Question

Table 1 shows sales data for a company involves with the manufacturing of Product X. Analyze the given information to forecast sales for the month of July using the following methods: (i) 3-period simple moving average. (ii) 3-period weighted moving average using weightage value of 0.5, 0.3 and 0.2 (highest value for the most recent period). (iii) Simple exponential smoothing using \( \alpha = 0.2 \). Assume the forecast for month of April is 24. (iv) Linear trend method. \( m = \frac{n \sum xy - \sum x \sum y}{n \sum x^2 - (\sum x)^2} \) \( c = \frac{\sum y - m \sum x}{n} \) Table 1: Sales data for Product X January February March April May June 18 19 24 21 25 27

          Table 1 shows sales data for a company involves with the manufacturing of
Product X. Analyze the given information to forecast sales for the month of
July using the following methods:
(i) 3-period simple moving average.
(ii) 3-period weighted moving average using weightage value of 0.5, 0.3
and 0.2 (highest value for the most recent period).
(iii) Simple exponential smoothing using \( \alpha = 0.2 \). Assume the forecast for
month of April is 24.
(iv) Linear trend method.
\( m = \frac{n \sum xy - \sum x \sum y}{n \sum x^2 - (\sum x)^2} \)
\( c = \frac{\sum y - m \sum x}{n} \)
Table 1: Sales data for Product X
January February March April May June
18 19 24 21 25 27

Table 1 shows sales data for a company involves with the manufacturing of
Product X. Analyze the given information to forecast sales for the month of
July using the following methods:
(i) 3-period simple moving average.
(ii) 3-period weighted moving average using weightage value of 0.5, 0.3
and 0.2 (highest value for the most recent period).
(iii) Simple exponential smoothing using α = 0.2. Assume the forecast for
month of April is 24.
(iv) Linear trend method.
m = (n ∑ xy - ∑ x ∑ y)/(n ∑ x^2 - (∑ x)^2)
c = (∑ y - m ∑ x)/(n)
Table 1: Sales data for Product X
January February March April May June
18 19 24 21 25 27

Added by Olga P.

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