DEMAND FORCASTING Chapter 5 Prepared by Cynthia Wisner, MBA 1 © 2019 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use. Principles of Supply Chain Management (5e) Learning Objectives You should be able to: Explain the role of demand forecasting in a supply chain Identify the components of a forecast Compare & contrast qualitative & quantitative forecasting techniques Calculate and Assess the accuracy of forecasts Explain collaborative planning, forecasting, & replenishment 2 © 2019 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use. Principles of Supply Chain Management (5e) Chapter Outline Introduction The Importance of Demand Forecasting Forecasting Techniques Forecast Accuracy Collaborative Planning, Forecasting, & Replenishment (CPFR) Useful Forecasting Websites Forecasting Software Cloud-Based Forecasting Summary 3 © 2019 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use. Principles of Supply Chain Management (5e) Introduction Organizations moving to a more effective demand-driven supply chain Suppliers must find ways to better match supply & demand Improved forecasts benefit all trading partners in the supply chain & mitigates supply-demand mismatch problems. . 4 © 2019 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part, except for use as permitted in a license distributed with a certain product or service or otherwise on a password-protected website or school-approved learning management system for classroom use. Principles of Supply Chain Management (5e) The Importance of Demand Forecasting A forecast is an estimate of future demand & provides the basis for planning decisions The goal is to minimize deviation between actual demand and forecast The factors that influence demand must be considered when forecasting Buyers and sellers should share all relevant information to generate a single consensus forecast Good forecasting provides reduced inventories, costs, & stockouts, & improved production plans & customer service 5 © 2019 Cengage Learning. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in.

1. DEMAND FORCASTING
Chapter 5
Prepared by Cynthia Wisner, MBA
1
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Learning Objectives
You should be able to:
Explain the role of demand forecasting in a supply chain
Identify the components of a forecast
Compare & contrast qualitative & quantitative forecasting
techniques
Calculate and Assess the accuracy of forecasts
Explain collaborative planning, forecasting, & replenishment
2
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.

2. Principles of Supply Chain Management (5e)
Chapter Outline
Introduction
The Importance of Demand Forecasting
Forecasting Techniques
Forecast Accuracy
Collaborative Planning, Forecasting, & Replenishment (CPFR)
Useful Forecasting Websites
Forecasting Software
Cloud-Based Forecasting
Summary
3
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Introduction
Organizations moving to a more effective demand-driven supply
chain
Suppliers must find ways to better match supply & demand
Improved forecasts benefit all trading partners in the supply
chain & mitigates supply-demand mismatch problems.
.
4
© 2019 Cengage Learning. May not be scanned, copied or

3. duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
The Importance of Demand Forecasting
A forecast is an estimate of future demand & provides the basis
for planning decisions
The goal is to minimize deviation between actual demand and
forecast
The factors that influence demand must be considered when
forecasting
Buyers and sellers should share all relevant information to
generate a single consensus forecast
Good forecasting provides reduced inventories, costs, &
stockouts, & improved production plans & customer service
5
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques

4. Qualitative forecasting - based on opinion & intuition
Quantitative forecasting - uses mathematical models &
historical data to make forecasts
Time series models - most frequently used among all the
forecasting models
6
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Qualitative Forecasting Methods
Used when data are limited, unavailable, or not currently
relevant
Forecast depends on skill & experience of forecaste & available
information
Four qualitative models used are –
Jury of executive opinion
Delphi method
Sales force composite
Consumer survey
7

5. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Jury of executive opinion
Group of senior management executives collectively develop
the forecast
Good for long-range planning and new product introductions
Delphi method
Internal and external experts are surveyed during several rounds
summary of responses is sent out to all the experts & they can
modify their responses in next round
Good for high-risk technology forecasting; large, expensive
projects; or major new product introductions
8
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

6. Forecasting Techniques (Continued)
Sales force composite
Based on sales force’s knowledge of the market & estimates of
customer needs
Tendency for sales force to under-forecast
Consumer survey
Questionnaire uses inputs from customers on future purchasing
needs, new product ideas, & opinions about existing or new
products
9
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Quantitative Methods
Time series forecasting – assumes the future is an extension of
the past
Historical data is used to predict future demand
Cause & Effect forecasting – assumes one or more factors
(independent variables) predict future demand
All quantitative methods become less accurate as forecast’s
time horizon increases
For long-time horizon forecasts, use a combination of
quantitative & qualitative techniques
10

7. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Components of Time Series
Data should be plotted to detect for the following components –
Trend variations: increasing or decreasing over many years
Cyclical variations: wavelike movements that are longer than a
year (e.g., business cycle)
Seasonal variations: show peaks & valleys that repeat over
consistent interval (ie. hours, days, weeks, months, seasons, or
years)
Random variations: due to unexpected or unpredictable events
such as natural disasters
11
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

8. Forecasting Techniques (Continued)
Time Series Forecasting Models
Naïve Forecast – the estimate of the next period is equal to the
demand in the past period.
Ft+1 = At
WhereFt+1 = forecast for period t+1
At = actual demand for period t
12
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Time Series Forecasting Models
Simple Moving Average Forecast – uses historical data to
generate a forecast. Works well when demand is stable over
time.
WhereFt+1 = forecast for period t+1
At = actual demand for period t
n= number of periods to calculate moving average

9. 13
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Simple Moving Average
14
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duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Time Series Forecasting Models

10. Weighted Moving Average Forecast – based on an n-period
weighted moving average
WhereFt+1 = forecast for period t+1
Ai = actual demand for period i
n= number of periods to calculate moving average
wi= weight assigned to period i (Σwi = 1)
15
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Weighted Moving Average
16
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

11. Forecasting Techniques (Continued)
Time Series Forecasting Models
Exponential Smoothing Forecast – type of weighted moving
average - only two data points are needed
- –
Where Ft+1= forecast for Period t + 1
Ft = forecast for Period t
At = actual demand for Period t
17
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Exponential Smoothing
18
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole

12. or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Time Series Forecasting Models
Linear Trend Forecast – trend can be estimated using simple
linear regression to fit a line to a time series
Ŷ = b0 + b1x
Where Ŷ = forecast or dependent variable
x = time variable
b0 = intercept of the line
b1 = slope of the line
19
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)

13. Linear Regression
(Fig. 5.4)
20
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Cause & Effect Models
One or several external variables are identified & related to
demand
Simple regression – Only one explanatory variable is used & is
similar to the previous linear trend model.
x variable is no longer time but an explanatory variable
Ŷ = b0 + b1x
Where Ŷ = forecast or dependent variable
x= explanatory or independent variable
b0 = intercept of the line
b1 = slope of the line
21

14. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecasting Techniques (Continued)
Cause & Effect Models
Multiple regression – several explanatory variables are used to
predict the dependent variable
Ŷ = b0 + b1x1 + b2x2 + . . . Bkxk
Where
Ŷ = forecast or dependent variable
xk = kth explanatory or independent variable
b0 = intercept of the line
bk = regression coefficient of the independent variable xk
22
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy

15. Forecast error - the difference between actual quantity & the
forecast
Forecast error, et = At - Ft
Whereet = forecast error for Period t
At = actual demand for Period t
Ft = forecast for Period t
23
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy (Continued)
Mean absolute deviation (MAD)- MAD of 0 indicates the
forecast exactly predicted demand
Whereet= forecast error for period t
At = actual demand for period t
n = number of periods of evaluation
24
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duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-

16. protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy (Continued)
Mean absolute percentage error (MAPE) – provides a
perspective of the true magnitude of the forecast error
Whereet= forecast error for period t
At = actual demand for period t
n = number of periods of evaluation
25
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy (Continued)
Mean squared error (MSE) – analogous to variance, large
forecast errors are heavily penalized
Whereet= forecast error for period t
n = number of periods of evaluation
26

17. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy (Continued)
Running Sum of Forecast Errors (RSFE) – indicates bias in the
forecasts or the tendency of a forecast to be consistently higher
or lower than actual demand.
Running Sum of Forecast Errors, RSFE =
Where et= forecast error for period t
27
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

18. Forecast Accuracy (Continued)
Tracking signal determines if forecast is within acceptable
control limits
If tracking signal falls outside pre-set control limits
there is bias problem with the forecasting method
evaluation of forecast generation is warranted
Tracking Signal =
28
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Forecast Accuracy (Continued)
Biased forecast will lead to excessive inventories or stockouts
Key to accurate forecasts is collaboration with different
partners inside outside the firm working together to eliminate
forecasting error
Collaboration can lead to significant improvements in
forecasting accuracy
29

19. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Collaborative Planning, Forecasting, & Replenishment (CPFR)
What is CPFR?
It is a business practice that combines the intelligence of
multiple trading partners in the planning & fulfillment of
customer demands.
It links sales & marketing best practices, such as category
management, to supply chain planning processes to increase
availability while reducing inventory, transportation & logistics
costs.
30
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

20. Real value of CPFR comes from sharing of forecasts among
firms
Eliminates shifting of inventories among trading partners that
suboptimizes the supply chain
Provides the supply chain with a plethora of benefits but
requires a fundamental change in the way that buyers & sellers
work together.
Collaborative Planning, Forecasting, & Replenishment
(Continued)
31
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
CPFR Benefits
Strengthens partner relationships
Provides analysis of sales & order forecasts
Uses point-of-sale data, seasonal activity, promotions, to
improve forecast accuracy
Manages demand chain & eliminates problems before they
appear
Allows collaboration on future requirements & plans
Collaborative Planning, Forecasting, & Replenishment
(Continued)
32

21. © 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
CPFR Benefits (continued)
Uses joint planning & promotions management
Integrates planning, forecasting and logistics activities
Provides efficient category management & understanding of
consumer purchasing patterns
Collaborative Planning, Forecasting, & Replenishment
(Continued)
33
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
CPFR Benefits (continued)

22. Provides analysis of key performance metrics to:
reduce supply chain inefficiencies
improve customer service
increase revenues and profitability
Collaborative Planning, Forecasting, & Replenishment
(Continued)
34
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Top three challenges for CPFR implementation
Difficulty of making internal changes
Cost
Trust
Collaborative Planning, Forecasting, & Replenishment
(Continued)
35
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duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management

23. system for classroom use.
Principles of Supply Chain Management (5e)
Useful Forecasting Websites
Institute for Business Forecasting & Planning
https://ibf.org/
International Institute of Forecasters
www.forecasters.org
Forecasting Principles
www.forecastingprinciples.com
Several forecasting blogs:
Business Forecasting (www.businessforcastingblog.com)
No Hesitations: A Blog by Francis Diebold
(http://fxdiebold.blogspot.com.au)
36
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
Software

24. Solution
s
Forecasting Software simplifies the calculation processes and
saves a great deal of time
Business Forecast Systems - www.forecastpro.com
John Galt - www.johngalt.com
Just Enough - www.justenough.com
Avercast, LLC - www.avercast.com
SAS - www.sas.com/en_us/home.html
37
© 2019 Cengage Learning. May not be scanned, copied or
duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)

25. Cloud-Based Forecasting
Cloud-based forecasting - supplier-hosted or software-as-a-
service (SaaS) advanced forecasting applications
Provided on a subscription basis
Benefits include:
Increase data storage & data analysis capabilities
New capabilities without extensive training
Reduction in IT costs
Improvement in forecast accuracy
Reduction in stockout & inventory carrying costs
Improvement in employee productivity
38
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duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management

26. system for classroom use.
Principles of Supply Chain Management (5e)
End of Chapter 5
39
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duplicated, or posted to a publicly accessible website, in whole
or in part, except for use as permitted in a license distributed
with a certain product or service or otherwise on a password-
protected website or school-approved learning management
system for classroom use.
Principles of Supply Chain Management (5e)
å
=
n
t
t
e
1

27. MAD
RSFE
MAD
RSFE
CASE
· 3.
Hammerstein University—Enrollment
Hammerstein University president Blake Sherman was worried
about the survival of his university. The state of Illinois had
been having financial troubles for years. For the past two years
it was at an impasse in passing a budget. One consequence was
that students were not getting the state tuition funds they had
received in past years. This issue had multiple results.
· (1)
Less students were enrolling because of the lack of state funds.
· (2)
Those students who did enroll were tentative about remaining in
school and this generated a high dropout rate.
· (3)
To support the students, Hammerstein University increased its
own tuition assistance program.
All three results were affecting Hammerstein University’s
financial well-being.

28. The key to survival was simple. Enrollment has to be
maintained at a level to support the current level of degree
programs, staff, and faculty. In addition, there still are the basic
expenses of running Hammerstein University such as building
repairs, grounds upkeep, and travel costs for sporting events. If
the enrollment could not be maintained at the required level,
Blake knew he would have to make some significant cuts.
Degree programs that were not paying for themselves may need
to be cut. This obviously would affect faculty. Cosmetic repairs
for building or the grounds may need to be deferred to later.
The potential financial crisis may make it necessary to
temporarily shut down some sports. Blake feared that while
these types of actions would ease the financial crisis in the
short term, the same actions could further reduce enrollment
and result in a slow downward spiral.
Blake had his admissions staff provide the enrollment for the
past six semesters. The following table provides the data.
SEMESTER
ENROLLMENT
2017 fall
2017 spring
3,120
2016 fall
3,249

29. 2016 spring
3,716
2015 fall
3,520
2015 spring
3,170
2014 fall
3,094
Blake has the vice president of finance, Leroy Hardy, to
calculate how many students represented a breakeven point
under the current financial situation. He then asked Leroy to
calculate what the enrollment breakeven point would be if the
state began to provide tuition assistance again to their students.
Leroy said for the enrollment breakeven point for no-state
tuition was 3,265. The enrollment breakeven point for state
tuition being paid was 3,000.
Blake had not yet been told the forecast for fall 2017. However,
as he looked over the past enrollments, he was not optimistic.
Both spring 2017 and fall 2016 enrollments were below the
breakeven point for no-state tuition. Although he would have to
wait for the figures from admissions staffs, he believed he needs
to begin looking at options if the state didn’t pass a budget that
included tuition assistance.
Discussion Questions
1. Calculate the potential enrollment for fall 2017 using a three-

30. semester weighted moving average, with weights 0.1, 0.3, and
0.6, with 0.06 for the most recent semester. Start your forecast
for the spring 2016 semester and continue to fall 2017.
2. Calculate the potential enrollment for fall 2017 using
exponential smoothing with a forecast for fall 2014 of 3,094 and
a smoothing constant of 0.2. Which forecast do you think is the
best? Why?
3. Which forecast or forecasts match or exceed the enrollment
breakeven point if the state continues to not pay tuition
assistance? Which match or exceed the enrollment breakeven
point if the state pays tuition assistance? What is the next task
Blake should have his staff do, based on these forecasts?
Case
· 1.
Quincy Snodgrass Enterprises—Forecasting
Quincy Snodgrass is an entrepreneur and a lover of the
outdoors. He has worked for various companies since he
graduated college with his business administration degree in
management. Over the years, he has saved every extra penny
and now has the starting capital he needs; consequently, he
plans to open his own business. Quincy plans to open a
landscaping business. The primary services he’ll offer are grass
cutting, edging, and bush trimming. Obviously, this will only

31. provide income in the spring, summer, and early fall. Therefore,
he plans to offer snow removal in the winter. His goal is to
continue to provide those baseline services and expand into
actual landscaping work.
Quincy’s initial challenge is to develop a forecast of how many
customers he’ll have each month. This is essential to determine
if he needs to hire any additional labor throughout the season.
Unfortunately, none of the jobs Quincy has had involved
forecasting. Quincy is digging deep into his memory to recall
his supply chain management course and the chapter on
forecasting. He knows he has two methods to choose from,
qualitative and quantitative.
Quincy is a numbers guy and is partial to using a quantitative
method if possible, but he doesn’t rule out the option of using
one of the qualitative methods. He had worked many summers
for other yardwork companies. Knowing he wanted to own his
own business someday, Quincy took notes on how things went.
Since his customer base would be in a series of small towns,
Quincy knows he cannot charge as much as the companies that
served larger communities. Consequently, volume is necessary
to earn the revenue he will need.
Quincy focused on three small towns, Smithburg with a
population of 700, Emeryville with 1,800, and Golf Creek with
2,500. He believes he can get 10 percent of the homes in each
town to hire him. Quincy used information from the county files

32. to estimate that on average, the number of homes is equal to
about 25 percent of the population, meaning in Smithburg (700)
the potential number of homes is about 175. Quincy believes his
calculations are reasonable and could be the foundation for
using a qualitative method to kick off his forecasting.
Discussion Questions
1. Since Quincy doesn’t have any historical data (only an
estimate of the number of customers he’ll serve each month),
which specific type of qualitative method is he using? What
would be the total number of customers based on his
assumptions? Is this a realistic number to allow the business to
survive? He estimates his average fee will be $25.
2. Because of the type of communities, overtime Smithburg will
provide 20 percent of the homes as customers, Emeryville still
10 percent, and Golf Creek only 7 percent. Assuming he was
charging $25 per home, what effect does this change in monthly
forecast have on his monthly revenue? Should he change his
price either up or down? Explain.
3. Quincy has been gathering data for over five years now. He
has a record of how many customers he had each month. He also
has information on the weather, for example, which month has
the most rain. Quincy wants to use these data to improve his
forecasting. He has a choice of cause-and-effect models. Based
on what data he has and what he wants to do, what would be the
best method from the choices he has? Explain the elements of

33. the forecasting equation.