Overbooking policy for an Airline


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Overbooking policy for airline is one of the main channels that enhance revenue and improve profit; it should be carefully implemented to avoid any negative effects of denied boarding impact especially on the brand name for the airline. It should be address the best strategy that minimize cost between no-show cost and denied boarding cost, by implement cost based overbooking model ( U curve technique )

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Overbooking policy for an Airline

  1. 1. DefiningAn OptimumOverbooking PolicyDefiningAn OptimumOverbooking PolicyCost Based Overbooking ModelBy : Mohammed S. AwadResearch Scholar
  2. 2. Defining An Optimum Overbooking PolicyThree factors lead to the best earning of revenue inaviation, they are, right flight scheduling, optimum faremaxing and proper inventory control. While the mainprinciple of airline revenue management is to sell theright service to the right customer at the right time forthe right fare, and can be achieved by developing byoptimum overbooking policy that minimize the cost oftwo main cost elements. i.e No Show Cost and DeniedBoarding Cost, the problem is solved by implementingU curve technique which define the right overbookingpolicy, so by analysis the historical data of specifiedroute, defining the existing overbooking policy thatalso may reflect a denied boarding cases and thecorresponding no-shows distribution. A goodoverbooking strategy will be the one that minimize the expected of denied boarding andopportunity cost of spoilage, this clearly leading to define Overbooking and No-show curves.Introduction:Revenue Management (RM) is the process of understanding, anticipating and influencingpassenger behavior in order to maximize revenue or profits from a fixed, perishable resource asavailability of airline seats. The problem is to sell the available seats to the passengers at the righttime for the right fare. So revenue management is a set of revenue maximization strategies andtactics meant to improve the profitability of certain businesses.Airline Revenue Management:Based on the revenue management theory, the crossfunctional of managing revenue is impact three main factors:1- Flight Scheduling2- Pricing3- Inventory ControlThe product of an airline offers to a great extent defined byScheduling, Pricing and Capacity. Scheduling defines therouting, the frequency, the departure time, whether it is anon-stop or a connection. The role of Revenue Managementis to match the demand with the capacities given byscheduling. This is done by determining the availability ofthe capacity aircraft . In order to optimize the availability,Revenue Management has to know how much money thePricingFlightScheduleInventoryControlREVENUE
  3. 3. company will get when this product is sold.Yield Management (YM) involves the tactical controlof an airline’s seat inventory for each future flightdeparture. YM is the airline’s last chance to maximizerevenue.Yield Management (YM)It is a process determines the number of seats to bemade and available for each fare class by settingbooking limits on low fare seat. Usually YM systemstake a set of differential prices/products, schedules andassigned flight capacities. Figure ( 1 ). Shows Normal Booking Curve.Yield Management System:Four Steps describe typical Yield Management System.Data Collection-- The Basic collected data of revenue management are: Revenue Data , Historical Booking,No-Show Data, Actual BookingForecasting –- It is for No-Show Data keeping in mind the capacity constrainsOptimization –- Cost based Overbooking modelReservation:- The reservation procedure is related to theairline patterned, it is legacy or low costcarriers, and with the advanced, so feeding bythe outcomes of the optimization models todefine the overbooking level, terms as AU(Authorized Capacity), CAP (PhysicalCapacity), BKD (conformed booking) and NSR( No-Show Rate), are interfere in overbooking issue.Overbooking Problem:The goal of overbooking is to minimize the risk of spilled revenue due to passenger cancellationsand no-shows, to accomplish this, airlines routinely overbook flights to balance the need ofgenerating additional revenue while minimizing the risk of over sales.
  4. 4. Cost-based Overbooking Model:The objective of Cost-based overbooking model is to findthe optimum overbooking policy that minimize the totalcombined cost of denied boarding and spoilage ( no-show )cost.Optimum Overbooking Policy =MIN Cost of DB + Cost of SP ………1WhereDB : Denied BoardingSP : SpoilageA simple overbooking algorithm takes the no-show forecast and overbooking to compensate forthose no-shows.A more sophisticated overbooking takes the different costs of no-shows and denied boarding intoaccount as well as the uncertainty of the no-show forecasts. It calculates the expected costs ofspoiled seats and denied boarding for each possible overbooking level and selects that withminimum expected costs.Figure shows the two cost elements.The risk of spoilage, that is empty seats despite high demand is the greater, the smaller theoverbooking limit is. On the other hand the risk of denied boarding increases with increasingoverbooking limits.The sum of both costs has a minimum and the corresponding booking limit minimizes theexpected total costs.Case Study :Based on actual data of Yemenia for sector SAH-DXB, the no-show data for the period Oct.2010. It is a complex issue to forecast the number of no-show per flight, as mentioned above, demand can beforecast, likely wise No-Show passengers can beforecasted in the same manner, to get No-showpassengers per month, assuming the process is followsPoisson Sampling, so by considering a historical data ofNo-show of one month, and fitted to a Poisson byminimum least square analysis and chi square test orKologorov test based on the number of sampling.The collection data represented by histogram, Figure no.
  5. 5. ( ) these no-show data are related to the environmental / operational pattern, that mean we haveto restricted to capacity of aircraft, time of departure,route connectivity and other factors.The data analysis first based on average value ofLAMDA i.e 2.143 then adjusted to reached optimumvalue 3.055 to us it in Overbooking Lose Table. Thefollowings figures shows the collection of LAMDA.ANALYSIS:The analysis is based on Cost Based Overbooking Model based on the following inputs:1- No-show Passenger Cost:This is an opportunity lose revenue cost due tothe no-show of passenger it is the revenuealmost in hand, as empty flown seat never getback. So it can be calculated as the fare ofSAH-DXB = 270 USD per no-showpassenger.2- Denied Boarding Cost:This is a critical cost, caused by oversellspolices of airlines, and its includes a variety ofelements, some of them are not quantifiable in monetary terms.o Cash compensation paid to involuntary denied boarding.o Free travel vouchers as incentive for involuntary denied boardingo Meals and hotel costs for displaced passengers.o Space on other airlines to accommodate displaced passengers.o Cost of lost passengers goodwill.Based on Yemenia compensation program, it cost =150 USD for SAH-DXB sector.So by developing Overbooking lose table, Table ( 4 ) probability of no-show is calculated basedon Poisson distribution and accordingly cost.So – first we have to represent the data by Poisson distribution, and accordingly to utilize theprobability function of Poisson distribution in Overbooking Lose Table.
  6. 6. Two cost are evaluated1- No-Show Cost:The loss of opportunity may calculate as the followingFare SAH-DXB = 270 USDSo the expected cost of lose opportunity0 × 0.47 + 1 × 0.134 + 2 × 0.219 … … . . +7 × 0.024 × 270 = 2.958 × 270= 798 USD per flightSo No Show Cost = ( No. of No-show -- No. of Overbooking ) * Probability of No Show *Cost of no show cost per seat.Provided that No Show is greater than Overbooking2- Denied Boarding CostAirline Estimate the cost incurred per overbooking procedure per reservation is 150 USDper passenger.So Denied Boarding Cost = ( No. of Overbooking – No. of Noshow) * Probability ofNoshow* Cost of denied boarding per passenger.Provided that Overbooking is greater than No show.3- No Show passengers equal Overbooking reservation:Net cost result is ZeroThat’s lead us to develop an overbooking lose table. This shows clearly the ZeroDiagonal Values across the table.
  7. 7. Results:Based on Yemenia No-show data of Oct. 2010 for sector SAH-DXB, and a initial costs ofno-shows and denied boarding as inputs, two main curves are plotted, no-show cost curveand denied boarding cost, resulting a U shape curve that define the optimumoverbooking policy i.e Three overbooking reservation. The analysis based on monthlydata and should be repeated on monthly bases taking in consideration the seasonality’s,shocks and trends keeping in mind the other environmental operation and other constrainsfactors are not change.
  8. 8. Summary:The study shows the importance of no-show rates and its sampling / art of fit withPoisson distribution. The historical data is collected and demonstrated by frequencydistribution, which analysis by minimum least square analysis using cdf data ( cumulativedensity function), the data examine by the tentative average value of the sample thenfitted by kolomogorovo test to get the optimum value of LAMDA ( parameter of Poissondistribution, which is used in the cost-based overbooking model) .Finally the ratio of Denied Boarding Cost to No-show Cost, play a major rules in shapingthe U curve approach, which give a clear picture for the top management of airlines toselect the right policy, and the real impacts on the performance of airline especially in thecommercial side.