Chapter
8 Forecasting
DISCUSSION QUESTIONS
1. a. There is no apparent trend in the data. The naïve forecast method, exponential
smoothing or the simple moving average would be appropriate for estimating the
average.
b. The primary external factors that can be forecasted three days in advance and can
appreciably affect air quality are wind velocity and temperature inversions.
c. Weather conditions cannot be forecast two summers in advance. Medium-term causal
factors affecting air quality are population, regulations and policies affecting wood
burning, mass transit, use of sand and salt on roads, relocation of the airport, and
scheduling of major tourism events such as parades, car races, and stock shows.
d. In the area of technological forecasting, qualitative methods of forecasting are best. One
such approach is the Delphi method, whereby the consensus of a panel of experts is
sought. Here we would survey experts in the fields of electric-powered vehicles, coal-
fired combustion for electric utilities, and development of alternatives to sand and salt on
roads. We hope to determine whether to expect any technological breakthroughs
sufficient to affect air quality within the next 10 years.
2. What’s Happening? Our objective in writing this discussion question is to ensure students
recognize the difference between sales and demand. Demand forecasting techniques require
demand data. Michael is making the common mistake of using sales data as the basis for
demand forecasts. Sales are generally equal to the lesser of demand or inventory. Say that
inventory matches average demand at a particular location and is 100 newspapers. However,
for the current edition, demand is less than average, say 90. Michael enters sales (which
happens to be equal to demand in this period) into the forecasting system, resulting in an
inventory reduction at that location for the next edition. Now suppose that demand for the
next edition is 110. But because inventory has been reduced to 90, only 90 newspapers will
be sold. Michael would then enter sales (which happens to be equal to inventory, not
demand) into the forecasting system. This approach ratchets downward and tends to starve
the distribution system. Because the publication is not reliably available, some customers
eventually stop looking for What’s Happening? and demand truly declines. It is important
that data used for demand forecasting are demand data, not sales data.
8-1
Copyright © 2016 Pearson Education, Inc.
,8-2 PART 2 Managing Customer Demand
PROBLEMS
Causal Methods: Linear Regression
1. Garcia’s Garage
a. The results, using the Regression Analysis Solver of OM Explorer, are:
The regression equation is Y = 42.464 + 2.452X
b. Forecasts
Y (Sep) = 42.464 + 2.452 (9) = 64.532 or 65
Y (Oct) = 42.464 + 2.452 (10) = 66.984 or 67
Y (Nov) = 42.464 + 2.452 (11) = 71.888 or 72
2. Hydrocarbon Processing Factory
Using the Regression Analysis Solver of OM Explorer, we get:
a. Relationship to forecast Y from X
Y = 0.888 + 0.622 X
b. Strength of relationship between Y and X is moderate as indicated by
R2 = 0.450
Copyright © 2016 Pearson Education, Inc.
, Forecasting l CHAPTER 8 l 8-3
R = 0.671
Standard Error of Estimate = 0.331
3. Ohio Swiss Milk
The results from the Regression Analysis Solver are:
a. Y = 1,121.212. − 0.282 X
b. R2 = 0.888
R = −0.942 indicates a fairly strong negative relationship. Increases in costs explain
89% of the decreases in gallons sold
c. Y = 1,121.212 − 0.282 (325) = 1,029.562
Because these numbers are in terms of thousands of gallons, the cost per gallon is $1.03
at this production level.
4. Manufacturing firm skills test
The results from the Least Square Linear Regression module of POM for Windows are:
Copyright © 2016 Pearson Education, Inc.
, 8-4 PART 2 Managing Customer Demand
a. From the output, the relationship is
Rating = 4.184 + 0.943 (Score)
b. Score = 80
Rating = 4.184 + 0.943 (80) = 79.624
c. R = R2 = 0.934 = 0.966
There is a very strong positive relationship. Increases in scores explain 93% of increases
in ratings.
5. Materials handing
The results from the POM for Windows’ least squares-linear regression module are:
Copyright © 2016 Pearson Education, Inc.
8 Forecasting
DISCUSSION QUESTIONS
1. a. There is no apparent trend in the data. The naïve forecast method, exponential
smoothing or the simple moving average would be appropriate for estimating the
average.
b. The primary external factors that can be forecasted three days in advance and can
appreciably affect air quality are wind velocity and temperature inversions.
c. Weather conditions cannot be forecast two summers in advance. Medium-term causal
factors affecting air quality are population, regulations and policies affecting wood
burning, mass transit, use of sand and salt on roads, relocation of the airport, and
scheduling of major tourism events such as parades, car races, and stock shows.
d. In the area of technological forecasting, qualitative methods of forecasting are best. One
such approach is the Delphi method, whereby the consensus of a panel of experts is
sought. Here we would survey experts in the fields of electric-powered vehicles, coal-
fired combustion for electric utilities, and development of alternatives to sand and salt on
roads. We hope to determine whether to expect any technological breakthroughs
sufficient to affect air quality within the next 10 years.
2. What’s Happening? Our objective in writing this discussion question is to ensure students
recognize the difference between sales and demand. Demand forecasting techniques require
demand data. Michael is making the common mistake of using sales data as the basis for
demand forecasts. Sales are generally equal to the lesser of demand or inventory. Say that
inventory matches average demand at a particular location and is 100 newspapers. However,
for the current edition, demand is less than average, say 90. Michael enters sales (which
happens to be equal to demand in this period) into the forecasting system, resulting in an
inventory reduction at that location for the next edition. Now suppose that demand for the
next edition is 110. But because inventory has been reduced to 90, only 90 newspapers will
be sold. Michael would then enter sales (which happens to be equal to inventory, not
demand) into the forecasting system. This approach ratchets downward and tends to starve
the distribution system. Because the publication is not reliably available, some customers
eventually stop looking for What’s Happening? and demand truly declines. It is important
that data used for demand forecasting are demand data, not sales data.
8-1
Copyright © 2016 Pearson Education, Inc.
,8-2 PART 2 Managing Customer Demand
PROBLEMS
Causal Methods: Linear Regression
1. Garcia’s Garage
a. The results, using the Regression Analysis Solver of OM Explorer, are:
The regression equation is Y = 42.464 + 2.452X
b. Forecasts
Y (Sep) = 42.464 + 2.452 (9) = 64.532 or 65
Y (Oct) = 42.464 + 2.452 (10) = 66.984 or 67
Y (Nov) = 42.464 + 2.452 (11) = 71.888 or 72
2. Hydrocarbon Processing Factory
Using the Regression Analysis Solver of OM Explorer, we get:
a. Relationship to forecast Y from X
Y = 0.888 + 0.622 X
b. Strength of relationship between Y and X is moderate as indicated by
R2 = 0.450
Copyright © 2016 Pearson Education, Inc.
, Forecasting l CHAPTER 8 l 8-3
R = 0.671
Standard Error of Estimate = 0.331
3. Ohio Swiss Milk
The results from the Regression Analysis Solver are:
a. Y = 1,121.212. − 0.282 X
b. R2 = 0.888
R = −0.942 indicates a fairly strong negative relationship. Increases in costs explain
89% of the decreases in gallons sold
c. Y = 1,121.212 − 0.282 (325) = 1,029.562
Because these numbers are in terms of thousands of gallons, the cost per gallon is $1.03
at this production level.
4. Manufacturing firm skills test
The results from the Least Square Linear Regression module of POM for Windows are:
Copyright © 2016 Pearson Education, Inc.
, 8-4 PART 2 Managing Customer Demand
a. From the output, the relationship is
Rating = 4.184 + 0.943 (Score)
b. Score = 80
Rating = 4.184 + 0.943 (80) = 79.624
c. R = R2 = 0.934 = 0.966
There is a very strong positive relationship. Increases in scores explain 93% of increases
in ratings.
5. Materials handing
The results from the POM for Windows’ least squares-linear regression module are:
Copyright © 2016 Pearson Education, Inc.
