IBM Operations Forecasting Technique

Forecasting is the process of predicting the future based on the past data. Typically, forecasting uses the statistical technique employing different methods such as time series, moving average, linear regression and exponential smoothing. The study uses the 15-year dataset of IBM (International Business Machine) revenues from 1999 to 2016 fiscal years. The study collects large dataset because of the larger the dataset, the better the accuracy of the results.

The researcher collects revenue data of the IBM between 1999 and 2016 from the Statista (2016) website, and the dataset used for the analysis is as follow: Revenue ($Billion) Different methods are used for the forecast. The linear regression, exponential smoothing and moving average are used for analysis. Linear Regression The linear regression is the forecasting technique that assists in enhancing the relationship between dependent and independent variables. The benefits of the linear regression is that it assists in providing accurate results if large data are obtained.

The study uses the data in Table 1 to produce revenue forecast using the Linear regression technique. The output is revealed in fig 1. Fig 1: Linear Regression Forecasting SUMMARY OUTPUT Regression Statistics Multiple R 0,24 R Square 0,06 Adjusted R Square 0,00 Standard Error 5,34 Observations 18,00 ANOVA df SS MS F Significance F Regression 1,00 28,93 28,93 1,02 0,33 Residual 16,00 455,57 28,47 Total 17,00 484,50 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95,0% Upper 95,0% Intercept 1992,97 14,47 137,69 0,00 1962,28 2023,65 1962,28 2023,65 X Variable 1 0,16 0,16 1,01 0,33 -0,17 0,49 -0,17 0,49 Exponential Smoothing The exponential smoothing is another forecasting technique used and the result is presented in Fig 2 and Table 2.

Fig 2: Exponential Smoothing Forecasting Table 2: Forecast using Exponential Smoothing Year Revenue ($ Billion) Forecast with Exponential Smoothing 1999 87,55 2000 88,4 2001 83,07 87,635 2002 81,19 87,867 2003 89,13 82,882 2004 96,29 81,984 2005 91,13 89,846 2006 91,42 95,774 2007 98,79 91,159 2008 103,63 92,157 2009 95,76 99,274 2010 98,87 102,843 2011 106,92 96,071 2012 104,51 99,675 2013 99,75 106,679 2014 92,8 104,034 2015 81,7 99,055 2016 79,9 91,69 2017 81,52 Moving Average Moving Average is a technique that assists in forecasting the future revenues of the IBM, and the major benefits of the moving average are its simplicity. (Lucey 2002). The output of the moving average forecasting method is revealed in table 3.

Table 3: Forecasting Using Moving Average Year Revenue ($Billion) Moving Average 1999 87,55 2000 88,4 2001 83,07 2002 81,19 86,34 2003 89,13 84,22 2004 96,29 84,46333 2005 91,13 88,87 2006 91,42 92,18333 2007 98,79 92,94667 2008 103,63 93,78 2009 95,76 97,94667 2010 98,87 99,39333 2011 106,92 99,42 2012 104,51 100,5167 2013 99,75 103,4333 2014 92,8 103,7267 2015 81,7 99,02 2016 79,9 91,41667 2017 84,8 Fig 3: Moving Average Forecasting Compare and Contrast the Forecasting Method Overview of the linear regression, exponential smoothing and moving average show that all the three models can assist in forecasting future revenue for the IBM. Moreover, all the three forecast methods have the ability to produce accurate results if a large number are available. Moreover, both the exponential smoothing and moving average have the ability to produce short-term forecasting. The.