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IBM Operations Forecasting Technique Essay

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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. 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...

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svg-one

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…

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