SUPPORTING
DECISION MAKING
Chapter (12 8E, International)
Information Systems Management In
Practice 8E
McNurlin & Sprague
PowerPoints prepared by Michael Matthew
Visiting Lecturer, GACC, Macquarie University – Sydney Australia
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This part consists of three chapters that discuss
supporting three kinds of work – decision making,
collaboration, and knowledge work
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
PART IV: SYSTEMS FOR SUPPORTING
KNOWLEDGE-BASED WORK
As shown in the book’s framework figure, we
distinguish between procedure-based and knowledgebased information-handling activities
The two previous chapters, in Part III, dealt mainly
with building systems for procedure-based work
This part focuses on supporting knowledge-based
activities: the systems that support people in
performing information-handling activities to solve
problems, work together, and share expertise
11-2
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
11-3

Chapter 12 discusses supporting decision making by
first presenting five underlying technologies and some
examples of their use:
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
PART IV: SYSTEMS FOR SUPPORTING
KNOWLEDGE-BASED WORK CONT.
Decision Support Systems (DSS)
Data Mining
Executive Information Systems (EIS), and
Expert Systems (ES)
Agent-based Modelling
The chapter then discusses the fascinating subject of
the real-time enterprise, which has a goal of gaining
competitive edge by learning of an event as soon as
possible and then responding to that event quickly, if 11-4
necessary
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This lecture / chapter discusses technologies for
supporting decision making:
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Decision Support Systems (DSS)
Data Mining
Executive Information Systems (EIS), and
Expert Systems
Agent-based Modelling
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
CHAPTER 12
It then discusses IT issues related to creating the realtime enterprise
 Case examples include: a problem-solving scenario,
Ore-Ida Foods, a major services company, Harrah’s
Entertainment, Xerox Corporation, General Electric,
American Express, Delta Air Lines, a real-time
11-5
interaction on a website, and Western Digital

©2006 Barbara C. McNurlin.
Published by Pearson
Education.
INTRODUCTION
 Most
computer systems support decision
making because all software programs
involve automating decision steps that
people would take
 Decision
making is a process that involves a
variety of activities, most of which handle
information
A
wide variety of computer-based tools and
approaches can be used to confront the
problem at hand and work through its
solution
11-6
CASE EXAMPLE – SUPPORTING DECISION MAKING
 Using
an executive information system,
(EIS) to compare budget to actual sales
 Discover
a sale shortfall in one region
 Searches
for the cause of the shortfall
 But
©2006 Barbara C. McNurlin.
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A PROBLEM-SOLVING SCENARIO
couldn’t find an answer
11-7
CASE EXAMPLE – SUPPORTING DECISION MAKING CONT.
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
A PROBLEM-SOLVING SCENARIO
Investigate – several possible causes
 Economic Conditions – through the EIS & the Web accesses:
Wire services
Bank economic newsletters
 Current business and economic publications
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Competitive Analysis – through the same sources
investigates whether competitors:
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
Written Sales Report – browses the reports

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Have introduced a new product
Have launched an effective ad campaign
“Concept based” text retrieval system makes this easier
A Data Mining Analysis

Looking for any previously unknown relationships
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CASE EXAMPLE – SUPPORTING DECISION MAKING CONT.

Then accesses a marketing DSS – includes a set of
models to analyze sales patterns by:
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
A PROBLEM-SOLVING SCENARIO
Product
Sales representative
Major customer
Result – no clear problems revealed.
Action – hold a meeting, in an electronic meeting
room supported by group DSS (GDSS) software
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This scenario illustrates:
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The wide variety of activities involved in problem solving,
and
The wide variety of technologies that can be used to assist
decision makers and problem solvers
11-9

The purpose of tractors, engines, machines etc. = to
enhance humans’ physical capabilities
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION
MAKING
The purpose of computers has been to enhance our
mental capabilities
 Hence, a major use of IT is to relieve humans of
some decision making or help us make more
informed decisions

11-10
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
DECISION SUPPORT SYSTEMS
Systems that support, not replace, managers in their
decision-making activities
 Decision modeling, decision theory, and decision
analysis, attempt to make models from which the
‘best decision’ can be derived, by computation
 DSS are defined as: Computer-based systems
 That help decision makers
 Confront ill-structured problems
 Through direct interaction
 With data and analysis models

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Wide range of technologies can be used to assist
decision makers and problem solvers
11-11
THE ARCHITECTURE FOR DSSS
 Figure
11-1 shows the relationship between
the three components of the DDM model
 Software system in the middle of the figure
consists of:
©2006 Barbara C. McNurlin.
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DECISION SUPPORT SYSTEMS
The database management system (DBMS)
 The model base management system (MBMS)
 The dialog generation and management system
(DGMS)

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Published by Pearson
Education.
11-13
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
DECISION SUPPORT SYSTEMS
THE ARCHITECTURE FOR DSSS CONT.
The Dialog Component
 The DSS contains a dialog component to link the user to the system
 Was ‘mouse’ (Mac) now = browser interface
The Data Component
 Data sources – as the importance of DSS has grown, it has become
increasingly critical for the DSS to use all the important data sources
within and outside the organization
 Data warehousing
 Data mining

Much of the work on the data component of DSS has taken the form of
activities in this area
The Model Component

Models provide the analysis capabilities for a DSS

Using a mathematical representation of the problem, algorithmic
processes are employed to generate information to support decision
making
11-14
TYPES OF DSS
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The size and complexity of DSS range from large
complex systems that have many of the attributes of
major applications down to simple ad hoc analyses
that might be called end user computing tasks
Institutional DSSs tend to be fairly well defined

They are based on pre=defined data sources
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
DECISION SUPPORT SYSTEMS
Heavily internal with perhaps some external data
Use well established models in a prescheduled way
Quick-hit DSSs are developed quickly to help a
manager make either a one-time decision or a
recurring one
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Can be every bit as useful for a small or large company
Most today = Excel spreadsheets (and not ‘called’ DSS)
11-15
CASE EXAMPLE – INSTITUTIONAL DSS
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Frozen food division of H.J. Heinz
Marketing DSS must support 3 main tasks in the
decision making process:
1.
2.
3.
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
ORE-IDA FOODS
Data retrieval – helps managers find answers to the question,
“what has happened?”
Market analysis – addresses the question, “Why did it
happen?”
Modeling – helps managers get answers to, “What will
happen if…?”
Modeling for projection purposes, offers the greatest
potential value of marketing management
For successful use – line managers must take over the
ownership of the models and be responsible for keeping
them up-to-date
11-16
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
A MAJOR SERVICES COMPANY
CASE EXAMPLE – “QUICK HIT” DSS – SHORT ANALYSIS PROGRAMS
Considering – new employee benefit program: an
employee stock ownership plan (ESOP).
 Wanted a study made to determine the possible
impact of the ESOP on the company and to answer
such questions as:
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How many shares of company stock will be needed in 10,20
and 30yrs to support the ESOP?
What level of growth will be needed to meet these stock
requirements?
The information systems manager wrote a program
to perform the calculations & printed the results
 Results = showed the impact of the ESOP over a 30yr
period
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Surprising results
11-17
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
DATA MINING
A
promising use of data warehouses is to let the
computer uncover unknown correlations by
searching for interesting patterns, anomalies, or
clusters of data that people are unaware exist
 Called
data mining, its purpose is to give people
new insights into data
 Also
covered in Chapter 7
 Most
frequent type of data mined = customer data
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CASE EXAMPLE – DATA MINING (CUSTOMER)
To better know its customers, Harrah’s encourages
them to sign up for its frequent-gambler card, Total
Rewards
 Harrah’s mined its Total Rewards database to
uncover patterns and clusters of customers
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©2006 Barbara C. McNurlin.
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Education.
HARRAH’S ENTERTAINMENT
It has created 90 demographic clusters, each of
which is sent different direct mail offers –
encouraging them to visit other Harrah’s casinos

Profit and loss for each customer calculating the likely
‘return’ for every ‘investment’ it makes in that customer
11-19
CASE EXAMPLE – DATA MINING (CUSTOMER) CONT.
 Much
of its $3.7B in revenues (and 80% of
its profits) comes from its slot machines and
electronic gaming-machine players

 It
©2006 Barbara C. McNurlin.
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Education.
HARRAH’S ENTERTAINMENT
Found = locals who played often
was not the ‘high rollers’ who were the
most profitable
 Within
the first two years of operation of
Total Rewards, revenue from customers
who visited more than one Harrah’s casino
increased by $100 million
11-20
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
EXECUTIVE INFORMATION SYSTEMS (EIS)

As the name implies EISs are for use by executives

They have been used for the following purposes:
1.
2.
Gauge company performance: sales, production,
earnings, budgets, and forecasts
Scan the environmental: for news on government
regulations, competition, financial and economics
developments, and scientific subjects
11-21
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
EXECUTIVE INFORMATION SYSTEMS (EIS) CONT.

EIS can be viewed as a DSS that:
1.
2.
3.
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Provides access to summary performance data
Uses graphics to display and visualize the data in
an easy-to-use fashion, and
Has a minimum of analysis for modeling beyond
the capability to “drill down” in summary data to
examine components
In many companies, the EIS is called a
dashboard and may look like a dashboard of
a car
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CASE EXAMPLE – EXECUTIVE INFORMATION SYSTEM
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
XEROX CORPORATION
The EIS at Xerox began small and evolved to the
point where even skeptical users became avid
supporters
 Its objective was to improve communications and
planning, such as giving executives pre-meeting
documents
 It was also used in strategic planning and resulted in
better plans, especially across divisions
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXECUTIVE INFORMATION SYSTEMS (EIS)
PITFALLS IN EIS DEVELOPMENT
1.
Lack of executive support: executives must provide
the funding, but are the principal users and supply
the needed continuity
2.
Undefined system objectives: the technology, the
convenience, and the power of EIS are impressive,
but the underlying objectives and business values
of an EIS must be carefully thought through
3.
Poorly defined information requirements: EIS
typically need non - traditional information sources
- judgments, opinion, external text-based
documents - in addition to traditional financial and
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operating data
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXECUTIVE INFORMATION SYSTEMS (EIS)
PITFALLS IN EIS DEVELOPMENT CONT.
4.
Inadequate support staff: support staff must:
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Have technical competence
Understand the business, and
Have the ability to relate to the varied responsibilities and work
patterns of executives
5.
Poorly planned evolution: highly competent system professionals
using the wrong development process will fail with EIS

EIS are not developed, delivered, and then maintained
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They should evolve over a period of time under the leadership of a
team that includes:
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The executive sponsor
The operating sponsor
Executive users
The EIS support staff manager, and
The IS technical staff
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WHY INSTALL AN EIS?
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXECUTIVE INFORMATION SYSTEMS (EIS)
Attack a critical business need: EIS can be viewed as an
aid to dealing with important needs that involve the
future health of the organization
A strong personal desire by the executive: The executive
sponsoring the project may
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Want to get information faster than he/she is now getting it, or
Have a quicker access to a broader range of information, or
Have the ability to select and display only desired information
and to probe for supporting detail, or
To see information in graphical form
11-26
 “The
thing to do”: An EIS is seen as
something that modern management must
have, in order to be current in management
practices
rationale given is that the EIS will
increase executive performance and reduce
time that is wasted looking for information
and by such things as telephone tag
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXECUTIVE INFORMATION SYSTEMS (EIS)
A WEAK REASON TO INSTALL AN EIS
 The
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WHAT SHOULD THE EIS DO?

A Status Access System: Filter, extract, and
compress a broad range of up-to-date internal and
external information
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXECUTIVE INFORMATION SYSTEMS (EIS)
It should call attention to variances from plan.
It should also monitor and highlight the critical
success factors of the individual executive user
EIS is a structured reporting system for
executive management, providing the executive
with the data and information of choice and
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desired form
CASE EXAMPLE – EXECUTIVE INFORMATION SYSTEM

Most senior GE executives have a real-time view of
their portion of GE via an executive dashboard

©2006 Barbara C. McNurlin.
Published by Pearson
Education.
GENERAL ELECTRIC
Each dashboard compares expected goals (sales, response
times, etc) with actual, alerting the executive when gaps of a
certain magnitude appear
GE’s goal is to gain better visibility into all its
operations in real time and give employees a way to
monitor corporate operations quickly and easily
 The system is based on complex enterprise software
that interlinks existing systems
 GE’s actions are also moving its partners and
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business ecosystem closer to real-time operation

©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
EXPERT SYSTEMS

A real-world use of artificial intelligence (AI)
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AI is a group of technologies that attempts to mimic our senses
and emulate certain aspects of human behavior such as
reasoning and communication
Promising for 40 years +. Now = finally living up to promise
An expert system is an automated type of analysis or
problem-solving model that deals with a problem the
way an “expert” does

Note: Expert Systems are not new


LISP
Prolog

Languages in the ’70s
11-30
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
EXPERT SYSTEMS CONT.


The process involves consulting a base of
knowledge or expertise to reason out an
answer based on the characteristics of the
problem
Like DSSs, they have:
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A user interface
An inference engine, and
Stored expertise (in the form of a knowledge base)
The inference engine is that portion of the
software that contains the reasoning methods
used to search the knowledge base and solve
the problem
11-31
KNOWLEDGE REPRESENTATION

Knowledge can be represented in a number of
ways:
©2006 Barbara C. McNurlin.
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EXPERT SYSTEMS
1.
One is as cases; case-based reasoning expert systems
using this approach draw inferences by comparing a
current problem (or case) to hundreds or thousands of
similar past cases
2.
A second form is neural networks, which store knowledge
as nodes in a network and are more intelligent than the
other forms of knowledge representation because they
can learn
3.
Third, knowledge can be stored as rules (the most
common form of knowledge representation), which are
obtained from experts drawing on their own expertise,
experience, common sense, ways of doing business,
regulations, and laws
11-32
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
11-33
CASE EXAMPLE – EXPERT SYSTEM
 One of the first commercially successful ESs and a
fundamental part of the company’s everyday credit
card operation
 Authorizer’s Assistant is an expert system that
approves credit at the point of sale
 It has over 2,600 rules and supports all AmEx card
products around the world
 Authorizes credit by looking at:
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
AMERICAN EXPRESS
Whether cardholders are creditworthy
Whether they have been paying their bills
Whether a purchase is within their normal spending patterns
It also assesses whether the request for credit could
be a potential fraud
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CASE EXAMPLE – EXPERT SYSTEM CONT.
 The
most difficult credit-authorization
decisions are still referred to people
 Avoids ‘sensitive’ transactions
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
AMERICAN EXPRESS
Restaurants
Airline queues
 The
rules were generated by interviewing
authorizers with various levels of expertise –
comparing good decisions to poor decisions
 The system can be adapted quickly to meet
changing business requirements
11-35
DEGREE OF EXPERTISE
1.
As an assistant, the lowest level of expertise, the
expert system can help a person perform routine
analysis and point out those portions of the work
where the expertise of the human is required
2.
As a colleague, the second level of expertise, the
system and the human can “talk over” the
problem until a “joint decision” has been reached
3.
As an expert, the highest level of expertise, the
system gives answers that the user accepts,
perhaps without question
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
EXPERT SYSTEMS
11-36

A simulation technology for studying emergent
behaviour (e.g. traffic jam) that emerges from the
decisions of a large number of distinct individuals
(drivers)

Simulation contains computer generated agents, each
making decisions typical of the decisions an individual
would make in the real world
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©2006 Barbara C. McNurlin.
Published by Pearson
Education.
AGENT –BASED MODELLING
Trying to understand the mysteries of why businesses,
markets, consumers, and other complex systems behave as
they do
Some examples:
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Nasdaq; Change its tick size
Retailer = redesign its incentive program
Southwest Airlines = revamp its cargo operations
Company changing its recruiting practices
11-37
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TECHNOLOGIES THAT SUPPORT DECISION MAKING
CONCLUSION

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This section has discussed five seemingly
competing technologies that support decision
making
In reality they often overlap and combine
The next section demonstrates how these
decision support technologies and other
technologies are being mixed and matched to
form the foundation for the real-time
enterprise
11-38

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Through IT, organizations have been able to see the
status of operations more and more toward real
time
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
The Internet is giving companies a way to
disseminate closer-to-real-time information about
events
The essence of the phrase real-time enterprise is
that organizations can know how they are doing at
the moment, rather than have to wait days, weeks,
or months for analysis results
11-39
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
CONT.

It is occurring on a whole host of fronts, including:

Enterprise nervous systems

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Straight-through processing


To reduce distortion in supply chains
Real-time CRM

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To coordinate company operations
To automate decision making relating to customers, and
Communicating objects

To gain real-time data about the physical world
11-40
ENTERPRISE NERVOUS SYSTEMS
 These are the technical means to a real-time
enterprise
 They are:



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Message based - because sending messages is efficient and
effective in dispersing information among parties
simultaneously
Event driven - when an event occurs, it is recorded and
made available
Use a publish and subscribe approach - the event is
“published” to an electronic address and any system,
person, or device authorized to see that information can
“subscribe” to that address’s information feed, and
Use common data formats - data formats from disparate
systems are reduced to common denominators that can be
understood by other systems and hence shared
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
11-41
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
DELTA AIRLINES
CASE EXAMPLE – ENTERPRISE NERVOUS SYSTEMS

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


Delta has built an enterprise nervous system to manage its gate
operations by incorporating the disparate systems the airline
had in the late 1990s
Information about each flight is managed by the system, in real
time, and everyone who needs to know about a change can get
the data
The system uses a publish-and-subscribe approach using
enterprise application integration (EAI) products, whereby the
messaging middleware allows disparate applications to share
data
When an event occurs, it ripples to everyone
Delta is now expanding those ripples out to their partners who
11-42
serve their passengers, such as caterers and security companies
STRAIGHT-THROUGH PROCESSING
 The
notion of a real-time enterprise has
generated two “buzzwords”
 One is zero latency, which means reacting
quickly to new information (with no wait
time)
 The second is straight-through processing,
which means that transaction data are
entered just once in a process or a supply
chain (like at Delta)
 The goal is to reduce lags and latency in
supply chains
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
11-43
REAL-TIME CRM

©2006 Barbara C. McNurlin.
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Education.
TOWARD THE REAL-TIME ENTERPRISE
Another view of a real-time response might occur
between a company and a potential customer
-
Perhaps via a customer call center or a Website
11-44
©2006 Barbara C. McNurlin.
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Education.
A REAL-TIME INTERACTION ON A WEB SITE
CASE EXAMPLE – REAL-TIME CRM
 E.piphany
CRM software example
 A potential guest visits the Website of a hotel
chain, checking for a hotel in Orlando

The real-time CRM system initiates requests to
create a profile of the customer
All past interactions with that customer
 Past billing information
 Past purchasing history

 Using
this information, it makes real-time
offers to the Website visitor, and the visitor’s
responses are recorded and taken into
account for future Website visitors
11-45
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
11-46
COMMUNICATING OBJECTS


©2006 Barbara C. McNurlin.
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Education.
TOWARD THE REAL-TIME ENTERPRISE
These are sensors and tags that provide
information about the physical world via realtime data
A communicating object can tell you:




What it is attached to
Where it is located
Where it belongs, and
A lot more information about itself
 It
is a radio frequency identification device
(RFID), also called “smart tags”

Based on WW2 technology
11-47
COMMUNICATING OBJECTS

CONT.
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
In Singapore, cars carry smart tags, and
drivers are charged variable prices for where
they drive in the city and when


The prices are set to encourage or discourage
driving at different places at different times
Also proposed for Sydney’s new toll ways
 It’s
an example of real-time traffic control
 Smart
tags will transform industries because
they will talk to one another (object-to-object
communication), changing how work is
11-48
handled
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
VIGILANT INFORMATION SYSTEMS
 The
premise of the real-time enterprise is
not only that it can capture data in real
time, but that it has the means to act on
that data quickly
 US Air Force pilot = bet he could win any
dogfight


Never lost a bet, even to superior aircraft
Called his theory OODA
Observe where his challenger’s plane is
 Orient himself and size up his own vulnerabilities and
opportunities
 Decide which manoeuvre to take
 Act to perform it before the challenger could go through 11-49
the same four steps

CASE EXAMPLE: VIGILANT INFORMATION SYSTEMS
(OODA)
 PC
disk manufacturer used OODA type of
thinking to move itself closer to operating
in real time with a sense-and-respond
culture that aims to operate faster than
its competitors
 Built what they call a Vigilant
Information System (VIS) which they
define as a system that is “alertly
watchful”


Complex and builds on the firm’s legacy
systems
Essentially has four layers – Figure 11-5
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
WESTERN DIGITAL
11-50
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
11-51
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
WESTERN DIGITAL
CASE EXAMPLE: VIGILANT INFORMATION SYSTEMS (OODA) CONT.
VIS had to be complemented by appropriate business
processes



To operate inside it competitors OODA loops
Three new company policies were drafted
1.
2.
3.






Shop-Floor OODA loop
Factory OODA loop
Corporate OODA loop
Benefits of the VIS
Quickened all 3 OODA loops and helped to link decisions
across them
Corporate performance improved measurably


Company’s strategic goals must be translated into time based
objectives and aligned across the company
KPIs must be captured in real time and be comparable
Collaborative decision making to co-ordinate actions companywide
Margins doubled since introduction 3 years ago
Sense and response culture where Western digital learns and
adapts quickly in a coordinated fashion
11-52
THE DARK SIDE OF REAL TIME
 What

are the drawbacks of real-time activities?
Object-to-object communication could compromise
privacy, since knowing the exact location of a
company truck every minute of the day and night can
be construed as invading the driver’s privacy


©2006 Barbara C. McNurlin.
Published by Pearson
Education.
TOWARD THE REAL-TIME ENTERPRISE
That’s a political issue, not a technical issue, and many CEOs
are going to face this question in the future
Also, in the era of speed, a situation can become very
bad very fast, so people must be constantly watching
for signals that something negative is likely to
happen
 Need
for circuit breakers? e.g. NYSE
11-53
 Use
of IT to support decision making covers
a broad swath of territory
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
CONCLUSION
 Some
technologies aim to alert people to
anomalies, discontinuities, and shortfalls
 Others
aim to make decisions, either as
recommendations to people or to act on
behalf of people
 Handing
over decisions to systems has its
pros and cons, thus their actions need to be
monitored
11-54
 CIOs
need to alert their management team
of potential social and economic effects of
computer-based decision making because
errant computer-based decisions have
devastated corporate reputations and cost a
lot of money
©2006 Barbara C. McNurlin.
Published by Pearson
Education.
CONCLUSION CONT.
 With
vendors pushing toward the real-time
enterprise, this is a use of computers that
should give pause to explore the
ramifications
11-55
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McNurlin - 7th Edition