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Introduction to MIS 1
Copyright © 1998-2002 by Jerry Post
Introduction to MIS
Chapter 8
Models and Decision Support
Introduction to MIS 2
Models
Data
Model
f xx
( ) exp
1
2
1
2
2
020406080
100
1st Qtr 2nd Qtr 3rd Qtr 4th Qtr
Actual Forecast
Decision
Output
Strategy
Operations
Tactics
Company
Introduction to MIS 3
Outline 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 5
Choose a Stock
Stock Price
90
95
100
105
110
115
120
125
130
1 2 3 4 5 6 7 8 9 10 11 12
Month
CompanyA
CompanyB
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 6
Human Biases Acquisition/Input Data availability Selective perception Frequency Concrete information Illusory correlation
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
Introduction to MIS 7
Optimization
1 2 3 4 5 6 7 8 9 101
3
5
0
5
10
15
20
25
Ou
tpu
t
Input Levels
Maximum
Model: definedby the data pointsor equation
Control variables
Goal or outputvariables
File: C08Fig08.xls
Why Build Models? Understanding the Process Optimization Prediction Simulation or "What If"
Scenarios Dangers
Introduction to MIS 8
Prediction
0
5
10
15
20
25
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2
Time/quarters
Ou
tpu
t
Moving AverageTrend/Forecast
Economic/regressionForecast
File: C08Fig09.xls
Introduction to MIS 9
Simulation
0
5
10
15
20
25
1 2 3 4 5 6 7 8 9 10
Input Levels
Ou
tpu
t
Goal or outputvariables
Results from alteringinternal rules
File: C08Fig10.xls
Introduction to MIS 10
Object-Oriented Simulation Models
Customer
purchaseorder
Order Entry
Custom Manufacturing
purchaseorder
routing& scheduling
Production
Inventory
Shipping
PartsList
ShippingSchedule
Invoice
Introduction to MIS 11
DSS: Decision Support Systems
sales revenueprofit prior154 204.5 45.32 35.72163 217.8 53.24 37.23161 220.4 57.17 32.78173 268.3 61.93 47.68143 195.2 32.38 41.25181 294.7 83.19 67.52
Sales and Revenue 1994
Jan Feb Mar Apr May Jun0
50
100
150
200
250
300
LegendSalesRevenueProfitPrior
Database
Model
Output
data
to a
nalyz
e
results
File: C08Fig11.xls
Introduction to MIS 12
Data Mining: Spotfirehttp://www.spotfire.com
Introduction to MIS 13
Data Warehouse
OLTP Database3NF tables
Operationsdata
Predefinedreports
Data warehouseStar configuration
Daily datatransfer
Interactivedata analysis
Flat files
Introduction to MIS 14
Multidimensional OLAP Cube
TimeSale Date
CustomerLocation
Categ
ory
Pet StoreItem SalesAmount = Quantity*Sale Price
Introduction to MIS 15
Microsoft SQL Server Cube Browser
Introduction to MIS 16
Microsoft Pivot Table
Introduction to MIS 17
Digital Dashboard
http://www.microsoft.com/business/casestudies/dd/honeywell.asp
Stock market
Exceptions
Plant or management variables
Equipment details
Products
Quality control
Plant schedule
Introduction to MIS 19
Executive IS
ProductionDistribution
Sales
Central Management
Executives
DataData
SalesProduction CostsDistribution Costs
Fixed Costs
Production CostsSouthNorth
Overseas
0500
100015002000250030003500400045005000
1993 1994 1995 1996
South
North
Overseas
Production: NorthItem# 1995 1994
1234 542.1 442.32938 631.3 153.57319 753.1 623.8
Data for EIS
Data
Data
Introduction to MIS 20
Marketing Research Data
Introduction to MIS 21
Marketing Sales Forecast
GDP and Sales
1000
1200
1400
1600
1800
2000
2200
2400
2600
2800
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
Quarter
GD
P
30
40
50
60
70
80
90
100
Sal
es
GDP
Sales
Forecast
forecast
Note the fourth quarter sales jump.
The forecast should pick up this cycle.
File: C08-10 Marketing Forecast.xls
Introduction to MIS 22
Regression Forecasting
Sales = b0 + b1 Time + b2 GDPModel:
Data: Quarterly sales and GDP for 10 years.
Analysis: Estimate model coefficients with regression.
Forecast GDP for each quarter.
Output: Compute Sales prediction.
Graph forecast.
Coefficients Standard Error t StatIntercept -98.175 15.895 -6.176Time -1.653 0.304 -5.444GDP 0.102 0.012 8.507
Introduction to MIS 23
Human ResourcesFile: C08-19 HRM.xls
Introduction to MIS 24
Raises
0500
1000
1500200025003000
35004000
Caulkins Jihong Louganis Naber Spitz Weissmuller
dolla
rs
0.0%10.0%20.0%30.0%40.0%50.0%60.0%70.0%80.0%90.0%
Raise Raise pct Performance
Human Resources
Introduction to MIS 25
Finance Example: Project NPVProject A NPV=$18,475
-250,000
-200,000
-150,000
-100,000
-50,000
0
50,000
100,000
0 1 2 3 4 5 6
Year
Costs-A
Revenue-A
Project B NPV=$6,064
-120,000
-100,000
-80,000
-60,000
-40,000
-20,000
0
20,000
40,000
60,000
80,000
0 1 2 3 4 5 6
Year
Costs-B
Revenue-B
Project C NPV = -$3,814
-100,000
-80,000
-60,000
-40,000
-20,000
0
20,000
40,000
60,000
0 1 2 3 4 5 6
Year
Costs-C
Revenue-C
Rate = 7%
Can you look at these cost and revenue flows and tell if the project should be accepted?
File: C08-14 Finance NPV.xls
Introduction to MIS 26
Accounting
Balance Sheet for 2003
Cash 33,562 Accounts Payable 32,872 Receivables 87,341 Notes Payable 54,327 Inventories 15,983 Accruals 11,764 Total Current Assets 136,886 Total Current Liabilities 98,963
Bonds 14,982 Common Stock 57,864
Net Fixed Assets 45,673 Ret. Earnings 10,750 Total Assets 182,559 Liabs. + Equity 182,559
File: C08-15 Accounting.xls
Introduction to MIS 27
AccountingIncome Statement for 2003
Sales $97,655 tax rate 40%Operating Costs 76,530 dividends 60%Earnings before interest & tax 21,125 shares out. 9763
Interest 4,053 Earnings before tax 17,072 taxes 6,829 Net Income 10,243
Dividends 6,146 Add. to Retained Earnings 4,097
Earnings per share $0.42
Introduction to MIS 28
Accounting Analysis
Results in a CIRCular calculation.
Cash $36,918Acts Receivable 96,075Inventories 17,581
Net Fixed Assets 45,673
Total Assets $196,248
Accts Payable $36,159Notes Payabale 54,327Accruals 12,940
Total Cur. Liabs. 103,427
Bonds 14,982Common Stock 57,864Ret. Earnings 14,915
Liabs + Equity 191,188
Add. Funds Need 5,060
Bond int. rate 5%
Added interest 253
Balance Sheet projected 2004Income Statement projected 2004
Sales $ 107,421Operating Costs 84,183
Earn. before int. & tax 23,238Interest 4,306
Earn. before tax 18,931taxes 8,519
Net Income 10,412
Dividends 6,274
Add. to Ret. Earnings $ 4,165
Earnings per share $0.43
Tax rate 45%Dividend rate 60%Shares outstanding 9763
Sales increase 10%Operations cost increase 10%
Forecast sales and costs.
Forecast cash, accts receivable, accts payable, accruals.
Add gain in retained earnings.
Compute funds needed and interest cost.
Add new interest to income statement.
1
2
3
4
5
12
4
2
3
5Total Cur. Assets 150,576
Introduction to MIS 29
Geographic Models
File: C08-25 GIS.xls
City 1990 pop 2000 pop 1990 per capita income2000 per capita income1990 soft sales1990 hard sales2000 hard sales2000 soft salesClewiston 6085 8549 13598 15466 562.5 452.0 367.6 525.4Fort Myers 45206 59491 16890 20256 652.9 535.2 928.2 1010.3Gainesville 84770 101724 13672 19428 281.7 365.2 550.5 459.4Jacksonville 635230 734961 15316 19275 849.1 990.2 1321.7 1109.3Miami 258548 300691 16874 18812 833.4 721.7 967.1 1280.6Ocala 42045 55878 12027 15130 321.7 359.0 486.2 407.3Orlando 164693 217889 16958 20729 509.2 425.7 691.5 803.5Perry 7151 8045 11055 14144 267.2 300.1 452.9 291.0Tallahassee 124773 155218 14578 20185 489.7 595.4 843.8 611.7Tampa 280015 335458 15081 19062 851.0 767.4 953.4 1009.1
Introduction to MIS 30
Tampa
Miami
Fort Myers
JacksonvilleTallahassee
Gainesville
Ocala
Orlando
Clewiston
Perry
17,000
15,800
14,600
13,400
12,200-
1990 2000
20,700
19,400
18,100
16,800
15,500-
per capita income
Red
3.2
Yellow
2.3
Blue
1.9
Green
2.3
Red
1.7
Yellow
1.1
Blue
1.0
Green
1.1
Red
2.1
Yellow
1.7
Blue
1.1
Green
1.4
Red
5.0
Yellow
4.2
Blue
3.2
Green
3.7
Red
1.8
Yellow
1.5
Blue
1.2
Green
1.4
Red
2.6
Yellow
3.0
Blue
1.9
Green
1.6
Red
3.6
Yellow
3.8
Blue
3.2
Green
2.9
Red
3.5
Yellow
3.8
Blue
2.5
Green
2.0
Red
1.4
Yellow
2.0
Blue
2.1
Green
1.7
Red
3.7
Yellow
4.8
Blue
3.2
Green
2.7
2000HardGoods
2000SoftGoods
1990HardGoods
1990SoftGoods
Introduction to MIS 31
Cases: Computer Hardware Industry
Introduction to MIS 32
Cases: Dell Computer Gateway 2000, Inc.
What is the company’s current status?
What is the Internet strategy?
How does the company use information technology?
What are the prospects for the industry?
www.dell.com
www.gateway.com
Introduction to MIS 33
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
Introduction to MIS 34
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
Q
PS
DD’
Increase in incometime
sales
trend
Introduction to MIS 35
Structural Equations Demand is a function of
Price Income Prices of related products
QD = b0 + b1 Price + b2 Income + b3 Substitute
QD = 1114 - 0.1 Price + 1.2 Income - 1.0 Substitute
Model
Estimate
Data
Forecast 33318 = 1114 - 0.1 (155) + 1.2 (20000) - 1.0 (160)
Need to know (estimate) future price, income, and substitute price.
Introduction to MIS 36
Time Series Components
time
sales
Dec Dec Dec Dec1. Trend2. Seasonal3. Cycle4. Random
Trend
Seasonal
A cycle is similar to the seasonal pattern,but covers a time period longer than a year.
Introduction to MIS 37
Exponential Smoothing
Exponential Smoothing
800
900
1000
1100
1200
1300
1400
1500
1600
1 3 5 7 9 11 13 15 17 19 21
Raw Data
Smooth:0.20
St = Yt + (1 - ) St-1
S is the new data point is the smoothing factor
Use Excel:Tools, Data AnalysisExponential Smoothing
Introduction to MIS 42
Regression Analysis
=$F$20+$F$21*B6
Time Sales Forecast
Tools + Data Analysis + Regression
Dependent = Sales
Independent = Time