SHA541 printable

SHA541: Price and Inventory Controls Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners This course includes • Nine self-check quizzes • Two discussions • Eleven tools to download and use on the job • One Ask the Expert interactive • One video transcript file Completing all of the coursework should take about five to seven hours What you'll learn Estimate the marginal value of capacity Evaluate the effects of price, length of stay, demand, and availability controls on revenue Incorporate uncertainty into current and future pricing decisions Analyze the effects of multiple resource controls- rate and length of stay controls Course Description This course provides a rigorous foundation in traditional revenue management control of room and rate availability You will begin by exploring inventory control, focusing on controlling rate but not length of stay (LOS) You will then add uncertain demand and discuss traditional availability controls (e.g., minimum LOS) Finally, you will explore optimization and illustrate methods for full rate and availability control This foundation is necessary if you want to develop your own Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners revenue management systems or to engage effectively and fully with commercially available revenue management systems Chris Anderson Associate Professor, School of Hotel Administration, Cornell University Chris Anderson is an associate professor at the Cornell School of Hotel Administration Prior to his appointment in 2006, he was on faculty at the Ivey School of Business in London, Ontario Canada His main research focus is on revenue management and service pricing He actively works with industry, across numerous industry types, in the application and development of RM, having worked with a variety of hotels, airlines, rental car and tour companies as well as numerous consumer packaged goods and financial services firms Anderson's research has been funded by numerous governmental agencies and industrial partners and he serves on the editorial board of the Journal of Revenue and Pricing Management and is the regional editor for the International Journal of Revenue Management At the School of Hotel Administration, he teaches courses in revenue management and service operations management Start Your Course Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Module Introduction: Single Resource Pricing and Control Revenue management is used to sell the right unit to the right customer, at the right price, at the right time In this module you discover techniques to maximize revenue by controlling price or duration of stay You examine revenue controls first assuming your demand is certain and then assuming demand is uncertain You then evaluate upgrading and upselling to customers After completing this module, you will be able to: Use fundamental approaches to demand control Determine appropriate capacity or inventory levels when demand is uncertain Maximize expected revenue when there are uncertain levels of demand Calculate when upgrading contributes the most revenue Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Read: Parking Lot Dilemma You can think of revenue management as analogous to parking your car on a very busy shopping day In the U.S a day called "Black Friday" is one of the busiest shopping days of the year, and parking spaces can be hard to find Imagine it's Black Friday-you are driving through the parking lot and immediately see a parking spot You may think, "Great, it's a spot, I can park my car, get to the store, and buy my new Gucci bag." But then you might think, "Well, this parking spot is far away from the store I wonder if I can find a closer spot?" You face a dilemma: Should you park your car in the first spot you find, or should you continue to drive and look for a spot closer to the store entrance? You look down the rows and not see any other parking spaces closer to the store Now it's really a difficult decision If there are any closer spots, they are unknown to you Should you take the first parking spot, or should you continue to look for a closer spot? Chances are that if you drive down the lot and circle back to the first spot, the driver behind you will have parked his or her car in that spot In other words, if you don't take this spot, somebody else will, and it will be gone As a revenue manager, you make many decisions in settings similar to this one From a revenue management perspective, the question you would ask yourself is: "Should I accept the early discount request (take the first parking spot), or should I wait for a later arriving, higher paying request (continue to look for a closer spot)?" If you leave the first revenue opportunity, somebody else may take it There may be a better revenue opportunity in the near future-or there may not Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Read: Review of Revenue Management Key Points "RevPAR" is revenue per available room "RASM" (a similar measure) is revenue per available airline seat mile Uses of Revenue Management When we think about revenue management (RM), basically we're trying to maximize revenue per available time-based inventory unit We calculate variations on this all-inclusive measure differently in different contexts, and we use these calculations in all applications of revenue management to analyze a business's ability to maximize its revenue capacity Necessary Conditions for Revenue Management The revenue management process can be applied to any business that has a relatively fixed capacity, a perishable inventory, and demand that varies over time Fixed capacity In our parking lot dilemma, the number of parking spaces in the lot is fixed; when they are all filled with cars, there is no more capacity Consider a hotel that has sold all its rooms for a particular night They cannot add any more rooms instantly, and thus they cannot accept any more guests Perishable inventory If the inventory is not used within a timeframe, then we lose a revenue opportunity The hotel room that is not sold on Wednesday, November 14, is a lost opportunity on November 15 Time-variable demand Demand varies by time of year, time of day, and by the length of customer stays or use For example, a business traveler commonly stays in a hotel for one or two nights during the week, rather than over the weekend The time used in midweek and the length of stay may be one to three days Leisure travelers, on the other hand, generally stay over the weekend and often for many consecutive days The time they use includes a weekend and the length of stay may be four to seven days Revenue Management Objectives When we discuss RM for hotels we talk about RevPAR-revenue per available room RevPAR is simply the average selling price or average daily rate (ADR) times what fraction of our inventory is used (occupancy) Thus, if our average daily rate is €100 and 15 of our 20 of rooms are occupied our RevPAR would be €75 Maximizing RevPAR is analogous to maximizing revenue - sometimes at the expense of ADR as a firm may make more total revenue while selling to some guests at lower values Another example is the equivalent RevPAR for an airline-RASM revenue per available seat mile The airline can choose to Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners fly a plane from and to different locations Passengers may fly from Ithaca, New York to Philadelphia (about 200 miles) or from Ithaca to Detroit (about 500 miles) Where they choose to fly influences the revenue per available seat mile Segmentable Market To fully use RM, we must be able to segment our market and set prices according to different cutomer types The objective is to expand our market and increase our revenue potential in two ways We want to charge higher prices to market segments that not respond to changes in price level And we want to lower prices to market segments that will respond to a price reduction by increasing their purchases enough to more than offset the revenue reduction occasioned by the discount Strategic Levers of RM Different combinations of duration, price, and reach can be used as strategic levers in the revenue management process Duration, how long someone uses your capacity, can be predictable or unpredictable Using duration controls, such as a minimum length of stay, a firm can maximize overall revenue over all time periods Variable pricing requires two basic considerations One is the prices charged and the second is who pays which price With variable pricing people pay different prices, such as weekday rates or senior rates This lever can be used to control demand Reach is how a company sells to its customers This can be property direct, through an online travel agent, or via the call center Reach is not just about how you perform the transaction, but how you create a stimulant and create business We may have 50 percent property direct, but much of it may have originated from an Internet search followed by booking property direct All three of these strategic levers are linked to segmentation and in essence how well we utilize these levers dictates how well we are segmenting our market (and increasing our revenue) Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Watch: Price and Duration Controls Learn how variable pricing and capacity management can be used to enhance profitability Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Tool: Demand Control for Rooms Review Download the Tool Completed Chart On the previous page you developed a demand-control chart for using trigger points of 70% and 100% In the attached document are the occupancy percentages, minimum rates, and hot and cold periods we determined for the hotel If you had trouble with this quiz exercise on the previous page, please review the completed chart and try the quiz again Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners Read: Demand Controls in Other Business Areas Key Points Demand control can be useful in the absence of historic data Demand controls can help you manage parts of your hotel beyond the rooms Demand control is a relatively straightforward and simple form of inventory control It was commonly used in the early stages of inventory control (specifically by hotel and cruise lines), but most industries have moved beyond this form of control It is still useful in some situations, however One example is firms that realize they have revenue opportunities, particularly in non-room areas, but not have sophisticated systems to evaluate them Demand control is also useful when the company does not have a large amount of historic data Demand control charts are relatively simple to construct Tables and show sample charts for parts of a hotel not normally actively managed for revenue They display aggregate utilization of meeting space, first by day of week and month, and then by monthly average At the aggregate level, use of meeting space should be fairly easy to summarize for any property, but as the first two tables show, it provides a wealth of information Days of Week Month Sun Mon Tues Wed Thurs Fri Sat Average by Month January 0% 0% 0% 40% 25% 0% 100% 24% February 25% 0% 0% 200% 0% 25% 75% 46% March 0% 13% 0% 25% 40% 40% 40% 23% April 30% 0% 25% 63% 25% 25% 50% 31% May 0% 25% 20% 0% 0% 100% 50% 28% June 0% 25% 25% 25% 50% 40% 100% 38% July 40% 0% 25% 0% 0% 0% 60% 18% August 25% 25% 40% 20% 0% 38% 100% 35% Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 10 address changes in availability By managing the flow of reservations using availability controls, you can minimize the need to apply closed-to-arrival controls Returning to the 4-box matrix we discussed earlier, we see that our revenue management goal is to be in Quadrant 2, controlling both price and demand We know that if occupancy is high, we can require a minimum night stay But that is only going to move us part of the way to quadrant We will still be operating in Quadrant 4, not really controlling availability but using a sort of approximation Remember that our goal is to truly be up in Quadrant and to forecast by rate class, length of stay, and availability Once we've done that we can calculate the marginal value and use the marginal value to determine what will and will not be available This approach may be challenging for some people to implement because logically we want to take a sequential approach-focusing on rooms and on days that have peak demand and ignoring other days But in reality, if we have a sequence of days that are reaching capacity and they are close to each other, then we need to manage those days simultaneously rather than sequentially Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 35 Watch: Using Rate and Availability Controls Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 36 Tool: Rate and Length of Stay Controls Download the Tool Review Spreadsheet In the previous assignment you practiced using shadow prices to manage rate and availability controls The following table shows the days and rates that are open and closed to arrival If you had trouble with the assignment, please click the link on the right to download and review the spreadsheet When you are ready, please return to the quiz to try again Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 37 Read: Welcome to Grand Casino de France Grand Casino de France operates a highly diversified network of gambling facilities in France, the United States, and the United Kingdom, and it is planning major international facilities in Spain and the Bahamas It is the largest provider of branded casino entertainment in the world, with about million square feet of casino space, 40,000 hotel rooms, and 85,000 employees Grand Casino de France uses an extensive set of revenue management (RM) tools to allocate hotel rooms at its properties around the world These tools, built on a shadow price system based on customers' projected gaming activities, differ from the allocation methods typically used in hospitality and airline RM systems Traditionally, hospitality companies view hotel rooms as their most important asset and seek to maximize hotel revenues In contrast, the casino views hotel rooms as a facilitating commodity and aims to offer hotel rooms as a means of supporting gaming activity and profits Rather than allocating rooms to customer segments based on room rates, the casino bases its rates on the expected profit generated by customers' projected gaming activities Average Daily Tier Theoretical €881 €285 €69 unknown Grand Casino de France's RM system tracks the availability of rooms at a given property and constructs detailed demand forecasts for that property using the average daily theoretical (ADT) gaming profit data (from its customers' activity) A customer's ADT value represents the casino's estimate of the profit it will clear on one day of his or her gaming activity The casino segments customers into four ADT profit tiers Customers in all tiers win and lose in their gambling The estimated revenue is the amount a customer in that tier loses to the casino on average per day of gambling Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 38 Read: Revenue Management at Grand Casino de France Key Points Tracking customer spending can help you with inventory control Revenue management forecasts demand by tier Across all its properties, Grand Casino de France tracks what its guests spend gaming Similar to other casinos, it keeps track of how much money people spend gambling and how much they spend elsewhere in the casino They use this information to help control inventory in their revenue management system Thus, in addition to rates and lengths-of-stay, the casino has gaming revenue that, for some casinos, is more than the room rates Tier Tier Tier Tier €881 €285 €69 unknown Table Grand Casino de France divides customers into four tiers, and they calculate the average amount of money the members of each tier spend gambling per day they stay in the hotel Table shows the revenues for three tiers; tier includes retail customers that the casino does not track Historically, the casino ignored room rates and simply forecast gambling revenue For a given night their revenue management system forecast unconstrained demand for each tier, by arrival date and by length-of-stay (LOS) Although LOS was explicitly used as a forecast criterion, the system did not use LOS information when allocating rooms Table provides a small example of how the system translated LOS-based forecasts into total numbers of rooms demanded each night The table shows five arrival dates, three lengths-of-stays, and three different tiers For example, on Aug 31 the forecast is for 432 tier-zero guests arriving for a one-night stay This group will provide average daily theoretical (ADT) gambling revenue of €881 each Tier (€881) Tier (€285) Tier (€69) Date LOS_1 LOS_2 LOS_3 LOS_1 LOS_2 LOS_3 LOS_1 LOS_2 LOS_3 8/31 432 149 104 280 213 157 369 148 49 9/1 486 147 84 251 168 118 333 47 16 9/2 655 189 98 321 210 146 310 170 57 9/3 20 60 48 36 32 215 72 9/4 20 35 26 352 164 55 Table Cumulative Demand by Tier and Date Tier ADT (€) 8/31 9/1 9/2 9/3 9/4 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 39 881 685 970 1,277 404 146 285 650 907 1120 618 295 69 566 593 649 562 915 1,901 2,470 3,046 1,584 1,356 Total Table Table provides a small example of how the system translates LOS forecasts into total room demand for each night In this table we see five arrival dates and three different tiers Given each tier's ADT forecast by occupancy date, the system compiles an overall forecast by ADT tier and occupancy For example, 649 tier customers are forecast to stay in the hotel on 9/2 These 649 Tier customers consist of 537 Tier gamers who arrive on 9/2 (310 + 170 + 57), plus the 63 (47 + 16) Tier guests arriving on 9/1 and staying for two or three days, plus 49 guests from 8/31 staying three nights (see Table 2) The total demand by tier (last row in Table 3) can be sorted to determine which guests should have access to rooms Demand is sorted by ADT from highest to lowest and allocated to that night's available rooms Date Available Rooms 8/31 1180 9/1 1062 9/2 1082 9/3 450 9/4 493 Table The clearing price for a night is the maximum value at which the forecast demand will occupy all free rooms The room rate quoted to a gamer for each night of a requested stay reflects the difference between his or her tier value and the clearing price The clearing price on any given night is determined by finding the ADR at which the available rooms will sell out Table shows how many rooms are available (capacity minus reservations on hand) for the same five arrival dates Table shows clearing prices for these five days as a function of rooms available and demand by tier For example, on 9/1, there are 1,062 available rooms These rooms could be filled using only tiers zero and customers The demand from tier is 970 rooms and the demand from tier is 907, which creates a total demand from these two tiers of 1877 rooms-more than the 1062 available In this case the 1062nd room is generating gaming revenue of €285 (ADT of the tier guest) If a tier guest requests a room, he will be quoted the difference between his ADT and the clearing price (€285 €69) as his room rate Cumulative Demand by Tier and Date Tier Value (€) 8/31 9/1 9/2 9/3 9/4 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 40 881 685 970 1,277 404 146 285 650 907 1120 618 295 69 566 593 649 562 915 Available rooms 1180 1062 1082 450 493 Clearing price 285 285 881 285 69 Table For example, if a tier customer were to make a reservation for 9/1, he or she would be quoted a room rate of €216 for that night (€285 €69 = €216) If a tier customer, with an ADR of €881, requests a reservation for that same night, he or she will be offered a complimentary room, since €285 €881 < €0 For customers making reservations for multiple-night stays, the rate quoted will be the sum of the quoted prices for the individual nights For instance, a tier customer wanting a room for the nights of 9/3 and 9/4 will be quoted a rate of €216 as: (€285 €69) + (€69 €69) = €216 + €0 = €216 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 41 Tool: Maximizing Revenue Download the Tool Review Spreadsheet In the last exercise you used a model to determine the optimal number of reservations to accept by gaming tier and length of stay (LOS) Click the link on the right to download the spreadsheet and see the completed solution Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 42 Read: Grand Sky Airlines Revenue Management Key Points Consider pricing, demand, and capacity in the airline industry How can historical data help the airline forecast demand and availability? Another example of revenue optimization can be seen in the airline industry Consider Grand Sky Airline, a regional airline operating in the south of France They fly three airplanes between Marseille and three towns: Nice, Paris, and St Tropez These three towns are the "spokes" connected by the Marseille "hub." A few times each day the three airplanes fly from the spoke cities to Marseille They arrive simultaneously at Marseille, connecting passengers change aircraft during a 45-minute layover, and then the three airplanes depart for the spokes One set of six flights (three inbound to Marseille and three outbound) is called a bank Each bank can serve passengers flying on 12 different routes: Three inbound direct routes: Nice-Marseille (N-M), Paris-Marseille (P-M), and St Tropez-Marseille (S-M) Three outbound direct routes: Marseille-Nice (M-N), Marseille-Paris (M-P), and Marseille-St Tropez (M-S) Six routes requiring two flights each For example Paris to Nice requires the passenger to connect through the hub (fly Paris-Marseille, then Marseille-Nice) Grand Sky charges a single fee for a one-way coach-class ticket on each passenger route Table shows the prices Destination Marseille Marseille Nice Paris St Tropez - €197 €110 €125 Nice €190 - €282 €195 Paris €108 €292 - €238 St Tropez €110 €192 €230 - Origin Table 1: Price for each passenger route Each of the three planes currently has 240 coach seats Table shows demand for the routes in a bank Because passengers must all connect through the hub, passenger demand exceeds airplane capacity on every flight Total demand Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 43 Marseille Marseille Nice Paris St Tropez for flight to hub 123 80 110 - Nice 130 98 88 316 (N-M) Paris 72 105 68 245 (N-M) St Tropez 115 90 66 271 P-M) - 318 244 266 Origin Total demand for flights to spoke (M-N) (M-P) (M-S) Table 2: Demand for Routes in a Bank For example, on the flight from Nice to the Marseille hub (N-M), the total demand is the sum of demands for three passenger routes, N-M, N-P, and N-S, totaling 130 + 98 + 88 = 316 passengers (this is the sum of the second row of Table 2) Because only 240 passengers can travel on the N-M flight, at least 76 passengers must be turned away We have used this information to build an optimization model in the Grand Sky spreadsheet This is what you will find in the spreadsheet Cells H16 to H18 are row sums for the demand on the inbound flights into Marseille Cells E19 to G19 are column sums for Marseille outbound flights (see Table 3) Table Figure summarizes the optimization used to determine how much demand to accept on each leg Cells D26-G29 show our decisions-which leg demands to accept The total revenue (H6) is the price of each route multiplied by the seats sold on each route Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 44 Figure Figure shows the optimization model where the decision variables are changed to maximize revenue while ensuring that seats sold on each leg not exceed the plane's capacity and the route demand Figure Constraints Cell Name $E$19 - Nice Final Shadow Price 240 Price 192 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 45 $F$19 - Paris 240 92 $G$19 - St Tropez 240 $H$16 Nice - 240 190 $H$17 Paris - 240 100 $H$18 St Tropez - 198 Table Table displays the shadow prices or marginal value of capacity on each of the six legs Five out of six flights are full, with St Tropez-Marseille being the only leg that is not full-only 198 of the 240 seats are occupied Because this flight is not full, it has a zero marginal value (an additional seat is worthless) The other five flights, which are full, all have positive marginal values-we can increase revenue if we have more seats and we lose revenue if we reduce seats For each additional seat we gain (lose), we increase (decrease) the revenue by the value of the shadow price This is similar to controlling rates and LOS at the Hotel Ithaca We can use these shadow prices to determine the minimal acceptable rates on each route The act of taking a reservation decreases by one the effective capacity of each leg used, and hence must generate revenue at least as large as the shadow prices If passengers want to fly from Paris to Marseille, they must pay at least €100 (the shadow price of the Paris-Marseille leg) If passengers want to fly from Marseille to Paris, they must pay €92 As a result, a round-trip ticket from Paris to Marseille should cost at least €192 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 46 Read: Thank You and Farewell Congratulations on completing this course You now have a keen appreciation of marginal analysis and how it pertains to pricing and inventory control This course focused on inventory control for both a single constraining resource as well as introduced approaches for managing more complex settings as guests stay multiple nights or planes make layovers in connecting cities Thank you for your interest in this course I wish you the best of luck in your future educational and professional pursuits Chris Anderson Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 47 Stay Connected Additional Resources The Center for Hospitality Research provides focused whitepapers and reports based on cutting-edge research Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 48 Copyright © 2012 eCornell All rights reserved All other copyrights, trademarks, trade names, and logos are the sole property of their respective owners 49