The director of marketing research needs to determine which forecast method is the most accurate in forecasting sales for the year 2008 based on the collected data on quarterly sales for the previous four years. After running four different methods of forecasting: regression with time series, regression with economic factors, Holt-Winters additive model, and Holt-Winter multiplicative model. Based on the error the most appropriate method of forecasting is regression tit economic factors. Based on this model, sales for the year BIBB decrease significantly, which may be indicative of possible recession. Therefore, it is highly recommended that auto parts plans efficiently with the available resources to prevent large loss of money.
Background Forecast is “a planning tool that helps management in its attempts to cope with the uncertainty of the future, relying mainly on data from the past and present and analysis of trends” (Objectifications. Com)_ A good forecast helps companies prepare to prevent large amount of money loses by planning more efficiently. In the Auto Parts forecasting case study, the director of marketing of a large manufacturer of spare parts for automobiles understands the consequences of forecasting errors and wishes to forecast the sales as accurate as possible. After collecting sales data for each quarter Of the past four years, he ran a number of forecasts using the method of times series.
However, there are some factors such as economic activity and Oil prices that may have a significant impact on auto parts sales for which he is concerned. Therefore, the director Of marketing research decided to use econometric variables to check if sales recast are better predicted using this model. Problem The large manufacturer of spare parts for automobiles must decide which forecast method is the most accurate in forecasting sales for the year 2008 based on the collected data on quarterly sales for the previous four years. Analysis The information provided for the auto parts case study in Excel included: quarterly sales, non-Tara activity index and oil prices for the years 2004, 2005, 2006, and 2007.
Four different models were used to forecast sales for 2008: regression with time series, regression with economic doctors, Bolt-Winters additive model, and Bolt-Winters multiplicative model. Regression with time series: Time series is a sequence of obeseВ»actions which are ordered in time or space (Young, 1997). There are two types of time series data: continuous such as electrocardiograms and discrete which are spaced intervals. The main features of time series are trend and seasonality. Trend is a long term enactment in a time series. The trend is the direction and rate of change in the time series. Trends may be identified by taking averages over a period of time in seasonal data. If the averages change over time, then a trend is identified.
For example, in economics the GAP has a positive trend in the long term While resources and fix cost has a negative trend in the long term. Seasonality is the component of variation in a time series Which is dependent on the time Of the year. There are four seasons: spring. Summer, fall and winter. Dummies are used for seasonality. For the auto parts case study, regression with time series method was ran where Y the dependent variable is sales while X, XSL the independent variables are trend and seasonality respectively. Dummies were used for spring, summer, fall and winter. If a season is non-significant 8>0. 05, then it does not have an impact on sales. After running the first regression, winter (Q) is Nan-significant because it has a P value greater than 0. 5 and a t value less than absolute 2; therefore, winter (Q) does not have an impact on sales. After the first regression based on the F statistics the model is good; however, one of the independent variables (Q) was non-significant. Subsequently, Squaws eliminated and a second regression was ran, After running the second regression without Q, based on the statistics the model is good. The R Square value means how much the independent variable explains the behavior of the dependent variable. In this model, the R square value represents how much trend and seasonality explain the behavior of sales. R square is equal to 95. 47, which means that the model explanatory power is high.