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Demand and Revenue Management

Demand and Revenue Management. Anton J. Kleywegt April 2, 2008. Revenue Management. What is Revenue Management Why do Revenue Management Pricing Optimization Demand Modeling and Forecasting. What is Revenue Management.

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Demand and Revenue Management

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

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

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

  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. 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. 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. 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. 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. 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. 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. 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. 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. Hospital Contract Case Study • Example of setting per-diem rates

  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. 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. 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. 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 1960’s, 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. 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. 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. Consumer surplus=1800 70 Firm profits=3600 Deadweight loss=1800 60 What is Revenue Management • Revenue Management may involve price discrimination, but it does not have to 130 P=130-Q Unit cost = 10 Firm’s profits under single price: (130-Q-10)Q P MC=10 q 130

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

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

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

  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. What is Revenue Management • It is not spam

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

  33. Demand Modeling • 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

  34. Demand Modeling • 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

  35. Demand Modeling • 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”

  36. Demand Modeling • 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

  37. Demand Forecasting • Forecasting methods • Judgmental methods • Statistical forecasting methods

  38. Demand Forecasting • 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

  39. Demand Forecasting • 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

  40. Demand Forecasting • Forecasting software surveys: • Yurkiewicz, J., “Forecasting: Predicting Your Needs”, OR/MS Today, volume 31, number 6, pp. 44-52, December 2004, <http://lionhrtpub.com/orms/surveys/FSS/fss-fr.html>. • 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, <http://lionhrtpub.com/orms/surveys/sa/sa-survey.html>.

  41. Questions?

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