TM 745 Forecasting for Business & Technology

Paula Jensen

South Dakota School of Mines and Technology, Rapid City

1st Session 1/11/2012: Chapter 1 Introduction to Business Forecasting

Agenda Class Overview/Syllabus highlights Assignment Chapter 1 by Guest Lecturer Dr.

Stuart Kellogg

Business Forecasting 6th Edition J. Holton Wilson & Barry KeatingMcGraw-Hill

Instructor Information

Instructor Paula Jensen

Office Location IE/CM 320

Office Hours CM 320 M,W 2:00-3:00 pmIER T,TH, F 11:00-11:50 AME-mail for an appointment outside of office hours.

Office Phone 605-394-1770

E-mail paula.jensen@sdsmt.eduWebsite pjensen.sdsmt.edu

Course Materials Powerpoints & Class Information

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 KeatingMcGraw-Hill

One case from Harvard Business Review

Prerequisites

1) Probability and Statistics2) Understanding of

Excel/Spreadsheet software.3) It is expected that students will be

able to access and download internet files.

Course Objective 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 Exams20% - 1 Project20% - Interaction

A 90-100B 80-89C 70-79D 60-69F < 60

Exams Students signed up for the on-campus

section are required to take the test at the given time.

Make-up Exams available for University-Approved 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

Project Criteria to be discussed through Class

Interaction Assignments will include discussions, quizzes, and other assignments

Email Policy:

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

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

Send me a contact info e-mail. Include all important contact information phones, e-mail, 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

Intuition alone no longer acceptable.

Used in Future Sales Inventory needs Personnel requirements

Judgment still is needed

Forecasting in Business Today

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

mid-80’s 94% large American firmsused sales forecasts

Krispy Kreme New stores model with errors of < 1%

Bell Atlantic Data warehouse (shared) of monthly

history Subjective, regression, time series, Forecasts monitored & compared

Forecasting in Business Today

Columbia Gas (natural gas company) Design Day Forecast (supply)

Gas supply, transportation capacity, storage capacity, & related

Daily Operational (demand) Regression on temperatures,

wind speed, day of the week, etc.

Forecasting in Business Today

Segix Italia (Pharmaceutical company) Marketing forecasts for seven main drugs Targets for sales representatives

Pharmaceuticals in Singapore Glaxo-Wellcome, Bayer, Pfizer,

Bristol-Myers Squibb HR, Strategic planning, sales Quantitative & judgments

Forecasting in Business Today

Fiat Auto (2 million vehicles annually) All areas use centrally prepared forecasts Use macro-economic data as inputs From totals sales to SKU’s

Douglas Aircraft Top down (miles flown in 32 areas) Bottom up (160 Airlines studied)

Forecasting in Business Today

Trans World Airlines Uses a top down (from total market)

approach for sales Regression & Trend models

Brake Parts Inc. 250,000 SKU’s Forecast system saves $6M/mo. 19 time series methods

Forecasting in the Public and Not-for-Profit Sectors

Police calls for service by cruiser district

State government Texas: Personal income, electricity sales,

employment, tax revenues California: national economic

models, state submodel, tax revenues, cash flow models

Hospitals: staff, procedures,

Collaborative Forecasting Manufacturer’s forecast > Retailers

Retailer’s extra info > Manufacturers1. Lower Inventory2. Fewer unplanned shipments or runs3. Reduced Stockouts4. Increase customer satisfaction5. Better sales promotions6. Better new product intros7. Respond to Market changes

Computer Use and Quantitative Forecasting 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

Forecasting 80% math, 20% judgment

Subjective Forecasting Methods

Only way to forecast 40 years out Sale-Force Composite

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

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 ErrorsStandard Deviation

SX nX

nt

( )

.

.

2

1

0 408

11

0193

Soda Demand (1,000,000's)

Month t At At2

(At-Abar)2

Jul 1 2.47 6.1009 0.002934Aug 2 2.31 5.3361 0.011201Sep 3 2.24 5.0176 0.030917Oct 4 2.27 5.1529 0.021267Nov 5 2.15 4.6225 0.070667Dec 6 2.34 5.4756 0.005751Jan 7 2.23 4.9729 0.034534Feb 8 2.48 6.1504 0.004117Mar 9 2.46 6.0516 0.001951Apr 10 2.58 6.6564 0.026951May 11 2.74 7.5076 0.105084Jun 12 2.72 7.3984 0.092517

Sum = 78 28.99 70.44 0.408

Avg = 6.5 2.416

St. Dev = 0.193

Measurement ErrorsStandard Deviation

SX nX

nt

2 2

2

1

70 44 12 2 416

11

0193

. ( . )

.

Soda Demand (1,000,000's)

Month t At At2

(At-Abar)2

Jul 1 2.47 6.1009 0.002934Aug 2 2.31 5.3361 0.011201Sep 3 2.24 5.0176 0.030917Oct 4 2.27 5.1529 0.021267Nov 5 2.15 4.6225 0.070667Dec 6 2.34 5.4756 0.005751Jan 7 2.23 4.9729 0.034534Feb 8 2.48 6.1504 0.004117Mar 9 2.46 6.0516 0.001951Apr 10 2.58 6.6564 0.026951May 11 2.74 7.5076 0.105084Jun 12 2.72 7.3984 0.092517

Sum = 78 28.99 70.44 0.41

Avg = 6.5 2.416

St. Dev = 0.193

Measurement ErrorsMAE

| |

| 2.47 2.416 | | 2.31 2.416 | ...

12

0.159

tX XMAE

n

Soda Demand (1,000,000's)

Month t At |At - Abar|

Jul 1 2.47 0.0542

Aug 2 2.31 0.1058

Sep 3 2.24 0.1758

Oct 4 2.27 0.1458

Nov 5 2.15 0.2658

Dec 6 2.34 0.0758

Jan 7 2.23 0.1858

Feb 8 2.48 0.0642

Mar 9 2.46 0.0442

Apr 10 2.58 0.1642

May 11 2.74 0.3242

Jun 12 2.72 0.3042

Sum = 78 28.99 1.91

Avg = 6.5 2.416 0.159

St. Dev = 0.193

Measurement ErrorsMAE

| |

| 2.47 2.416 | | 2.31 2.416 | ...

12

0.159

tX XMAE

n

Soda Demand (1,000,000's)

Month t At |At - Abar|

Jul 1 2.47 0.0542

Aug 2 2.31 0.1058

Sep 3 2.24 0.1758

Oct 4 2.27 0.1458

Nov 5 2.15 0.2658

Dec 6 2.34 0.0758

Jan 7 2.23 0.1858

Feb 8 2.48 0.0642

Mar 9 2.46 0.0442

Apr 10 2.58 0.1642

May 11 2.74 0.3242

Jun 12 2.72 0.3042

Sum = 78 28.99 1.91

Avg = 6.5 2.416 0.159

St. Dev = 0.193

In general,

0.8(.193) = 0.154

0.8MAE S

Measurement ErrorsSoda Demand (1,000,000's)

Month t At (At - Ahat)

Jul 1 2.47 0.0542

Aug 2 2.31 -0.1058

Sep 3 2.24 -0.1758

Oct 4 2.27 -0.1458

Nov 5 2.15 -0.2658

Dec 6 2.34 -0.0758

Jan 7 2.23 -0.1858

Feb 8 2.48 0.0642

Mar 9 2.46 0.0442

Apr 10 2.58 0.1642

May 11 2.74 0.3242

Jun 12 2.72 0.3042

Sum = 78 28.99 0.00

Avg = 6.5 2.416 0.000

St. Dev = 0.193

Mean Error

MEe

nt

( . . ) ( . . ) ...

.

2 47 2 416 2 31 2 416

12

0 0

Measurement ErrorsSoda Demand (1,000,000's)

Month t At Ft et |et| et2

Jul 1 2.47 2.416 0.054 0.054 0.003

Aug 2 2.31 2.416 -0.106 0.106 0.011

Sep 3 2.24 2.416 -0.176 0.176 0.031

Oct 4 2.27 2.416 -0.146 0.146 0.021

Nov 5 2.15 2.416 -0.266 0.266 0.071

Dec 6 2.34 2.416 -0.076 0.076 0.006

Jan 7 2.23 2.416 -0.186 0.186 0.035

Feb 8 2.48 2.416 0.064 0.064 0.004

Mar 9 2.46 2.416 0.044 0.044 0.002

Apr 10 2.58 2.416 0.164 0.164 0.027

May 11 2.74 2.416 0.324 0.324 0.105

Jun 12 2.72 2.416 0.304 0.304 0.093

Sum = 78 28.99 28.99 0.00 1.91 0.41

Avg = 6.5 2.416 2.416 0.000 0.159 0.034

St. Dev = 0.193

Using Multiple Forecasts

Use judgment Reference:

Combining Subjective andObjective Forecasts.

Sources of Data

Internal records Timeliness & formatting problems

Government & syndicated services (good)

Web 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 FundamentalsSoda Demand (1,000,000's)

Month t At

Jul 1 2.47

Aug 2 2.31

Sep 3 2.24

Oct 4 2.27

Nov 5 2.15

Dec 6 2.34

Jan 7 2.23

Feb 8 2.48

Mar 9 2.46

Apr 10 2.58

May 11 2.74

Jun 12 2.72

Consider the followingsales data over a 12 month period.

Summary StatisticsSoda Demand (1,000,000's)

Month t At

Jul 1 2.47

Aug 2 2.31

Sep 3 2.24

Oct 4 2.27

Nov 5 2.15

Dec 6 2.34

Jan 7 2.23

Feb 8 2.48

Mar 9 2.46

Apr 10 2.58

May 11 2.74

Jun 12 2.72

XX

nt

2 47 2 31 2 72

12

2 42

. . ... .

.

Mean

Summary StatisticsMedian

Sorted Demand

t At

5 2.15

7 2.23

3 2.24

4 2.27

2 2.31

6 2.34

9 2.46

1 2.47

8 2.48

10 2.58

12 2.72

11 2.74

Xm

2 34 2 46

2

2 40

. .

.

Summary StatisticsSoda Demand (1,000,000's)

Month t At

Jul 1 2.47

Aug 2 2.31

Sep 3 2.24

Oct 4 2.27

Nov 5 2.15

Dec 6 2.34

Jan 7 2.23

Feb 8 2.48

Mar 9 2.46

Apr 10 2.58

May 11 2.74

Jun 12 2.72

Mode

No number repeats no mode

Summary StatisticsModal Range

Sorted Demand

t At

5 2.15

7 2.23

3 2.24

4 2.27

2 2.31

6 2.34

9 2.46

1 2.47

8 2.48

10 2.58

12 2.72

11 2.74

2.31 - 2.47

Summary Statistics

Modal Range

2.5 to 3.0

Soda Sales

0

10

20

30

40

0.5 1.0 1.5 2.0 2.5 3.0 More

Volume

Fre

qu

en

cy

Overview of the Text Ch 1 Intro Ch 2 Forecast Process (more Intro) Ch 3 MA & Exponential Smoothing Ch 4 Regression Ch 5 Multiple Regression Ch 6 Time-Series Decomposition Ch 7 ARIMA Box-Jenkins Ch 8 Combining Forecasts Ch 9 Forecast Implementation

Upcoming Events 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