Information and Decision Support Systems Chapter 6

Information and Decision Support Systems

Chapter 6

Key Topics

Decision making stages– Technical support for the stages

DSS GDSS Psychological processes

DSS

Decision support systems typically collections of tools that can help you create optimization and simulation models

They often have database clients, so you can the get the data you need for your models – their parameters– E.g., manufacturing process times, truck

capacity, projected interest rates, …

Components of a DSS

Dialogue Manager Model Base

– Financial Models– Statistical Analysis Models– Graphical Models– Project Management Models

Database

Model of a DSS

Decision Making and Problem Solving

Intelligence

Identify problems and opportunities– Problems sometimes identify themselves

• People complain, profits fall, customers go elsewhere

• But waiting for problems to show themselves makes it harder to solve them

• Sometimes it’s too late

Intelligence

Helps to look for problems that are brewing– Look at key indicators, that is, measures that

can help warn you when there might be a festering problem

– If you were an OU administrator looking at enrollments, what are some key indicators that you would look at to try to predict whether there might be problems on the way?

Key Indicators

External indicators– Predictors of problems outside the firm– What are some key external indicators for

OU administrators? Internal

– Internal indicators of problems– What are some key internal indicators for

OU administrators?

Watching Key Indicators

If you don’t watch, you won’t know

Managers should define key indicators ahead of time, then make sure they watch them

But – there are always new problems

What Can IT Do?

Monitor key indicators automatically, and alert a manager when the indicator is out of bounds– Send an email automatically

Remind the manager to pay attention to certain indicators– Produce regular reports– Email reminders

This Helps, But…

You never can know what all the key indicators are

New problems come up all the time– You can’t be sure you know how to watch

for them There is no substitute for paying

attention

So Far…

Intelligence– Key indicators of problems

• External• Internal

– Opportunities• External• Internal

Opportunities

Are there ways to do new things, or do the same things differently?

Harder to define the key indicators here, but not impossible

If you were an OU administrator, what key indicators would you watch to look for new opportunities?

DIY Many key indicators already exist

– Government data, data from existing systems (accounting, sales, …)

Sometimes you need to create your own key indicators– Gather your own data

What data might an OU administrator gather that is not already available from existing sources?


Design

Identifying and analyzing alternatives Key ideas:

– Group decision making– Surfacing assumptions– Sensitivity analysis

Group Decision Making

Identifying alternatives is one of the most common reasons for group decision meetings

But how can you make sure that the group performs well?

What does it even mean for a group to perform well?– Come up with good alternatives

Brainstorming

Technique for generating alternatives– Gather people with different backgrounds– Ask them to generate ideas, without

evaluating them• Encourage participation• Keep going until people run out of ideas, or for

a fixed time

– Evaluate the ideas, after the idea generation phase

Group Problems

Domination by an individual or clique– Anonymity helps

Group Think– Group develops distorted view of reality– Group members reinforce each other

• E.g., Bay of Pigs

– Devil’s advocate helps• Rotate this unpopular role!

Group Problems Many people are unwilling to abandon a

position once it is taken publicly – Need to defend your position– Takes maturity to change your mind– Takes a boss and a culture that allows you to

change your mind Fear of offering uncertain or unpopular ideas

– Results in a loss of face– Management sets the tone: what is the norm for

reacting to potentially bad ideas?

Modify Brainstorming

Anonymous idea generation Anonymous idea ranking in evaluation

phase Allow people to anonymously change or

retract their ideas Randomly assign the devil’s advocate

role

IT Can Help

Group Decision Support System Software application that consists of

most elements in a DSS, plus software needed to provide effective support in a group decision making

Imagine Excel with features to support brainstorming

GDSS Characteristics Special design Ease of use Flexibility

– Support various group processes

Decision-making support– Spreadsheets, access

corporate DB, access Web, …

Anonymous input

Reduction of negative group behavior

Parallel communication– Several

conversations at once

Automated record keeping

Communication Choices Face-to-face vs. dispersed

– Are group members in the same room? Synchronous vs. asynchronous

– Does everyone meet at the same time? Media

– Text, voice, video Consider cost and effectiveness for different

decisions– E.g., face-to-face offers rich communication, but can

be expensive (travel, hotels, disrupted schedules…)

GDSS Decision Room

Delphi

A well-known method Groups of experts generate alternatives,

and rank them over a series of rounds Rankings can be anonymous Discussion between rounds Try to converge (although may not be

able to) Can be done over a network

Other Technology

Less formal techniques are more likely to be subject to error, but are quick and cheap

Chat Forum Email list Teleconferencing

Chat Useful features:

– Record discussion, so you know what was discussed and decided

– Private rooms– Banning (for public chats)– Ability to exchange files– Emoticons

The most important: social standards– Rules for how people treat each other– More important than the technology

Forums Useful features

– Threaded discussion– Searchable – forum used as a knowledge

base as well as a communication mechanism

– Email alerts – forum sends an email to you when there are new postings to a thread you mark as important

– Spell checking, cuss filters, … Social rules!!

Teleconferencing

Ability to see and hear other people in real time

Sometimes ability to work on applications together

Much cheaper than face-to-face meetings, but gives you relatively rich communication

Design



Surfacing Assumptions

Sometimes it’s hard to see what you are assuming about a situation that may not be true

In IT, it’s easy to assume that users know things, have resources, have time to learn, …

A project can be in trouble if these assumptions turn out to be wrong

Example: IT for Voluntary NPOs

Suppose you’re creating an IS for small voluntary organizations

You cannot assume that:– They have office space to house PCs

• Much work is done on volunteers’ home PCs

– The home PCs are maintained• Virus protection, backups, …

– Management can force workers to use IT– Workers will see the value of IT

• “Bite me, nerd child”

ACK!

Any of these things can sink a project– How do you deal with the mix of hardware,

software, connectivity, expertise, …?– How do you train people who don’t want to

be trained? It’s enough to drive you nuts!

What To Do?

1. Involve people who know the operational constraints in system development

2. Try the system out in operational environments early in the project

3. “Laboratory” testing Usability testing – give people tasks to do

with a system prototype, and video tape them talking aloud as they do it

Design



Sensitivity Analysis

What happens if you’re wrong?– If exchange rates fluctuate– If competition is stronger than predicted– If enrollments are less than you thought– If costs are higher than you forecast?

What’s the effect on your decision?

Sensitivity Analysis

Identify the assumptions that have the most effect on outcomes– Hopefully surfacing assumptions will help

See how much violations of your assumptions change the value of various alternatives


Choice

Ranking the alternatives Issues:

– Identifying outcomes of interest• Account for all important effects of each

alternative

– Valuation of outcomes• Objective functions

– Types of models• Optimization, simulation

Alternative/Attribute Matrix

A B B

1

2

3

AlternativesA

ttrib

utes

Buying a Llama

Pete Zynox Banther

Price 925 850 995

Height 64 58 68

Cuteness Medium Medium High

Eliminating Alternatives

Constraints– Criteria that an alternative absolutely must

meet– E.g., marketing plan is legal and ethical

Dominated solutions– If alternative A is better than alternative B

in all respects, then no need to consider B

Objective Function

Specifies the relative importance of the various outcomes of an alternative

Ideally, summarizes the value of an alternative in one number

May not work that way in practice – Important qualitative variables that are

difficult to measure– Uncertainty

Objective Function Example

Suppose you make hats and booties for llamas

The more hats you make, the fewer booties you can make, and vice versa

Suppose you make $12 for every hat and $8 for every pair of booties.

Objective Function Example

The objective function is:

You want to maximize Z for the various combinations of H and B that you can make.

Z = 12H + 8B

Objective Function Example Suppose you have the following three

production possibilities:

Which one do you pick?

1 2 3

H 33 42 25

B 48 21 62

Objective Function

In general:

Where:Z = value of alternative i

k = number of attributes

b = importance of attribute j

x = rating of alternative i on attribute j Linear functions are the most common in

business

Zi = b1xi1 + b2xi2 + …+ bkxik

Objective Function

So you need to know:– What the alternatives are– What attributes you will judge each

alternative on– How to weight the attributes

Choice

Ranking the alternatives Issues:

– Identifying outcomes of interest• Account for all important effects of each

alternative

– Valuation of outcomes• Objective functions

– Types of models• Optimization, simulation

Optimization Models

Guaranteed to give you the best choice– Linear programming– Dynamic programming– Queuing models– Inventory models– Data envelope analysis– …

Simple Example You have a supply depot on a river You want to install a security fence You have 1,000 feet of left-over fencing What’s the most space you can

enclose?Jetty

Buildings

A = W ( 1000 – 2 W )

A = 2W2 – 1000 W

dA / dW = 4W – 1000

dA / dW = 0 when W = 250

Jetty

BuildingsW

1,000 – 2W

Another Way

W B A

247 506 124,982

248 504 124,992

249 502 124,998

250 500 125,000

251 498 124,998

252 496 124,992

253 494 124,982

Build a fence with this width

Get this area

In Other Words…

Create a mathematical model that acts like the one you are interested in– Outputs from an input, e. g., area resulting

from building a fence of a given width Try different inputs, and watch the

output This is simulation

Visual Simulation - Traffic

http://www.flexsim.com

Manufacturing

Warehousing

Differences?

Optimization models have strict mathematical assumptions– E. g., linear programming assumes that all

constraints can be represented by linear equations

Simulation models are more flexible– But they won’t give you the “right answer”– If the model doesn’t reflect the real

situation, they are of limited value

Everything Depends on the People

The tech is useless without savvy people

To use DSS well, they need to know the business, the tech, and themselves

People exhibit systematic psychological biases when making decisions

If you know about them, you can do allow for them

Psychological Processes

EmotionsConfirmation

Cognitivelimits

Schemas

Confidence

Confirmation

Tendency to look for confirming information rather than disconfirming information.– Given a frequency question, search our

memories for matches– Think that the real frequency matches what

we recall– Usually makes sense

Confirmation Problems

But:– We hear about some things more than

others. E.g., the media is more likely to cover murder than suicide

– We remember more salient than less salient things, i.e, some things draw attention more than others.

– A murder in your neighborhood might get more attention than a suicide. Why?

Confirmation

If an issue is important, try to get some data

Don’t assume that what comes to mind first is right

The contingency table is a useful tool

Contingency TableDoes not wearing hats cause accidents?

Wearing

Accident

NoAccident

10

NotWearing

50

120 2,200

60

2,320

130 2,250 2,380



Cognitivelimits

Schemas

Confidence

Emotions Emotions set goals

– Fear of getting fired – don’t take risk– Don’t like Bill – do the reverse of what he

says– Dominate – say an idea is stupid to put

someone in their place Managers who want their employees to

make good decisions need to intentionally create that environment

Emotions

Emotions can set positive goals– Service– Mastery

Applies to all of your life– People who have goals are more satisfied

with the lives– What are some personal goals people

have?

Personal Goals

Emotions

Emotions change the way you process information– Anger can reduce your ability to think

• Emotional cascade

You cannot fully control your emotions– Amygdala

Emotional Control

But there are things you can do Most important: be mindful

– Pay attention to yourself, so you can detect when you are getting out of control

– When others are out of control, ask yourself why

Cool off - 30 minutes Talk about assumptions



Cognitivelimits

Schemas

Confidence

Schemas

Schemas are mental templates

How do you know what they are talking about?

A: Did you order it? B: Yeah, it will be here in about 45 minutes. A: Oh... Well, I've got to leave before then. But save me a couple of slices, okay?

Expertise

Experts have more and better schemas than novices

Experts know which schemas to apply when– Diagnosticity

Experts can ask the right questions– The questions that will tell when what the

problem is (i.e., choose the right schema)

Schemas and Stereotypes

Schemas can have negative consequences

Example – women are bad at math Affects not only how people with this

stereotype view women, but how women view themselves, and how they perform

Sample Study

Women perform as much as 12 percent better on math problems when tested in a setting without men…

…women tested in single-sex groups scored a 70-percent accuracy rate on math exams; women tested in groups in which they were outnumbered by men scored a 58-percent accuracy rate

From ScienceDaily, 9/13/2000

Sample Study

Stanford psychology professor Claude Steele has conducted experiments in which he brings in black students and white students to take a standardized test.

The first time, he tells the students that they will be taking a test to measure their verbal and reasoning ability. The second time, he tells them the test is an unimportant research tool.

Steele has found that the black students do less well when they are told that the test measures their abilities.

Frontline, 1999

Schemas and Stereotypes

Schema use is unavoidable– Built in to the way we think

When you see a Chinese man or a black woman, your stereotypes will be activated

But those who show less prejudice add a second step– They question their own thinking



Cognitivelimits

Schemas

Confidence

Confidence

Evolution has equipped us with the ability to make fast, confident decisions– The leopard and the human

Social pressure to be sure– “If you’re not sure, you can’t be competent”

Result: People are more sure of some things than they should be

Confidence

People who are sure can lead you to disaster– Many people want certainty from their

leaders– But the world is an uncertain place– Ask why you or someone else is so certain,

and what the consequences of error are



Cognitivelimits

Schemas

Confidence

Cognitive Limits

The magic number seven plus or minus two

Keep things as simple as possible, but no simpler

Allow people time to make good decisions

Confidence

People who are sure can lead you to disaster– Many people want certainty from their

leaders– But the world is an uncertain place– Ask why you or someone else is so certain,

and what the consequences of error are

Key Topics

Decision making stages DSS GDSS Psychological processes

Documents

Information and Decision Support Systems Chapter 6