Trend Line Forecasting Case Study

e. Trend line forecasting using the time series approach
i. The time series approach uses historical data from previous years’ performance. The main intention of using this approach is to determine a trend arising from past sales demand patterns and using that to predict the level of sales in the future. ii. The time series examines previous years’ data and uses than to establish a trend line from the past to the present and then project this line into the future. It is possible that the trend line uses raw data and may not have been adjusted for seasonal factors and other random events. In those situations the sales forecasts are likely to be exaggerated and the consequences can be serious for the lending bank or investor, let alone the company’s management. iii. Therefore it is a common routine for the reader or analyst to establish how the forecasts are arrived at and whether appropriate adjustments have been made for various factors including seasonality and random events. On the other hand, the reader or analyst would be prudent to consider testing the forecast sales for a possible range of deviation from the forecast. This ought to be done regardless of the explanations given for the forecast sales.

f. Trend line forecasting using

Unlike the time series that attempts to predict the future sales based on a trend over a period of time, the causal approach begins by establishing a relationship between past years’ sales performance and a crucial underlying variable such as per capita income, national economic growth or population increase. As an example one can possibly agree that an older car would require more maintenance and repair expenditure than a newer one. As a car gets older, more costs would be incurred. If this proposition is reasonable and acceptable, a relationship would be established by the regression analysis technique. In this way the car owner can predict the cost of maintenance when his car is 5 years old or when it is at 8 years