FORECASTING AND SIMULATION

Forecasting and Simulation

Forecasting and Simulation

Description of Data

Data that has been examined in this research represents the hotel occupancy level. Quarter-wise data have been collected from year 2007to year 2011. The data represent scale item variable for the number of occupancy level that placed in every quarter for the defined period from year 2007 to 2011. Hotel revenue is highly dependent on the number of occupancy it receives from customers.

Occupancy Level (%)

Year

Q1

Q2

Q3

Q4

2007

64

77

72

2008

62

70

80

76

2009

60

75

86

71

2010

57

67

88

80

2011

75

60

90

Forecasting Techniques

Future demand for the product is extremely variable to the company, which is beyond their control. The appropriate demand forecasting is important because it will enable the company to take appropriate actions based on the forecasting manufacturer's orders. Forecast is essential for any knowledge of the historical demand of the product. The data selected involve time factor which includes seasonal and dimensional changes in productivity level.

The purpose of the non-causal forecasting techniques is to obtain estimates or forecasts of future values of a time series from the historical information contained in the observed series to date (Creswell, 2009). These techniques do not require specification of the factors that determine the behavior of the variable, but based solely on modeling the systematic behavior of the series. The appropriate technique depends on the model prediction of behavior of the series. The assumptions underlying the forecasting method selection is the stability to assess the systematic behavior of the series (Silverman, 2004). Secondly, the value of the variable observed in any period is the result of systematic behavior and a random disturbance (Kevin, 2003).

Selected Models

Three techniques that are suitable to assess the forecasting pattern of the selected data include Expert Modeler, Exponential Smoothing, and Seasonal Moving Average Model.

Discussion and Justifications for the Selected Models

Expert Modeler

The data selected involve time factor which includes seasonal and dimensional changes in orders level. Considering the impact of seasonality and trend component for the given case, Expert Modeler will provide the best-fitting model for analysing the future trend. Expert Modeler transforms the data using the square root or log transformation in order to have statistically significant relationship with dependent series. It involves both ARIMA and Exponential Smoothing of data, which makes this trend curve fit for forecasting the output of manufacturing industries (Murugan, 2007). It excludes the trend of seasonality by identifying the errors component associated with the seasonal time. It provides adjusted seasonal series for the data that increases the probability to predict the seasonal factor component effectively; providing significant value in forecasting even in unpredictable situations. Error identified in this modelling technique can be adjusted in forecasted value to enhance the accuracy of forecasted value (David, 2006).

Expert Modeler curve help analyze the current sales figure and set a trend of the industry adjusted with the change in output. Fitted value derived from the seasonal factor adjustment and trend cycle provides an advantage to have exponential trend curve for the forecasted value (Armstrong, 2001). The Expert Modeler sets the best-?tting model for each dependent series. When predictor variables (independent) are defined, Expert Modeler ...