when there is a shortage of flight numbers Make tactical market/flight changes during major schedule change Airport-pair passenger forecasts List of flights with best Codeshare Revenue Inputs Outputs Objective Codeshare Optimizer is a strategic decision-making tool to determine the best set of flights to code share based on market share and prorate agreements.

15 15 Exception Scheduling Model The model uses a Mixed Integer Linear Program to model the weekend schedule and maximize the profitability subject to operational and other constraints Associated business process changes have resulted in independent construction of optimal weekday and weekend schedules Methodology and Key Capabilities UA Schedule Demand and Fare Forecasts The model ensures that the weekend schedule meshes seamlessly with the surrounding weekday schedules The model recaptures demands from canceled flights and moves the demand to neighboring flights in the market Operational Constraints Fully Fleeted Weekend Schedule Inputs Outputs Objective Optimize exceptions on weekends to improve profitability while adhering to operational constraints

16 16 Schedule Improver (Simon) Given an aircraft inventory and a list of potential flights to fly, SIMON selects flight legs and assigns fleet types to flight legs in order to maximize contribution. Simon honors a host of operational constraints including those related to maintenance, noise, and crew availability. In addition, users can specify schedule structure constraints. Methodology and Key Capabilities Mandatory and optional flights O&D level demand Cost model By varying the amount of the schedule that is considered mandatory, users can control the amount of changes to an existing schedule in an incremental manner. Simon can intelligently determine the best pattern of flights to retain in any market O&D level fares Optimal Schedule Inputs Outputs Objective Simon determines the optimal schedule to fly from a given base schedule and a large superset of potential flight opportunities.

17 Revenue Management Automation Suite

18 18 This Section Will Focus on Yield (Inventory) Management Yield Management Objective: Given a schedule and estimated demand/fares, optimally allocate the seat inventory on each flight to ensure revenue-maximizing passenger mix Schedules Objective: Develop optimal schedule network based on market forces, estimated demand/fares, available capacity, operational imperatives, etc. Pricing Objective: Set the fares to maximize revenue across customer segments and to effectively compete in the market place

19 19 Passenger Segmentation Strategy Higher Lower FARESFARES Business travelers Frequent schedules Last minute availability Full service Global access Recognition Leisure travelers Low fares Quality service Low High Price sensitive Low High Willingness to commit in advance And schedule flexibility

20 20 Business Leisure Sale 14 14 7 3 0 No. of advance purchase days Travel restrictions 95 110 187 334 Fares 17 13 17 26 Demand High 56 passengers paying an average fare of $238; total revenue $13,328 69 passengers paying an average fare of $75; total revenue $5,175 Sale 7 60 79 28 24 125 passengers paying an average fare of $148; total revenue $18,503 Capacity Control Problem: UA881 on Sep 16 2004

21 21 What is O&D Control ? SFO LAX ORDLGA Itinerary Fare Demand LGA-ORD $100 5 ORD-LAX $100 2 ORD-SFO $100 1 LGA-ORD-LAX$150 5 LGA-ORD-SFO$225 1 (1 Seat)

22 22 O&D Control Yields Better Revenue SFO LAX ORDLGA Itinerary Fare Demand LGA-ORD $100 5 ORD-LAX $100 2 ORD-SFO $100 1 LGA-ORD-LAX$150 5 LGA-ORD-SFO$225 1 (1 Seat) Leg BasedORION 1 1 1 0 0 $300 0 1 0 0 1 $325

23 23 United has been the Leader in Adopting Cutting Edge Yield (Inventory) Management Technologies Overbooking systems Leg based Inventory Management systems with fare class control reservation systems AA, SAS implemented O&D systems in the 1990s. CO, LH started using O&D controls in the mid 1990s Enhancements to systems to compete with Low Cost Carriers Overbooking systems Static O&D system with O&D control Orion Development Orion implementation included path based forecast, network optimization and dynamic passenger valuation Strategic research to compete with Low Cost Carriers Major Airlines 1980s1990 - 19951996 - 20002001 - 20032004 and Beyond

24 24 Uniteds Yield Management System - Orion Travel Agents United Res. Online Agencies Passenger Valuation Optimization Demand Forecasting Pricing and Accounting Systems Aircraft Scheduling Inventory System (Apollo) Orion RM Planners tickets, data published fares rules adjustments controls schedule PV parameters bookings cancellations schedule change departure data Base Fares adjustments Path level demand & no-show forecast AU Levels Displacement Costs

25 25 Flight Network Orion optimizes revenue on approximately 3,600 UA and UAX daily departures About 27,000 unique paths are flown each day by Uniteds customers Forecast and Optimization Statistics Orion produces 13 million forecasts for all 336 future departure dates All future departure dates are optimized every day Orion produces flight level controls for nearly 1.1 million flights in the future Options exist for analysts to load changes into Apollo throughout the day Passenger valuation produces new base fares every two weeks Hardware infrastructure A dedicated IBM supercomputer complex is utilized to run the forecasting and optimization algorithms High-Level Orion Statistics

26 26 Advanced Availability Processing Consumers are price conscious and conditioned to shop for travel Availability of internet outlets is increasing shopping activity Most airlines are experiencing higher look to book ratios, stretching computing capability Opportunity to further tailor product offering to passenger segments Increased inventory control capabilities Improved channel control Customer centric RM Distribution capabilities Manages dramatic growth of availability requests and reduces processing costs Maintains revenue integrity through real- time application of inventory controls Open system architecture for faster development Advanced Availability Processing Challenges and Opportunities

27 Day of Operations Automation Suite

28 28 Airport Manpower Assignment Models How many employees do we need at the airport for daily Operations? Passengers OR-Based Assignment Model Demand & Schedule How many employees? Their respective assignments Output Input Customer Service Gate Agents Baggage Handlers Airport Employees Considerations Multiple start times Overtime/Parttime Employees call in sick IRROPS (Bad Weather) Overestimating Need Costly, Idle employees Underestimating Need Long lines, dissatisfied customers

29 29 Block Time Forecasting Model How many minutes should United take to fly between a City Pair? Lets Use JFK-LAX as an example Block Time Forecasting Demand Fuel cost Crew Cost # minutes to fly Output Input Initial Response to the Question above: Why doesnt United fly the most fuel efficient route and use that time? The range used for a 767 is anywhere between 5:10 & 5:30 Statistical Forecasting Techniques Going Too Fast: Higher fuel cost Going Too Slow: Higher crew costs Missed connections Complications: Enroute Air traffic delays FAA re-routes Weather

30 30 Real-time IRROPS Management Models Q: When things go wrong on the day-of-operations, what is the best way to Respond and Recover ? What can go wrong? 1.Bad Weather (60 days out of 360 days) 2.Aircraft needs maintenance 3.Crew shortage 4.Runway closedowns What are the choices? 1.Cancel the flight(s) 2.Delay a flight 3.Get a Spare Aircraft 4.Get Reserve Pilots/Flight attendants Challenges: All of this has to be done in close to real time All Resources have to be re- positioned so that the next day Operations can run smoothly United has built a whole host of math-based Applications to assist in these decisions

31 31 Irregular Operations Management at United Operations Data Store Pilot Apps Aircraft Reassignment Flight Attendant Recovery Passenger Recovery Resource Recovery Arrival Sequencing Delay Vs Cancels Optimized set of Cancellations Optimized Re-sequencing of Arrivals at ORD SkyPath Analyze the Impact of Proposed Cancellations & Recovery Analyze the Impact of Proposed Re-ordering Operations Data Warehouse FAA ODS Real-time Information Feedback to Planning GDP Issued for ORD A Bad Day at ORD DynaBlock All these tools work interactively to provide the overall solution

32 The Future for Operations The Future for Operations The Operations Holy Grail: Can there be one Global application that can make ALL these decisions?

33 33 Irregular Operations Management at united Operations Data Store Pilot Apps Aircraft Reassignment Flight Attendant Recovery Passenger Recovery Resource Recovery Arrival Sequencing Delay Vs Cancels Optimized set of Cancellations Optimized Re-sequencing of Arrivals at ORD SkyPath Analyze the Impact of Proposed Cancellations & Recovery Analyze the Impact of Proposed Re-ordering Operations Data Warehouse FAA ODS Real-time Information Feedback to Planning GDP Issued for ORD A Bad Day at ORD DynaBlock

34 34 Irregular Operations Management at united Operations Data Store Arrival Sequencing Optimized Re-sequencing of Arrivals at ORD SkyPath Analyze the Impact of Proposed Re-ordering Operations Data Warehouse FAA ODS Real-time Information Feedback to Planning GDP Issued for ORD A Bad Day at ORD DynaBlock Ops Global Solver At United, we are working on building this Global Solver

35 35 Next Frontiers – A Sample Game theoretic models to predict and respond to competitor actions Multiple Criteria Decision Making Modeling trade-offs between key decision variables Data Mining

36 Operations Research at United Airlines

37 37 Experts in optimization and forecasting techniques dedicated to solving complex business problems Approximately 45 people Advanced degrees in Mathematics, OR, Statistics, Transportation Science, Industrial Engineering, and related fields 19 PhDs Mix of employees from academia, the airline industry, and management consulting Partnerships with universities Enterprise Optimization - Overview Mission. Provide thought leadership and ground breaking research capabilities that challenge the status quo ; partner with business units and delivery groups to create value through excellence in modeling and research. The Activities Solve complex business problems using math modeling, forecasting, stochastic modeling, heuristic optimization, statistical modeling, game theory modeling, artificial intelligence, data mining, and other numerical techniques Review business processes in high- leverage areas Rapidly develop model prototypes to validate theories and provide quick returns Partner with IT professionals to build full blown, robust production systems The Group

38 38 Profitability forecasting to make long term business plan decisions including market selection and frequency of operations. Fleet Assignment models for fleet planning and profit maximization. Aircraft Routing models to operationally route aircraft Codeshare Optimization to effectively manage the growing revenue opportunity through partner airline relationships. Enterprise Optimization – Business Areas Aircraft Scheduling Revenue Management Crew Planning Models to efficiently plan trips and monthly schedules for pilots and flight attendants. Crew Manpower Planning Models for pilots and flight attendants to manage complex decisions including staffing levels, training levels, vacation allocations and distribution of crew among geographically dispersed bases. Revenue Optimization models focused on inventory, pricing, and yield. O&D Demand forecasting to feed decision making in revenue optimization models. Next Generation Revenue Management model to more effectively compete with growing airline segment of Low Cost Carriers that have a dramatically different and uniquely simplified price and inventory strategy. Supply Chain Management Models to balance reduction in inventory costs while maintaining and improving the reliability of our operation.

39 39 Summary The airline industry presents many high-value opportunities for Operations Research systems United has historically invested, and continues to heavily invest in state-of-the-art tools United has also consistently partnered with academia to develop cutting edge models Increasing computing power at lower cost many high value opportunities remain


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