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1 Demand and Revenue Management Anton J. Kleywegt April 2, 2008.

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Presentation on theme: "1 Demand and Revenue Management Anton J. Kleywegt April 2, 2008."— Presentation transcript:

1 1 Demand and Revenue Management Anton J. Kleywegt April 2, 2008

2 2 Revenue Management What is Revenue Management Why do Revenue Management Pricing Optimization Demand Modeling and Forecasting

3 3 What is Revenue Management Management of inventory, distribution channels and prices to maximize profit over the long run Selling the right product to the right customer at the right time at the right price

4 4 What is Revenue Management Revenue Management involves the following activities Demand data collection Demand modeling Demand forecasting Pricing optimization System implementation and distribution

5 5 What is Revenue Management Airline industry How many seats to make available at each of the listed fares, depending on the OD pair, time of year, time of week, remaining seats available, remaining time until departure What contracts and prices to provide to corporations How many seats to make available to consolidators and travel agents (if at all), and at what prices How much capacity to make available to cargo shippers and freight forwarders, and at what prices

6 6 What is Revenue Management Hotel industry How much to charge for a room depending on the location, type of room, time of year, time of week, duration of stay

7 7 What is Revenue Management Ocean cargo industry Which types of contracts to enter into with shippers How much capacity to commit to each shipper Which contract prices to have for each shipper How to vary prices as a function of direction of trade, commodity, and time of year

8 8 What is Revenue Management Car rental industry How much to charge for a rental car depending on the class of car, time of year, time of week, duration of rent Restaurant industry How much to charge for lunch vs dinner

9 9 What is Revenue Management Manufacturing industry Make-to-stock: dynamic pricing of inventory Make-to-order: dynamic pricing of orders, how much discount to give for orders in advance Make-to-stock and make-to-order: prices of advance orders vs prices of inventory

10 10 What is Revenue Management Retail industry Example: fashion apparel industry Products in fashion for a single season Retailer wants to sell available inventory for maximum profit Prices higher at start of season Retailer has to decide when to mark prices down, and by how much

11 11 What is Revenue Management Entertainment ticket pricing Example: opera houses let their ticket prices depend on The performance The reviews received so far Location of seat in opera house Day of the week of the performance Time of the day of the performance Time of performance in the season Remaining time until the performance Number of remaining seats available

12 12 What is Revenue Management Golf courses Variable pricing: Choose prices to vary by time of day day of week season of year Round duration control control tee-time interval control uncertainty in arrival time control uncertainty in duration

13 13 Hospital Contract Case Study Major customers of hospitals Insurance companies Medicare Medicaid Individuals Hospital contracts with major customers Discount-off-listed-charges contracts Per-diem contracts Case-rate contracts Capitation contracts

14 14 Hospital Contract Case Study Example of setting per-diem rates

15 15 Hospital Contract Case Study Example of setting per-diem rates Observe that most patients stay for only a few days, although a few patients make the average length of stay quite high Stratified per-diem rates Charge more per day to patients who stay for only a few days Results Higher average revenue Lower standard deviation of revenue

16 16 Hospital Contract Case Study Higher average revenue clearly beneficial to the hospital Lower standard deviation of revenue Beneficial to the hospital? Yes. More predictable revenue Beneficial to the insurance company? Yes. More predictable costs

17 17 What is Revenue Management Overbooking may be part of revenue management Overbooking important practice in many industries that use reservations, and where cancellations or no-shows may occur airlines hotels car rental cruise lines restaurants contractors (construction etc)

18 18 What is Revenue Management Overbooking Important trade-off between opportunity cost of unused resources if cancellations or no-shows cause resources to be wasted, and cost of oversales In 1960s, Simon and Vickrey proposed the use of auctions to allocate airline seats in case of oversales Airlines rejected idea for many years Nowadays, reverse Dutch auctions are widely used to allocate airline seats in case of oversales, and seem to be widely accepted

19 19 What is Revenue Management Dynamic pricing and the bullwhip effect Dynamic pricing can increase demand variability The case of Campbell Soup Wild swings in demand and in shipments of chicken noodle soup from the manufacturer to distributors and retail stores Increase in production, storage and logistics costs Frequent stockouts resulting in lost sales The culprit: Trade promotions!

20 20 What is Revenue Management Dynamic pricing and the bullwhip effect Dynamic pricing can be used to decrease demand variability Peak load pricing: lower prices during off-peak times, higher prices during peak times Airlines Hotels Golf courses Electricity wholesale market Oil/gasoline?

21 21 What is Revenue Management Revenue Management may involve price discrimination, but it does not have to P=130-Q Unit cost = 10 Firms profits under single price: (130-Q-10)Q P q MC=10 130 60 70 Consumer surplus=1800 Deadweight loss=1800 Firm profits=3600

22 22 Price Discrimination (continued) P=130-Q Unit cost = 10 What if the firm could segment the market and charge two different prices? P q MC=10 130 80 90 Consumer surplus=1600 Deadweight loss=800 Firm profits=4800 50 40

23 23 Price Discrimination (continued) P q MC=10 130 80 110 Consumer surplus=1000 Deadweight loss=200 Firm profits=6000 90 40 70 50 30 20 60 100

24 24 Price Discrimination (continued) Perfect price discrimination P q MC=10 130 Consumer surplus=0 Deadweight loss=0 Firm profits=7200

25 25 What is Revenue Management The same product sold at different times for different prices is not necessarily price discrimination, because at different times... the production or distribution costs may be different inventory costs were incurred to keep the product in stock until a later time the product value may change over time, such as perishable or maturing or seasonal products, fashion goods, antiques. the remaining inventory may be different interest is earned if product is sold at an earlier time consumers value products differently at different points in time locking sales in early reduces uncertainty

26 26 What is Revenue Management It is not spam

27 27 Fairness and Legal Issues Depending on the industry, there may be legal obstacles to revenue management Examples Regulated prices of utilities (this is changing) Prices in airline industry were regulated until 1978 - price and quantity changes had to be approved by CAB Pricing in ocean cargo industry was regulated until 1999 - carriers had to provide all shippers with the same essential contract terms Spot market pricing in ocean cargo industry is still regulated - 30 days notice required for price increases

28 28 Fairness and Legal Issues Golf course examples Kimes and Wirtz survey results (1 = extremely fair, 7 = extremely unfair) Time-of-day pricing: 3.41 Varying price (for example, as function of bookings on hand): 6.16 Two-for-one coupons for off-peak use: 1.80 Time-of-booking pricing: 5.12 Reservation fee/Charge for no-shows: 3.19 Tee-time interval pricing: 3.95

29 29 Fairness and Legal Issues Amazon.com example Fall 2000, Amazon conducted experiment to try to determine price sensitivity of demand for DVDs Discounts between 20% and 40% offered randomly Customers who visited amazon.com multiple times noticed changing prices Furious response by customers and press, suspecting Amazon varied price by demographics Why are varying airline prices accepted by most, and not varying DVD prices?

30 30 Why do Revenue Management Success stories American Airlines increased annual revenue with $500 million through revenue management Delta Airlines increased annual revenue with $300 million through revenue management Marriott hotels increased annual revenue with $100 million through revenue management National Car Rental was saved from liquidation with revenue management Canadian Broadcasting Corporation increased revenue with $1 million per week

31 31 Why do Revenue Management Increasing competition Fewer restriction on international trade More efficient international transportation Low cost foreign competitors Competitors use revenue management Use revenue management to stay on top

32 32 Why do Revenue Management At many companies, little cost-cutting juice can easily be extracted from operations. Pricing is therefore one of the few untapped levers to boost earnings, and companies that start now will be in a good position to profit fully from the next upturn. – McKinsey Quarterly, 2003

33 33 Revenue Management Optimization Control Methods Resource Bucket Control Methods Bid Price Control Methods Dynamic Programming Software

34 34 Revenue Management Optimization Control Methods/Optimization Methods Static Dynamic Deterministic Stochastic Leg Based OD based

35 35 Revenue Management Optimization Resource Bucket Control Methods If supply of different products are related, for example if different products use shared resources or capacity, then revenue management should not be done separately for the different products Also if demand for products are related, for example complementary goods or substitutes Examples Airlines: Itineraries with different origin-destination pairs share the same flight legs (resource) Hotels and rental cars: Multiple day bookings share capacity

36 36 Revenue Management Optimization Bid price methods Simple single-stage deterministic LP model Input: Lines of flight (LOF) The flights (legs/segments) each LOF traverses (flight-LOF incidence matrix A) Fares f 1,f 2,…,f k for each LOF Demand D j for each LOF-fare combination j (not well-defined notion) Capacity Q i of each flight (leg/segment) i Primal decision variables: x j = number of seats allocated to LOF-fare combination j

37 37 Revenue Management Optimization Dynamic programming State of process: current bookings/seats available for each flight, competitor information Transitions: take place through bookings and cancellations Decisions: which prices/fares are quoted when booking requests are received Policy: decision for each state x and time t Objective: determine optimal policy Value function: expected value V(x,t) as function of state x and time t Solving problem involves computing optimal value function V*(x,t) Another benefit: Optimal policy very simple: accept booking request if fare > V*(x,t) - V*(x-1,t)

38 38 Revenue Management Optimization Optimization Software Surveys Fourer, R., Linear Programming, OR/MS Today, volume 32, number 3, pp. 46-55, June 2005,. Nash, S. G., Nonlinear Programming, OR/MS Today, volume 25, number 3, pp. 36-45, June 1998,. Grossman, T.A., Spreadsheet Add-Ins for OR/MS, OR/MS Today, volume 29, number 4, pp. 46-51, August 2002,.

39 39 Revenue Management Optimization Decision Support Software Surveys Aksoy, Y. and Derbez, A., Software Survey: Supply Chain Management, OR/MS Today, volume 30, number 3, pp. 34-41, June 2003,. Buede, D., Decision Analysis Software Survey: Aiding Insight IV, OR/MS Today, volume 25, number 4, pp. 56-64, August 1998. Hall, R., Vehicle Routing Software Survey: On the Road to Recovery, OR/MS Today, volume 31, number 3, pp. 40-49, June 2004,. Maxwell, D.T., Decision Analysis: Aiding Insight VII, OR/MS Today, volume 31, number 5, pp. 44-55, October 2004,. Swain, J. J., 'Gaming' Reality: Biennial survey of discrete-event simulation software tools, OR/MS Today, volume 32, number 6, pp. 44-55, December 2005,. Swain, J. J., Power Tools for Visualization and Decision-Making, OR/MS Today, volume 28, number 1, pp. 52-53, February 2001.

40 40 Demand Forecasting The first law of forecasting: The forecast is always wrong Sources of forecast error: Modeling error Parameter error Measurement error

41 41 Demand Forecasting Modeling error The basic form of the demand model is wrong Example Suppose we want to forecast demand d as a function of price p The true demand function is d = exp(3-2p) / (1 + exp(3- 2p)) We try to estimate a linear demand model d = a – bp, with parameters a and b that are estimated with data No matter what values we estimate for a and b, the estimated model is wrong – modeling error

42 42 Demand Forecasting Parameter error The basic form of the demand model is correct, but we do not know the correct values of the parameters Example The true demand function is d = exp(3-2p) / (1 + exp(3- 2p)) We try to estimate a demand model d = exp(a-bp) / (1 + exp(a-bp)), with parameters a and b that are estimated with data If we estimate a=3 and b=2 (for example, with good data and a good statistical technique), then the estimated model is correct

43 43 Demand Forecasting Measurement error The basic form of the demand model is correct, but we do not know the correct values of the parameters, and data errors make it impossible to use statistical techniques to estimate the parameter values Example The true demand function is d = exp(3-2p) / (1 + exp(3- 2p)) We try to estimate a demand model d = exp(a-bp) / (1 + exp(a-bp)), with parameters a and b that are estimated with data Because of bad data, we estimate a=4 and b=-1

44 44 It is very important to understand and model customer behavior accurately Incorrect models of customer behavior can lead not only to suboptimal prices, but can lead to the systematic deterioration of models, prices, and profits over time – the spiral-down effect Demand Modeling

45 45 Spiral-down effect in airline revenue management For many years, airlines have used following simple model of customer behavior Some time before departure, customer requests a ticket in a particular fare class Airline accepts or rejects the request Above model describes the way airline reservations systems work However, it does not accurately describe the way customers behave Demand Modeling

46 46 Spiral-down effect in airline revenue management Low fare tickets and high fare tickets Airlines set aside chosen number of seats for high fare tickets Airlines use observed sales to estimate the supposed demand for high fare tickets Demand Modeling

47 47 Spiral-down effect in airline revenue management Spiral-down effect: Airline allows some low fare sales Some flexible customers (not modeled by the airlines) willing to buy high fare if that is the only option, now buy low fare tickets Airlines observe more low fare sales and less high fare sales – decrease their estimate of high fare demand Airlines set aside fewer seats for high fare tickets, and allow more low fare sales More customers buy low fare tickets, and the spiral down continues Spiral-down effect is the consequence of an incorrect model of customer behavior Demand Modeling

48 48 Forecasting methods Judgmental methods Statistical forecasting methods Demand Forecasting

49 49 Judgmental forecasting methods Expert opinion Questionable: See the articles Armstrong, J.S., How Expert Are the Experts?, Inc, pp.15-16, 1981 Armstrong, J.S., The Seer-Sucker Theory: The Value of Experts in Forecasting, Technology Review, pp.16-24, 1980 Consensus methods, such as Delphi technique Demand Forecasting

50 50 Statistical forecasting methods Non-causal methods Exponential smoothing Time series methods Causal methods Linear regression Nonlinear regression Discrete choice models (logit, probit, etc) Whatever the method, the basic approach is to find systematic behavior in data that one has reason to believe will continue in the future Demand Forecasting

51 51 Forecasting software surveys: Yurkiewicz, J., Forecasting: Predicting Your Needs, OR/MS Today, volume 31, number 6, pp. 44-52, December 2004,. Swain, J. J., Desktop Statistics Software: Serious Tools for Decision Making, OR/MS Today, volume 26, number 5, pp. 50- 61, October 1999. Swain, J. J., Looking for Meaning in an Uncertain World, OR/MS Today, volume 28, number 5, pp. 48-49, October 2001. Swain, J. J., 2005 Statistical Software Products Survey: Essential Tools of the Trade, OR/MS Today, volume 32, number 1, pp. 42- 51, February 2005,. Demand Forecasting

52 52 Revenue Management Implementation Business case: assessment of Revenue opportunity Development and support personnel needs Development cost Maintenance cost Hardware Software DBMS Forecasting Optimization Interfaces

53 53 Revenue Management Implementation Distribution system Communication network hardware Interfaces with revenue managers Interfaces with customers Management of customer awareness and customer perceptions Management of organizational change

54 54 Questions?


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