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Hotel Industry Efficiency: Linear Programming Research Paper


Introduction

It is important to determine the efficiency of a business operation, because of the assumption that inefficient management of resources leads to business failure in the long run. However, this assertion seems true only if the enterprise competes in a business environment characterized by perfect competition. Thus, it is also imperative to point out that there are various cases wherein an enterprise manages to survive in spite of inefficiency.

It is perhaps the end result of imperfect competition brought about by differences in location and product quality (Anderson, Fok, & Scott, 2000). The hotel industry is an example of an imperfectly competitive industry, and in this case the best candidate for an efficiency study.

Methods

The proponent of the study attempted to determine the managerial efficiency of the hotel industry through the use of a linear programming procedure. The said procedure was called the Data Envelopment Analysis of DEA (Anderson, Fok, & Scott, 2000). In a nutshell, a DEA process enables the researcher to figure out the minimum costs necessary to produce a certain level of input. Based on this analysis framework the use of inputs in excess of the minimum requirement tells researchers that a particular hotel is inefficient.

The proponent of the study was able to measure the level of efficiency based on an “efficient frontier” framework. Under the DEA technique one can find the following efficiency measures: 1) overall efficiency or OE; 2) technical efficiency or TE; 3) allocative efficiency or AE; 4) pure technical efficiency or PTE; and 5) scale efficiency or SE (Anderson, Fok, & Scott, 2000).

Data was taken from Ward’s Business Directory of U.S. companies (Anderson, Fok, & Scott, 2000). The proponent of the study selected hotels that provided complete financial records. After using this filtering process, the final sample reached the 48 mark. Nevertheless, the 48 hotels that were included in the final sample represented a wide cross section of hotels from all over the country. In addition, the firms were heterogeneous in terms of size.

In order to generate pertinent data related to the “efficiency frontier” information regarding the cost, the output produced and the input prices. Six inputs were identified and these are listed as follows:

  1. number of full-time employees;
  2. the number of rooms;
  3. total gaming related expenses;
  4. total food and beverage expenses;
  5. hotel operation expense;
  6. other expenses (Anderson, Fok, & Scott, 2000).

Furthermore, the wage cost per employee was derived by dividing the total revenue of the hotel by the number of full-time employees. On the other hand, the room price was derived by dividing the hotel revenues by the number of rooms, the occupancy rate, and the number of days per year. However, gaming, food, and beverage, and other expenses were calculated by deriving each value as a percentage of total revenue (Anderson, Fok, & Scott, 2000).

Results

It is interesting to note that overall efficiency scores were very low. In fact, the mean efficiency score was only at 42 percent. Therefore, the hotels deemed efficient can afford to lower the input cost by 58 percent. In addition, the mean AE was 51 percent. On the other hand the mean TE score was 81 percent. PTE and SE score were 90 and 87 percent respectively (Anderson, Fok, & Scott, 2000).

Data from 48 Samples OE AE SE PTE TE
Mean 0.424 0.515 0.901 0.869 0.814
Std. Dev. 0.306 0.295 0.183 0.262 0.285
Maximum 1 1 1 1 1
Minimum 0.000118 0.003914 0.18369 0.002104 0.000952

Table 1. Results.

It is interesting to note that the outcome of the study revealed high levels of inefficiency exists within the hotel industry. Thus, the proponent of the study wanted to point out that the said industry is not an example of a perfectly competitive industrial market. The proponent of the study also found out that inefficient hotels were able to allocate more resources to hotel operations and other forms of expenses. In addition, inefficient hotels had more rooms and more employees working for the said firm. In stark contrast the efficient hotels allocated more resources to food and beverage operations.

Discussion and Conclusion

The proponent of the study discovered the inefficiency of the hotel industry due to the efficiency measure of around 42 percent. In other words, the average hotel uses or inefficiently utilizes as high as 58 percent of resources. Thus, even a 50 percent reduction in expenditures will allow the average hotel proprietor to earn a profit. A significant root cause of the problem was the failure to determine the most appropriate input mix.

For example, the inefficient hotels employ a large number of workers and have to maintain a large number of rooms. Allocative efficiency measures reveal As a result, a large portion of the resources was utilized to manage the operations side of the business.

On the other hand, the efficient hotels did not focus on using the bulk of their resources to build more rooms and hire more people (Morey & Dittman, 1995). In contrast, the focus was on food and beverage operations. Therefore, hotel managers must do a better job in allocating resources or improving the input mix (Lambert, Lambert, & Cullen, 1989). It was also a good idea to invest more on the food and beverage operations, because it is an area that has higher profit margins.

It is interesting to note the significant levels of inefficiency, because in the beginning of the study, it was hypothesized that inefficient managers are going to drive their companies to the ground. In this context, assuming that the assumption held true for efficiency, the hotel managers that were leading inefficient hotels are about to bring them to the brink of financial ruin in the long run. However, this was not the case.

This assertion was based on the realization that the 48 hotels included in the study were the ones that submitted complete financial data. Thus, these hotels are still open for business even if there was a significant inefficiency in the management of certain hotels. As a result, the assumption made earlier that the hotel industry can afford to work under inefficient circumstances because of the nature of the business, especially with regards to the location and the different quality of the products.

Nevertheless, the hotel industry can increase its net profit if certain measures concerning the elimination of inefficient business strategies are implemented and monitored by the manager. Nonetheless, it has been made clear that inefficiency does not automatically destroy a business enterprise as suggested in the hypothesis. Imperfect competitive environments creates a marketing atmosphere that allows inefficient managers to survive, but at a tremendous cost to the company and stakeholders involved.

References

Anderson, R., Fok, R., & Scott, J. (2000). Hotel industry efficiency: An advanced linear programming examination. American Business Review, 18(1): 40-47.

Lambert, C., Lambert, J., & Cullen, T. (1989). The overbooking question: A simulation. Cornell Hotel & Restaurant Administration Quarterly, 30(2), 15-20.

Lefever, M. (1988). The gentle act of overbooking. Hotel industry efficiency: An advanced linear programming examination. American Business Review, 18(1): 40-47.

Morey, R., & Dittman, D. (1995). Evaluating a hotel GM’s performance: a case study in benchmarking. Cornell Hotel Restaurant & Administration, 36(5), 14-19.

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IvyPanda. (2020, October 1). Hotel Industry Efficiency: Linear Programming. Retrieved from https://ivypanda.com/essays/hotel-industry-efficiency-linear-programming/

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"Hotel Industry Efficiency: Linear Programming." IvyPanda, 1 Oct. 2020, ivypanda.com/essays/hotel-industry-efficiency-linear-programming/.

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IvyPanda. "Hotel Industry Efficiency: Linear Programming." October 1, 2020. https://ivypanda.com/essays/hotel-industry-efficiency-linear-programming/.

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IvyPanda. 2020. "Hotel Industry Efficiency: Linear Programming." October 1, 2020. https://ivypanda.com/essays/hotel-industry-efficiency-linear-programming/.

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IvyPanda. (2020) 'Hotel Industry Efficiency: Linear Programming'. 1 October.

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