Operations and Supply Chain Management The Core

84 OPERATIONS AND SUPPLY CHAIN MANAGEMENT
15. Historical demand for a product is
DEMAND
January
February
March
April
May
June
12
11
15
12
16
15
a. Using a weighted moving average with weights of 0.60, 0.30, and 0.10, find
the July forecast.
b. Using a simple three-month moving average, find the July forecast.
c. Using single exponential smoothing with α = 0.2 and a June forecast = 13,
find the July forecast. Make whatever assumptions you wish.
d. Using simple linear regression analysis, calculate the regression equation for
the preceding demand data.
e. Using the regression equation in part (d), calculate the forecast for July.
16. The tracking signals computed using past demand history for three different
products are as follows. Each product used the same forecasting technique.
1
2
3
4
5
6
7
8
9
10
TS 1 TS 2 TS 3
−2.70
−2.32
−1.70
−1.10
−0.87
−0.05
0.10
0.40
1.50
2.20
1.54
−0.64
2.05
2.58
−0.95
−1.23
0.75
−1.59
0.47
2.74
0.10
0.43
1.08
1.74
1.94
2.24
2.96
3.02
3.54
3.75
Discuss the tracking signals for each and what the implications are.
17. Here are the actual tabulated demands for an item for a nine-month period
(January through September). Your supervisor wants to test two forecasting
methods to see which method was better over this period.
MONTH ACTUAL MONTH ACTUAL
January
February
March
April
110
130
150
170
May 160
June
July
August
September
180
140
130
140
a. Forecast April through September using a three-month moving average.
b. Use simple exponential smoothing with an alpha of 0.3 to estimate April
through September, using the average of January through March as the initial
forecast for April.
c. Use MAD to decide which method produced the better forecast over the
six-month period.