Lecture Management information systems: Solving business problems with information technology – Chapter 8

In this chapter you will learn: How can information systems be used to support teams of workers? How many different ways are there to communicate with team members? Which method is best for each type of message? How can several people work on the same documents?,...

Copyright © 1998-2002 by Jerry Post

Introduction to MIS

Chapter 8 Models and Decision Support

0 20 40 60 80 100

1st Qtr 2nd Qtr 3rd Qtr 4th Qtr Actual Forecast

Outline

Decision Levels

Business Operations

Tactical Management

Strategic Mgt.

EIS

ES

T

action

P

ssing

trolModels

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?

 Heuristics: Rules of thumb

 Anchoring and adjustment

1 2 3 4 5 6 7 8 9

10 1 3

5 0

5 10 15 20 25

File: C08Fig08.xls

Why Build Models?

0 5 10 15 20 25

Economic/

regression Forecast

File: C08Fig09.xls

0 5 10 15 20 25

Object-Oriented Simulation Models

Customer

purchase order

Order Entry

Custom Manufacturing

purchase order

Shipping Schedule

Invoice

DSS: Decision Support Systems

sales revenue profit prior

Sales and Revenue 1994

Jan Feb Mar Apr May Jun 0

50 100 150 200 250 300 Legend Sales Revenue Profit Prior

results

File: C08Fig11.xls

Data Mining: Spotfire

http://www.spotfire.com

Data Warehouse

OLTP Database3NF tables

Operations

data

Predefinedreports

Data warehouseStar configuration

Daily datatransfer

Interactivedata analysis

Flat files

Multidimensional OLAP Cube

Time

CustomerLocation

Cate

gory

Pet StoreItem SalesAmount = Quantity*Sale Price

Microsoft SQL Server Cube Browser

Microsoft Pivot Table

Plant schedule

EIS: Executive Information System

Executive IS

ProductionDistribution

Production Costs

SouthNorthOverseas

0 500 1000 1500 2500 3000 4000 4500

1993 1994 1995 1996

South North Overseas

Marketing Research Data

Marketing Sales Forecast

GDP and Sales

1000 1200 1400 1600 1800 2000 2200 2400 2600 2800

GDP Sales Forecast

forecast

Note the fourth quarter sales jump

The forecast should pick up this cycle.

File: C08-10 Marketing Forecast.xls

Regression Forecasting

Sales = b0 + b1 Time + b2 GDP Model:

Forecast GDP for each quarter.

Human Resources

File: C08-19 HRM.xls

0 500

Finance Example: Project NPV

P ro je c t  C  N P V = ­ $ 3 , 8 14

-100,000 -80,000 -60,000 -40,000 -20,000 0 20,000 40,000 60,000

Ye a r

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

Balance Sheet for 2003

Total Current Assets 136,886 Total Current Liabilities 98,963

File: C08-15 Accounting.xls

Income Statement for 2003

Earnings before interest & tax 21,125 shares out 9763

Add to Retained Earnings 4,097

Earnings per share $0.42

Balance Sheet projected 2004

Income Statement projected 2004

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.

4

2

3

5 Total Cur Assets 150,576

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

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

Hard

Goods

1990 Soft Goods

Cases: Computer Hardware Industry

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

Appendix: Forecasting Uses

trend

Structural Equations

QD = b0 + b1 Price + b2 Income + b3 Substitute

QD = 1114 - 0.1 Price + 1.2 Income - 1.0 Substitute

Model

EstimateData

Forecast 33318 = 1114 - 0.1 (155) + 1.2 (20000) - 1.0 (160)

Need to know (estimate) future price, income, and substitute price

Time Series Components

A cycle is similar to the seasonal pattern,but covers a time period longer than a year

St = Yt + (1 - ) St-1

S is the new data point

is the smoothing factor

Use Excel:

Tools, Data AnalysisExponential Smoothing

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

(A2-D2)*(A2-D2)

Larger factors placemore importance onrecent data, which results in less

smoothing

Smoothing with Trends

Double Exponential Smoothing

20000 22000 24000 26000 28000 30000 32000 34000

1 3 5 7 9 11 13 15 17 19

Raw Data Smooth:0.20

Apply exponential smoothing and choose smoothing factor ( )

Apply exponential smoothing a second time to the smoothed data

Forecasting with Exponential Smoothing

Forecast for time T+

] 2

[

1

1 1

S20 = 32,064 (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

Moving average captures all

elements, but lags the actual pattern.

Regression Analysis

=$F$20+$F$21*B6

Tools + Data Analysis + Regression Dependent = Sales

Independent = Time

Estimating Trend

Yt = b0 + b1(t)

Use regression to estimate b0 and b1

Plug t into equation to estimate new value (on trend):