Forecast Case Study: The Hamilton Hotel

The Hamilton Hotel is a large downtown business hotel with 1 877 rooms. This property is a part of the hotel chain Mariott a huge company which operates 180 hotels and resorts all around the world.

Hotel seeking to get a higher profit, need to look ahead and try to forecast a future business situation.

The forecasting procedure is done to get a forecast the demand of the product (room) in the market depending on different factors (seasonality, trend, etc). This process can be done anytime of the year for any kind of hotel.

Those data will be compared to the budget of the hotel and define the new rate for rooms and organised strategies for the hotel to maximize the profit, so the revenue.

The accuracy of a forecast is very important because

(Weatherford and Kimes, 2003).

Issue Statement

1 As seen in the exhibit 1 the amount of no- the show is an issue when it comes to doing the forecast of the booking this also makes it difficult for the managers to decide that weather they should over a book or not. As we know The Hamilton Hotel is a conference center hotel this kind of structure has some consequences on the forecast and the bookings of the hotel.

To prevent this issue the hotel can create some offers and package for the week-end bookings such as spa massage and breakfast included being surer that they will keep their reservations.

This idea could also resolve the problem of the overbooking. In the way to prevent no-show, The Hamilton Hotel is often making overbooking to be sure of any way keep the right number of booking. While taking the risk of sending some clients into sister hotels and losing money (transportation to another hotel,etc), losing credit and reputation of management of guest

It is a technique that calculates the overall trend in a data set. In operations management, the data set is the sales quantity by the historical data of the hotel. This method is useful for short-term forecasting.

We called the “moving” average because as a new demand number is calculated for an forthcoming time period. (Christian George / http://study.com/academy/lesson/demand-forecasting-techniques-moving-average-exponential-smoothing.html). Then, the calculations method I purpose in this case study for the moving average is to take two type of data “5 Same DOW” that is mean take on 5 weeks of data the average of each (Saturday, Sunday, Monday, Tuesday, Wednesday, Thrusday,Friday). Also, tried with “3 Same DOW” that is mean take on 3 weeks of date the average of each day (Saturday, Sunday,,Friday).

Then the conclusion is to analyse the Error Measurement, the forecast error can be measured in a percentage (MAPE) establish by (Armstrong and Collopy). From that we should compare the both result of MAPE for the both test of data and try to find the minimum MAPE