INFSY540.1
Information Resources in Management
Lesson #4
Chapters 8
Models and Decision Support
Copyright © 1998 by Jerry Post
1
Information Systems & Technology
An information system (IS) is an arrangement of people, data,
processes, communications, and information technology that
interact to support and improve day-to-day operations in a
business as well as support the problem-solving and decision
making needs of management and users.
Information technology is a contemporary term that describes
the combination of computer technology (hardware and
software) with telecommunications technology (data, image, and
voice networks).
A practical way of making data useful.
2
What is an information system?
3
What is an information system?
Information System
Transaction
Processing System
Decision Support System
Data-Driven DSS
Model-Driven DSS
4
Information Systems

Transaction Processing Systems


aka Data Processing Systems
Decision Support Systems




Executive Information Systems
Management Information Systems
Expert Systems
Office, Workgroup, Personal Information Systems
Our text does not have any of these being DSS subsets
5
Data-Driven Decision Support

Using Transaction Processing Systems for anything
but processing transactions is hard:

Not easily accessible





Mainframes Cost
Mainframe Complexity
Mainframes open to many users is risky
Data spread to many databases and computers
But users now have powerful PCs with user friendly
analysis tools & they want to use them
6
Data-Driven Decision Support

History:


On Line Transaction Processing (OLTP)
DataBase Management System (DBMS)
Indexed Sequential Access Method (ISAM)

Relational DataBase Management System (RDBMS)
Structured Query Language (SQL)
Executive Information Systems (EIS)

Data Warehouse
On Line Analytical Processing (OLAP)
7
Front- and Back-Office Information Systems

Front-office information systems support business functions
that reach out to customers (or constituents).




Marketing
Sales
Customer management
Back-office information systems support internal business
operations and interact with suppliers (of materials, equipment,
supplies, and services).




Human resources
Financial management
Manufacturing
Inventory control
8
What is a model?


Webster’s New American Dictionary (1995)
 One who poses for an artist.
 An example for imitation or emulation
 A miniature representation
 A structural design
 Model ( verb): to shape, fashion, construct
“A model is a simplification of something else.”
Bob Kilmer
9
Models and Analysis
INPUTS
MODEL
OUTPUTS
ASSUMPTIONS
10
Assumptions and Conclusions
The aviation instructor had just delivered a
lecture on the use of parachutes.
“And if it doesn’t open?” someone asked.
“If it doesn’t open?” replied the instructor, “Well,
... that is what’s known as jumping to a
conclusion.”
11
GIGO
INPUTS
MODEL
OUTPUTS
ASSUMPTIONS
INPUTS
Constants
Parameters
OUTPUTS
Criteria or MOE
Additional Statistics
Variables
12
Types of Models





Mental
Symbolic
Mathematical
Computer
Physical
13
Sample Model
$
Determining Production Levels
in Perfect Competition
Marginal cost
Average total
cost
price
Q*
Quantity
14
Order Model
vice-presidents
Decide if we
should produce
warehouse manager
marketing
manager
sales
manager
sales
staff
summarize
sales orders
review
sales orders
receive
sales orders
customer
check stock
to match order
production
manager
decide steps
to produce
accounting
manager
review costs
add fixed costs
compute costs
to produce
engineers
bill
customers
Simple Model of Evaluating Custom Orders
15
Models of
Physical Items:
CAD
Computer-aided design.
Designers traditionally build
models before attempting to
create a physical product. CAD
systems make it easier to create
diagrams and share them with
multiple designers. Portions of
drawings can be stored and used
in future products. Sample
products can be evaluated and
tested using a variety of computer
simulations.
16
Statistical Decision Models
Strategy
Decision
100
80
60
40
20
0
1st Qtr
2nd Qtr
Actual
3rd Qtr
4th Qtr
Forecast
Output
1
f ( x) 
 2
 1  x    2 
 exp 2    
Model
Tactics
Data
Operations
Company
17
File: C08Fig08.xls
Why Build Models?






Understand the Process
Prediction
Optimization
Simulation
To conduct "What If" analysis
Dangers
18

Acquisition/Input






Data availability
Selective perception
Frequency
Concrete information
Illusory correlation
Human Biases




Processing














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
Output


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
19
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
20
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
21
File: C08Fig08.xls
Optimization
Maximum
Goal or output
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
1
9 10
Control variables
22
Figure 10.2
23
Simulation

Webster’s New American Dictionary (1995)
 An object that is not genuine
 The imitation by one system or process of the way in which
another system or process works.
 Simulate (verb): imitate, create the effect or appearance of

Handbook of Systems Analysis (1985), E. S. Quade
 “The process of representing item by item and step by step
the essential features of whatever it is we are interested in.”
24
Bob Kilmer’s Simple Definitions:


Model: simplified representation of something else.*
Simulation: means of using or operating a model.**
* Something else = a real or proposed entity or system
** Must have inputs and outputs.
25
Building Models
Input
Process
Equation:
Output
output = f(input,time)
Define System
Input - Process - Output
Simplifying assumptions
System boundary
Build Equations
Identify parameters (variables you can control)
Identify variables you cannot control
Define equations for the variables
Estimate parameters from data
Use Model to transform Inputs into Outputs
26
Modeling Limitations



Model complexity
Cost of building model
Errors in model



Data
Equations
Presentation and interpretation
27
Models are for...

“Models are for thinking with.” -- Sir M. G. Kendall

“Models are for experimenting with.”

“Models are for communicating with.”

“Models always have assumptions.”
(Even though they might not be stated)

“Models are always wrong. They always have error.”
(Question: Is the level of error acceptable?)
28
EOQ Model
29
Appendix: Forecasting Uses

Marketing




Future sales
Consumer
preferences/trends
Sales strategies
Finance



Interest rates
Cash flows
Financial market conditions

HRM




Labor costs
Absenteeism
Turnover
Strategy



Rivals’ actions
Technological change
Market conditions
30
Forecasting Methods

Structural Models





Derive underlying models
Estimate parameters
Evaluate model
Focus on explanation and
cause
Time Series




Collect data over time
Identify trends
Identify seasonal effects
Forecast based on patterns
sales
P
trend
S
D’
D
Q
time
Increase in income
31
Structural Equations

Demand is a function of



Price
Income
Prices of related products
Model
QD = b0 + b1 Price + b2 Income + b3 Substitute
Time
Quantity Price
Income
Substitute
1
24926
134
20000
155
Data
2
26112
150
21000
155
3
27313
142
22000
135
4
26143
141
21000
150
5
26741
144
21500
150
Estimate
QD = 11146- 0.1 26149
Price + 1.2 Income
- 21000
1.0 Substitute
137
155
7
27893
140
22500
143
Forecast
33318 = 1114
0.1
(155)
+
1.2
(20000)
1.0
(160)
8
26397
142
21200
153
9
24895
147
20000
155
Need to know (estimate) future price, income, and substitute price.
10
28501
148
23000
160
11
29747
150
24000
165
12
29175
134
23500
15332
Time Series Components
sales
Seasonal
Trend
time
Dec
1. Trend
2. Seasonal
3. Cycle
4. Random
Dec
Dec
Dec
A cycle is similar to the seasonal pattern,
but covers a time period longer than a year.
33
Exponential Smoothing
Exponential Smoothing
1600
1500
1400
1300
1200
1100
1000
900
800
Raw Data
Smooth:0.20
1
3
5
7
9
11 13 15 17 19 21
St = Yt + (1 - ) St-1
S is the new data point
 is the smoothing factor
Use Excel:
Tools, Data Analysis
Exponential Smoothing
34
Exponential Smoothing
Choosing the smoothing factor ():
It is usually between 0.01 and 0.20
Test multiple values and compare errors:
(actual - smooth) * (actual - smooth)
Compute the sum. Choose the factor with
the least total sum-of-squared error.
Larger factors place
more importance on
recent data, which
results in less
smoothing.
(A2-D2)*(A2-D2)
Data
1350.782
1138.733
1104.254
1488.808
1193.076
1304.652
1089.714
1182.478
1225.365
1417.266
1079.631
1162.129
0.01
#N/A
1350.782 44964.83
1140.854 1339.502
1104.62 147600.4
1484.967 85200.11
1195.995 11806.3
1303.565 45732.48
Sum
1091.852 8213.119
1181.572929,916
1917.838
1224.927 36994.37
1415.343 112702.6
1082.988 6263.334
0.1
#N/A
1350.782
1159.938
1109.823
1450.91
1218.859
1296.072
1110.349
1175.265
1220.355
1397.575
1111.425
44964.83
3100.666
143630.1
66478.32
7360.322
42583.99
Sum
5202.577
848,686
2509.985
38774.04
101088.6
2570.888
0.2
#N/A
1350.782 44964.83
1181.143 5911.847
1119.632 136291.1
1414.973 49238.37
1237.455 4515.339
1291.212 40601.79
Sum
1130.013 2752.577
1171.985 769,265
2849.41
1214.689 41037.54
1376.751 88280.28
1139.055 532.425935
Smoothing with Trends
Double Exponential Smoothing
34000
32000
30000
28000
Raw Data
26000
Smooth:0.20
24000
22000
20000
1
3
5
7
9
11
13
15
17
19
Apply exponential smoothing and choose smoothing factor ().
Apply exponential smoothing a second time to the smoothed data.
36
Forecasting with Exponential Smoothing
Forecast for time T+
 
  [ 2]



yT    2 
 S T  1 
 ST
 1 
 1 
T = 20
=1
 = 0.2
S20 = 32,064
last of the raw data
forecast one period ahead
smoothing factor
(value at time 20, after one smoothing)
S[2] = 33,141
(value at time 20, after second smoothing)
Y21 = (2.25)32,064 - (1.25)33,141
= 30,718
37
Time Quantity Trend Difference
1 24917 24484
432
2 26152 24983
1169
3 27297 25482
1816
4 26157 25980
177
5 26710 26479
231
6 26103 26977
-874
7 27981 27476
505
8 26327 27975
-1647
9 24913 28473
-3560
10 28524 28972
-448
11 29774 29470
303
12 29136 29969
-833
13 29332 30468
-1136
14 30306 30966
-660
15 32133 31465
669
16 33329 31963
1366
17 34522 32462
2060
18 34769 32961
1808
19 33355 33459
-104
20 32684 33958
-1274
21
34456
22
34955
23
35454
24
35952
Estimating Trend
Yt = b0 + b1(t)
Use regression to estimate b0 and b1.
Intercept
Time
Coefficients Std Error t Stat P-value
23985.81
652.48 36.76 2.2E-18
498.60
54.47
9.15 3.4E-08
Plug t into equation to estimate new value (on trend):
Y21 = 23,986 + 498.6 * (21)
= 34,456
Result is the prediction on the trend, with no
random factors and no cycles.
38
An Overview of Decision Support
Systems
File: C08Fig11.xls
DSS: Decision Support Systems
Sales and Revenue 1994
300
Model
250
Legend
200
150
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
Sales
Revenue
Profit
Prior
100
50
0
Jan
Feb
Mar
Apr
May
Jun
Output
Database
40
Characteristics of Decision Support Systems






Handle lots of data from various sources
Report & presentation flexibility
Text and graphics capabilities
Support drill down analysis
Complex analysis, statistics, and forecasting
Optimization, satisficing, heuristics



Simulation
What-if analysis
Goal-seeking analysis
41
Figure 10.14
42
Capabilities of a DSS




Support all problem-solving phases
Support different decision frequencies
Support different problem structures
Support various decision-making levels
43
The Model Base

Financial models



Statistical analysis models





Cash flow
Internal rate of return
Averages, standard deviations
Correlations
Regression analysis
Graphical models
Project management models
44
Table 10.3
45
Group Decision Support Systems
Characteristics of a GDSS




Special design
Ease of use
Flexibility
Decision-making support
47
Characteristics of a GDSS




Anonymous input
Reduction of negative group behavior
Parallel communication
Automated record keeping
48
Figure 10.18
49
Executive Support Systems (ESS)







Tailored to individual executives
Easy to use
Drill down capabilities
Access to external data
Can help when uncertainty is high
Future-oriented
Linked to value-added processes.
50
Capabilities of an ESS





Support for defining an overall vision
Support for strategic planning
Support for strategic organizing & staffing
Support for strategic control
Support for for crisis management
51




Easy access to data
Graphical interface
Non-intrusive
Drill-down capabilities
EIS: Enterprise
Information System
EIS Software
from Lightship
highlights easeof-use GUI for
data look-up.
52
Enterprise IS
Sales
Production Costs
Distribution Costs
Fixed Costs
Executives
Production Costs
South
North
Overseas
5000
4500
4000
3500
3000
2500
2000
1500
1000
500
0
Central Management
South
North
Overseas
1993
1994
1995
1996
Production: North
Data
Data
Sales
Data
Distribution
Data
Item#
1995
1994
1234
2938
7319
542.1
631.3
753.1
442.3
153.5
623.8
Production
53