Historical demand for a product is: a. Using a weighted moving average with weig

Question

Historical demand for a product is:

a. Using a weighted moving average with weights of 0.40 (June), 0.50 (May), and 0.10 (April), find the July forecast. (Round your answer to 1 decimal place.)

July forecast            

b. Using a simple three-month moving average, find the July forecast. (Round your answer to 1 decimal place.)

July forecast            

c. Using single exponential smoothing with ? = 0.20 and a June forecast = 10, find the July forecast. (Round your answer to 1 decimal place.)

July forecast            

d. Using simple linear regression analysis, calculate the regression equation for the preceding demand data. (Do not round intermediate calculations. Round your intercept value to 1 decimal place and slope value to 2 decimal places.)

Y =   +   t

e. Using the regression equation in d, calculate the forecast for July. (Do not round intermediate calculations. Round your answer to 1 decimal place.)

July forecast            

DEMAND January 11 February 10 March 14 April 11 May 15 June 14

Explanation / Answer

Answer to question a :

Forecast for July

= 0.4 x Demand for June + 0.5 x Demand for May + 0.1 x Demand for April

= 0.4 x 14 + 0.5 x 15 + 0.1 x 11

= 5.6 + 7.5 + 1.1

= 14.2

Answer to question b :

Forecast for July

= ( Demand for April + Demand for May + Demand for June ) /3

= ( 11 + 15 + 14 ) /3

= 13.33 ( 13,3 rounded to 1 decimal place )

Answer to question C :

Following to be noted about forecast for period t :

Ft = alpha x Dt-1 + ( 1 – alpha ) x Ft-1 = 0.2 x Dt-1 + 0.8 x Ft-1

Where,

Ft = Forecast for period t

Ft-1 = Forecast for period t-1

Dt-1 = demand for period t-1

Alpha = Exponential smoothing constant = 0.2

Since Demand ( Dt-1 ) for June = 14 and Forecast for June ( t-1 ) = 10

Forecast for July = 0.2 x 14 + 0.8 x 10 = 2.8 + 8 = 10.8

JULY FORECAST = 10.8

Answer to question d and e :

Let the Linear regression equation be :

Y = a + b.t

Where,

Y ( Dependent variable ) = Forecasted demand

T = Serial number of month ( January = 1, February = 2 , March = 3 … June = 6 , July = 7 etc )

We now place all values of serial number of month and corresponding demand ( as mentioned in the problem ) in 2 adjacent columns in excel and apply the formula LINEST ( 0 to obtain the values of a and b .

Accordingly , values are as follows :

A = 9.8

B = 0.77

Therefore ,

Y = 9.8 + 0.77.t

To calculate forecast for July , we need to put t = 7 .

Therefore ,

Forecast for July = 9.8 + 0.77 x 7 = 9.8 + 5.39 = 15.19 ( 15.20 rounded to 1 decimal place )

Y = 9.8 + 0.77.t

JULY FORECAST = 15.20

JULY FORECAST = 10.8

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