My prediction for the quarterly unit production in the next one year is 12. 00, 14. 00, 13. 00 and 20. 00 million respectively. This prediction is based on the time series methodologies and analysis of the previous data. The centered moving averaging model is used considering the previous quarterly data to make the prediction. To get the values for each quarter, I have conducted a three phase research. In phase one, the variable explaining the advertising budget was determined. Regression analysis and a correlation co-efficient of 0. 96 were used to determine this variable. With sales as the independent variable, regression analysis found the standard error of the prediction to be 11. 00 and this is lower than standard error of the estimation of the other two variables. Phase two studied market size data of the previous twelve years to predict the Blue Inc sales based on the averaging models. A study was done on the mean square error and patterns of trend graphs to select best model and make the right prediction and moving averaging model was chosen through trial and error method. An averaging model of two periods with weights of 0. 1 and 0. 9 was chosen.
Using this model and a market share of 6%, I made the decision that we need to set our least product unit for the coming year as 47 million. The third phase used the centered moving averaging model to analyze the quarterly data for the previous 12 years. Studying cyclic disparity of data found that consumers tend to buy more in the fourth quarter. Trend graphs helped make the best forecast. Due to the cyclic disparity in the data, a centered moving average model was used to make the data non-seasonal. Data for the previous six years was used for analysis since data beyond this period could not show recent trend and predicted value would be lesser than usual. The product level for the coming four quarters should be put as12. 00, 14. 00, 13. 00 and 20. 00 million units. According to the company policy, every predicted number of production units was increased by 10% to cater for safety stock.