Download Chap 12

Supporting InformationCentric Decision
Making
Chapter 12
Information Systems
Management in Practice
8th Edition
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
Part IV: Systems for Supporting
Knowledge-Based Work
This part consists of three chapters that
discuss supporting three kinds of
work—decision making, collaboration,
and knowledge work
 Procedure-based versus knowledgebased information-handling activities



Part III dealt with procedural-based work
Part IV focuses on knowledge-based work
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-2
Framework For IS Management
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-3
Chapter 12


Introduction
Technologies-supported decision making






Building timely business intelligence
Decision support systems
Data mining
Executive information systems
Expert systems
Agent-based modeling
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-4
Chapter 12 cont’d

Toward the real-time enterprise







Enterprise nervous systems
Straight-through processing
Real-time CRM
Communicating objects
Vigilant information systems
Requisites for successful real-time management
Conclusion
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-5
Introduction



Decision making is a process that involves a
variety of activities, most of which handle
information
Most computer systems support decision
making by automating decision processes
A wide variety of computer-based tools and
approaches can be used to solve problems.
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-6
A Problem-Solving Scenario
Case Example: Supporting decision making
1.
Use of executive information systems (EIS) to
compare budget to actual sales
2.
Discovery of a sale shortfall in one region
3.
Analysis of possible cause(s) of the shortfall



4.
5.
Economic conditions, competitive analysis, data mining,
sales reports
Sales pattern via marketing DSS
Brainstorming session via GDSS
No discernable singular cause
Solution: Multimedia sales campaign
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-7
Technologies-Supported
Decision Making
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-8
Building Timely Business
Intelligence

Business intelligence (BI) is a broad set of concepts,
methods and technologies to improve contextsensitive business decisions


Gather, filter and analyze large quantities of data from
various sources
Sense-making is central to BI

Ability to be aware and assess situations that seem
important to the organization
 Awareness: Inductive process (data-driven)
 Assessment: Fitting observed data into a pre-determined
model
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-9
Decision Support Systems


Computer-based systems that help decision
makers confront ill-structured problems
through direct interaction with data and
analysis models.
Architecture for DSS

Dialog-Data Model (DDM)


Ad hoc information requests
Specific data query
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-10
Components of a Decision
Support System
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-11
ORE-IDA Foods
Case Example: Institutional DSS
 Frozen food division of H.J. Heinz
 Marketing DSS must support three main tasks in
decision making process:
1.
2.
3.

Data retrieval
•
“What has happened?”
Marketing analysis (70% of DSS function)
•
“Why did it happen?”
Modeling
•
“What will happen if…?”
Modeling for projection purposes offers greatest
potential value of marketing management
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-12
A Major Services Company
Case Example: ‘Quick Hit’ DSS - short analysis
programs
 Employee stock ownership plan (ESOP)


Determine possible impact of the ESOP on the company
and answer questions including
 How many company shares needed in 10-30 years?
 Level of growth needed to meet stock requirements?
IS manager wrote a program to perform calculations
 Program produced impact projections of ESOP over 30year period (surprising results)
 DSS program subsequently expanded to other employee
benefit programs
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-13
Data Mining

Use of computers to uncover unknown
correlations from a large data set





Classes
Clusters
Associations
Sequential patterns
Data mining gives people insights into data

Customer data
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-14
Harrah’s Entertainment
Case Example: Data Mining (Customer)
 Total Rewards Program

Mined customer data to create 90 demographic
clusters for different direct mail offers



Calculates the ROI on each customer
Found that 80% of profits from slot machine and
electronic game machine players rather than ‘high
rollers’
Within first two years of program, revenue from
repeat customers increased by $100 million
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-15
Executive Information
Systems
EIS an “executive summary” form of DSS


Used to gauge company performance, address a
critical business need and scan the environment
1.
2.
3.
Provides access to summary performance data
Uses graphics to display and visualize the data in a
user-friendly fashion
Has a minimum of analysis for modeling capability
beyond that for examining summary data
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-16
Xerox Corporation
Case Example: Executive Information Systems
 Objective for EIS at Xerox was to improve
communications and strategic planning



Quick access to related information at the right time
 Executive meetings
More efficient and better planning, especially across
divisions
 Explore relationships between plans and activities at
several divisions
Xerox corporate chief of staff was executive sponsor
of EIS development
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-17
Executive Information
Systems cont’d
Pitfalls for EIS development

1.
2.
3.
4.
5.
Lack of executive support
Undefined system objectives
Poorly defined information requirements
Inadequate support staff
Poorly planned evolution (expansion of EIS)
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-18
General Electric
Case Example: Executive Information Systems
 Most senior GE executives have a real-time view of
their portion of GE via “dashboard”
 GE’s goal is to gain visibility into all its operations in
real time and give managers a way to monitor
operations quickly and easily


EIS based on complex enterprise software that interlinks
existing systems
GE’s actions are also moving its partners and
business ecosystem closer to real-time operations
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-19
Expert Systems

Expert systems are a real-world use of
artificial intelligence (AI)

AI mimics human cognition and communication to
analyze a situation or solve a problem


e.g. MIT’s Commonsense Computing project
Expert system components


User interface
Inference engine


Reasoning methods
Knowledge base
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-20
Expert Systems cont’d

Knowledge representation

Cases


Neural networks


Knowledge from hundreds or thousands of cases to
draw inferences from
Knowledge stored as nodes in a network (adaptive
learning)
Rules

Knowledge obtained from human experts drawing on
own expertise, experience, common sense,
regulations, laws and regulations
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-21
Neural Networks
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-22
American Express
Case Example: Expert System
 Authorizer’s Assistant one of most successful
commercial uses of expert system


Approves all AmEx credit card transactions and assesses
for fraud based on over 2600 rules
 Credit worth of card holders
 Bill payment
 Purchases within normal spending pattern
Rules derived from authorizers with various levels of
expertise
 Customer sensitive (to avoid customer embarrassment)
 Can be changed to meet changing business demands
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-23
Agent-Based Modeling

A simulation technology for studying emergent
behavior (from large number of individuals)


Simulation contains “software agents” making decisions to
understand behavior of markets and other complex
systems
Nasdaq Example

Performed simulation to investigate effect of switch in tick
size from fixed eighths (.125) to decimals
 Found increase in buy-ask price spread instead of initially
predicted decrease because of the reduction in market’s
ability to do price discovery
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-24
Toward Real-Time Enterprise

This section builds on the five different types
of decision support technologies and
demonstrates how they can be mixed and
matched to form the foundation for the realtime enterprise
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-25
Toward the Real-Time
Enterprise


IT, especially the Internet, is giving companies a
way to know how they are doing “at the moment”
and disseminate the closer-to-real-time information
about events
Occurring on a whole host of fronts including



Enterprise nervous systems
 Coordinate company operations
Straight-through processing
 Reduce distortion in supply chains
Communicating objects
 Gain real-time data about the physical world
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-26
Enterprise Nervous Systems

A kind of network that connects people, applications
and devices (buzz phrase?)




Message-based
 Messages are efficient and effective for dispersing
information
Event driven
 Events are recorded and made available
Publish and subscribe approach
 Events are published to electronic address, which can be
subscribed to as an information feed
Common data formats
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-27
Delta Airlines
Case Example: Enterprise Nervous Systems
 Delta integrated existing disparate systems to
build an enterprise nervous system to
manage gate operations



Information about each flight is managed in realtime by the system
System uses a publish-and-subscribe approach
using messaging middleware
Delta is now expanding system out to their
partners who serve their passengers
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-28
Straight-Through Processing

Real-time information

Zero latency



Quick reaction to new information
Straight-through processing means
transaction data are entered just once in a
process, especially a supply chain
Goal is to reduce bullwhip effect from process
lags and latency
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-29
Real-Time CRM

Another view of real-time response might
occur between a company and a potential
customer (touch points)


Customer call
Web site
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-30
A Real-Time Interaction On a
Web Site

An illustration of how real-time CRM works

A potential guest visits the Web site of a hotel
chain

The real-time CRM system initiates requests to create
profile of customer




Past interactions with the customer
Past billing information
Past purchasing history
Using this information, it makes real-time offers to
the visitor, and visitor’s responses are recorded
and taken into account for Web site visits
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-31
A Real-Time Interaction On a
Web Site cont’d
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-32
Communicating Objects

These are “smart” sensors and tags that
provide information about the physical world
via real-time data

radio frequency identification device (RFID)



pet micro-chips (satellite GPS), product tags
A tag can be passive (read-only) or active
(send out signals)
Carries far more information than bar codes

Item code, price and history
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-33
Communicating Objects cont’d

Example: Real-time electronic road pricing
(ERP) system in Singapore to control traffic
congestion



Cars have smart card devices attached to their
windscreens
Smart cards are debited (wirelessly) when cars
pass through gantries in certain areas of the city
Variable pricing dependent on when and where
you drive
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-34
Vigilant Information Systems


The premise of a real-time enterprise is not only
having the ability to capture data in real time, but
also acting on that data quickly
US Air Force pilot’s OODA framework

Never lost a dog-fight even to superior aircraft!
 Observe where his challenger’s plane is
 Orient himself and size up his own vulnerabilities and
opportunities
 Decide which maneuver to take
 Act to perform before the challenger through the same four
steps
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-35
OODA Loop
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-36
Western Digital
Case Example: Vigilant Information Systems
 PC disk manufacturer used OODA type of
thinking to move itself closer to operating in
real-time with a sense-and-respond culture
for competitive advantage
 Built “alertly watchful” vigilant information
system (VIS)


Complex and builds on the firm’s legacy systems
Essentially four layers
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-37
Western Digital’s Vigilant
Information Systems
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-38
Western Digital cont’d

Changed business processes to complement VIS to
give Western Digital a way to operate inside
competitors’ OODA loops



Established new company policies
 Translate strategic goals to time-based objectives
 Capture real-time key performance indicators (KPIs)
 Collaborate decision making and coordinate actions
Three levels of OODA loops to maximize VIS “alerts”
 Shop-floor, Factory, Corporate
Benefits of VIS

Quickened all OODA loops and helped link decisions
across them, which ultimately led to significant increase in
firm performance
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-39
Requisites for Successful
Real-Time Management

Real-time data and real-time performance
metrics

Focus on high value-added data


Identify key activities and performance indicators that
are needed in real time
Technology readiness

Substantial computing resources

Integrated and seamless system that is capable of
selecting, filtering and compiling data to send them in
real time to designated users on demand.
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-40
Conclusion

Use of IT to support decision making covers
a variety of functions including



Alert, recommendation or decision making itself
Computer-supported decision making needs
to be monitored
IS managers must comprehend the potentials
and limitations of these technologies
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-41
All rights reserved. No part of this publication may be reproduced, stored in a
retrieval system, or transmitted, in any form or by any means, electronic,
mechanical, photocopying, recording, or otherwise, without the prior written
permission of the publisher. Printed in the United States of America.
Copyright © 2009 Pearson Education, Inc.
Publishing as Prentice Hall
© 2009 Pearson Education, Inc. Publishing as Prentice Hall
12-42