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Copyright © 1998 by Jerry Post

INFSY540.1Information Resources in Management

Lesson #4

Chapters 8

Models and Decision Support

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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.

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What is an information system?

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What is an information system?

Information System

Transaction Processing System

Decision Support System

Model-Driven DSSData-Driven DSS

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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

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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

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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)

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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

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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

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Models and Analysis

INPUTS MODEL OUTPUTS

ASSUMPTIONS

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Assumptions and ConclusionsThe 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.”

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GIGO

INPUTS MODEL OUTPUTS

ASSUMPTIONS

INPUTS Constants

Parameters

Variables

OUTPUTS

Criteria or MOE

Additional Statistics

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Types of Models Mental Symbolic Mathematical Computer Physical

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Sample Model

Average totalcost

Marginal cost

$

Quantity

price

Q*

Determining Production Levelsin Perfect Competition

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Order Model

Simple Model of Evaluating Custom Orders

salesmanager

warehouse managerproductionmanager

vice-presidents

engineers

Decide if weshould produce

summarizesales orders

reviewsales orders

receivesales orders

marketingmanager

salesstaff

customer

compute coststo produce

check stockto match order

decide stepsto produce

review costsadd fixed costs

accountingmanager

billcustomers

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Models ofPhysical 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.

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Statistical Decision 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

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File: C08Fig08.xls

Why Build Models?

Understand the Process Prediction Optimization Simulation To conduct "What If" analysis Dangers

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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

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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

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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

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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

Optimization

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Figure 10.2

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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.”

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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.

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Building ModelsProcess

Equation:output = f(input,time)

InputOutput

Define SystemInput - Process - OutputSimplifying assumptionsSystem boundary

Build EquationsIdentify parameters (variables you can control)Identify variables you cannot controlDefine equations for the variablesEstimate parameters from data

Use Model to transform Inputs into Outputs

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Modeling Limitations Model complexity Cost of building model Errors in model

Data Equations Presentation and interpretation

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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?)

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EOQ Model

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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

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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

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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.

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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.

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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

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Exponential SmoothingChoosing the smoothing factor ():It is usually between 0.01 and 0.20Test multiple values and compare errors:(actual - smooth) * (actual - smooth)Compute the sum. Choose the factor with the least total sum-of-squared error.

Sum Sum Sum

(A2-D2)*(A2-D2)

929,916 848,686 769,265

Larger factors placemore importance onrecent data, which results in less smoothing.

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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.

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Forecasting with Exponential Smoothing

Forecast for time T+

]2[

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12 TTT SSy

T = 20 last of the raw data = 1 forecast one period ahead = 0.2 smoothing factorS20 = 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

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Estimating Trend

Yt = b0 + b1(t)

Use regression to estimate b0 and b1.

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 norandom factors and no cycles.

Time Quantity Trend Difference1 24917 24484 4322 26152 24983 11693 27297 25482 18164 26157 25980 1775 26710 26479 2316 26103 26977 -8747 27981 27476 5058 26327 27975 -16479 24913 28473 -3560

10 28524 28972 -44811 29774 29470 30312 29136 29969 -83313 29332 30468 -113614 30306 30966 -66015 32133 31465 66916 33329 31963 136617 34522 32462 206018 34769 32961 180819 33355 33459 -10420 32684 33958 -127421 3445622 3495523 3545424 35952

Coefficients Std Error t Stat P-valueIntercept 23985.81 652.48 36.76 2.2E-18Time 498.60 54.47 9.15 3.4E-08

An Overview of Decision Support Systems

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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

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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

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Figure 10.14

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Capabilities of a DSS Support all problem-solving phases Support different decision frequencies Support different problem structures Support various decision-making levels

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The Model Base Financial models

Cash flow Internal rate of return

Statistical analysis models Averages, standard deviations Correlations Regression analysis

Graphical models Project management models

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Table 10.3

Group Decision Support Systems

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Characteristics of a GDSS Special design Ease of use Flexibility Decision-making support

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Characteristics of a GDSS Anonymous input Reduction of negative group behavior Parallel communication Automated record keeping

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Figure 10.18

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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.

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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

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EIS: EnterpriseInformation System

Easy access to data Graphical interface Non-intrusive Drill-down capabilities

EIS Softwarefrom Lightshiphighlights ease-of-use GUI fordata look-up.

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Enterprise 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