Transcript Introduction to MIS Chapter 10

Introduction to MIS
Chapter 10
Business Decisions
Technology Toolbox: Forecasting a Trend
Technology Toolbox: PivotTable
Cases: Financial Services
Copyright © 1998-2002 by Jerry Post
Introduction to MIS
1
Outline
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How do businesses make decisions?
How do you make a good decision? Why do people make bad
decisions?
How do you find and retrieve data to analyze it?
How can you quickly examine data and view subtotals without
writing hundreds of queries?
How does a decision support system help you analyze data?
How do you visualize data that depends on location?
Is it possible to automate the analysis of data?
Can information technology be more intelligent? Can it analyze
data and evaluate rules?
How do you create an expert system?
Can machines be made even smarter? What technologies can be
used to help managers?
What would it take to convince you that a machine is intelligent?
How can more intelligent systems benefit e-business?
Introduction to MIS
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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
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Sample Model
$
Determining Production Levels
in Perfect Competition
Marginal cost
Average total
cost
price
Q*
Introduction to MIS
Quantity
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?
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Human Biases
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Acquisition/Input
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Data availability
Selective perception
Frequency
Concrete information
Illusory correlation
Processing
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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
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Output
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Question format
Scale effects
Wishful thinking
Illusion of control
Feedback
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Learning on irrelevancies
Misperception of chance
Success/failure attribution
Logical fallacies in recall
Hindsight bias
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Model Building
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Understand the Process
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Optimization
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Models are used to search for the best solutions: Minimizing
costs, improving efficiency, increasing profits, and so on.
Prediction
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Models force us to define objects and specify relationships.
Modeling is a first step in improving the business process.
Model parameters can be estimated from prior data. Sample
data is used to forecast future changes based on the model.
Simulation
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Models are used to examine what might happen if we make
changes to the process or to examine relationships in more
detail.
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File: C10Optimum.xl
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Understanding the Process
Optimization
Prediction
Simulation or "What If"
Scenarios
Dangers Goal or output
Why Build Models?
Optimization
Maximum
variables
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Output
20
Model: defined
by the data points
or equation
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10
5
5
3
0
1 2 3
4
5
Input Levels
6
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8
1
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Control variables
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File: C10Fig05.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
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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
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Object-Oriented Simulation Models
Custom Manufacturing
purchase
order
routing
& scheduling
purchase
order
Customer
Order Entry
Invoice
Parts
List
Production
Shipping
Schedule
Shipping
Inventory
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Data Warehouse
Predefined
reports
Interactive
data analysis
Operations
data
Daily data
transfer
OLTP Database
3NF tables
Data warehouse
Star configuration
Flat files
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Multidimensional OLAP Cube
Pet Store
Item Sales
Amount = Quantity*Sale Price
Customer
Location
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File: C10DSS.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
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Microsoft Pivot Table
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Marketing Research Data
Internal
1. Sales
2. Warranty cards
3. Customer service
lines
4. Coupons
5. Surveys
6. Focus groups
Introduction to MIS
Purchase
1. Scanner data
2. Competitive market
analysis
3. Mailing and phone lists
4. Subscriber lists
5. Rating services (e.g.,
Arbitron)
6. Shipping, especially
foreign
Government
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•
•
•
•
•
•
•
Census
Income
Demographics
Regional data
Legal registration
Drivers license
Marriage
Housing/construct
ion
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File: C10-11 Marketing Forecast.xls
Marketing Sales Forecast
GDP and Sales
2800
2600
100
GDP
90
Sales
2400
Forecast
80
2000
70
1800
60
Sales
GDP
2200
1600
50
1400
40
1200
30
1
2
3
4
5
6
7
8
9
10
11
12
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1000
Quarter
forecast
Note the fourth quarter sales jump.
The forecast should pick up this cycle.
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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
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File: C08-19 HRM.xls
Human Resources
Name
Caulkins
Jihong
Louganis
Naber
Spitz
Weissmuller
Department
Corporate
Performance
r1
r2
r3
9
7
6
3
6
7
8
7
7
9
8
8
3
4
3
5
4
6
6
6
6
5
6
5
Introduction to MIS
Merit Pay
Raise pool
Pct
Perf
73%
53%
73%
83%
33%
50%
Current
Salary
35.8
17.9
29.5
19.8
17.5
53.2
21.7
18.9
Salary range ($000)
High
Low
Avg
37.5
28.4
36.4
18.9
15.4
16.3
30.2
26.7
28.9
23.2
19.5
21.4
22.4
17.3
18.4
60.4
32.5
45.2
32.1
22.2
21.9
124
9.2
18.9
$10000
Merit
$100
733
533
733
833
333
500
3665
Market
Adjust.
Total
Raise
733
533
733
833
333
500
3665
Raise%
2.0%
3.0%
2.5%
4.2%
1.9%
0.9%
2.4%
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Human Resources
dollars
Raises
4000
3500
3000
2500
2000
1500
1000
500
0
100.0%
80.0%
60.0%
40.0%
20.0%
0.0%
Caulkins
Jihong
Raise
Introduction to MIS
Louganis
Naber
Raise pct
Spitz
Weissmuller
Performance
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File: C08-14 Finance NPV.xls
Finance Example: Project NPV
Rate = 7%
P r oj e c t C N P V = - $ 3 , 8 14
P r oj 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
0
Cost s-A
-20, 000
0
1
2
3
Revenue-A
-100, 000
4
5
6
Cost s-C
Revenue-C
-40, 000
-150, 000
-60, 000
-200, 000
-80, 000
-250, 000
-100, 000
Y ear
Y ear
P r oj e c t B N P V=$ 6 , 0 6 4
80, 000
60, 000
40, 000
20, 000
0
-20, 000
Cost s-B
0
1
2
3
4
-40, 000
5
6
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
Y ear
Introduction to MIS
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File: C08-15 Accounting.xls
Accounting
Balance Sheet for 2003
Cash
Receivables
Inventories
Total Current Assets
Net Fixed Assets
Total Assets
Introduction to MIS
33,562
87,341
15,983
136,886
Accounts Payable
Notes Payable
Accruals
Total Current Liabilities
45,673
182,559
Bonds
Common Stock
Ret. Earnings
Liabs. + Equity
32,872
54,327
11,764
98,963
14,982
57,864
10,750
182,559
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Accounting
Income Statement for 2003
Sales
$97,655
Operating Costs
76,530
Earnings before interest & tax 21,125
Interest
Earnings before tax
Taxes
Net Income
40%
60%
9763
4,053
17,072
6,829
10,243
Dividends
Add. to Retained Earnings
6,146
4,097
Earnings per share
$0.42
Introduction to MIS
Tax rate
Dividends
Shares out.
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Accounting Analysis
Balance Sheet projected 2004
Income Statement projected 2004
Cash
$36,918
Acts Receivable
96,075
Inventories
17,581
2
Accts Payable
Notes Payabale
Accruals
$36,159
54,327
12,940
Total Cur. Assets 150,576
Total Cur. Liabs. 103,427
Net Fixed Assets
Bonds
Common Stock
Ret. Earnings
14,982
57,864
14,915
Liabs + Equity
191,188
Total Assets
45,673
$196,248
Sales
Operating Costs
2
5
3
$ 107,421
84,183
Earn. before int. & tax
Interest
23,238
4,306
Earn. before tax
taxes
18,931
8,519
Net Income
10,412
Dividends
Add. Funds Need
6,274
5,060
Add. to Ret. Earnings
Bond int. rate
5%
Added interest
4
Earnings per share
$ 4,165
$0.43
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1
Forecast sales and costs.
2
Forecast cash, accts receivable, accts payable, accruals.
3
Add gain in retained earnings.
4
Compute funds needed and interest cost.
5
Add new interest to income statement.
Introduction to MIS
1
Tax rate
Dividend rate
Shares outstanding
45%
60%
9763
Sales increase
Operations cost increase
10%
10%
Results in a CIRCular calculation.
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File: C08-25 GIS.xls
Geographic Models
City
Clewiston
Fort Myers
Gainesville
Jacksonville
Miami
Ocala
Orlando
Perry
Tallahassee
Tampa
1990 pop
2000 pop 1990 per capita
2000 income
per capita
1990 income
soft sales
1990 hard sales
2000 soft sales
2000 hard
6085
8549
13598
15466
562.5
452.0
367.6
525.4
45206
59491
16890
20256
652.9
535.2
928.2
1010.3
84770
101724
13672
19428
281.7
365.2
550.5
459.4
635230
734961
15316
19275
849.1
990.2
1321.7
1109.3
258548
300691
16874
18812
833.4
721.7
967.1
1280.6
42045
55878
12027
15130
321.7
359.0
486.2
407.3
164693
217889
16958
20729
509.2
425.7
691.5
803.5
7151
8045
11055
14144
267.2
300.1
452.9
291.0
124773
155218
14578
20185
489.7
595.4
843.8
611.7
280015
335458
15081
19062
851.0
767.4
953.4
1009.1
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R ed
Ye llow
R ed
3.2
2.3
5.0
4.2
2.3
1.9
3.7
3.2
Green
Tallahassee
Blu e
R ed
Ye llow
Green
2.1
1.7
1.4
1.1
Green
R ed
Blu e
Ye llow
R ed
2000
Soft
Goods
1990
Hard
Goods
1990
Soft
Goods
1.7
1.1
1.1
1.0
Green
Blu e
Jacksonville
Perry
2000
Hard
Goods
Ye llow
Gainesville
Ye llow
1.8
1.5
1.4
1.2
Blu e
Ocala
Green
Blu e
R ed
Orlando
R ed
2.6
3.0
1.6
1.9
Ye llow
3.6
3.8
2.9
3.2
Green
Green
Tampa
20,700
19,400
14,600
18,100
R ed
R ed
16,800
12,200-
15,500-
1990
2000
Ye llow
3.5
3.8
2.0
2.5
Green
13,400
Blu e
Blu e
per capita income
17,000
15,800
Ye llow
Fort Myers
Clewiston
Ye llow
1.4
2.0
1.7
2.1
Green
Blu e
Miami
Blu e
R ed
3.7
4.8
2.7
3.2
Green
Introduction to MIS
Ye llow
Blu e
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Data Mining
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Automatic analysis of data
Statistics
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Correlation
Regression (multiple correlation)
Clustering
Classification
Nonlinear relationships
More automated methods
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Market basket analysis
Patterns: neural networks
Introduction to MIS
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Data Mining: Clusters
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http://www.spotfire.com
Introduction to MIS
Data Mining Tools: Spotfire
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Market Basket Analysis
What items do customers buy together?
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Data Mining: Market Basket Analysis
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Goal: Measure association between two items
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Classic examples
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What items do customers buy together?
What Web pages or sites are visited in pairs?
Convenience store found that on weekends, people often buy
both beer and diapers
Amazon.com: shows related purchases
Interpretation and Use
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Decide if you want to put those items together to increase
cross-selling
Or, put items at opposite ends of the aisle and make people
walk past the high-impulse items
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Link: http://www.exsys.com/
Expert System Example
Camcorder selection by ExSys
Test It
http://www.exsys.com/demomain.html
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Expert System
Expert
Knowledge Base
Expert decisions
made by
non-experts
Symbolic &
Numeric Knowledge
Rules
If income > 20,000
or expenses < 3000
and good credit history
or . . .
Then 10% chance of default
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ES Example: Bank Loan
Welcome to the Loan Evaluation System.
What is the purpose of the loan? car
Forward Chaining
How much money will be loaned? 10,000
For how many years? 5
The current interest rate is 10%.
The payment will be $212.47 per month.
What is the annual income? 24,000
What is the total monthly payments of other loans? Why?
Because the payment is more than 10% of the monthly income.
What is the total monthly payments of other loans? 50.00
The loan should be approved—there is only a 2% chance of default.
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Decision Tree (bank loan)
Payments
< 10%
monthly income?
No
Yes
Other loans
total < 30%
monthly income?
Yes
Credit
History
Good
Bad
No
So-so
Approve
the loan
Introduction to MIS
Job
Stability
Good
Poor
Deny
the loan
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ES Examples
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United Airlines
American Express
Stanford
DEC
Oil exploration
IRS
Auto/Machine repair
Introduction to MIS
GADS: Gate Assignment
Authorizer's Assistant
Mycin: Medicine
Order Analysis + more
Geological survey analysis
Audit selection
(GM:Charley) Diagnostic
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ES Problem Suitability
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Narrow, well-defined domain
Solutions require an expert
Complex logical processing
Handle missing, ill-structured data
Need a cooperative expert
Repeatable decision
Introduction to MIS
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ES Development
ES Shells
Guru
Exsys
Custom Programming
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LISP
PROLOG
Rules
and
decision
trees
entered
by designer
Forward
and
backward
chaining
by ES shell
Maintained by expert system shell
Expert
ES screens
seen by user
Knowledge
database
Knowledge
engineer
Programmer
(for (k 0 (+ 1 k) )
exit when ( ?> k cluster-size) do
(for (j 0 (+ 1 j ))
exit when (= j k) do
(connect unit cluster k output o -A
to unit cluster j input i - A ))
... )
Custom program in LISP
Introduction to MIS
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Some Expert System Shells
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CLIPS
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Jess
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Originally developed at NASA
Written in C
Available free or at low cost
http://www.ghg.net/clips/CLIPS.html
Written in Java
Good for Web applications
Available free or at low cost
http://herzberg.ca.sandia.gov/jess/
ExSys

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Commercial system with many features
www.exsys.com
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Limitations of ES
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Fragile systems
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Small environmental.
changes can force revision.
of all of the rules.
Conflicting experts
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Mistakes

Who is responsible?
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Expert?
Multiple experts?
Knowledge engineer?
Company that uses it?
Vague rules

Rules can be hard to define.
Introduction to MIS
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With multiple opinions, who
is right?
Can diverse methods be
combined?
Unforeseen events

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
Events outside of domain
can lead to nonsense
decisions.
Human experts adapt.
Will human novice recognize
a nonsense result?
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AI Research Areas
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Computer Science
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Parallel Processing
Symbolic Processing
Neural Networks
Robotics Applications



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
Visual Perception
Tactility
Dexterity
Locomotion & Navigation
Introduction to MIS
Natural Language

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Speech Recognition
Language Translation
Language Comprehension
Cognitive Science
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Expert Systems
Learning Systems
Knowledge-Based Systems
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Neural Network: Pattern recognition
Output Cells
Input weights
7
3
-2
4
Hidden Layer
Some of the connections
6
Incomplete
pattern/missing inputs.
Introduction to MIS
Sensory Input Cells
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Machine Vision Example
http://www.redteamracing.org/
Carnegie Mellon, funded by Boeing, Intel, the Depart of Defense,
and several others leads the way in self-driving vehicles.
Red Team Racing is preparing for the second DOD Grand
Challenge in 2005.
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Language Recognition
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

Look at the user’s voice command:
Copy the red, file the blue, delete the yellow mark.
Now, change the commas slightly.
Copy the red file, the blue delete, the yellow mark.
I saw the Grand Canyon flying to New York.
Introduction to MIS
Emergency
Vehicles
No
Parking
Any Time
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Subjective (fuzzy) Definitions
Subjective Definitions
reference point
cold
hot
temperature
e.g., average
temperature
Moving farther from the reference point
increases the chance that the temperature is
considered to be different (cold or hot).
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DSS and ES
DSS
ES
Goal
Method
help user make decision
provide expert advice
data - model - presentation
Type of
problems
general, limited by user
models
asks questions,
applies rules, explains
narrow domain
Introduction to MIS
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DSS, ES, and AI: Bank Example
Data
Model
Output
Decision Support System
Expert System
Artificial Intelligence
Loan Officer
ES Rules
Determine Rules
Income
What is the monthly income?
Existing loans
3,000
Credit report
What are the total monthly
payments on other loans? 450
Lend in all but worst cases
Monitor for late and missing
payments.
Name
Brown
Jones
Smith
...
Loan #Late Amount
25,000 5
1,250
62,000 1
135
83,000 3
2,435
How long have they had the
current job? 5 years
Data/Training Cases
loan 1 data: paid
loan 2 data: 5 late
loan 3 data: lost
loan 4 data: 1 late
...
Neural Network Weights
Should grant the loan since there
is only a 5% chance of default.
Evaluate new data,
make recommendation.
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Software Agents
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
Independent
Networks/Communication
Uses



Search
Negotiate
Monitor
Locate &
book trip.
Software agent
Vacation
Resorts
Resort
Databases
Introduction to MIS
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AI Questions
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What is intelligence?
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






Creativity?
Learning?
Memory?
Ability to handle unexpected events?
More?
Can machines ever think like humans?
How do humans think?
Do we really want them to think like us?
Introduction to MIS
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Technology Toolbox: Forecasting a Trend
y = 5897x + 156750
R2 = 0.5198
Total Sales by Month
$2,500,000
$2,000,000
$1,500,000
$1,000,000
$500,000
19
94
19 01
94
19 08
95
19 03
95
19 10
96
19 05
96
19 12
97
19 07
98
19 02
98
19 09
99
19 04
99
20 11
00
20 06
01
20 01
01
20 08
02
20 03
02
20 10
03
20 05
03
20 12
04
20 07
05
20 02
05
20 09
06
20 04
06
20 11
07
-0
6
$0
Rolling Thunder query for total sales by year and month
Use Format(OrderDate, “yyyy-mm”)
In Excel: Data/Import/New Database Query
Create a line chart, right-click and add trend line
In the worksheet, add a forecast for six months
Introduction to MIS
C10TrendForecast.xls
53
Quick Quiz: Forecasting
1. Why is a linear forecast usually safer than nonlinear?
2. Why do you need to create a new column with month
numbers for regression instead of using the formatted
year-month column?
3. What happens to the trend line r-squared value on the
chart when you add the new forecast rows to the chart?
Introduction to MIS
54
Technology Toolbox: PivotTable
Excel: Data/PivotTable, External Data source
Find Rolling Thunder, choose qryPivotAll
Drag columns to match example. Play.
Introduction to MIS
C10PivotTable.xls
55
Quick Quiz: PivotTable
1. How is the cube browser better than writing queries?
2. How would you display quarterly instead of monthly data?
3. How many dimensions can you reasonably include in the
cube? How would you handle additional dimensions?
Introduction to MIS
56
Cases: Financial Services
Annual Revenue
120
100
Billion $
80
Citigroup
Merrill Lynch
60
Capital One
Providian
40
20
0
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
How do you use information technology to make better decisions?
Introduction to MIS
57