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Review View Help Tell me what you want to do nless you need to edit, it's safer to stay in Protected View. Enable Ed ecause it hasn't been activated. Activate An exercise in Forecasting... How many will attend Saturday night's concert? (Lionel a How many hot dogs will Sodexo cook Friday and Saturday How many schools (and students) will be attending? Hampton Jazz Festival Big Band) in the Kibbie Dome? What will be room availability in Lewiston because of the festival? a What should I charge for my hotel rooms during the busy weekend? Is there a threshold? 2 How much more should Casa Lopez be ordering for Thursday, Friday, and Saturday? 8 What will the economic impact be to the city of Moscow? Select one or two and write a double-spaced paper telling what you forecasted and how you camie up with the foreast you made. 3-21 LO 3.46 Notescomments

Explanation / Answer

Forecasting is the method used to predict future Trends of economic data related to an organisation. Quantitative forecasting utilizes compilation of past performance data of the organisation to arrived at accurate forecast for any given process. Qualitative forecasting is more like an expert opinion based on information of other organisations within the same sector, and maybe based largely on intuition. Qualitative forecast resorted to when historical data is not present due to the organisation of the product line being new.

One of the methods used for quantitative forecasting is trend analysis, which is based on data exhibiting a definite upward or downward Trend. Trend analysis me utilise different methodologies such as regression double exponential smoothing and triple smoothing. The method is utilised for forecasting sales data and Amazer disadvantage main be better does not take into account external factors which may greatly impact demand such as in production of a new competitive product, or general economic slowdown.

Graphical methods used very often for forecasting based on information of past performance which has been plotted into a graphical form thereby presenting the entire information in a visual format which makes it relatively easy to identify Trends and patterns, as well as spot any deviations. Graphical representation of data is much easier to analyse and interpret, making it convenient to make more accurate predictions, as also allowing for extrapolation of previous data of demand for a product for making accurate focus for future demand. The major disadvantage of utilising this method for forecasting is that all the base data used should has fair and equal representation within the graph. Any variance in according the correct proportion to each data set may result in erroneous analysis and results. Again as with all methods of forecasting it is based on the assumption of all internal and external factors remain stagnant and similar to the past.

Econometric modelling with a type of focus Mall where a set of equations are utilised simultaneously to represent the interaction of interrelated variables dependent as well as, independent. As the name suggests it utilizes a combination of mathematical as well as economic models. The model needs to have enough equations, to arrive at accurate predictions for all the variables involved in the model. The model depends upon historical data and consists of utilising data to represent real world processes, and arriving at predicted values of involved variables through application of statistics as well as economics. It is used for analysing as well as predicting, economic forces which impact the supply and cost factors, within an organisation or industry.

Life cycle modelling is another quantitative method of forecasting which depends upon application of available patterns of past performance of demand data which cover every stage of the life cycle of the product write from point of introduction, through the growth stages, upon attaining majority, upon reaching saturation point and finally decline of the product leading to new product family. This method of forecast is concentrated on demand forecasting which has as its basis analysis of product trains over the entire life cycle to be able to accurately analyse the product history and identify the future trends. It necessarily explains the consumption pattern of individuals through analysis of life cycle of products as all demands are related to consumption behaviour of individuals.

All the methods of forecasting have the same drawback as being dependent on data and compiling fresh data in sanitised lab like conditions without considering the impact of the real world dynamics on every process, product and organisation.

Room availibilty in Lewiston because of festival

room availability forecasting is a useful tool for managing reservations maintaining Goodwill by preventing loss of customers and an excellent tool open disk management as well as pre planning facilities, along with scheduling necessary activities for unexpected volume of business.

The information which proves to be when official in preparing a forecast for availability of rooms may be listed as under:

Market intelligence of the constituency within which Hotel operates

The major strengths of the hotel along with the surrounding area

Historical Occupancy data, especially for the same interval of prior years.

All details of reservations along with historical data and lead times

Profiling of existing clients especially Corporates

Any specific events which impacts the business within the geographical area

The present Occupancy rate along with cut off date and the current reservation status along with anticipated reservations

Any plans which may impact the forecast such as renovation which may lead to changes in the quantity of available rooms.

Details and data for competitive hotels within the same segment in the area.

The system of room availability forecasting will use the following data:

Number of expected room arrivals

Number of expected stroll ins

Number of anticipated room stayovers

Number of expected room No shows

Number of anticipated room under stays

Number of anticipated room over stays

The percentage of no suggests dividing the number of room No shows by the number of room reservations for a given period. This helps the front desk to take decisions on whether rooms should be offered to stroll in visitors.

Percent of walkins is calculated by number of rooms stroll ins by number of room arrivals.

Percent of over stays and under stays is calculated by dividing the relevant quantity of overstay/understay rooms by number of anticipated over stays and under stays.

Room availability forecasting method can be calculated by total quantity of rooms less quantity of out of order rooms

Less quantity of room stay over

Less quantity of room reservations

Add quantity of room reservations x percent of no shows

Add number of room under stays less number of room over stays

This gives the number of rooms available for sale for a given period.

In a hotel business the days are categorised into high season and low season which seasonality being the major factor that considerably impacts level of room demand. Bye mastering knowledge of the periods of demand, pricing and room allocation revenue Optimisation can be achieved in an efficient and logical manner. for this the data collected is deseasonalized after being subjected to computation of seasonal averages. The relative or normalised time series values calculated from the data and the forecasting model is applied on this time series. After the forecasting model is applied the normalising factors will be multiplied back to restore the seasonal impact within the forecasted portion of the time series. The classification of the data on the basis of seasonal resumes is done on the basis of information from Hotel managers and is possible to verify why utilisation of various statistical Tools and techniques. However, extensively it is need to be provided to information from managers as they may have extensive knowledge of events which occur locally and other activities impacting business. This help to obtain an accurate seasonal average. This can be them through a time series model using holes exponential smoothing with maximum likelihood approach for estimating the given parameters. it needs to be combined with the deseasonalization strategy.

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