IBM Operations Forecasting Technique Essay

Tags: forecasting revenue ibm short

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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.

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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

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Reference

Lucey T. (2002). Quantitative Technique. UK. BookPower.

Hidalgo, B., & Goodman, M. (2013). Multivariate or Multivariable Regression? American Journal of Public Health, 103(1), 39-40. doi:10.2105/ajph.2012.300897

Statista (2016). Revenue of IBM worldwide from 1999 to 2015 (in billion U.S. dollars) Statista Inc.

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