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