Revenue Management - Report Example

Revenue Management al Affiliation) ASSIGNMENT FRONT SHEET Cost, Price and Revenue Management Student Class: E-74 Faculty Student Name: Assessment Title: Inventory Management Due Date: Word Count: Due Time: Statement of Authorship I confirm that this work is my own and has not been submitted previously for any other assessment or institution. Additionally, I confirm that no part of this coursework, except where clearly quoted and referenced, has been copied from material belonging to any other person e.g. from a book, handout, another student. We are aware that it is a breach of regulations to copy the work of another without clear acknowledgement and that attempting to do so renders us liable to disciplinary procedures’ Signature: __________________ Date: _____________ Table of Contents Table of Contents 4 List of Figures 5 Property Location and Adjustments 7 The revenue manager will adjust the demand forecast from the 20th to 27th April 2014 for the American Inn, a hotel located in the Midtown part of Manhattan, 6 blocks to the Broadway Theatres and 4 blocks from the Empire State Building. This is due to the upcoming event of the Tribeca Film Festival. 7 Pricing Ideas 11 List of Figures Figure 1: Mean Demand Forecast (Adjusted Slide) ……………………….…….. 8 Figure 2: New Customers per rate class, day of arrival and length of stay….….…9 Figure 3: Capacity Allocation………………………………………………….….10 Figure 4: Different revenues of the models………………………………..………12 Figure 5: Adjusted data………………………………………………………..….15 Figure 6: Demand forecast…………………………………………………..……17 Introduction New York hosts the annual Tribeca Film Festival, which has become one of the most prestigious film festivals in the world (on the level of Cannes and Sundance). The 2014 chapter will be held on April 16th - 27th in New York. The Festival received over 8,600 film submissions and hosted 1,500 screenings in 2006 and 2007 alone, and its line up often includes wide range of independent films including shorts films, narrative features and documentaries. The Festival also features panel discussions with people in the entertainment industry and a music lounge created with ASCAP to hype artists. One of the Festival’s most unique features is its Artist Awards program in which upcoming and established artists celebrate filmmakers by submitting original works of art that are awarded to the winners of the filmmakers’ competition. Currently, the Festival attracts estimated 3 million people _including international celebrities _ and returns $600 million annually. This report is intended to assess the impact of arriving business travelers and tourists on the room allocations in the hotel in the last week of the Festival. In order to evaluate this impact, the EMSR-b method, together with the linear programming method will be used. Given the two outcomes obtained, a comparison between both methods will be carried out to assess the capacity allocation for the observed week. This will be followed by recommendations on each of the methods used. Property Location and Adjustments The revenue manager will adjust the demand forecast from the 20th to 27th April 2014 for the American Inn, a hotel located in the Midtown part of Manhattan, 6 blocks to the Broadway Theatres and 4 blocks from the Empire State Building. This is due to the upcoming event of the Tribeca Film Festival. Figure 1: Mean Demand Forecast (Adjusted Slide) Table 1: Daily mean demand forecast per fare class (including all lengths of stay Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Total Benchmark rate demand 52 67 45 38 49 47 51 Total Corporate rate demand 85 95 75 76 45 38 56 Total Special Transient rate demand 104 89 112 94 85 120 102 Total Deep Discount rate demand 203 185 221 223 230 210 173 Total demand 444 436 453 431 409 415 382 The alterations made to the data of a “normal” week are shown below, and include the number of nights and various fare classes (Origin destination fare – ODF). Figure 2: New Customers per rate class, day of arrival and length of stay. Table 2: New customers per rate class, day of arrival and length of stay ODF Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Benchmark rate, 1 night 19 11 2 9 8 17 14 Benchmark rate, 2 nights 12 11 0 2 14 1 9 Benchmark rate, 3 nights 9 9 0 3 3 0 9 Corporate rate, 1 night 25 21 11 18 1 6 3 Corporate rate, 2 nights 24 10 12 9 3 0 14 Corporate rate, 3 nights 16 10 8 8 4 2 22 Special Transient Rate, 1 night 31 12 37 38 10 38 36 Special Transient Rate, 2 nights 28 5 8 12 19 5 8 Special Transient Rate, 3 nights 31 6 9 10 9 16 7 Deep Discount Rate, 1 night 95 43 60 51 50 57 55 Deep Discount Rate, 2 nights 47 31 36 37 40 23 27 Deep Discount Rate, 3 nights 29 23 19 34 27 16 8 Presentation of Results Since 2 different techniques are being applied in determining the capacity allocation for the “Tribeca Film Festival Week”, the outcomes differ from each other. As illustrated in the table below, both methods provide different results, which will be elaborated on in greater detail in the discussion and comparison of results section. Figure 3: Capacity Allocation Allocation EMSR-b Class Mon Tue Wed Thur Fri Sat Sun Benchmark 250 220 228 224 243 236 241 Corporate 18 56 67 86 83 101 95 Transient 132 124 105 90 74 63 64 Discount 0 0 0 0 0 0 0 Total 400 400 400 400 400 400 400 Allocated Linear Programming Class Mon Tue Wed Thur Fri Sat Sun Benchmark 239 217 310 278 296 287 301 Corporate 29 33 45 62 71 59 84 Transient 132 150 45 60 33 54 15 Discount 0 0 0 0 0 0 0 Total 400 400 400 400 400 400 400 Discussion and comparison of results Capacity allocation is crucial whenever a hotel is offering the same unit of restricted capacity at 2 or more different prices (Talluri & Ryzin, 2004). Since hotels are not able to place the high demand at specific times in their property, maximization of revenue is possible when assessing which guest to host. When using the EMSR-b model, it is assumed that customers who are displaced by extra booking will be paying a fare that is equal to a weighted average of future rates. Linear programming, on the other hand, is a natural technique used in assigning limited resources to competing product demands (Phillips, 2005). It takes into consideration the length of stay (in contrast to the EMSR-b technique) and therefore accounts for different outcomes as illustrated by different revenues realized in the table below: Figure 4: Different revenues of the models Revenue EMSR-b $1,450,328.00 Linear Method $1,563,541.00 Limitations to both techniques include the fact that they do not consider cancellations or no-shows of customers. Pricing Ideas Since the hotel expects high occupancy rates, the pricing can be adjusted in such a way that the profits remain high and customers still get value for their money. Essentially, the hotel should focus on using pricing to make as much money as possible during the Festival without necessarily losing customers to other hotels or making losses. Pricing should be spread according to the occupancy rates of the different rooms. Pricing for rooms like Benchmark with very high occupancy should balance with that of rooms like corporate which have low occupancy. This will ensure that the hotel maximizes profit by taking full advantage of the increased occupancy rates, and still offering competitive rates. Managerial Recommendations The two techniques employ different approaches and therefore produce different outcomes when predicting the best distribution of fare classes whenever there is high demand. However, most revenue managers contend that it does not adequately account for aspects like length of stay or revenue assigned to a customer’s full stay (Talluri & Ryzin, 2004). However, the EMSR-b technique is a very effective instrument in cases of single resource allocation and facilitates forecasting consisting of the mean and the standard deviation. The linear programming technique facilitates the accommodation of more parameters. However, it is more difficult to apply it because it requires daily adjustment and is therefore perceived as more expensive to be used in hotel reservation systems (Talluri & Ryzin, 2004). The daily adjustments are critical because demand is assumed to be assured and dramatic changes should be accounted for to guarantee effective and efficient capacity allocation. References The Holloywood Reporter City Map. (2014). Tribeca Film Festival. Retrieved March 27, 2014, from The Hollywood Reporter: http://www.hollywoodreporter.com/package/tribeca-2014 Tribeca Film Festival. (2014). Tribeca Film Festival. Retrieved March 27, 2014, from http://tribecafilm.com/festival/ Phillips, R. L. (2005). Pricing and revenue optimization. Stanford, Calif.: Stanford Business Books. Talluri, K. T., & Ryzin, G. (2004). The theory and practice of revenue management. Boston, Mass.: Kluwer Academic Publishers. Appendices Kiel with a population of 235.000 inhabitants is a smaller town in the North of Germany and the host to the annual “Kiel week” (Kielnett, 2012). As mentioned earlier, it is the largest sailing event in the world with more than three million visitors in 9 days. Visitors include mainly tourists, competitive sailors, companies, locals, employees and groups. The city of Kiel does not have particular cultural attractions to offer for such a big amount of visitors besides the attractions during the “Kiel Week” which include sailing events, fireworks, an international market, games for children and theater (Kieler Woche, 2012). Figure 5: Adjusted data Table 2: New customers per rate class, day of arrival and length of stay ODF Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Benchmark rate, 1 night 19 11 2 9 8 17 14 Benchmark rate, 2 nights 12 11 0 2 14 1 9 Benchmark rate, 3 nights 9 9 0 3 3 0 9 Corporate rate, 1 night 25 21 11 18 1 6 3 Corporate rate, 2 nights 24 10 12 9 3 0 14 Corporate rate, 3 nights 16 10 8 8 4 2 22 Special Transient Rate, 1 night 31 12 37 38 10 38 36 Special Transient Rate, 2 nights 28 5 8 12 19 5 8 Special Transient Rate, 3 nights 31 6 9 10 9 16 7 Deep Discount Rate, 1 night 95 43 60 51 50 57 55 Deep Discount Rate, 2 nights 47 31 36 37 40 23 27 Deep Discount Rate, 3 nights 29 23 19 34 27 16 8 N.B: a) Benchmark various increase to achieve high occupancy b) -20% corporate for 1 and 2 nights c) -10% corporate for 3 nights d) Transient occupancy rate is 300% from Monday to Thursday, and 400% from Friday to Sunday. e) -90% discount for all nights. Figure 6: Demand forecast Daily Mean demand forecast per fare class (including all lengths of stay) Total Benchmark 40 52 31 23 30 38 36 Total Corporate 65 81 67 65 33 23 45 Total Special Transient 90 82 96 83 69 97 81 Total Deep Discount 171 173 198 200 207 197 154 Total demand 366 388 392 371 339 355 316 Adjusted Daily Mean demand forecast per fare class (including all lengths of stay) Total Benchmark 203 185 221 223 230 210 173 Total Corporate 85 95 75 76 45 38 56 Total Special Transient 104 89 112 94 85 120 102 Total Deep Discount 52 67 45 38 49 47 51 Total demand 444 436 453 431 409 415 382 Read More