Operations and Supply Chain Management The Core

68 OPERATIONS AND SUPPLY CHAIN MANAGEMENT
Causal relationship
forecasting
Forecasting using
independent
variables other than
time to predict future
demand.
Causal Relationship Forecasting
Causal relationship forecasting involves using independent variables other than time to
predict future demand. To be of value for the purpose of forecasting, any independent variable
must be a leading indicator. For example, we can expect that an extended period of
rain will increase sales of umbrellas and raincoats. The rain causes the sale of rain gear.
This is a causal relationship, where one occurrence causes another. If the causing element
is known far enough in advance, it can be used as a basis for forecasting.
The following shows one example of a forecast using a causal relationship.
Example 3.5: Forecasting Using a Causal Relationship
The Carpet City Store in Carpenteria has kept records of its sales (in square yards) each year, along
with the number of permits for new houses in its area.
NUMBER OF HOUSING STARTS
YEAR
PERMITS
SALES
(IN SQ. YDS.)
1 18 13,000
2 15 12,000
3 12 11,000
4 10 10,000
5 20 14,000
6 28 16,000
7 35 19,000
8 30 17,000
9 20 13,000
Carpet City’s operations manager believes forecasting sales is possible if housing starts are known
for that year. First, the data are plotted in Exhibit 3.13, with
X = Number of housing start permits
Y = Sales of carpeting
exhibit 3.13
Causal Relationship: Sales to Housing Starts
Y
20,000
18,000
Excel:
Forecasting
Sales
(square
yards of
carpet)
16,000
14,000
12,000
10,000
8,000
X
Y
6,000
4,000
2,000
0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36
Number of housing start permits
X