National Scan, Inc., sells radio frequency inventory tags.
Monthly sales for a seven-month period were as follows:
Month | Sales (000)Units |
Feb. | 17 |
Mar. | 20 |
Apr. | 14 |
May. | 22 |
Jun. | 21 |
Jul. | 25 |
Aug. | 29 |
b. Forecast September sales volume using each of the
following:
(1) A linear trend equation.(Round your intermediate
calculations and final answer to 2 decimal places.)
Yt _ thousands
(2) A five-month moving average. (Round your answer to 2
decimal places.)
Moving average _ thousands
(3) Exponential smoothing with a smoothing constant equal to
.10, assuming a March forecast of 15(000). (Round your
intermediate forecast values and final answer to 2 decimal
places)
Forecast _ thousands
(4) The naive approach.
Naive approach _ thousands
(5) A weighted average using .55 for August, .20 for July, and
.25 for June. (Round your answer to 2 decimal
places.)
Weighted average _ thousands
(1) A linear trend equation.
Month | t | y | ty | t^2 |
Feb | 2 | 17 | 34 | 4 |
Mar | 3 | 20 | 60 | 9 |
Apr | 4 | 14 | 56 | 16 |
May | 5 | 22 | 110 | 25 |
Jun | 6 | 21 | 126 | 36 |
Jul | 7 | 25 | 175 | 49 |
Aug | 8 | 29 | 232 | 64 |
Sum | 35 | 148 | 793 | 203 |
Yt = a + bt
where
t = specified number of time periods from t=0
Yt = forecast for period t
a=value of Yt at t=0
b=slope of the line
b =
a =
n=number of periods
y=value of time series
So, y(9) = 28.7 thousands
2) A five-month moving average.
So, Moving Average = 22.2 thousands
(3) Exponential smoothing with a smoothing constant equal to .10, assuming a March forecast of 15(000).
where, Ft-1 = Forecasted of previous month
and At-1 = Actual of previous month
Month | t | Actual | Forecasted (Ft) |
Feb | 2 | 17 | |
Mar | 3 | 20 | 15 |
Apr | 4 | 14 | 15+0.1*(20-15)= 15.5 |
May | 5 | 22 | 15.5+0.1*(14-15.5)= 15.35 |
Jun | 6 | 21 | 15.35+0.1*(22-15.35)= 16.015 |
Jul | 7 | 25 | 16.015+0.1*(21-16.015)= 16.5135 |
Aug | 8 | 29 | 16.5135+0.1*(25-16.5135)= 17.362 |
Sep | 9 | 17.362*0.1*(29-17.362)= 20.21 |
Forecast = 20.21 thousands
(4) The naive approach.
In Naive approach, the last period's actuals are used as this period's forecasts.
Month | (000)Units (Actual) | Forecast (Actual in Past) |
Feb. | 17 | |
Mar. | 20 | 17 |
Apr. | 14 | 20 |
May. | 22 | 14 |
Jun. | 21 | 22 |
Jul. | 25 | 21 |
Aug. | 29 | 25 |
Sep | 29 |
Naive approach :29 thousands
(5) A weighted average using .55 for August, .20 for July, and .25 for June.
Month | Sales | |
(000)Units | wt | |
Feb. | 17 | |
Mar. | 20 | |
Apr. | 14 | |
May. | 22 | |
Jun. | 21 | 0.25 |
Jul. | 25 | 0.2 |
Aug. | 29 | 0.55 |
F(t) =
(weight*actual)
= (0.55*29)+(0.20*25)+(0.25*21)
= 5.25+5+15.95
= 26.2
Weighted average is 26.2 thousands
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven-month period were as...
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Need some help with this operations management question. It'd
also be nice if you could explain how you got your answer. Thanks.
National Scan, Inc., sells radio frequency inventory tags. Monthly sales for a seven-month period were as follows: Month Feb Mar Sales (000)Units 16 15 12 May Jun Jul Aug 19 23 25 b. Forecast September sales volume using each of the following (1) A linear trend equation. (Round your intermediate calculations and final answer to 2 decimal places.)...
ASSIGNMENT: #2 a through c. NOTE: Problem 2b. (1), change
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