Historical demand for a product is a. Using a weighted moving average with weigh
ID: 325990 • Letter: H
Question
Historical demand for a product is
a. Using a weighted moving average with weights of 0.60 (June), 0.20 (May), and 0.20 (April), find the July forecast. (Round your answer to 1 decimal place.)
b. Using a simple three-month moving average, find the July forecast. (Round your answer to 1 decimal place.)
c. Using single exponential smoothing with ? = 0.30 and a June forecast = 11, find the July forecast. (Round your answer to 1 decimal place.)
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.)
e. Using the regression equation in d, calculate the forecast for July. (Do not round intermediate calculations. Round your answer to 1 decimal place.)
DEMAND January 15 February 12 March 16 April 15 May 17 June 16a. Using a weighted moving average with weights of 0.60 (June), 0.20 (May), and 0.20 (April), find the July forecast. (Round your answer to 1 decimal place.)
b. Using a simple three-month moving average, find the July forecast. (Round your answer to 1 decimal place.)
c. Using single exponential smoothing with ? = 0.30 and a June forecast = 11, find the July forecast. (Round your answer to 1 decimal place.)
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.)
e. Using the regression equation in d, calculate the forecast for July. (Do not round intermediate calculations. Round your answer to 1 decimal place.)
Explanation / Answer
Answer to Question a :
Forecast for July
= 0.60 x Demand for June + 0.2 x demand for May + 0.2 x demand for April
= 0.60 x 16 + 0.2 x 17 + 0.2 x 15
= 9.6 + 3.4 +3
= 16
Answer to question b :
Forecast for July using a 3 month simple moving average
= Demand for April + Demand for May + demand for June ) / 3
= ( 15 + 17 + 16 ) / 3
= 16
Answer to problem c:
The equation for exponential smoothing forecast :
Ft = alpha x D t-1 + ( 1 – alpha) x Ft-1
Ft, Ft-1 = Forecast for period t and t-1 respectively
Dt-1 = Demand for period t-1
Alpha = Exponential smoothing forecast = 0.3
Therefore ,
Ft = 0.3xDt-1 + 0.7.Ft-1
Given are following data :
Dt-1 = Demand for June =16
Ft-1 = Forecast for June = 11
Ft = 0.3 x 16 + 0.7 x 11 = 4.8 + 7.7 = 12.5
FORECAST FOR JULY = 12.5
Answer to problem d:
We denote months in terms of numbers such as :
January = 1 , February = 2 , March = 3 , April = 4 , May = 5 , June = 6 , July = 7
Let the linear regression equation is :
Y = a + b.t
Y ( dependent variable ) = Demand
T =Serial number of month
A, b = Constants
We place all the values of months ( in terms of numbers ) and Demands as given in 2 parallel columns in excel . and apply theformula LINEST( ) to find out the values of a and b
Accordingly relevant values are :
A =13.266 ( 13.27 rounded to 2 decimal places )
B =0.542
Therefore , the regression equation will be :
Y = 13.27 + 0.542.t
Answer to question e :
To calculate forecast for July, we need to putt= 7
Accordingly, forecast for July :
Y = 13.266 + 0.542 x 7 = 13.266 + 3.794 = 17.06
FORECAST FOR JULY = 17.06 ( 17.1 rounded to 1 decimal place)
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