Introduction to MIS
Chapter 8
Models and Decision Support

Copyright © 1998-2002 by Jerry Post

Introduction to MIS

1


Models
Strategy

Decision
100
80
60
40
20
0
1st Qtr

2nd Qtr

Actual

3rd Qtr

4th Qtr

Forecast

Output
1
2

f ( x)

1 x
exp
2

2

Model
Data

Tactics
Operations

Company

 

Introduction to MIS

 

2



Outline
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





 

Biases in Decisions
Introduction to Models
Why Build Models?
Decision Support Systems: Database, Model, Output
Data Warehouse
Data Mining and Analytical Processing
Digital Dashboard and EIS
DSS Examples
Geographical Information Systems
Cases: Computer Hardware Industry
Appendix: Forecasting

Introduction to MIS


 

3


Tactical

Models

Management

Business Operations

 

Introduction to MIS

 

Tr
a
Pr nsa
Pr
o
oc
c
es ces tion
s C sin
on g
t ro

l

Mgt.

DS
S

Strategic

ES

EI
S

Decision Levels

4


Choose a Stock
Stock Price
130
125
120
115

CompanyA

110


CompanyB

105
100
95
90
1

2

3

4

5

6

7

8

9

10 11 12

Month

Company A’s share price increased by 2% per month.
Company B’s share price was flat for 5 months and then increased by

3% per month.
Which company would you invest in?

 

Introduction to MIS

 

5












Data availability
Selective perception
Frequency
Concrete information
Illusory correlation








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











Inconsistency
Conservatism
Non-linear extrapolation
Heuristics: Rules of thumb
Anchoring and adjustment
Representativeness
Sample size
Justifiability
Regression bias
Best guess strategies
Complexity
Emotional stress

Social pressure
Redundancy

Introduction to MIS

Output


Processing


 

Human Biases

Acquisition/Input

 



Question format
Scale effects
Wishful thinking
Illusion of control

Feedback







Learning on irrelevancies
Misperception of chance
Success/failure attribution
Logical fallacies in recall
Hindsight bias

6


File: C08Fig08.xls








Understanding the Process
Optimization
Prediction
Simulation or "What If"
Scenarios
Dangers Goal or output

Why Build Models?
Optimization

Maximum

variables

25

Output

20

Model: defined
by the data points
or equation

15
10
5

5
3

0
1 2 3
4

5

Input Levels

6


7

8

9 10

1

Control variables

 

Introduction to MIS

 

7


File: C08Fig09.xls

Prediction
25

20

Economic/
regression
Forecast


Output

15

10

5

Moving Average
Trend/Forecast

0
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Time/quarters

 

Introduction to MIS

 

8


File: C08Fig10.xls

Simulation
Goal or output
variables


25

Output

20
15

Results from altering
internal rules

10
5
0
1

2

3

4

5

6

7

8


9 10

Input Levels

 

Introduction to MIS

 

9


Object-Oriented Simulation Models
Custom Manufacturing

purchase
order
routing
& scheduling

purchase
order

Customer

Order Entry

Invoice


Parts
List
Production

Shipping
Schedule

Shipping

Inventory

 

Introduction to MIS

 

10


File: C08Fig11.xls

DSS: Decision Support Systems
Sales and Revenue 1994
300

Model

250


Legend
200

d

a
at

to

an

y
al

ze

sales
154
163
161
173
143
181

revenue profit
204.5 45.32
217.8 53.24
220.4 57.17
268.3 61.93

195.2 32.38
294.7 83.19

prior
35.72
37.23
32.78
47.68
41.25
67.52

su
re

lt s

150

Sales
Revenue
Profit
Prior

100
50
0
Jan

Feb


Mar

Apr

May

Jun

Output

Database

 

Introduction to MIS

 

11




 

Introduction to MIS

 

Data Mining: Spotfire


12


Data Warehouse
Predefined
reports

Interactive
data analysis

Operations
data
Daily data
transfer
OLTP Database
3NF tables

Data warehouse
Star configuration

Flat files

 

Introduction to MIS

 

13



Multidimensional OLAP Cube
ry
o
eg
t
Ca

Pet Store
Item Sales
Amount = Quantity*Sale Price

Customer
Location

Time
Sale Date

 

Introduction to MIS

 

14


Microsoft SQL Server Cube Browser


 

Introduction to MIS

 

15


Microsoft Pivot Table

 

Introduction to MIS

 

16


Digital Dashboard
Stock market
Equipment details

Exceptions

Quality control

Plant or
management variables


Products
Plant schedule

/>
 

Introduction to MIS

 

17







Easy access to data
Graphical interface
Non-intrusive
Drill-down capabilities

EIS: Executive
Information System

EIS Software
from Lightship
highlights easeof-use GUI for

data look-up.

 

Introduction to MIS

 

18


Executives

5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0

Sales

Data
Distribution


 

South
North
Overseas

1993

1994

1995

1996

Production: North

Data

Data

Introduction to MIS

Production Costs
South
North
Overseas

r EIS
o
f

 
a
t
Da

Central Management

 

Executive IS

Sales
Production Costs
Distribution Costs
Fixed Costs

Data

Item#

1995

1994

1234
2938
7319

542.1
631.3

753.1

442.3
153.5
623.8

Production

19


Marketing Research Data

 

Introduction to MIS

 

20


File: C08-10 Marketing Forecast.xls

Marketing Sales Forecast
GDP and Sales
2800

100
GDP


2600

90

Sales

2400

Forecast

GDP

2000

70

1800

60

1600

Sales

80

2200

50


1400
40

1200

30
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23

24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48

1000

Quarter


forecast

Note the fourth quarter sales jump.
The forecast should pick up this cycle.

 

Introduction to MIS

 

21


Regression Forecasting
Data:

Quarterly sales and GDP for 10 years.

Model:

Sales = b0 + b1 Time + b2 GDP

Analysis:

Estimate model coefficients with regression.
Intercept
Time
GDP


Coefficients Standard Error
-98.175
15.895
-1.653
0.304
0.102
0.012

t Stat
-6.176
-5.444
8.507

Forecast GDP for each quarter.
Output:

Compute Sales prediction.
Graph forecast.

 

Introduction to MIS

 

22


File: C08-19 HRM.xls


Human Resources

 

Introduction to MIS

 

23


Human Resources

dollars

Raises
4000
3500
3000
2500
2000
1500
1000
500
0

90.0%
80.0%
70.0%
60.0%

50.0%
40.0%
30.0%
20.0%
10.0%
0.0%
Caulkins

Jihong

Raise

 

Introduction to MIS

 

Louganis

Naber

Raise pct

Spitz

Weissmuller

Performance


24


File: C08-14 Finance NPV.xls

Finance Example: Project NPV

Rate = 7%

P r o j e c t  C  N P V = ­ $ 3 , 8 14

P r o j e c t  A  N P V=$ 18 , 4 7 5

100,000

60,000

50,000

40,000
20,000

0

0

-50,000

1


2

3

4

5

6

Costs-A
Revenue-A

-100,000

0
-20,000

0

1

2

3

4

5


6

Costs-C
Revenue-C

-40,000

-150,000

-60,000

-200,000

-80,000

-250,000

-100,000
Ye a r

Ye a r

P r o j e c t  B  N P V=$ 6 , 0 6 4

80,000
60,000
40,000
20,000
0
-20,000

-40,000

0

1

2

3

4

5

6

Costs-B

Can you look at these cost and
revenue flows and tell if the
project should be accepted?

Revenue-B

-60,000
-80,000
-100,000
-120,000
Ye a r


 

Introduction to MIS

 

25