4 Mishra

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Published on January 28, 2008

Author: Dora

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Revenue Management with Restriction-free Pricing:  Revenue Management with Restriction-free Pricing AGIFORS Reservations and Yield Management 2003 Honolulu, June 2-5, 2003 Shankar Mishra & Vish Viswanathan Revenue Management Environment:  Revenue Management Environment Y Y6S KVE14N KSE14N KFSE21N KUE14N SVVE14N SA3S WGE30N WSE30N WGE14AN Fare Class Alignment Availability is controlled by Validity of Fares Fare Restrictions Inventory Control Fare Restrictions Pricing Restrictions:  Pricing Restrictions Saturday Night Stay Min Stay Requirements Max Stay Requirements Adv. Purchase Restrictions Routing Restrictions Exchange, Cancellation Penalties Point of Sale Restrictions Distribution Channel Restrictions Simplification starts in a rather unexpected manner … Proliferation of Low Cost Carriers:  Proliferation of Low Cost Carriers 4 “How do you get to be a millionaire? Be a billionaire and start an airline.” Sir Richard Branson Low Cost Carriers:  Low Cost Carriers 1985 Enhanced Product, One-way Fares The Pioneer (since 1971) Reduced number of fare types (<10) Europe Low Cost Carriers Effect:  Low Cost Carriers Effect A simpler pricing structure Changes to “pricing fences” None at some airlines None for a price range Non-refundable bookings Exchange fee Possibility of a higher fare Lower distribution costs Inventory Control with revenue management Price is the main differentiator in market segmentation From LCC to Majors:  From LCC to Majors LCC aren’t the only ones offering fares with fewer restrictions Majors from Europe: Alitalia, Lufthansa, British Airways Majors within US New one-way fares with minimal restrictions from Delta, United, US Airways, Hawaiian Two main reasons pressure from Southwest, jetBlue, AirTran Research into launching their own LCC Time to take a closer look into applicability of traditional revenue management techniques Role of Revenue Management:  Role of Revenue Management 8 “If I can get a £7 flight to somewhere within 200 miles of Venice, well I’ll *@%* take it. Seven quid, I don’t care where I *@%* go.” Sir Bob Geldof Role of Revenue Management:  Role of Revenue Management No Pricing Restrictions All non-refundable sales One fare per O&D,Cabin at a time When to close the current available fare in any market? After x number of bookings? At y days before departure? Role of Revenue Management being re-defined Traditional RM Approach:  Traditional RM Approach Assumption of demand independence among all classes within a cabin Protection for higher classes assumes “Infeasibility of Sell-down” Market Segmentation Assumptions Segment restrictions included in pricing definitions Independence among market segments (distribution channel, point-of-sale) Effect of external conditions on market segmentation Issues with Traditional RM:  Issues with Traditional RM Total demand < Capacity High dilution possibility High Demand Flights Over-estimation of demand in higher classes – high spoilage, some dilution Under-estimation of demand in higher classes – high dilution, some spoilage Even with a perfect total demand forecast – arrival order will cause dilution & spoilage Demand from multiple classes may materialize in one time-period buying the only available fare in marketplace Any dilution may also give rise to spoiled seats which are protected for higher classes Roadmap to a new solution:  Roadmap to a new solution 12 “Informed decision-making comes from a long tradition of guessing and then blaming others for inadequate results.” Scott Adams Solution Ingredients:  Solution Ingredients Inventory Control Given a pricing structure, how many seats to sell at each price (Segment, Compartment)? Optimality Maximizes Revenue Maximizes Profit (variable cost of sale is factored in) Minimizes Dilution & Spoilage Current Practice Closure of a price point by “Days-to-departure” Booking Limits Time Limits A fare class is closed if bookings reach a pre-defined limit Close a fare class at “N” days before departure Solution Expectations:  Solution Expectations Fare Structure Cum. Exp. Bookings W K B S Fare Structure / Exp Bookings Class Closure W: either at day 84 or after10 bookings K: either at day 35 or after 27 bookings B: either at day 14 or after 60 bookings S: either at day 5 or after 72 bookings Influencing Factors:  Influencing Factors Price points and related bookings Historic and current, if applicable Current price points and observed bookings Inventory controls Grouping of historical data – Seasonality Relationship between Price & Demand with respect to Time Available capacity to optimize External factors Competitor pricing, load factors, schedule etc Optimization Module:  Optimization Module $75 $10 $84 $40 $25 Open/Close Periods Price-Demand Relationship A Dynamic Programming Formulation Rn(Space,Price,Demand) = Max0kSpacern(k) + Rn-1(Space – k, Price, Demand) Function of Allocation k Re-calculated after each allocation Revenue/Profit Optimization Inputs:  Optimization Inputs Demand estimation for each price point at a point in time (days to departure) Concept of Sell-up and Sell-down Demand for a $50 fare deduced from observed bookings at all <$50 fare in a time-band Sell-up Demand for a $50 fare deduced from observed bookings at all >$50 fare in a time-band Sell-down Calibration of Sell-up & Sell-down curves Depends upon Proximity of Fares Relationship with days to departure Unconstraining at cabin level only Post-Optimization Analysis:  Post-Optimization Analysis Calculation of Time Limits Booking and Time Limits are in sync at each optimization Time Limits may be re-calculated in between optimization points Ability to enhance price structure periodically Factors being changing booking patterns & competitor activities Estimation of Bid Price Curve from optimization OA Fares included to better estimate bid prices Generation of an Initial Bid Price Curve based on historical data Updates to Bid Price Curve with every optimization Possibility of changing the price structure in marketplace Post-optimization Analysis:  Post-optimization Analysis Cum. Exp. Bookings Bid Price Curve Price Structure Update Enhanced Price Structure Updates to Booking & Time Limits Additional Complexity:  Additional Complexity Not all classes in the cabin may follow “restriction-free pricing” Calculation of available space for these group of classes Moving Curtain Aircrafts Moving Curtain Aircrafts with “Variable Seating Configuration” Group Handling & Integration with RM Enhancements with External Data Competitor Pricing, Load Factors, Schedule etc. Measuring Performance:  Measuring Performance Analysis of Price Structure offered in the marketplace on a post-departure basis Comparison with post-departure Bid Price Curve Loss of opportunity on the pricing front Measuring Revenue & Profit against pre-defined targets Initial target based on historical data (grouped by entity, seasonality etc.) Continuous revision of target based on observed booking & competitor activities Provision of Feedback to Revenue Management Solution Improved estimation of price-demand relationship w.r.t. time Continuous updates to Bid Price Curve linked to revenue opportunity Acknowledgements:  Acknowledgements Suzanne Donnelly Chris Binnion John Wardman Jonathan Downes Donna Clarkstone Andy Bosworth Stuart Mason, Prism Inc. Dr. Miguel Anjos Christine Currie Questions?:  Questions? 23 “It never occurred to them that there was a fourth class out there called the human race who just wanted to fly at the lowest fare.” Sir Freddie Laker

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