This paper aims at conducting a time series analysis and forecasting the results of the sales of the two firms, that is, Kellogg and Majesco. A time series can be understood as the collection of data recorded over a certain period of time, which can be weekly, monthly, quarterly, or on a yearly basis (Pincus, & Goldberger, 1994). The sales by quarter of the two companies, kellogg and Majesco represents a perfect example of the time series data. The time series analysis can be used by the management to make current decisions and the plans which are based on the long-term forecasting.

In this analysis, there is an assumption that the past patterns will continue into the future. These long-term predictions are very essential to the management as they allow sufficient time for the procurement, sales, finance and other departments within the organizations to create plans for possible new plans, financing, development of a new product, and new techniques of assembling the company’s products (Box, Jenkins, & Reinsel, 2011). The sales forecasting levels of both the short-term and long term, are determined by the nature of business in the two organizations (Pincus, & Goldberger, 1994). It is important to understand that in business activities, time is money, since from the analysis, it is clear that that time and money are directly related (Keogh, Lonardi, & Chiu, 2002).

When making decisions under uncertainty, there is a need of making forecast. It is often the case that people may not realize that the choices they make from their anticipation of outcomes of their actions or inactions are all dictated by time (Keogh, Lonardi, & Chiu, 2002). This data therefore provides the management with better work of anticipating and thus a better work of managing the uncertainty in the business, through the use of efficient forecasting and other predictive methods.

This paper aims at conducting a time series analysis and forecasting the results of the sales of the two firms, that is, Kellogg and Majesco. A time series can be understood as the collection of data recorded over a certain period, which can be weekly, monthly, quarterly, or on a yearly basis (Pincus, & Goldberger, 1994). The sales by a quarter of the two companies, Kellogg and Majesco represents a perfect example of the time series data. The time series analysis can be used by the management to make current decisions and the plans which are based on the long-term forecasting (Pincus, & Goldberger, 1994).

In this analysis, there is an assumption that the past patterns will continue into the future. These long-term predictions are very essential to the management as they allow sufficient time for the procurement, sales, finance and other departments within the organizations to create plans for possible new plans, financing, development of a new product, and new techniques for assembling the company’s products (Box, Jenkins, & Reinsel, 2011).. The sales forecasting levels of both the short-term and long-term, are determined by the nature of business in the two organizations. It is important to understand that in business activities, time is money, since, from the analysis, it is clear that that time and money are directly related.

When making decisions under uncertainty, there is a need for making a forecast. It is often the case that people may not realize that the choices they make from their anticipation of outcomes of their actions or inactions are all dictated by time. This data, therefore, provides the management with better work of anticipating and thus a better work of managing the uncertainty in the business, through the use of efficient forecasting and other predictive methods.

In the analysis for Majesco Net sales, the Moving Average indicates that the sales are affected by the seasonal trend. The MA takes the closing sales of the products of Majesco and factor them into the calculations. The analysis shows that the average sales have been increasing with time from the first quarter all the way to the fourth quarter. In the analysis, the MA affects the net sales as the company may prefer to use the MA for the quarters to gauge the sales in short-term momentum (Box, Jenkins, & Reinsel, 2011). For instance, when the MA is 18912 in 2012 in the second quarter, the management can predict that the next average sales in the same quarter will be lower 18912. However, this MA is useless when the trend is moving sideways, making it difficult to make a proper decision based on the Net Sales.

It is seen that the MA fluctuates over each quarter making it difficult to predict the correct sales figure. For example, there are certain instances where the first quarter is higher than the other quarters and within the sales data, there are certain instances where there is a mix. It is seen that in 2012, 35315.75 is the first quarter 32715.75 is the second quarter, and 33071.75 is the third quarter. In this trend, the first quarter and the third quarter are all bigger than the second quarter. This makes moving average unreliable in this case when predicting the future net sales. The same cases happen to the Cumulative Moving Average (CMA). However, for Kellogg things are a bit different as the MA indicates a decreasing trend in the net sales from the first quarter of the fourth quarter. For instance, in 2012in the first quarter, the MA is 3310.25, and there is a decrease of sale to the fourth quarter that is 3654.5. The CMA also decreases with time from each quarter. The trend data shows an increasing net sales from the first quarter of the fourth quarter. The forecasted net sales are higher and increase from the first quarter to the fourth quarter.

The trend data shows that the net sales increases with time. However, the interval is not constant as it keeps on changing. There is a trend of increasing sales, for instance, taking 2011 as the base year, the sales in the first quarter are 26430.89889 while in the fourth quarter the sales are 28724.83027. Again in the forecasted sales figure are expected to be higher as seen in 2013 all the way to 2014. There is a prediction that the sales will increase for instance from 20716.4 in the second quarter of 2013 to the fourth quarter of 2014.

From the analysis, the trend shows that the PAT for Kellogg is decreeing with time from the first quarter to the fourth quarter. The Net profit for Kellogg may be reducing as a result of the impact of the interest, depreciation, and tax imposed on the company’s products. For instance, in 2010 trend in the first quarter is 321.6282051. This figure has decreased to 304.5866841. The interval is not constant as it changes with time. The trend shows that the forecasted net profits will be affected will increase but at some point fluctuate. The forecasted figures for PAT of Kellogg are higher than the net profit by 41.33724 which represents 13. 77908 percent higher. The SI also shows an increasing trend in the net sales for each quarter. It is seen the third quarter ranks the best in predicting the neat sales in the future. The sales data in the third quarter are more stable as compared to the other quarters. The PBIT shows that the third quarter ranks first, followed by Q4, Q1 and lastly Q2 in predicting future PBIT. The EBIT shows a decreasing trend of the earnings before tax. This is indicated by a decreasing MA and CMA. The interval is fluctuating, and there is no stable interval. The forecasted earnings before tax are also decreasing a similar manner to Q4. The same thing happens to PAT of Majesco (Arsham, H2003). .

The regression analysis for Majesco indicates that the model in the sales data represents 19.7150761 percent of the total variation in the outcomes of the regression analysis. The sales data are significant in this analysis. This is shown by the F-statistic which is more than 0.5 (Chatfield, 2013). The regression analysis also shows that changing the values of the data, the results. The model, in this case, is given by the question Y^=21078.39234+764.644*TIME. The equation shows that there is always a constant sale of 21078.39234 and that increasing the time by one unit result in a 764.644 increases the net sales. For Kellogg, the net sales model represents 90.5 percentage of the variation in net sales. Time is significant at a 95 percent level of confidence. The equation is given by Y^=2890.8574+49.83173*TIME. It shows that there is always a constant net sale of 2890.8574 and a one-unit change in time increases the net sales by 49.83173.

For EBIT of Kellogg, the model represents 72.24 variations in earnings before profits of the company. The F-statistics is greater than 0.5. Thus, the model is significant at 95 level of significance. For EBIT of Kellogg, the equation is Y^=557.26253-10.94755*TIME. It shows that an increase in the time reduces the profits after tax considerably by 10.94755 billion. The regression analysis on PAT indicates that the model only explains 66.96 variations. The equation is Y^=338.66973-5.680507*TIME. The equation indicates that an increase in time by one-quarter reduces the profit after tax by 5.680507. There is also a constant profit after tax of 338.66973 regardless of the tax and the interest rates.

Finally, the graph on the trend of the three variables, that is, PAT, nest sales, and EBIT are seen to follow linear with the net sales being highest. The net sales are followed by PAT and lastly EBIT.