TM 745 Forecasting for
Business & Technology
Paula Jensen
1st Session 1/11/2012:
Chapter 1 Introduction to Business
Forecasting
South Dakota School of Mines and
Technology, Rapid City
Agenda
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Class Overview/Syllabus highlights
Assignment
Chapter 1 by Guest Lecturer Dr. Stuart
Kellogg
Business Forecasting 6th Edition
J. Holton Wilson & Barry Keating
McGraw-Hill
Instructor Information
Instructor
Paula Jensen
Office Location
IE/CM 320
Office Hours
Office Phone
CM 320 M,W 2:00-3:00 pm
IER T,TH, F 11:00-11:50 AM
E-mail for an appointment outside of
office hours.
605-394-1770
E-mail
Website
paula.jensen@sdsmt.edu
pjensen.sdsmt.edu
Course Materials
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Powerpoints & Class Information
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Website: pjensen.sdsmt.edu via the ENGM 745
Engineering Notebook – 9-3/4" x 7-1/2", 5x5
quad-ruled, 80-100 pp. (approx.)
Engineering/Scientific calculator
Book: Business Forecasting 6th Edition J.
Holton Wilson & Barry Keating
McGraw-Hill
One case from Harvard Business Review
Prerequisites
1)
2)
3)
Probability and Statistics
Understanding of Excel/Spreadsheet
software.
It is expected that students will be
able to access and download internet
files.
Course Objective
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to educate prospective managers about the
philosophies and tools of sound forecasting
principles
to provide technical managers with a
theoretical basis for statistical forecasting
to provide technical managers with the
fundamentals methods available for
technological and qualitative forecasts
Evaluation Procedures
60% - 2 Exams
20% - 1 Project
20% - Interaction
A
B
C
D
F
90-100
80-89
70-79
60-69
< 60
Exams
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Students signed up for the on-campus
section are required to take the test at
the given time.
Make-up Exams available for UniversityApproved reasons.
All exams are open engineering
notebook, and use of a scientific
calculator is encouraged.
Distance Students need proctors- See
Syllabus for further details
Project & Interaction Grades
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Project Criteria to be discussed through
Class
Interaction Assignments will include
discussions, quizzes, and other
assignments
Email Policy:
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If you are writing about issues relating
to the class, make sure the subject line
reads ENGM 745: (subject info) so I can
sort my e-mails and answer accordingly.
Please be professional in your e-mails.
(no texting lingo!)
Academic Honesty
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Cheating: use or attempted use of unauthorized
materials, information or study aids
Tampering: altering or interfering with
evaluation instruments and documents
Fabrication: falsification or invention of any
information
Assisting: helping another commit an act of
academic dishonesty
Plagiarism: representing the words or ideas of
another as one's own
ADA
Students with special needs or requiring
special accommodations should contact
the instructor and/or the campus ADA
coordinator, Jolie McCoy, at 394-1924 at
the earliest opportunity.
First Assignment
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Send me a contact info e-mail. Include all
important contact information phones, email, and mail addresses. Preferred mode.
Send via e-mail a Current Resume
Problems 1,4, & 8 in chapter 1 – I don’t
need these sent. I will post solutions.
Introduction to Business
Forecasting
Quantitative Forecasting Has
Become Widely Accepted
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Intuition alone no longer acceptable.
Used in
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Future Sales
Inventory needs
Personnel requirements
Judgment still is needed
Forecasting in Business Today
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Two Professional Societies
Accountants: costs, revenues (tax plans)
Personnel: recruitment, changes in
workforce
Finance: cash flows
Production: raw-material needs &
finished goods inventory
Marketing: sales
Forecasting in Business Today
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mid-80’s 94% large American firms
used sales forecasts
Krispy Kreme
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New stores model with errors of < 1%
Bell Atlantic
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Data warehouse (shared) of monthly history
Subjective, regression, time series,
Forecasts monitored & compared
Forecasting in Business Today
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Columbia Gas (natural gas company)
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Design Day Forecast (supply)
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Gas supply, transportation capacity, storage
capacity, & related
Daily Operational (demand)
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Regression on temperatures,
wind speed, day of the week, etc.
Forecasting in Business Today
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Segix Italia (Pharmaceutical company)
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Marketing forecasts for seven main drugs
Targets for sales representatives
Pharmaceuticals in Singapore
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Glaxo-Wellcome, Bayer, Pfizer,
Bristol-Myers Squibb
HR, Strategic planning, sales
Quantitative & judgments
Forecasting in Business Today
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Fiat Auto (2 million vehicles annually)
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All areas use centrally prepared forecasts
Use macro-economic data as inputs
From totals sales to SKU’s
Douglas Aircraft
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Top down (miles flown in 32 areas)
Bottom up (160 Airlines studied)
Forecasting in Business Today
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Trans World Airlines
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Uses a top down (from total market)
approach for sales
Regression & Trend models
Brake Parts Inc.
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250,000 SKU’s
Forecast system saves $6M/mo.
19 time series methods
Forecasting in the Public and
Not-for-Profit Sectors
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Police calls for service by cruiser district
State government
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Texas: Personal income, electricity sales,
employment, tax revenues
California: national economic
models, state submodel, tax
revenues, cash flow models
Hospitals: staff, procedures,
Collaborative Forecasting
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1.
2.
3.
4.
5.
6.
7.
Manufacturer’s forecast > Retailers
Retailer’s extra info > Manufacturers
Lower Inventory
Fewer unplanned shipments or runs
Reduced Stockouts
Increase customer satisfaction
Better sales promotions
Better new product intros
Respond to Market changes
Computer Use and
Quantitative Forecasting
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Computer use common by mid 80’s
Packages run from $100 to thousands
PC systems generally have replaced
mainframes for state government work
PC’s dominant at conferences
Chase of Johnson & Johnson
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Forecasting 80% math,
20% judgment
Subjective Forecasting
Methods
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Only way to forecast 40 years out
Sale-Force Composite
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Inform sales staff of data
Bonus for beating the forecast ??
Surveys of Customers/Population
Jury of Executive Opinion
The Delphi Method (Experts)
New-Product Forecasting
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A special consideration
Surveys
Test marketing ( Indy, K-zoo, not KC)
Analog Forecasts: movie toys
New Product Short Life Cycle
New Product Short Life Cycle
New Product Short Life Cycle
Product Life Cycle
Bass Model
Two Simple Naive Models (4th)
Two Simple Naive Models (4th)
Evaluating Forecasts
Evaluating Forecasts
Evaluating Forecasts
Measurement Errors
Soda Demand (1,000,000's)
2
Month
t
At
At
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
6.1009
5.3361
5.0176
5.1529
4.6225
5.4756
4.9729
6.1504
6.0516
6.6564
7.5076
7.3984
70.44
(At-Abar)
2
0.002934
0.011201
0.030917
0.021267
0.070667
0.005751
0.034534
0.004117
0.001951
0.026951
0.105084
0.092517
0.408
Standard Deviation
S

(X
t
 nX ) 2
n 1
0.408
11
 0193
.
Measurement Errors
Soda Demand (1,000,000's)
2
Month
t
At
At
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
6.1009
5.3361
5.0176
5.1529
4.6225
5.4756
4.9729
6.1504
6.0516
6.6564
7.5076
7.3984
70.44
(At-Abar)
2
0.002934
0.011201
0.030917
0.021267
0.070667
0.005751
0.034534
0.004117
0.001951
0.026951
0.105084
0.092517
0.41
Standard Deviation
S
X
2
t
 nX 2
n 1
70.44  12(2.416) 2

11
 0193
.
Measurement Errors
Soda Demand (1,000,000's)
Month
t
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
At
|At - Abar|
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
0.0542
0.1058
0.1758
0.1458
0.2658
0.0758
0.1858
0.0642
0.0442
0.1642
0.3242
0.3042
1.91
0.159
MAE
|X

MAE 
t
X |
n

| 2.47  2.416 |  | 2.31  2.416 |  ...
12
 0.159
Measurement Errors
Soda Demand (1,000,000's)
Month
t
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
At
|At - Abar|
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
0.0542
0.1058
0.1758
0.1458
0.2658
0.0758
0.1858
0.0642
0.0442
0.1642
0.3242
0.3042
1.91
0.159
MAE
MAE 

| X
t
X |
n
| 2.47  2.416 |  | 2.31  2.416 |  ...
12
 0.159
In general,
MAE  0.8S
0.8(.193) = 0.154
Measurement Errors
Soda Demand (1,000,000's)
Month
t
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
At
(At - Ahat)
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
0.0542
-0.1058
-0.1758
-0.1458
-0.2658
-0.0758
-0.1858
0.0642
0.0442
0.1642
0.3242
0.3042
0.00
0.000
Mean Error
e

ME 
t
n

(2.47  2.416)  (2.31  2.416)  ...
12
 0.0
Measurement Errors
Soda Demand (1,000,000's)
Month
t
At
Ft
et
|et|
e t2
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
Sum =
Avg =
St. Dev =
1
2
3
4
5
6
7
8
9
10
11
12
78
6.5
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
28.99
2.416
0.193
2.416
2.416
2.416
2.416
2.416
2.416
2.416
2.416
2.416
2.416
2.416
2.416
28.99
2.416
0.054
-0.106
-0.176
-0.146
-0.266
-0.076
-0.186
0.064
0.044
0.164
0.324
0.304
0.00
0.000
0.054
0.106
0.176
0.146
0.266
0.076
0.186
0.064
0.044
0.164
0.324
0.304
1.91
0.159
0.003
0.011
0.031
0.021
0.071
0.006
0.035
0.004
0.002
0.027
0.105
0.093
0.41
0.034
Using Multiple Forecasts
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Use judgment
Reference:
Combining Subjective and
Objective Forecasts.
Sources of Data
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Internal records
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Timeliness & formatting problems
Government & syndicated services (good)
Web
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Used by gov’t & syndicated
Sites changes
Domestic Car Sales (4th ed ex.)
Domestic Car Sales (4th ed ex)
Domestic Car Sales (4th ed ex)
Forecasting Fundamentals
Soda Demand (1,000,000's)
Month
t
At
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
1
2
3
4
5
6
7
8
9
10
11
12
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
Consider the following
sales data over a 12
month period.
Summary Statistics
Mean
Soda Demand (1,000,000's)
Month
t
At
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
1
2
3
4
5
6
7
8
9
10
11
12
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
X

X
t
n

2.47  2.31  ...  2.72
12
 2.42
Summary Statistics
Sorted Demand
t
At
5
7
3
4
2
6
9
1
8
10
12
11
2.15
2.23
2.24
2.27
2.31
2.34
2.46
2.47
2.48
2.58
2.72
2.74
Median
Xm 
2.34  2.46
2
 2.40
Summary Statistics
Mode
Soda Demand (1,000,000's)
Month
t
At
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Feb
Mar
Apr
May
Jun
1
2
3
4
5
6
7
8
9
10
11
12
2.47
2.31
2.24
2.27
2.15
2.34
2.23
2.48
2.46
2.58
2.74
2.72
No number repeats
no mode
Summary Statistics
Sorted Demand
t
At
5
7
3
4
2
6
9
1
8
10
12
11
2.15
2.23
2.24
2.27
2.31
2.34
2.46
2.47
2.48
2.58
2.72
2.74
Modal Range
2.31 - 2.47
Summary Statistics
Soda Sales
Frequency
40
30
20
10
0
0.5
1.0
1.5
2.0
2.5
Volume
Modal Range
2.5 to 3.0
3.0
More
Overview of the Text
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Ch
Ch
Ch
Ch
Ch
Ch
Ch
Ch
Ch
1
2
3
4
5
6
7
8
9
Intro
Forecast Process (more Intro)
MA & Exponential Smoothing
Regression
Multiple Regression
Time-Series Decomposition
ARIMA Box-Jenkins
Combining Forecasts
Forecast Implementation
Upcoming Events
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No Class next week
Figure out what your log-in/password is
to D2l if you have not yet. It is the
same as WebAdvisor - (Here is the
website for D2L:
https://d2l.sdbor.edu/)
Watch U-tube videos posted on Website
Discussions on D2L- Ready 1/20/2012
Read Chapter 2 for Class on 1/25/2012