Non-pricing RM tools

Capacity management usually lays under jurisdiction of operation department rather than Revenue Management, however, decisions are made based on forecasted demand and RM strategies. The fit between capacity and demand has a direct impact on customer satisfaction and company’s profitability. Excess capacity will decreased profitability by underutilized workforce or physical resources, while inadequate capacity can decrease customer satisfaction because of degradation of facilities, crowd or increased waiting time. Strategic operational capacity decisions are long-term and includes the psychical and labour decisions as well as optimal utilization of a facility. They also include capacity flexibility, which is the most important for RM. It represents ability to respond to fluctuating demand by short-term adjustments to workforce or psychical space.

Strategic physical capacity decisions are often made in hotel group’s headquarter and include size, configuration, layout and built-in flexibility. Those decisions are made in early stage and have long-term implications. Moreover the relationship between demand, capacity, managerial objectives and visitor experience is complex and difficult to evaluate (Pullman, 178). In order to correctly estimate physical capacity, it’s recommended to use one of the following methods. Liu and Var (1980) adapted break even formula :

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where is total profit, is quantity (volume of sales), is average sales price per room, is variable costs per room and is total fixed costs. Carey (1989) used regression analysis

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where Oro stands for optimal room occupancy, B1, B2, B3 are the parameters, is the price, is the overall market capacity. It’s worthy to mention that those methods are used for long-term analysis.

Labour capacity strategies can be divided into three groups based on the nature of the hotel. The first – level – exists in higher class hotels, when regardless of demand hotel retain major of their staff during the year. It keeps high service level and morals and does not increase opportunity costs when the demand is known before. Usually work forces are scheduled due to flights departure or bus arrivals. The second – chase – usually used in budget-hotels staff only when they need it. It decreases the costs but increased turnover can decrease service level and morals. The third – mixed strategies – are used when there are stable seasonal trends during the year, and hotel can adjust labour capacity due to seasonality. While, capacity estimation for level strategies is pretty straight-forward and is calculated by such factors as average demand, number of rooms and other facilities and average number of employees necessary to keep distinct function. The estimation in chase strategy usually involves calculating trade-off between full – and part-time employees, over-time, hiring and layoff costs. Those factors may be seen as in variables in linear programming in which goal is to minimize cost function. (Pulmann, 2010, p.183)

As it was mentioned before, short-term adjustments are used to quickly respond to fluctuating demand, therefore they are the most important non-pricing RM tools. As long-term strategies they are also divided into physical and human (labour and visitor) capacities. To physical we could include: renting and sharing capacity, hiring sub-contractors, changing resource allocation, changing hour operation or partitioning visitors (status or length of transaction). On the other hand, short-term human capacity decisions include such approaches as: allowing over-time, idle time, cross-training employees, hiring and using temporary and part-time employees. The other side of the coin is customer perspective. To address demand bigger than anticipated hotel can: allow waiting or bulking, provide rewards or incentives for inconveniences, provide diversions or complementary services, camouflage the queue, pay for VIP queues, move guests to different hotel (Klassen and Rohleder, 2002, p. 530), (Adenso-Diaz et al., 2002, p. 288).

A powerful tool to manage physical capacity is overbooking. Despite its disadvantages, it is widely used to overcome the problem of cancellations and no-shows. Even if from ethical point of view it brings some concerns, it is indeed a RM strategic tool which generate additional revenue. Normally tour operators were the ones to include over-bookings in there tour packages, however, because of the decreasing importance of intermediaries and cost savings which can rise from adapting over-booking directly in the hotels  and more and more hotels use it on daily basis. The biggest problem is to properly model the optimal level of overbooking. Overbooking models can be divided into: static and dynamic[1]. Very interesting static overbooking model was proposed by Netessine and Shumsky (2002). The formula for optimal number of rooms overbooked takes form (is the smallest value Y*, such that):

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where F(x) – known distribution function of no-shows (or cancellations) Y – number of rooms to overbooked, B – loss, when room is not sold (fare of the room or fare of the room – amount returned to customer due to early cancellation), C – ill-will and cost of turning down customer who had reservation.

A simple approach to overbooking was proposed by Ivanov (2006), he considers two types of reservation (guaranteed and non-guaranteed) and influence of one additional booking request on the optimal number of overbooked rooms. The general formula for expected capacity is a follows:

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where C – expected capacity; N – amount of non-guaranteed reservations at the specific time; n – average percentage of unused non-guaranteed reservations; G – amount of guaranteed reservations at the specific time; g – average percentage of unused non-guaranteed reservations

 

Example of overbooking calculations is here: Download

 

The next RM non-pricing tool is “length of stay”, however besides certain event is rarely used in nowadays RM. The basic concept lies in setting minimal and rarely maximal number of nights in hotel. It protects the hotel from losing revenue when customers book short periods on high demand periods and “block” the capacity for customers who would book more nights. It’s usually used during New Year’s Eve, Christmas or festivals. It can also create demand in days when it’s constantly very low, but it implies the risk that customers will reject paying for additional night and choose other hotel. The example of such a practice would be business hotels, which require two days stay over weekend – Friday, and Saturday night. The biggest disadvantage of such practices is their static nature; as a result it cannot react flexible to changing demand. (Vinod, 2004)

“Availability guarantees” is a RM strategy, which is widely used in practice, but it has not got adequate attention in academic literature. This strategy might be described as loyalty program for the best clients, with which hotel protects itself from loss of valuable customers due to lack of inventory. Members of such a program have e.g. 48 hours or 72 hours reservation guarantee (Example from Platinum program from Marriott hotel).  The question is however, when availability should take precedence over demand-based availability controls derived from traditional RM (Noon, et. al., 2003, 14), and which customers qualify for such a program. It is important to remember, that such a tactic will always leave couple of rooms empty before e.g. 72 or 48 hours before arrival date, what as consequence can lead to opportunity cost in case of no premium members show.

The problem, which had considerably small attention in RM papers, is the distribution channels’ effect on hotel revenue management. Especially it concerns electronic channel, which is the most popular within big hotels. Myung, Li and Bai (2009) showed that hotels perceived e-wholesalers as partners that enable them selling their inventories more effectively and with greater exposure. However, this research showed also possible conflicts of control over room prices. Moreover, it could create a specific logic error in building RM strategy when rates are set due to forecasted demand but the distributor sales rooms at different (often discounted) levels. The question is then, do the additional factor of distribution channel will improve forecasts and as result revenue? The research conducted by: Choi and Kimes (2002) shows that using pick-up method used directly on rate and length of stay with additional factor of distribution channel level gives more accurate forecast and significantly more revenue and contribution. However, the same research showed that extended Bid-price model did not give significant improvement in accuracy and additional revenue. But the researchers emphasize that failure to achieve additional revenue in second model could be due to limitations of simulation study, and the result could be different on empirical data.

 


 

References:

  1. Adenso-Diaz, B., Gonzales-Torre, P., Garcia, V. (2002) A capacity management model in service industries. International Journal of Service Industry Management 13 (3), p. 286–302.
  2. Carey, K. (1989) Tourism development in LDCs: hotel capacity expansion with reference to Barbados. World Development, vol. 17,  no. 1, pp. 59–67.
  3. Choi, S. and Kimes, S. (2002) Electronic distribution channels’ effect on hotel revenue management. The Cornell hotel and restaurant administration quarterly, vol. 43, no. 3, pp. 23-31.
  4. Ivanov, S. (2006) Management of Overbookings in the Hotel Industry-Basic Concepts and Practical Challenges. Tourism Today, Vol. 6, pp. 19-2.
  5. Klassen, K., Rohleder, T. (2002) Demand and capacity management decisions in services: how they impact on one another. International Journal of Operations & Production Management vol. 5, no. 6, pp. 527–548
  6. Liu, J., Var, T. (1980) The use of lodging ratios in tourism. Annals of Tourism Research, vol. 7, no. 3, pp. 406–427.
  7. Myung, E., Li, L. and Bai, B. (2009) Managing the distribution channel relationship with E-Wholesalers: Hotel Operators’ Perspective. Journal of Hospitality Marketing & Management, vol. 18, no. 8, pp. 811-828
  8. Netessine, S., R. Shumsky (2002) Introduction to the theory and practice of yield management. INFORMS Transactions on Education, vol. 3, no. 1, pp. 34-44
  9. Noone, B.M., Kimes, S.E. and Renaghan, L.M. (2003). Integrating customer relationship management with revenue management: A hotel perspective. Journal of Revenue and Pricing Management, vol. 2, no. 1, pp. 7-21.
  10. Pullman, M. and Rodgers, S. (2010) Capacity management for hospitality and tourism: A review of current approaches. International Journal of Hospitality Management, vol. 29, no.1, pp. 177-187
  11. Vinod, B. (2004) Unlocking the value of revenue management in the hotel industry. Journal of Revenue and Pricing Management, vol. 3, no. 2, pp. 178-190

 

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