Presentation on theme: "OPSM 405 Service Management Class 12: Yield management: discount allocation and pricing Koç University Zeynep Aksin"— Presentation transcript:
OPSM 405 Service Management Class 12: Yield management: discount allocation and pricing Koç University Zeynep Aksin email@example.com
Announcements Next group case assignment due next Monday (groups of 2-3) –Instructions on last slide –Data on student CD of the textbook and handouts section of course webpage –There is no one right answer, though there are better answers.. –… groups will compete in class
Yield Management System Reservation SystemForecasting Overbooking LevelsDiscount Allocation current demand cancellations cancellation rate estimates overbooking levels future demand estimates fare class allocations
The displacement cost method A general framework for allocation Attempt to evaluate the opportunity cost (displacement cost/bid prices) of using resources required to meet current demand Accept current request if... Revenue > Displacement Cost Advantages –intuitive –conceptually simple –sophisticated applications O-D control (airlines) multi-night stays (hotels) group evaluations –near-optimal (provided correct displacement costs are used!)
Generic procedure STEP 1: Forecast demand-to-come for each... - product (e.g. fare-class/booking class) - resource (e.g. flight leg, day-of-week) STEP 2: Using forecast, determine best allocation of remaining capacity to products. STEP 3: Using the results of STEP 2, calculate the displacement cost of the capacity required by a new request to the revenue it brings in to evaluate accept/deny decisions.
A rough-cut approach: Simple deterministic displacement Assumptions –Forecast is perfect –Future demand for each resource (flight-leg, hotel room-day) is independent Procedure - Determine revenue (net contribution) of each demand class - Rank demand from highest revenue to lowest - Greedy allocation/displacement - allocation: highest revenue classes first - displacement: lowest revenue classes first
Example: ABC Forecast of Leg Demand Discount $60 Full Fare $100 KEY 0 15 85 100 70 A reservation agent has a group that wants to book 20 seats from A to C at a rate of $80 per person. Should we accept the group? 0 60 70 Remaining Capacity
0 60 70 Analysis: ABC Forecast of Demand to Come Discount $60 Full Fare $100 KEY 0 15 85 100 70 20 Forecasted revenue displacement: 15 x $0 + 5 x $60 = $300 Forecasted revenue displacement: 10 x $60 + 10x $100 = $1600 Net Revenue = New Revenue - Total Displacement Cost = 20x$80 - $300 - $1600 = - $300 ==> DO NOT accept the group.
Result depends on remaining capacity.... ABC Forecast of Demand to Come Discount $60 Full Fare $100 KEY 0 15 85 100 80 20 Forecasted revenue displacement: 15 x $0 + 5 x $60 = $300 Forecasted revenue displacement: 20 x $60 + 0x $100 = $1200 Net Revenue = New Revenue - Total Displacement Cost = 20x$80 - $300 - $1200 = $ 100 ==> DO accept the group. 60 80 20
and the forecast.... ABC Forecast of Demand to Come Discount $60 Full Fare $100 KEY 0 15 95 100 80 20 Forecasted revenue displacement: 5 x $0 + 15 x $60 = $900 Forecasted revenue displacement: 20 x $60 + 0x $100 = $1200 Net Revenue = New Revenue - Total Displacement Cost = 20x$80 - $900 - $1200 = - $500 ==> DO NOT accept the group. 60 80 20
Hedging against forecast error Assumptions: fare classes full-fare discount revenue r 1 r 2 demand X 1 X 2 Sequence of Events: discount demand arrives accept/reject discount res. S 1 protection level A 2 = C-S 1 discount allocation full-fare demand arrives
Approach 1: Deterministic Allocation If we knew demand for high fare with certainty, Approximation: Analysis
Approach 2: Optimal Allocation S seats remaining: accept low fare? no yes $r 2 $r 1 $0 Accept if Optimal protection level is smallest value of S satisfying this condition.
Example r 1 =$250 r 2 =$100 Demand for high fare uniformly distributed between 10 and 50. C=100 seats Demand for low fare uniformly distributed between 50 and 90.
Example $250 10 50 $100 34 Reserve 34 seats for full fare demand. Allocate 100-34=66 seats to discount fare demand.
Set protection levels to satisfy …. Average fare of classes i and higher Aggregate demand of classes i and higher This is the heuristic used in many commercial systems. F(z) standard normal dist.
Example: ClassFareMeanVariance 1$1003050 2$803080 3$4050120 Set protection level 1: 1$100 30 50 2 $90 60130 3 $67.3110250 Weighted average fares and aggregate mean & variance..
Set protection level 2 (for classes 1 & 2 combined): There is not protection level for the lowest class (class 3) Accept all three classes Accept class 1 and 2 only Accept class 1 only #seats remaining
Allocation Procedure Alternative: demand control chart based on history 0 Days before arrival demands Accept discount fare demands Do not accept discount fare demands
Some complications in pricing Multiple products are more complex –Diversion/demand shifting Other products Competitors products Same product on different day Ex: Peak load pricing –Cross-elasticity: demand for one product is affected by price of other available products –Joint capacity constraints often mean incremental sales of one product require reduction in sales of other products shadow price of joint capacity constraint is important to understand
Competition often forces price matching (e.g. discount airline fares) As a result of all these factors, pricing is often done at an aggregate level considering long-term supply/demand balances and competitors actions. Capacity allocation is then used to manage short-run fluctuations.
Example: Pricing interacts with capacity allocation Premium customer information Price10011090 Demand 100 80 120 Scenario 1: unlimited capacity, only premium customers 10000 8800 10800
Example cont. Premium customer information Price10011090 Demand 100 80 120 Scenario 2: capacity=100, discount unlimited demand at $50 Premium 10000 8800 9000 Discount0 1000 0
Example cont. Premium customer information Price10011090 Demand 100 80 120 Scenario 3: capacity=100, discount unlimited demand at $75 Premium 10000 8800 9000 Discount 0 1500 0
Discount allocation example During the recent economic slump, Blackjack Airline discovered that airplanes on its Los Angeles-to-Las Vegas route have been flying with more empty seats than usual. To stimulate demand, it has decided to offer a special, nonrefundable, 14-day advance-purchase gamblers fare for only $49 one-way based on a round-trip ticket. The regular full-fare coach ticket costs $69 one-way. The Boeing 737 used by Blackjack, has a capacity 95 in coach, and management wants to limit the number of seats that are sold at the discount fare in order to sell full-fare tickets to passengers who have not made advance travel plans. Considering recent experience, the demand for full-fare tickets appears to have a normal distribution, with a mean of 60 and a standard deviation of 15. Calculate the number of full-fare seats to reserve.
Overbooking example A commuter airline overbooks all its flights by one passenger (i.e., the ticket agent will take seven reservations for an airplane that only has six seats). The no-show experience for the past 20 days is shown below: No-shows 0 1 2 3 4 Frequency 6 5 4 3 2 Using the critical fractile P(d
"name": "Overbooking example A commuter airline overbooks all its flights by one passenger (i.e., the ticket agent will take seven reservations for an airplane that only has six seats).",
"description": "The no-show experience for the past 20 days is shown below: No-shows 0 1 2 3 4 Frequency 6 5 4 3 2 Using the critical fractile P(d
Solution No ShowsFrequencyProbabilityP(d
"name": "Solution No ShowsFrequencyProbabilityP(d
Summary: RM is a new twist on some old demand management ideas Old demand management ideas... –segmentation –peak-load pricing With some new twists... –tactical application of these concepts Small differences matter! –systematic/disciplined approach –data intensive/ IS intensive
For Monday Prepare MotherLand Air at the end of chapter (9 in old edition) Analyze the information provided and develop a dynamic policy on –Price (select from list provided in the case) –Overbooking level –Seat allocation (nested reservation limits) Inform me of your groups policy at least 2 hours before class (for each of the weeks away from takeoff on Table 9.8) If you want to start out with a static policy, I just need one set of price, overbooking, discount allocation numbers. Bring printout of data to class for use during the game Write up a report describing your analysis and justifying your choice for the above tactics. Clearly state all of your assumptions and explain all of your work. Also articulate how you plan to react to demand announcements in class; i.e. what is your plan. In class we will play a game: I will announce demand realizations, you as a group can update/change your strategy
Illustration of policy to be determined before class 2420161287654321 PriceFull 1000 Disc400 D. Disc100 SeatFull120 AllocationDisc50 D. Disc0