NEWSVENDOR & REVENUE MANAGEMENT

Transcription

1 NEWSVENDOR & REVENUE MANAGEMENT PROFESSOR DAVID GILLEN (UNIVERSITY OF BRITISH COLUMBIA) & PROFESSOR BENNY MANTIN (UNIVERSITY OF WATERLOO) Istanbul Technical University Air Transportation Management M.Sc. Program Logistic Management in Air Transport Module December 2014

2 THE NEWSVENDOR AND APPLICATIONS 2

3 THE NEWSVENDOR MODEL 3

4 O NEILL S HAMMER 3/2 WETSUIT TIMELINE Generate forecast of demand and submit an order to TEC Spring selling season Nov Dec Jan Feb Mar Apr May Jun Jul Aug Receive order from TEC at the end of the month Left over units are discounted Marketing s forecast for sales is 3200 units. 4

5 O NEILL S HAMMER 3/2 WETSUIT TIMELINE Marketing s forecast for sales is 3200 units. O Neill sells each suit for p = $190 O Neill purchases each suit from its supplier for c = $110 per suit Discounted suits sell for v = $90 This is also called the salvage value. How many units shall O Neill order?! O Neill is facing a too much/too little problem : Order too much and inventory is left over at the end of the season Order too little and sales are lost. 5

6 Probability Probability FORECASTING: DEMAND DISTRIBUTION Marketing's forecast is a single number. However, demand is more likely to follow some distribution. Traditional distributions from statistics include, e.g., the normal, gamma, Poisson distributions GAMMA NORMAL POISSON Demand Demand 6

7 FORECASTING: DEMAND DISTRIBUTION How can we uncover the underlying distribution? We can look at historical forecast performance at O Neill Actual demand Forecast Forecasts and actual demand for surf wet-suits from the previous season 7

8 FORECASTING: DEMAND DISTRIBUTION The idea: use historical actual to forecast ratio (the A/F ratio) from past observations. A/F ratio Actual demand Forecast Calculate average and standard deviation of A/F ratio. Set the mean of the normal distribution to Expected actual demand (Expected A/F ratio) Forecast Set the standard deviation of the normal distribution to Standard deviation of actual demand (Standard deviation of A/F ratios) Forecast Recall: 1 N N i 1 ( x i ) 2 8

9 FORECASTING: DEMAND DISTRIBUTION Product description Forecast Actual demand Error A/F Ratio JR ZEN FL 3/ EPIC 5/3 W/HD JR ZEN 3/ WMS ZEN-ZIP 4/ ZEN 3/ ZEN-ZIP 4/ WMS HAMMER 3/2 FULL Average Standard deviation Note: Full data in hidden slide Marketing s forecast for sales is 3200 units. Hence: Expected actual demand Standard deviation of actual demand O Neill can choose a normal distribution with mean 3192 and standard deviation 1181 to represent demand for the Hammer 3/2 during the Spring season. 9

10 Probability Probability DISTRIBUTION AND DENSITY FUNCTIONS 1.00 Distribution function Density function Demand Demand The probability the outcome will The probability of a particular be a particular value or smaller outcome occurring 11

11 Probability NORMAL DISTRIBUTION Characterized by mean ( ) and standard deviation ( ) All normal distributions are related to the standard normal that has mean = 0 and standard deviation = 1. For example: Let Q be some quantity N (, ) describes the demand distribution Prob{demand is Q or lower} = Prob{the outcome of a standard normal is z or lower}, where Q z or Q z Demand Look up z in the Standard Normal Distribution Function Table to get the desired probability or use Excel: Prob{} = normsdist(z) Q 12

12 THE STANDARD NORMAL DISTRIBUTION Standard Normal Distribution Function Table (continued), F(z ) z Q: What is the probability the outcome of a standard normal will be z = 0.28 or smaller? A: The answer is , or 61.03% 13

13 THE STANDARD NORMAL DISTRIBUTION Standard Normal Distribution Function Table (continued), F(z ) z Q: For what z is there a 70.19% chance that the outcome of a standard normal will be that z or smaller? A: the answer is z =

14 O NEILL S HAMMER 3/2 WETSUIT TIMELINE Marketing s forecast for sales is 3200 units. our demand model is a normal distribution with mean 3192 and standard deviation 1181 O Neill sells each suit for p = $190 O Neill purchases each suit from its supplier for c = $110 per suit Discounted suits sell for v = $90 This is also called the salvage value. How many units shall O Neill order?! O Neill is facing a too much/too little problem : What are the costs associated with too much/too little? 15

15 TOO MUCH AND TOO LITTLE COSTS C o = overage cost The consequence of ordering one more unit than what you would have ordered had you known demand. For the Hammer 3/2 C o = Cost Salvage value = c v = = 20 C u = underage cost The consequence of ordering one fewer unit than what you would have ordered had you known demand. For the Hammer 3/2 C u = Price Cost = p c = = 80 16

16 Expected gain or loss. TOO MUCH AND TOO LITTLE COSTS Idea: balance the risk vs. benefit of an additional unit Ordering one more unit increases the chance of overage Expected loss on the Q th unit = C o F(Q) but the benefit/gain of ordering one more unit is the reduction in the chance of underage: Expected gain on the Q th unit = C u (1-F(Q)) As more units are ordered, the expected benefit from ordering one unit decreases while the expected loss of ordering one more unit increases! Expected gain Expected loss Q th unit ordered

17 MAXIMIZE EXPECTED PROFIT Expected profit is maximized when the expected loss on the Q th unit equals the expected gain on the Q th unit: C o F( Q) C 1 F u Q Rearrange terms in the above equation: F( Q) Cu C C o u The ratio C u / (C o + C u ) is called the critical ratio. Hence, to maximize profit, choose Q such that the probability we satisfy all demand (i.e., demand is Q or lower) equals the critical ratio. For the Hammer 3/2 the critical ratio is Cu C C o u

18 HAMMER 3/2 S ORDER QUANTITY The critical ratio is 0.80 Find the critical ratio inside the Standard Normal Distribution Function Table: z If the critical ratio falls between two values in the table, choose the greater z-statistic this is called the round-up rule. Choose z = 0.85 Convert the z-statistic into an order quantity : Q z

19 NEWSVENDOR MODEL PERFORMANCE MEASURES For any order quantity we would like to evaluate the following performance measures: In-stock probability: Probability all demand is satisfied Stockout probability: Probability some demand is lost Expected lost sales: The expected number of units by which demand will exceed the order quantity Expected sales: The expected number of units sold. Expected left over inventory: The expected number of units left over after demand (but before salvaging) Expected profit 20

20 IN-STOCK PROBABILITY All demand is satisfied if demand is the order quantity, Q, or smaller. If Q = 3000, then to satisfy all demand, demand must be The distribution function tells us the probability demand is Q or smaller! What is the in-stock probability if the order quantity is Q = 3000? The z-statistic is: Q z Using the Std. Normal Distribution Function Table, we have F(-0.16) = That is, if 3000 units are ordered, then there is a 43.64% chance all demand will be satisfied. 21

21 IN-STOCK PROBABILITY Now, suppose that we wish to find the order quantity, Q, that satisfies are required in-stock probability Consider the Hammer 3/2. What is Q if a 99% in-stock probability is required? Find the z-statistic: from the Standard Normal Distribution Function Table we have F(2.33) = Hence z = Convert the z-statistic into an order quantity: Q = + z = =

22 OTHER MEASURES OF SERVICE PERFORMANCE The stockout probability is the probability some demand is not satisfied: Stockout probability = 1 F(Q) = = 56.36% The fill rate is the fraction of demand that can purchase a unit: The fill rate is also the probability a randomly chosen customer can purchase a unit. The fill rate is not the same as the in-stock probability! e.g. if 99% of demand is satisfied (the fill rate) then the probability all demand is satisfied (the in-stock) need not be 99% 23

23 EXPECTED LOST SALES Suppose demand can be one of the following values {0,10,20,,190,200} with 0.1 probabilities as per graph 0.09 Suppose Q = What is expected lost sales? 0.07 It depends on the actual demand, D: If D Q, lost sales = 0 If D = 130, lost sales = D Q = Expected lost sales = Prob{D = 130} + 20 Prob{D = 140} Prob{D = 200} Loss of 10 units Loss of 40 units

24 Expected Lost Sales EXPECTED LOST SALES: HAMMER 3/2S Suppose O Neill orders 3000 Hammer 3/2s. How many sales will be lost on average? Find its z-statistic. Q z Look up in the Standard Normal Loss Function: L(-0.16)= or, in Excel: L(z) =Normdist(z,0,1,0)-z (1-Normsdist(z)) Evaluate lost sales: Expected Lost Sales L( z) Everything is lost Order quantity Nothing is lost 25

25 OTHER MEASURES: HAMMER 3/2S Expected sales = - Expected lost sales = = 2620 Expected Left Over Inventory = Q - Expected Sales = = 380 Expected Profit Price - Cost Cost - $ $ Expected Sales Salvage value Expected left over inventory $202,000 Note: the above equations hold for any demand distribution 26

26 NEWSVENDOR MODEL SUMMARY The model can be applied to settings in which There is a single order/production/replenishment opportunity. Demand is uncertain. There is a too much vs. too little challenge The critical ratio trades off the overage and underage costs: F( Q) Cu C C The corresponding order quantity maximizes expected profit. The firm can determine other service measure such as instock probability or fill rate. o u 29

27 REVENUE MANAGEMENT With material partially adapted from Netessine and Shumsky; teaching note on Yield Management; Metin s class notes; Phillips (2005); Boyd (2007) 30

28 What is Revenue Management REVENUE MANAGEMENT technique to maximize revenue by matching fixed supply with uncertain demand A very successful application of the critical ratio Examples Airlines: AA credits RM for increasing revenue by $500 million per year. Delta airlines has attributed a $300 million gain. Hotels: Marriot Hotel attributes $100 million in additional revenue to RM car rental: National Car Rental used RM to rescue itself from the bank of bankruptcy commercials 31

29 THE DIFFERENT ASPECTS OF PRICING Strategic pricing Southwest THE Low-Fare Airline Operational pricing Day-to-day adjusting of prices to address demand realization and updating of expectations Revenue Management A technique to maximize revenue by matching fixed supply with uncertain demand 32

30 BRIEF HISTORY: AIRLINES Until 1978, the aviation industry in the US was regulated. Fares were published and controlled by Civil Aeronautics Board 33

31 Deregulation in 1978 BRIEF HISTORY: AIRLINES PeopleExpress established circa 1980 Charged 70% less than major airlines Fast growth over : targeting underserved Leisure market In 1984 entered AA s core routes: Newark-Chicago and New Orleans-LA In January 1985, AA s counterattack: Ultimate Super Saver Discount fare if reserving 2 weeks before departure and staying over a Saturday night. Restrictions on the number of seats available Leisure passengers used USS, Business travelers paid full fare. September 1985, on the verge of bankruptcy, PeopleExpress s CEO: We had great people, tremendous value, terrific growth. We did a lot of things right. But we didn t get our hands around the yield management and automation issues. 34

32 BRIEF HISTORY: AIRLINES Airline Reservation Before Computers 35

33 REVENUE MANAGEMENT: WHEN TO USE IT Fixed inventory or capacity that is expensive or impossible to store Inventory/capacity committed to a customer before all demand is known Different customer segments exist firm can differentiate and price-discriminate among customers Same unit of inventory or capacity can satisfy different customer segments 36

34 CUSTOMER SEGMENTATION Leisure Passengers vs. Business Passengers Leisure passengers are highly price sensitive They book earlier They are more flexible in choosing departure/arrival times More likely to stay over for Saturday night Can further segment within the segment Other segmentations: Government employees, senior citizens, children, frequent flyers, etc. International pricing: Same ticket at different prices in different countries. (what is the logic here?) Distribution channel based segmentation: Travel agent in person vs. Travel agent software/website vs. Airline s website. City-of-origin pricing: Hotels: Kamaaina rates for Hawaii residents Theme Parks: Disney s resident rates for Orlando residents 37

35 HOTEL EXAMPLE You manage a hotel with 200 rooms Your guests either book a room in advance. Or, wait till the last minute So you charge two rates: Advance booking: Bargain rate Late booking: Premium rate Capacity: 200 rooms These X rooms are protected for high paying customers Should the hotel fill up rooms with advance customers? 38 Why? How many rooms should be set aside for lastminute customers? What information do you need? 200-X is the booking limit for low paying customers Note: allocation is virtual. Room # 168 can serve both classes

36 HOTEL EXAMPLE Assume number of last minute customers is normally distributed with mean 75 and standard deviation 25 Advance booking: $200/night Late booking: $500/night How many rooms shall be protected? C u = =300 C o = 200 P=C u /(C u +C o )= 300/500=0.6 Z= Q LB = *25=81.3 =>82 39

37 What is the expected revenue? $500 E[high fare sales] + $200 Booking limit E[high fare sales]? Expected sales = Expected demand - Lost sales How many high-fare travelers will be rejected? Expected lost sales = σ L(z) L(z)? HOTEL EXAMPLE L(z)=f(z)-z (1- F(z)) =Normdist(z,0,1,0)-z (1-Normsdist(z)) 40

38 HOTEL EXAMPLE How many high-fare travelers will be rejected? Expected lost sales = σ L(0.253)= = 7.13 How many high-fare travelers will be accommodated? Expected sales = Expected demand - Lost sales = = How many rooms will remain empty? Expected left over inventory = Q - Expected sales = = What is the expected revenue? $500 Exp. sales + $200 Booking limit = $57,535. Note: without RM: all rooms sold in advance = $40,000 all rooms kept for late arrivals = $37,500 if 75 rooms kept (z=0): $57,513 Excel 41

39 OVERBOOKING You can easily fill up your hotel (200 rooms) Some people with reservations may not show up How many reservations should you take? More than 200? How many more? What information do you need? number of no-shows (x) is normally distributed with mean 5 and standard deviation 2, advance room rate is $200. How many rooms (y) should be overbooked? Same setup: Single decision when the number of no-shows is uncertain. Underage cost if x>y (insufficient number of rooms overbooked). Overage cost if x<y (too many rooms overbooked). 42

40 OVERBOOKING: EXAMPLE Critical Values and Overbooking Limits Critical Value Critical Value Overbooking Limit Cost of Com pensation Overbooking Limit The challenge is to estimate the ill-will and penalty cost. If no compensation (assume $1 processing fee) => overbook 11 rooms If overage cost is $200 => overbook 5 rooms If overage cost is $500 => overbook 4 rooms 43

41 RECAP The last question gives the same answer even when we can charge two prices. That is, in the calculation of C u we use the conservative value of $200. The first question implied that we need to protect 82 rooms for late arrivals. The last question asserts that we can overbook. Assume compensation is $350, then we can overbook 5 rooms. So what is the advance booking booking limit? Low-fare booking limit = =123 44

42 AIRLINE EXAMPLE Consider a plane with 100 (coach) seats utilizing two fare classes: Y priced at $500 and Q (discounted) priced at $200 On average 30 people show up looking for Y Partitioning Y:30 and Q:70 Nested inventory levels: all seats available for Q are also available for Y Accept all Y-class passengers as long as you have an empty seat, but accept no more than 70 Q-class passengers 45

43 EXAMPLE (CONTINUED) So we are using nested inventory level, but the question is how many seats to reserve for Y-class Use forecasting to predict demand levels # of Y-class Passengers Prob. 51% 50% 49% 47% 44% 40% 35% 30% Expected marginal revenue $255 $250 $245 $235 $220 $200 $175 $150 So the airline should reserve 34 seats for Y-class This is the EMSR concept developed by Peter Belobaba, MIT, a University of Waterloo graduate. 46

44 BOOKING LIMITS ARE NESTED Nesting Assume b {6} =10 and b {5,6} =20 After selling 10 rooms to class 5: b {6} =10 and b {5,6} =10 So can accept 10 for either class 5 or class 6. Without nesting: Assume b {6} =10, b {5} =10 After selling 10 rooms to class 5: b {6} =10 and b {5} =0 So can accept 10 for class 6, but reject requests for class 5... b 1 b 2 b 3 b 4 b 5 b6 47

45 Class Price Internet Free Fruit basket Newspaper Water (1 bottle/day) Bed choice: 2Q or K BOOKING LIMITS ARE NESTED How to segment standard rooms? b 1 1 $250 Free Y Y Y Y b 2 b 3 2 $220 $5/d Y Y Y Y 3 $190 $5/d N Y Y Y 4 $160 $5/d N N Y Y b 4 b 5 b6 5 $130 $5/d N N $3/b Y 6 $100 $5/d N N $3/b N Note: the differences in cost do not justify the price differential 48

46 BOOKING LIMITS ARE NESTED Booking limit b i limits bookings for classes j={i,,n}. Protection level y i protects future reservations or classes j={1,2,, i}. Booking limits {n} {n-1,n} {3,.,n} {2,.,n} {1,.,n} Protection levels {1,.,n} {1,.,n-1} {1,2,3} {1,2} {1} b 1 b 2 b 3 b n-1 b n 49

47 BOOKING LIMITS ARE NESTED Capacity = booking limits + protection levels Capacity = b i + y i-1 y n y n-1 y n-2 y 2 y 1 y 0 b 1 b 2 b 3 b n-1 b n b n+1 b n b n-1 b 3 b 2 b 1 50

48 REVENUE MANAGEMENT SUMMARY Yield management and overbooking give demand flexibility where supply flexibility is not possible. In the model used: Single decision in the face of uncertainty. Underage and overage penalties. These are powerful tools to improve revenue: American Airlines estimated a benefit of $1.5 billion over 3 years. National Car Rental faced liquidation in 1993 but improved via yield management techniques. Delta Airlines credits yield management with $300 million in additional revenue annually (about 2% of year 2000 revenue.) 51

49 REVENUE MANAGEMENT CHALLENGES Demand forecasting. seasonality, special events, changing fares and truncation of demand data. Consumer behavior (strategic waiting) Dynamic decisions. Variable capacity: Different aircrafts, ability to move rental cars around. Group reservations. How to construct good fences to differentiate among customers? One-way vs. round-trip tickets. Saturday-night stay requirement. Non-refunds. Advanced purchase requirements. Multi-leg passengers/multi-day reservations for cars and hotels: Not all customers using a given piece of capacity (a seat on a flight leg, a room for one night) are equally valuable. 52

50 RM SIMULATION GAME: MOTHERLAND AIR

51 BACKGROUND Owner leased a 400-seat 747 All tickets $400 Low fill rate; unprofitable Introduced segmentation: Full fare: $1,000 Discount: $400 Deep discount: $100 54

52 BACKGROUND Still unprofitable Full fare: $1, seats Discount: $ seats Deep discount: $ seats Switch to smaller and cheaper DC-9 (100 seats) How to revenue mange the seats on the plane? 55

53 DECISIONS Nested! 56

54 Full fare: HISTORIES Discount: Deep discount not necessary: can always sell all seats 57

55 Per-passenger penalty OVERBOOKING $200 (# of stranded) 2 So leaving 1 person stranded costs $200, two people costs $800, three people, $1,800, etc. 58

56 PRICING Current prices are $1000, $400, and $100 Sticking to the notion of price buckets, they will be pre determined: Full fare: $1100, $1000, and $900 Discount: $440, $400, and $360 Reflecting +/- 15% deviations in demand. 59

57 ADDITIONAL SLIDES 60

58 Airfare ($) Fare predictions CHANGING CHALLENGES Implications of price volatility ATL-LAS BOI-DEN 50 BUF-RSW CVG-LGA Days out

59 BOOKING LIMITS AND PROTECTION LEVELS 62

60 BOOKING LIMITS AND PROTECTION LEVELS 63

61 BOOKING LIMITS AND PROTECTION LEVELS The updating algorithm n classes The vector b of booking limits is given Set vector B to 0 While Remaining Capacity =b 1 -B 1 >0 do Suppose m requests for class i: x=[m,,m,0, 0] (note, m<0 implies a cancellation) If b B+x, accept request and set B:=B+x; else, reject the request End while 64

62 b=(b1, b2, b3, b4, b5)=(100, 73, 12, 4, 0). B=(B1, B2, B3, B4, B5)=(0, 0, 0, 0, 0) EXAMPLE Request x=(x1, x2, x3, x4, x5) condition Accept? Updating, B= 5 for C2 (5, 5, 0, 0, 0) b 2 -x 2 =68 0 b 1 -x 1 = for C2 (1, 1, 0, 0, 0) b2-b2-x2=67 0 b1-b1-x1= for C4 (1, 1, 1, 1, 0) b4-b4-x4=3 0, b3-b3-x3=11 0, b2-b2-x2=66 0, b1-b1-x1= for C3 (3, 3, 3, 0, 0) b3-b3-x3=8 0, b2-b2-x2=63 0, b1-b1-x1= for C4 (4, 4, 4, 4, 0) b4-b4-x4=-1<0, b3-b3-x3=4 0, b2-b2-x2=59 0 b1-b1-x1= for C1 (2, 5, 0, 0, 0) b-b-x= Adapted from Metin s class notes (88, 63, 8, 3, 0) 0 65 Yes (5, 5, 0, 0, 0) Yes (6, 6, 0, 0, 0) Yes (7, 7, 1, 1, 0) Yes (10, 10, 4, 1, 0) NO (10, 10, 4, 1, 0) Yes (10, 10, 4, 1, 0) +(2, 0, 0, 0, 0)= (12, 10, 4, 1, 0)

63 EXAMPLE Request x=(x1, x2, x3, x4, x5) condition Accept? Updating, B= 30 for C2 (30, 30, 0, 0, 0) b-b-x=(58, 33, 8, 3, 0) 0 Yes (12, 10, 4, 1, 0) +(30, 30, 0, 0, 0) =(42, 40, 4, 1, 0) 20 for C2 (20, 20, 0, 0, 0) b-b-x=(38, 13, 8, 3, 0) 0 Yes (42, 40, 4, 1, 0) +(20, 20, 0, 0, 0) =(62, 60, 4, 1, 0) 10 for C3 (10, 10, 10, 0, 0) b-b-x=(28, 3, -2, 3, 0) No (62, 60, 4, 1, 0) 6 for C3 (6, 6, 6, 0, 0) b-b-x=(32, 7, 2, 3, 0) 0 Yes (62, 60, 4, 1, 0) +(6, 6, 6, 0, 0) =(68, 66, 10, 1, 0) 3 for C2 (3, 3, 3, 0, 0) b-b-x=(29, 4, -1, 3, 0) No =(68, 66, 10, 1, 0) 6 for C2 (6, 6, 0, 0, 0) b-b-x=(26, 1, 2, 3, 0) 0 Yes (68, 66, 10, 1, 0) +(6, 6, 0, 0, 0) =(74, 72, 10, 1, 0) 1 for C1 (1, 1, 1, 0, 0) b-b-x=(25, 0, 1, 3, 0) 0 Yes (74, 72, 10, 1, 0) +(1, 1, 1, 0, 0) =(75, 73, 11, 1, 0) 66

64 Note: now we have that b-b=(25, 0, 1, 3, 0) Only first class requests are accepted from now on! EXAMPLE Request x=(x1, x2, x3, x4, x5) condition Accept? Updating, B= 1 for C5 (1, 1, 1, 1, 1) b-b-x=(24, -1, 0, 2, -1) No (75, 73, 11, 1, 0) 1 for C4 (1, 1, 1, 1, 0) b-b-x=(24, -1, 0, 2, 0) No (75, 73, 11, 1, 0) 1 for C3 (1, 1, 1, 0, 0) b-b-x=(24, -1, 0, 3, 0) No (75, 73, 11, 1, 0) 1 for C2 (1, 1, 0, 0, 0) b-b-x=(24, -1, 1, 3, 0) No (75, 73, 11, 1, 0) 25 for C1 (25, 0, 0, 0, 0) b-b-x=(0, 0, 1, 3, 0) 0 Yes (75, 73, 11, 1, 0) +(25, 0, 0, 0, 0) =(100, 73, 11, 1, 0) Note: now we have that b-b=(0, 0, 1, 3, 0) All classes are closed! Recover bookings: 1 st class: B 1 -B 2 =27; 2 nd class: B 2 -B 3 =62; 3 rd class: B 3 -B 4 =10; 4 th class: B 4 -B 5 =1; 5 th class: B 5 =0. 67

65 EXAMPLE: VISUALIZATION 68

66 EXAMPLE: VISUALIZATION 69

67 EXAMPLE: VISUALIZATION 70

68 EXAMPLE: VISUALIZATION 71

69 EXAMPLE: VISUALIZATION 72

70 SOME U.S. AIRLINE INDUSTRY OBSERVATIONS Since deregulation (1979), 137 carriers have filed for bankruptcy. (2000 data) From (the industry s best 5 years ever), airlines earned 3.5 cents on each dollar of sales: The US average for all industries is around 6 cents. From , the industry earned 1 cent per $ of sales. Carriers typically fill 72.4% of seats and have a break-even load of 70.4%. (2000 data) The difference between making and losing money is measured by a handful of passengers. 73

71 CHANGING FARE STRUCTURES Major shifts in airline pricing strategies since 2000 Growth of low-fare airlines with relatively unrestricted fares Matching by legacy carriers to protect market share and stimulate demand Increased consumer use of internet search engines to find lowest available fare options Greater consumer resistance to complex fare structures and huge differentials between highest and lowest fares offered Recent moves to simplified fares overlook the fact that pricing segmentation contributes to revenues: Fare simplification removes restrictions, resulting in reduced segmentation of demand 74

72 A SOLUTION TO THE MULTI-LEG CUSTOMER: BUCKETS O Hare JFK Heathrow Fare class O'Hare to JFK O'Hare to Heathrow Y $724 $1,610 M $475 $829 Q $275 $525 With segment control, there are only three booking limits for the O Hare-JFK leg, one for each fare class. But an O Hare-Heathrow customer may be more valuable, so you could have six booking limits, one for each fare-itinerary combination. But that leads to many booking limits, so group fare-itineraries combinations with similar values into buckets: Bucket Itinerary Fare class 0 O'Hare to Heathrow Y 1 O'Hare to Heathrow M O'Hare to JFK Y 2 O'Hare to Heathrow Q O'Hare to JFK M 3 O'Hare to JFK Q 75

73 ANOTHER SOLUTION TO MULTI-LEGS: BID PRICES O Hare JFK Heathrow Fare class O'Hare to JFK O'Hare to Heathrow Y $724 $1,610 M $475 $829 Q $275 $525 Assign a bid price to each segment: O'Hare to JFK JFK to Heathrow Bid price $290 $170 A fare is accepted if it exceeds the sum of the bid prices on the segments it uses: For example, an O Hare-JFK fare is accepted if it exceeds $290 A O Hare-Heathrow fare is accepted if it exceeds $290+$170 = $460 The trick is to choose good bid-prices. 76