Choose one of the forecasting methods and explain the rationale behind using it in real life. I would choose to use the exponential smoothing forecast method. Exponential smoothing method is an average method that reacts more strongly to recent changes in demand than to more distant past data. Using this data will show how the forecast will react more strongly to immediate changes in the data. This is good to examine when dealing with seasonal patterns and trends that may be taking place.
I would find this information very useful when examining the increased production of a product that appears to be higher in demand in the present than in the past Taylor (2011). For example, annual sales of toys will probably peak in the months of March and April, and perhaps during the summer with a much smaller peak. This pattern is likely to repeat every year, however, the relative amount of increase in sales during March may slowly change from year to year. During the month of march the sales for a particular toy may increase by 1 million dollars every year.
We could add to our forecasts for every March the amount of 1 million dollars to account for this seasonal fluctuation. Describe how a domestic fast food chain with plans for expanding into China would be able to use a forecasting model. By looking at the data of other companies the fast food chain would be able to put together a forecast to determine if their business venture was viable. They could examine the sales data and determine through a exponential smoothing forecast if it made sense for them to enter into the market.
This would show the trends and changes in the data more recently rather than in past time. The fast food industry of China is experiencing phenomenal growth and is one of the fastest growing sectors in the country, with the compounded annual growth rates of the market crossing 25%. Further, on the back of changing and busy lifestyle, fast emerging middle class population and surging disposable income, the industry will continue to grow at apace in coming years.
What is the difference between a causal model and a time- series model? Give an example of when each would be used. The time series model is based on using historical data to predict future behavior Taylor (2011). This method could be used by a construction work, retail store, fast food restaurant or clothing manufacturer to predict sales for an upcoming season change. For example, new homebuilders in US may see variation in sales from month to month.
But analysis of past years of data may reveal that sales of new homes are increased gradually over period of time. In this case trend is increase in new home sales. The causal model uses a mathematical correlation between the forecasted items and factors affecting how the forecasted item behaves. This would be used by companies who do not have access to historical data therefore they would use a competitors available data. For example, the sales of ice cream will increase when the temperature outside is high.
You will see more and more people going to the stores buying ice cream, freeze pops and other cold items when it is hot. When it is cold you will see more people buying coffee, hot chocolate, and cappuccino. What are some of the problems and drawbacks of the moving average forecasting model? One problem with the moving average method is that it does not take into account data that change due to seasonal variations and trends. This method works better in short run forecast…