Chapter 11: Executive Information
and Support Systems
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DSS have been rarely used by top
executives
Why?
What are the needs of top executives?
What is needed in computer-based
information systems for upper
management?
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Unique MSS Tools
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Executive Information Systems
(EIS)
Executive Support Systems (ESS)
and Organizational DSS (ODSS)
Plus
Client/Server Architecture (C/S)
Enterprise Computing
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.1 Opening Vignette:
The Executive Information System
at Hertz Corporation
The Problem
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High competition
Keys to Success - Marketing and flexible planning
Instantaneous marketing decisions (decentralized)
Based on information about cities, climates, holidays,
business cycles, tourist activities, past promotions,
and competitors' and customers' behavior
Must know competitors’ pricing information
The Problem - How to provide accessibility to this
information and use it effectively
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Initial Solution:
A Mainframe-Based DSS
(1987)
Later: The Executive
Information System (EIS) in
1988
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PC-based front-end to the DSS
Commander EIS (Comshare Inc.)
Tools to analyze the mountains of stored information
To make real-time decisions without help
Extremely user-friendly
Maintained by the marketing staff
Continuous upgrades and improvements
Conformed to how Hertz executives work
Implementation and acceptance were no problem
System allows Hertz to better use its information and IS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
resources
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.2 Executive Information
Systems: Concepts and
Definitions
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Tool that can handle the executives’
many needs for timely and accurate
information in a meaningful format (DSS
In Focus 11.1)
Most Popular EIS Uses
– Decision making (by providing data)
– Scheduling (to set agendas and schedule
meetings)
– Email and electronic briefing (to browse data
and monitor situations)
(Table 11.1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
TA B LE 11.1 R easons for U sing EIS
Purpose for Using E IS
Percent of E IS Users
Decision M aking
Scheduling
E -M ail
E lectronic B riefing
T ickler and Follow Up Functions
O ther
50.0
50.0
43.8
37.5
31.3
6.3
(Source: B ased on N ord and N ord [1996], Exhibit 4)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D S S I n F o c u s 1 1 .1 : W h y E I S ?
M o s t c o m m o n b e n e fits : I m p r o v e m e n t in th e q u a lity a n d q u a n tity o f
in fo r m a tio n a v a ila b le to e x e c u tiv e s . F a c to r s id e n tifie d b y W a ts o n e t
a l. [1 9 9 1 ] a n d W a ts o n e t a l. [1 9 9 7 ]:
I n fo r m a tio n N e e d s (I n te r n a l a n d E x te r n a l):
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M o r e tim e ly in fo r m a tio n
G r e a te r a c c e s s to o p e r a tio n a l d a ta
G r e a te r a c c e s s to c o r p o r a te d a ta b a s e s
M o r e c o n c is e , r e le v a n t in fo r m a tio n
N e w o r a d d itio n a l in fo r m a tio n
M o r e in fo r m a tio n a b o u t th e e x te r n a l e n v ir o n m e n t
M o r e c o m p e titiv e in fo r m a tio n
F a s te r a c c e s s to e x te r n a l d a ta b a s e s
F a s te r a c c e s s to in fo r m a tio n
R e d u c e d p a p e r c o s ts
E I S I m p r o v e m e n ts in E x e c u tiv e J o b P e r fo r m a n c e A b ility :
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E n h a n c e d c o m m u n ic a tio n s
G r e a te r a b ility to id e n tify h is to r ic tr e n d s
I m p r o v e d e x e c u tiv e e ffe c tiv e n e s s
I m p r o v e d e x e c u tiv e e ffic ie n c y
F e w e r m e e tin g s , a n d le s s tim e s p e n t in m e e tin g s
E n h a n c e d e x e c u tiv e m e n ta l m o d e ls
I m p r o v e d e x e c u tiv e p la n n in g , o r g a n iz in g , a n d c o n tr o llin g
M o r e fo c u s e d e x e c u tiv e a tte n tio n
G r e a te r s u p p o r t fo r e x e c u tiv e d e c is io n m a k in g
I n c r e a s e d s p a n o f c o n tr o l
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Majority of personal DSS support the
work of professionals and middle-level
managers
Organizational DSS support planners,
analysts, and researchers
Rarely do top executives directly use a
DSS
Executive Information Systems (EIS)
(or Executive Support Systems (ESS)
Technology emerged to meet executive
information needs
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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EIS - Rapid growth
Prime Tool for Gaining Competitive
Advantage
Many Companies - Sizable Increase in
Profits with EIS
Sometimes the Payback Period is Measured
in Hours
New Internet / World Wide Web and
Corporate Intranets EIS Developments
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
EIS and ESS Definitions
Executive Information System (EIS)
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A computer-based system that serves the
information needs of top executives
Provides rapid access to timely information and
direct access to management reports
Very user-friendly, supported by graphics
Provides exceptions reporting and "drill-down"
capabilities
Easily connected to the Internet
Drill down
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Executive Support System
(ESS)
A Comprehensive Support System that
Goes Beyond EIS to Include
 Communications
 Office automation
 Analysis support
 Intelligence
(DSS In Action 11.2)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.3 Executives’ Role and
Their Information Needs
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Decisional Executive Role (2 Phases)
1. Identification of problems and/or opportunities
2. The decision of what to do about them
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Flow Chart and Information Flow (Figure
11.1)
Use Phases to Determine the Executives’
Information Needs
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Methods for Finding
Information Needs
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Wetherbe's Approach [1991] (Figure 11.2)
1. Structured Interviews (Table 11.2)
– IBM's Business System Planning (BSP)
– Critical Success Factors (CSF)
– Ends/Means (E/M) Analysis
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2. Prototyping
Watson and Frolick's Approach [1992]
– .Asking (interview approach)
– .Deriving the needs from an existing information
system
– .Synthesis from characteristics of the systems
– .Discovering (Prototyping)
• Ten methods (Table 11.3)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .2 M eth o d s a n d S a m p le Q u estio n s fo r
S tru ctu red In terv iew s
M e th o d
1.
T he executiv e interv iew p o rtio n o f
IB M 's B usiness S y stem P lanning
(B S P ): S p ecify p ro b lem s and
d ecisio ns
S a m p le In te r v ie w Q u e stio n s
a.
b.
c.
d.
e.
2.
C ritical success facto rs (C S F ):
S p ecify critical success facto rs
a.
b.
c.
3.
E nd /m eans (E /M ) analy sis: S p ecify
effectiv eness criteria o r o utp uts and
efficiency criteria fo r p ro cesses used
to g enerate o utp uts
a.
b.
c.
d.
e.
f.
W hat are the m ajo r p ro b lem s enco untered in
acco m p lishing the p urp o ses o f the o rg anizatio nal unit
y o u m anag e?
W hat are g o o d so lutio ns to tho se p ro b lem s?
H o w can info rm atio n p lay a ro le in any o f tho se
so lutio ns?
W hat are the m ajo r d ecisio ns asso ciated w ith y o ur
m anag em ent resp o nsib ilities?
W hat im p ro v em ents in info rm atio n co uld result in
b etter d ecisio ns?
W hat are the critical success facto rs o f the
o rg anizatio nal unit y o u m anag e? M o st m anag ers hav e
fo ur to eig ht o f these.
W hat info rm atio n is need ed to ensure that critical
success facto rs are und er co ntro l?
H o w d o y o u m easure the sp ecific C S F s? F o r exam p le:
p ro m p t ship m ent o f o rd ers (a C S F ) is m easured b y
the p ercentag e o f tim e ship m ents are d eliv ered o n
sched ule.
W hat are the resulting g o o d s o r serv ices p ro v id ed b y
the b usiness p ro cess?
W hat m akes these g o o d s o r serv ices effectiv e to
recip ients o r custo m ers?
W hat info rm atio n is need ed to ev aluate that
effectiv eness?
W hat are the key m eans o r p ro cesses used to g enerate
o r p ro v id e g o o d s o r serv ices?
W hat co nstitutes efficiency in the p ro v id ing o f these
g o o d s o r serv ices?
W hat info rm atio n is need ed to ev aluate that
efficiency ?
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .3 M ethods for A ssessing Inform ation
R equirem ents
Intera ctio n
N o nco m puter R ela ted
C o m puter R ela ted
D irect E xecutive
Interaction
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P articip ation in strategic
p lanning sessions
F orm al C S F sessions
Inform al d iscussions of
inform ation need s
T racking executive
activities
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C ollab orative w ork
system sessions (e.g.,
G D S S , Intranet)
D iscussions w ith sup p ort
p ersonnel
E xam ination of
noncom p uter-generated
inform ation
A ttend ance at m eetings
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S oftw are tracking of
E IS usage
E xam inations of
com p uter-generated
inform ation
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Ind irect E xecutive
Interaction
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S ou rce: H . J. W atson an d M . F rolick, " D eterm in in g In form ation R eq u irem en ts for an
E xecu tive In form ation S ystem ," In form ation S ystem M an agem en t, S p rin g 1992. R ep rin ted
from Jou rn al of In form ation S ystem s M an agem en t (N ew Y ork: A u erb ach P u b lication s),
1992 R esearch In stitu te of A m erica In c. U sed w ith p erm ission .
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D S S In F o cu s 1 1 .3 : H o w to F in d E x ecu tiv es'
In fo rm a tio n N eed s
1 . A sk se n io r e x e c u tiv e s w h a t q u e stio n s th e y w o u ld a sk u p o n
th e ir r e tu r n fr o m a th r e e -w e e k v a c a tio n .
2 . U se th e c r itic a l su c c e ss fa c to r (C S F ) m e th o d o lo g y .
3 . In te r v ie w a ll se n io r m a n a g e r s to d e te r m in e w h a t d a ta th e y
th in k a r e m o st im p o r ta n t.
4 . L ist th e m a jo r o b je c tiv e s in th e sh o r t- a n d lo n g -te r m p la n s
a n d id e n tify th e ir in fo r m a tio n r e q u ir e m e n ts.
5 . A sk th e e x e c u tiv e s w h a t in fo r m a tio n th e y w o u ld le a st like
fo r th e ir c o m p e titio n to se e .
6 . E ith e r th r o u g h a n in te r v ie w o r o b se r v a tio n p r o c e ss,
d e te r m in e w h a t in fo r m a tio n fr o m c u r r e n t m a n a g e m e n t
r e p o r ts is a c tu a lly b e in g u se d b y th e e x e c u tiv e .
7 . P r o v id e m o r e im m e d ia te , o n lin e a c c e ss to c u r r e n t
m a n a g e m e n t r e p o r ts, a n d th e n a sk e x e c u tiv e s h o w y o u c a n
b e tte r ta ilo r th e sy ste m to th e ir n e e d s. (E x e c u tiv e s a r e m u c h
b e tte r a t te llin g y o u w h a t is w r o n g w ith w h a t y o u h a v e
g iv e n th e m th a n a t te llin g y o u w h a t th e y n e e d .)
8 . U se th e B u sin e ss S y ste m P la n n in g (B S P ) m e th o d .
9 . U se th e E n d s/M e a n s (E /M ) a n a ly sis m e th o d .
1 0 . U se th e S tr a te g ic B u sin e ss O b je c tiv e s (S B O ) a p p r o a c h .
1 1 . U se th e In fo r m a tio n S u c c e ss F a c to r s (IS F ) m e th o d .
1 2 . U se p r o to ty p in g (sh o w , c r itic iz e , im p r o v e ).
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Volonino and Watson’s Strategic
Business Objectives Approach
[1991]
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Attempts to address some potential
problems of the other methods
Ignoring soft information
Identifying the information timeliness
Independence of information and specific
executives
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Organization-wide view
Identify business objectives
Link them to the information needs of
individuals throughout the organization
EIS evolves into an enterprise-wide
system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
SBO Method
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Determine the organization’s SBOs
Identify related business processes
Prioritize the SBOs and their related business
processes
Determine the information critical to each business
process
Identify information linkages across the SBO business
processes
Plan for development, implementation and evolution
SBO method meshes well with Business Process
Reengineering
Requires extensive coordination of communication
between executive users and EIS developers
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Other Approaches
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Information Success Factors
Approach
Problem: Needs Change as
Executives’ Tasks and
Responsibilities Change
EIS Evolves
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.4 Characteristics of EIS
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Table 11.4
Important Terms Related to EIS
Characteristics
– Drill Down
– Critical Success Factors (CSF)
Monitored by five types of information
1. Key problem narratives
2. Highlight charts
3. Top-level financials
4. Key factors
5. Detailed responsibility reports
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .4 E IS C haracteristics and B enefits
Quality of information:
 Is flexible
 Produces correct information
 Produces timely information
 Produces relevant information
 Produces complete information
 Produces validated information.
User interface:
 Includes sophisticated graphic user interface (e.g., GUI)
 Includes a user-friendly interface
 Allows secure and confidential access to information
 Has a short response time (timely information)
 Is accessible from many places
 Includes a reliable access procedure
 M inimizes keyboard use; alternatively uses infrared controllers, mouse, touch pads,
and touch screen
 Provides quick retrieval of desired information
 Is tailored to management styles of individual executives
 Contains self-help menu.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Technical capability provided:
 Access to aggregate (global) information
 Access to electronic mail
 Extensive use of external data
 W ritten interpretations
 Highlights problem indicators
 Hypertext and hypermedia
 Ad hoc analysis
 M ultidimensional presentation and analysis
 Information presented in hierarchical form
 Incorporates graphics and text in the same display
 Provides management by exception reports
 Shows trends, ratios, and deviations
 Provides access to historical and most current data
 Organized around critical success factors
 Provides forecasting capability
 Produces information at various levels of detail (" drill down" )
 Filters, compresses, and tracks critical data
 Supports open-ended problem explanation.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Benefits:
 Facilitates the attainment of organizational objectives
 Facilitates access to information
 Allows the user to be more productive
 Increases the quality of decision making
 Provides a competitive advantage
 Saves time for the user
 Increases communication capacity
 Increases communication quality
 Provides better control in the organization
 Allows the anticipation of problems/opportunities
 Allows planning
 Allows finding the cause of a problem
 Meets the needs of executives.
Source: B ased on B ergeron et al. [1991]
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D SS In F ocus 11.4: T ypical K ey P erform ance Indicators
P rofitability
P rofitability m easures for each departm ent, product,
region, and so on; com parisons am ong departm ents and
products and w ith com petitors
F inancial
F inancial ratios, balance sheet analysis, cash reserve
position, rate of return on investm ent
M arketing
M arket share, advertisem ent analysis, product pricing,
w eekly (daily) sales results, custom er sales potential
H um an
R esources
T urnover rate, level of job satisfaction
P lanning
C orporate partnership ventures, sales grow th/ m arket
share analysis
E conom ic
A nalysis
M arket trends, foreign trades and exchange rates, industry
trends, labor cost trends
C onsum er
T rends
C onsum er confidence level, purchasing habits,
dem ographic data
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Status Access
– Analysis by
• Built-in functions
• Integration with DSS products
• Intelligent agents
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Exception Reporting
Use of Color
Navigation of Information
Communication
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.5 Comparing EIS and MIS
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Relationship between MIS and EIS (Figure 11.3)
– MIS is TPS based
– MIS typically lacks data integration across functional
areas
– Differences (Table 11.5)
– MIS does not accommodate many users’ decision
styles
– Often has slow response time
– Executive decision making is complex and
multidimensional
– MIS usually designed to handle fairly structured,
simpler configurations
– MIS do not usually combine data from multiple
sources
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
TABLE 11.5 M IS and EIS: A Comparison
System
Prim ary
Purpose
Prim ary
U sers
Prim ary
O utput
Prim ary
O perations
Tim e
O rientation
Exam ple
M IS
Internal
monitoring
M anagers
and
executives
Predefined
periodic reports
Summarize
information
Past
Sales report
EIS
Internal and
external
monitoring
Executives
Predefined
customized
periodic or ad
hoc reports,
presentations,
and queries
Integrate
present, track
CSF
Past, present
and future
M arket
share
tracking
Source: Reprinted with permission, M illet et al. [1991], “Alternative Paths to EIS,” DSS-91
Transactions, Eleventh International Conference on Decision Support Systems, Ilze Zigurs (Ed.),
The Institute of M anagement Sciences, 290 W estminster Street, Providence, RI 02903.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.6 Comparing and
Integrating EIS and DSS

Tables 11.6 and 11.7 compare the two
systems
– Table 11.6 - Typical DSS definitions related to
EIS
– Table 11.7 - Compares EIS and DSS
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EIS is part of decision support
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .6 D efinitions of D S S R elated to E IS
R elevant P ortion of D S S
D efinition
A uthor
C om p arison to E IS
" C B IS consisting of three
subsystem s: a problem -solving
subsystem . . ."
B onczek et al. [1980]
N o problem -solving subsystem
exists in an E IS .
" D S S can be developed only
through an adaptive process . . ."
K een [1980]
" M odel-based set of procedures .
. ."
L ittle [1970]
E IS m ay or m ay not be
developed through an adaptive
process.
E IS is not m odel-based.
" E xtendible system . ..
supporting decision m odeling. . .
used at irregular intervals."
M oore and C hang [1980]
E IS is not extendible, m ight
not have m odeling capabilities,
and is used at regular intervals.
" U tilizes data and m odels . . ."
S cott M orton [1971]
E IS does not utilize m odels.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 11.7 C om parison of E IS and D SS
Dimension
EIS
DSS
Focus
Status access, drill down
Analysis, decision support
Typical users
Senior executives
Analysts, professionals, managers (via
intermediaries)
Impetus
Expediency
Effectiveness
Application
Environmental scanning, performance
evaluation, identification of problems and
opportunities
Diversified areas where managerial decisions
are made
Decision support
Indirect support, mainly high-level and
unstructured decisions and policies
Supports semistructured and unstructured
decision making, and ad hoc, but some repetitive
decisions
Type of information
News items, external information on customers,
competitors, and the environment; scheduled
and demand reports on internal operations
Information to support specific situations
Principle use
Tracking and control; opportunity identification
Planning, organizing, staffing, and control
Adaptability to individual
users
Tailored to the decision-making style of each
individual executive, offers several options of
outputs
Permits individuals' judgments, what-if
capabilities, some choice of dialog style
Graphics
A must
Important part of many DSS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
User-friendliness
A must
A must if no intermediaries are used
Processing of information
Filters and compresses information, tracks
critical data and information
EIS triggers questions, answers are worked out
by using the DSS and fed back into the EIS
Supporting detailed
information
Instant access to the supporting details of any
summary ("drill down")
Can be programmed into the DSS, but usually is
not
Model base
Limited built-in functions
The core of the DSS
Construction
By vendors or IS specialists
By users, either alone or with specialists from
the Information Center or the IS Department
Hardware
Mainframe, RISC Workstation, LANs, or
distributed systems
Mainframe, RISC Workstation, PCs, or
distributed systems
Nature of software
packages
Interactive, easy access to multiple databases,
online access, sophisticated DBMS capabilities,
complex linkages
Large computational capabilities, modeling
languages and simulation, application and DSS
generators
Nature of information
Displays pregenerated info about past and
present, creates new information about past,
present, and future
Creates new information about the past,
present, and future
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Integrating EIS and DSS: An
Executive Support System
(ESS)



EIS output launches DSS
applications
Intelligent ESS
Users' roles
– Commander Decision (Figure 11.4)
– Commander OLAP
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Integrating EIS and Group Support
Systems
– EIS vendors - Easy interfaces with GDSS
– Some EIS built in Lotus Domino / Notes
– Comshare Inc. and Pilot Software, Inc. Lotus Domino/Notes-based enhancements
and Web/Internet/Intranet links
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.7 Hardware and Software

EIS Hardware
– Mainframe computers using graphics
terminals
– Personal computers connected to a
mainframe, a minicomputer, or a powerful
RISC workstation
– Departmental LAN or a client/server
architecture
– An enterprise-wide network, or on a
client/server enterprise-wide system.
Workstations perform high-speed graphics
displays

Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
EIS (Enterprise Information System,
EIS Software

Major Commercial EIS Software
Vendors
– Comshare Inc. (Ann Arbor, MI;
http://www.comshare.com)
– Pilot Software Inc. (Cambridge, MA;
http://www.pilotsw.com)

Application Development Tools
– In-house components
– Comshare Commander tools
– Pilot Software’s Command Center Plus and
Pilot Decision Support Suite
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D S S In F ocu s W 11 .*: R ep resen tative E IS / E S S In tegrated P rod u cts
1. C om m an der E IS . T h is sy stem h as several b u ilt-in an aly tical tools, su ch as forecastin g. In
ad d ition , it is d esign ed for easy in terface w ith D S S tools.
2. E IS T ool K it. T h is P C -b ased p rod u ct (from F erox M icrosy stem s In c.) w ork s w ith E n core P lu s, a
fin an cial m od elin g D S S from th e sam e ven d or. T h e E IS /D S S com b in ation is d esign ed p rim arily
to su p p ort fin an cial an d accou n tin g p lan n in g.
3. C om m an d C en ter E IS . T h is p rod u ct (from P ilot S oftw are, In c.), w h ich is lin k ed w ith a D S S
en gin e called F C S an d w ith D ecision S u p p ort S u ite (see S ection 11.12), is also lin k ed w ith a
softw are called A d van tage/G .
4. E xpress/E IS . T h is p rod u ct (from In form ation R esou rces In c.) is an en terp rise-w id e sy stem th at
in tegrates th e p ow erfu l E xp ress D S S gen erator w ith E IS II. T h is p rod u ct is b ased on a
m u ltid im en sion al relation al d ata m od el an d it p rovid es sin gle, u n ify in g, an d flexib le
arch itectu re.
5. G E N T IA . T h is clien t/server an d w eb -read y p rod u ct (from P lan n in g S cien ces) in tegrates D S S
cap ab ilities w ith E IS .
6. H olos. T h is p rod u ct (from H olistic S y stem s) com b in es E IS an d D S S activities in to on e sy stem ,
togeth er w ith d y n am ic lin k s to op eration al d ata.
7. T R A C K (E IS ). T h is p rod u ct (from D ecision W ork s L td ., L on d on , U K ) in terfaces w ith IB M 's D S S
tools.
8. P ow erP lay. T h is p rod u ct (from C ogn os In c.) in corp orates statistical an aly sis to an aly ze d ata.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Trend for EIS Software Vendors with
Third Party Vendors Producing
Specialized EIS Applications

Comshare, Inc.’s Commander Series
– Commander FDC for consolidation, reporting, and analysis of
financial information
– Commander Budget Plus for budget development and
multidimensional planning
– Commander Prism for personal multidimensional analysis and
modeling
– Arthur - a family of supply chain focused applications for
retailing (planning, allocation and tracking)
– Boost Application Suite - a decision support solution for the
consumer goods industry (Boost Sales and Margin Planning,
Boost Sales Analysis)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
More EIS Software

Pilot Software, Inc.
– Budget 2000 (with EPS, Inc.) is a budgeting application
that includes the power of Pilot Decision Support Suite
for budget preparation
– In Touch/2000 is a software agent that enables
organizations to instantly create personal cubes
(multidimensional databases), sales reports, budget
forecasts and marketing plans
– Sales & Marketing Analysis Library of Pilot Decision
Support Suite to perform detailed business reporting for
sales and marketing professionals
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Commercial EIS Software

Typically Includes
–
–
–
–
–

Office Automation
Electronic Mail
Information Management
Remote Information Access
Information Analysis
Representative List of EIS Software
Products
(Table W11.*)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E W 1 1 .x R e p r e se n ta tiv e E IS P r o d u c ts
Product
Vendor
Command Center
Pilot Software, Inc.
Commander DeskView, Commander
Decision
Comshare, Inc.
Commander EIS, EIS LAN;
Commander Decision
Comshare, Inc.
DSS Executive
M icro Strategy Inc.
Easel W orkbench, ENFIN
Easel Corp.
EIS Tool Kit
Ferox M icrosystems, Inc.
Executive Decisions
IBM Corp.
Express/EIS
Information Resources, Inc.
Focus/EIS; EDA/EIS
Information Builders, Inc.
Forest and Trees
Trinzic Corp.
GENTIA
Planning Sciences
Holos
Holistic Systems, Inc.
IM RS On Track
IM RS Inc.
Notes
Lotus Development Corp.
Pilot Decision Support Suite V5
Pilot Software, Inc.
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.8 EIS, Data Access, Data
Warehousing, OLAP, Multidimensional
Analysis, Presentation, and the Web



When data are delivered and viewed by an
executive, by definition, the software is
considered to be an EIS
Data warehouses as data sources for EIS
Advanced data visualization methods and
hypermedia within EIS
Comshare, Inc.’s Execu-View
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 11.8 E IS H ardw are O ptions
O ption
E IS Inform ation Source
U ser Interface
1
M ainfram e (or m inicom puter
or R ISC W orkstation)
G raphical term inal (dum b)
2
M ainfram e (or m inicom puter
or R ISC W orkstation)
PCs
3
L A N -based P C s (or servers) on P C s (regular, G U I)
a departm ental client/server
4
E nterprise-w ide netw ork
(m any possible databases)
P C s (regular, G U I)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Hypermedia over an Intranet
via a Web Browser within the
EIS



Comshare Commander DecisionWeb
Internet Publishing module of the Pilot
Decision Support Suite
On-line Analytical Processing (OLAP)
Tools
– Slice-and-dice multidimensional data cube
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
OLAP Packages











DSS Web (MicroStrategy, Inc.)
Oracle Express Server (Oracle Corp.)
Commander DecisionWeb (Comshare, Inc.)
DataFountain (Dimensional Insight Inc.)
Pilot Internet Publisher (Pilot Software, Inc.)
WebOLAP (Information Advantage Inc.)
Focus Fusion (Information Builders, Inc.)
Business Objects Inc. (Business Objects)
InfoBeaconWeb (Platinum Technology, Inc.)
BrioQuery (Brio Technology Inc.)
Data multidimensionality - In Touch/2000 - Pilot
personal cubes
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Pilot Software, Inc.’s
Decision Support Suite








Client/server, LAN-based, Windows-based software product
(was Lightship)
Pilot Desktop for ad hoc end-user data access
Pilot Designer for development of executive information
applications
Pilot Analysis Server for access to multidimensional data
models
Pilot Discovery Server for data mining and predictive
modeling
Pilot Internet Publisher for publishing multidimensional
data on the World Wide Web
Pilot Sales & Marketing Library for a specific vertical market
Excel Add-in - OLAP front end with Pilot Analysis Server
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.9 Enterprise EIS

Tool for Enterprise Support
– Executive-only EIS
– Enterprise-wide Information System
– Functional Management DSS Tools are
Integrated with EIS
– EIS is Diffusing Lower into Organization Levels


EIS = Enterprise Information System
EIS = Everybody's Information System
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 11.9 L evel of M anagem ent U sing E IS
L evel of M anagem ent U sing E IS
P ercent of E IS U sers
CEO
P resident
V ice-P resident
M iddle M anagem ent
O ther
50.0
31.3
93.8
87.5
18.8
(Source: B ased on N ord and N ord [1996], E xhibit 1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.10 EIS Implementation:
Success or Failure
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
EIS Development Success
Factors (Table 11.10)



Committed Executive Sponsor
Correct Definition of Information
Requirements
Top Management Support
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .1 0 T h e M a jo r S u ccessfu l E IS D ev elo p m en t
F a cto rs
Factors
E xecutive sponsor
D efine inform ation requirem ents
T op m anagem ent support
M anage data
C ost considerations
M anage system spread and evolution
M anage user expectations
D eliver first version quickly
M anage organizational resistance
L ink E IS and business objectives
E volutionary developm ent approach
E IS developm ent support team
A ppropriate technology
D ecide betw een vendor, custom softw are
S tart lim ited, not sm all
T otal C ount
(O ut of 214)
32
29
22
15
14
12
12
11
10
9
8
6
5
5
5
(S ou rce: C om piled from R ain er an d W atson [1995], T able 2.)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
1 1 .1 0 E IS Im p le m e n ta tio n : S u c c e s s o r F a ilu re
E IS Im p le m e n ta tio n - D iffe r e n t fr o m D S S , C B IS
C r itic a l S u c c e ss F a c to r s fo r Im p le m e n ta tio n o f E IS
1 . A c o m m itte d a n d in fo r m e d e x e c u tiv e sp o n so r
2 . A n o p e r a tin g sp o n so r
3 . A c le a r lin k to b u sin e ss o b je c tiv e (s)
4 . A p p r o p r ia te IS r e so u r c e s
5 . A p p r o p r ia te te c h n o lo g y
6 . M a n a g e m e n t o f d a ta p r o b le m s
7 . M a n a g e m e n t o f o r g a n iz a tio n a l r e sista n c e
8 . M a n a g e m e n t o f sp r e a d a n d sy ste m e v a lu a tio n
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
EIS Operational Success
Factors
(Table 11.11)







Deliver timely information
Improve efficiency
Provide accurate information
Provide relevant information
Ease of use
Provide access to the status of the organization
Provide improved communications
An IS for upper management must fit with their decision styles
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .1 1 S u ccessfu l O p era tio n a l E IS F a cto rs
Fa c to rs
T im e ly info rm a tio n
Im p ro ve e ffic ie nc y
A c c ura te info rm a tio n
R e le va nt info rm a tio n
E a se o f use
S ta tus a c c e ss
Im p ro ve c o m m unic a tio ns
M inim a l o r no tra ining
A d a p ta b le inte rfa c e
A d a p t to c ha nging info rm a tio n re q uire m e nts
E xc e p tio n re p o rting
C o nve nie nt info rm a tio n
S ta nd a rd d e finitio ns in the e nte rp rise
A c c e ss e xte rna l d a ta
D rill d o w n
M ultip le m o d e s o f p re se nta tio n
A c c e ss so ft, hum a n d a ta
A c c e ss inte rna l d a ta
A c c o unta b ility fo r p ro vid e rs
C o lo r
G ra p hic s
Fa st re sp o nse tim e
E a sy to o b ta in ha rd c o p y
T re nd a na lysis
Im p ro ve o p e ra tio na l c o ntro l
C o nc ise info rm a tio n
(S o u rce: C o m p iled fro m R a in er a n d W a tso n [1 9 9 5 ], T a b le 4 )
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T o ta l C o unt
(O ut o f 4 2 7 )
42
32
29
25
24
24
20
15
14
14
14
13
10
10
9
7
6
6
6
6
6
6
6
5
5
5
Motivations for Developing an
EIS





Internal in nature
Providing easier, faster access to
information
80 % - Evolving approach
Sequencing of the phases varies
More successful development efforts
include
– Initiation
– Definition of systems objectives
– Feasibility
analysis
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Determinates of EIS
Acceptance







Rapid Development Time
Staff Size
EIS Age
Not Ease of Use
Not High Usage
Not Many Features
Not a Staff Close to Users
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Factors Contributing to
EIS Failures
(Table 11.12)




Technology-related factors
Support-related factors
User-related factors
Most EIS fail because they do not
provide value for their high cost
though EIS benefits are difficult to
measure
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 1 1 .1 2 F actors C on trib u tin g to E IS F ailu res
1 . T echnolog y -R ela ted
F a ctors
a . T he E IS is no b etter tha n the orig ina l sy stem
b . T he technolog y is ina d eq ua te or ina p p rop ria te
c. T he interfa ces a re com p lica ted or m enus a re
extensiv e
2 . S up p ort-R ela ted
F a ctors
a . U sers’ inform a tion req uirem ents w ere ig nored
b . C ha ng es in users’ inform a tion need s w ere not
kep t up w ith
c. P rov id ing electronic rep orts id entica l to the
orig ina l p a p er rep orts w ith no enha ncem ents
d . Ina d eq ua te b usiness know led g e a m ong sup p ort
sta ff m em b ers
e. N ot a d d ressing a sig nifica nt b usiness p rob lem
f. A la ck of d a ta a v a ila b ility
g . L a te d eliv ery of op era ting d a ta
3 . U ser-R ela ted F a ctors:
1 . L im iting the focus of the E IS to one user
2 . L a ck of com m itm ent from users
3 . E xecutiv e sp onsor’s la ck of cla rity for the
p urp ose of the E IS
4 . N ot p rov id ing a m ea ns for executiv es to
com m unica te id ea s a nd insig hts
5 . U sers not a b le to com m unica te d ecisions
6 . P olitica l resista nce
7 . H a rd -to-use technolog y resisted b y users
8 . M id d le m a na g em ent fea rs executiv es w ill
m ed d le in their d a ily op era tions
(S o u rce: B a sed o n Y o u n g a n d W a tso n [1 9 9 5 ], T a b le 2 )
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Benefit and Cost
Assessment Practices in
EIS


Most Systems’ Realized Expected
Benefits Were Lower than Expectation
Greatest Problem - Information
Contents, Not Information Delivery
Issues
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Unexpected EIS Benefits




Enhancements to the enterprise-wide
information architecture
Consolidation of data into warehouses
Consolidation of analysis tools into OLAP
methods
Consistency of terminology across the
enterprise
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.11 Including Soft
Information in EIS
Soft information is fuzzy,
unofficial, intuitive, subjective,
nebulous, implied, and vague
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Soft Information Used in Most
EIS






Predictions, speculations, forecasts, and
estimates (78.1%)
Explanations, justifications, assessments,
and interpretations (65.6%)
News reports, industry trends, and external
survey data (62.5%)
Schedules and formal plans (50.0%)
Opinions, feelings, and ideas (15.6%)
Rumors, gossip, and hearsay (9.4%)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Soft Information Enhances
EIS Value

More in the Future
– External news services
– Competitor information
– Ease of entering soft information
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.12 The Future of EIS and
Research Issues









Toolbox for customized systems - Commander EIS LAN,
Forest and Trees, and Pilot Decision Support Suite
Multimedia support (databases, video and audio news
feeds, GIS)
Virtual Reality and 3-D Image Displays
Merging of analytical systems with desktop publishing
Client/server architecture
Web-enabled EIS (Comshare Commander DecisionWeb,
Pilot Decision Support Suite Internet Publishing module,
SAS Institute Internet support enterprise software suite)
Automated support and intelligent assistance
Integration of EIS and Group Support Systems
Global EIS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Research Issues





Relationship between the executive sponsor’s
organizational position and commitment level to
EIS success
Most important factors when selecting an
operating sponsor?
Prediction of EIS benefits in advance
How EIS software affects the development
process and system success
Best staffing level and organizational structure
for the builder/support staff
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ







Most effective methods to identify executives'
information requirements
Major EIS data management problems and their
solutions
Impact of soft data on EIS success
Major problems associated with spread and
evolution
How to increase EIS functionality while
maintaining ease of use
Effective use of emerging technologies with EIS
Most effective screen presentation formats
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Current Trends in EIS



More enterprise-wide EIS with
greater decision support
capabilities
Integration with other software
(Lotus Domino / Notes and World
Wide Web)
More intelligence - intelligent
software agents
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Other EIS Issues




How to assess EIS benefits and costs
How to cluster EIS benefits
depending on planned system uses
How EIS diffuses throughout the
organization
How to perform screen management creation, modification and elimination
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Five Broad Categories of EIS
Benefits
(Table W11.1)


Help developers in design and
development
(Iyer and Aronson [1995])
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E W 11.1 T h e F ive C ategories of E IS B en efits
C a teg o ries o f E IS B en efits
B en efits w ith in E a ch C a teg o ry
1. In form ation
a. M ore tim ely in form ation
b. F aster access to in form ation
c. M ore accu rate in form ation
d. M ore relevan t in form ation
e. M ore con cise in form ation
2. E n viron m en tal S can n in g
a. B etter access to soft in form ation
b. Im proved access to extern al data
c. B etter en viron m en tal scan n in g
d. M ore com petitive in form ation
3. Im provin g E xecu tives’ E ffectiven ess
a. Im proved com m u n ication s
b. Im proved execu tive perform an ce
c. S ave execu tive tim e
d. Im proved presen tation of data
4. M eetin g S trategic O bjectives
a. In creased span of con trol
b. Im proved plan n in g
c. Im proved decision m akin g
d. B etter problem u n derstan din g
e. B etter developm en t of altern atives
5. E con om y
a. C ost savin gs
b. L ess paper
c. S u pport T Q M program
d. M ore respon sive to ch an gin g cu stom er n eeds
e. S u pport dow n siz in g th e organ iz ation
S ou rce: Iy er an d A ron son [1995]
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.13 Organizational DSS
(ODSS)

Three Types of Decision Support
– Individual
– Group
– Organizational
Hackathorn and Keen [1981]
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E 11.13 D ifferences betw een R egular D S S and
O DSS
T AB L E 1 1 .1 4 D ifferences betw een R egular D S S and O D S S .
R egu lar (T raditional) D SS
O D SS
P urpose
Im prove perform ance of an
individual decision m aker,
or of a sm all group.
Im prove the efficiency and
effectiveness of organizational
decision m aking.
P olicies
M ust “sell” the system to an
individual.
T he system m ust be sold to the
organization.
C onstruction
U sually an inform al process
(except in large D SS).
Significant undertaking; requires
structured approach.
Focus
O n the individual and on his
or her objectives.
Focus on the functions to be
perform ed and not on the
individual users.
Support
Support is usually provided
to one individual, or one unit,
in one location.
D issem inate and coordinate
decision m aking across functional
areas, hierarchical levels, and
geographically dispersed units.
Source: B ased on W alker [1990].
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ



Organizational decision support
focuses on an organizational task or
activity involving a sequence of
operations and actors
Each individual's activities must mesh
closely with other people's work
Computer support is for
– Improving communication and coordination
– Problem solving
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Definitions of ODSS

A combination of computer and communication
technology designed to coordinate and
disseminate decision-making across functional
areas and hierarchical layers in order that
decisions are congruent with organizational goals
and management's shared interpretation of the
competitive environment (R. T. Watson [1990])

A DSS that is used by individuals or groups at
several workstations in more than one
organizational unit who make varied (interrelated
but autonomous) decisions using a common set of
tools Decision
(Carter
et al. [1992])
Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

A distributed decision support system
(DDSS). Not a manager's DSS, but supports
the organization's division of labor in
decision making (Swanson and Zmud [1990])

Apply the technologies of computers and
communications to enhance the
organizational decision-making process.
Vision of technological support for group
processes to the higher level of organizations
(King and Star [1990])
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Common Characteristics of
ODSS (George [1991])




Focus is on an organizational task or activity or
a decision that affects several organizational
units or corporate problems
Cuts across organizational functions or
hierarchical layers
Almost always involves computer-based
technologies, and may involve communication
technologies
Can Integrate ODSS with Group DSS and
Executive Information Systems
– Example: Egyptian Cabinet ODSS with EIS (DSS In
Action
11.15)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.14 The Architecture of ODSS


General Structure for ODSS (Figure
11.5)
Major Differences ODSS Structure and
Traditional DSS
– Case Management Component (CMS)
– Accessible by several users, in several
locations, via LANs
– May have an intelligent component
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Case Management




Run a model many times
Much output and many files
Helps the user manage the large
numbers of similar runs
Case = a specific run (scenario) of a
computer model
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
CMS Main Functions
1. Record keeping of the model cases
2. Documenting the changes from one
run to the next
3. Output comparison facilitation
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.15 Constructing an ODSS



Formal, structured approach
Large, complex, system programming
effort
Combination of the SDLC and iterative
approach
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Phases
1. Getting started (a structured,
organizational phase)
a) Needs assessment
b)
Getting management support
c) Getting organized. Set up steering
committee; identify project team members
d)
Getting a plan of action
2. Developing the conceptual design
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
3. Developing the system
a) Designing the physical system
b) Developing the system's models and database
4. Implementing and maintaining the
system:
a) Installation
b) Programming and updating system's modules
(programs)
c) Creating and updating the database
d) Documenting the modules and database
e) Training users
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.16 ODSS Example: The
Enlisted Force Management
System (EFMS)






Improve the effectiveness and efficiency Air Force staff
managing the enlisted force in decision-making and
information-processing
Objective: to provide a group of airmen that is best able to
support the missions and operational programs of the Air
Force
Iterative, continuous task
Decisions about force structure, promotion policies, and
the procurement, assignment, training, compensation,
separation, and retirement of personnel
Five major, independent organizational units (in three
geographically locations)
More than 125 person-years went into the EFMS
development
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
The Elements of EFMS

Model Base
– Authorization projection
– Grade allocation
– Aggregate planning, programming, and
oversight
– Skills management

Screening and Impact Assessment
Models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Hardware and Databases



EFMS's mainframe computer
DSS generator language, EXPRESS
Access databases and models on
PCs through EXPRESS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Databases from



Output from another EFMS model
Data supplied by other branches of
the Air Force
External data
– The EFMS and other Air Force computer
systems exchange data regularly
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
11.17 Implementing ODSS
Important ODSS Implementation
Issues
1. Steering committee for direction and
control
2. Project team members join on an ad hoc
basis
3. The System Management Office (SMO)
4. Conceptual design
a) Design principles
b) Functions to be supported
c) Models
Decision Support
andrequirements
Intelligent Systems, Efraim Turban and Jay E. Aronson
d)Systems
Data
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Model Base


Flexibility, adaptability and easy
maintainability
Interlinked system of many small
models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Database





Coordination and integration
Specification of a common,
consistent, easily accessed,
centralized database
All information from one module is
automatically (instantaneously)
available to others
Internal and external data
Many modules have their own
databases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
User Interface





Common for all elements
Menu driven
Easy to learn
Easy to use
Graphical User Interface (1993)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
ODSS Data





Understanding or defining the
problem situation
Estimating the nature of the models
Validating the models
Running the models (input data)
Database construction and data
cleaning:
25 % - 30 % of effort
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Integration and Networking

Many models and databases
Integration of models, data, and
knowledge can be complex

Artificial Intelligence in ODSS

– Ideal - especially in CMS and machine
learning (automatic rule induction)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Summary






EIS serves the information needs of top
executives and others
EIS provides rapid access to timely
information at various levels of detail
Very user friendly (user-seductive)
ESS also has analysis capabilities
Executives' work: finding problems
(opportunities) and making decisions
Finding the information needs of
executives is very difficult
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ







Methods: CSF (Critical Success Factors), BSP
(Business System Planning), SBO (Strategic
Business Objectives) and E/M (Ends/Means)
Many EIS benefits are intangible
Drill down
Management by exception approach, centered
on CSF, key performance indicators, and
highlight charts
In contrast to MIS, EIS has an overall
organizational perspective and uses external
data extensively
Trend to integrate EIS and DSS tools
EIS requires either a mainframe or a LAN
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ








Constructing an EIS can be difficult. Vendors or
consultants
EIS development tools
Intranets to deliver information to executives
Web-enabled EIS
EIS success - many factors ranging from
appropriate technology to managing
organizational resistance
The executive sponsor is crucial for the success
of an EIS
EIS failure - no value provided
An EIS must fit the executives’ decision styles
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ






Multidimensional analysis and
presentation
Access to database information by endusers, enterprise-wide
EIS technology and use diffusing to lower
levels of management
Data warehouses and client/server front
end environments make an EIS a useful
tool for end users
EIS can provide valuable soft information
Organizational DSS (ODSS) deals with
decision making across functional areas
and hierarchical organizational layers
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ








ODSS includes a case management system
(CMS)
ODSS is used by individuals and groups and
operates in a distributed environment
ODSS deals with organizational tasks
ODSS for similar, repetitive situations involves a
case management component
ODSS is frequently integrated with EIS and/or
GDSS
ODSS built using both traditional SDLC and
prototyping
Data and databases are critical to the success of
ODSS
ODSS usually use several quantitative and
qualitative
models
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Questions for the Opening
Vignette
1. Explain how Hertz added an EIS that is used as a
front end to the DSS
2. Why did the new DSS not satisfy the executives’
information needs?
3. Why was it so important for the new system to
provide information that conformed to the way
executives at Hertz worked? Do you think that the
system would have been acceptable otherwise?
Why or why not?
4. What capabilities did the PCs bring to the EIS?
5. Why is it important for Hertz to be able to monitor
competitors’ marketing strategies in real time?
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Appendix W11-A: The
Client/Server Architecture and
Enterprise Computing

Approach to organizing PCs, local
area networks, and possibly
mainframes, into a flexible, effective,
and efficient system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B L E W 11-A .1 T h e B en efits an d P oten tial P rob lem s of th e C lien t/S erver A rch itectu re
F eatu re
B en efit
P oten tial P rob lem s
N etw orked w ebs of sm all,
pow erfu l com pu ters
If on e m ach in e goes dow n , y ou r bu sin ess m ach in es stay
u p. T h e appropriate task m ay be ru n on th e
appropriate com pu ter
N o on e m ach in e m ay be capable of storin g th e en tire
database
N o on e m ach in e m ay be capable of perform in g
n ecessary com pu tation al tasks
P arts don ’t alw ay s w ork togeth er. T h ere are several
possible cu lprits w h en som eth in g goes w ron g
D esign in g th e division of w ork betw een clien t an d
server m ay be com plicated
C om pu ter array s w ith
th ou san ds of M IP S ; clien ts'
aggregate M IP S bey on d
calcu lation
S om e w orkstation s are as
pow erfu l as m ain fram es, bu t
cost 90% less
T h e sy stem provides th e pow er to get th in gs don e
w ith ou t m on opoliz in g resou rces. E n d-u sers are
em pow ered to w ork locally
C oordin ation of efforts an d com m u n ication con ten tion
m ay occu r
B y givin g y ou m ore pow er for less m on ey , th e sy stem
offers y ou th e flexibility to m ake oth er pu rch ases or to
in crease y ou r profits
Y ou locate or bu ild su pport tools y ou rself
T h e softw are developed for th e M ac or W in dow s is
differen t from th at for m ain fram es
T h e com pu tation al pow er m ay be u n deru tiliz ed
O pen sy stem s
Y ou can pick an d ch oose h ardw are, softw are, an d
services from variou s ven dors
T oo m an y option s an d / or in com patible sy stem s m ay
be difficu lt to m an age an d m ain tain
S y stem s grow easily an d are
in fin itely expan dable
It's easy to m odern iz e y ou r sy stem as y ou r n eeds
ch an ge. E xpan ded capacity m ay be added
in crem en tally
Y ou can m ix an d m atch com pu ter platform s to su it th e
n eeds of in dividu al departm en ts an d u sers
C on tin u al u pgrades m ay cau se in com patible softw are
problem s
O lder m ach in es m ay n ot ru n n ew er softw are
M an agin g an d m ain tain in g a variety of sm all sy stem s
can be difficu lt
In dividu al clien t operatin g
en viron m en ts
(S ou rce: B ased in part on B yte, Ju n e 1993, p. 100)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
C/S Characteristics





The clients are PCs or workstations, attached
to a network. Clients access network
resources
The user interfaces directly with the client
(via GUI)
Servers provide shared resources to several
clients
A server provides clients with service
capabilities (databases, large disk drives, or
communications)
Servers can be workstations, mainframes,
minicomputers, and/or LAN PC devices
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ







A client forms one or more queries or
commands, in a predefined language such as
SQL, for presentation to the server
Clients can send queries or commands to the
servers
Server transmits results to client's screen
Typical servers: database server, file server,
print server, image-processing server,
computing server, and communication server
(Web server)
Server only reacts to client's requests
Servers can communicate with each other
Tasks are split into two: front-end portion
(client), and back-end portion (server(s))
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Client / Server Computing

Changes the way people work

People are empowered to access
databases
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Client/Server Applications
Categories




Messaging applications, such as
electronic mail
Disseminating a database among several
computer networks
Offering file- or peripheral-sharing, or
remote computer access
Processing-intensive applications where
jobs are divided into tasks, each of which
is performed by a different computer
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Enterprise-wide Client/Server
Architecture





Computing systems that involve an entire
organization
Architecture for an integrated computer system to
serve the business needs of the enterprise
Technological framework that contains multiple
applications, hardware, databases, networks, and
management tools, usually from multiple vendors
Requires a consensus on a set of standards
ranging from operating systems to
telecommunication protocols
Requires a consensus on a common open
Decision Support Systems
and Intelligent Systems,
Efraima
Turban
and Jay E. Aronson
management
platform
and
strong
organizational
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Major Benefits of Enterprise
Computing






Reliable and responsive service
Smooth incorporation of new client/server
solutions with existing approaches
Frequent and rapid changes, and increasing
complexity
Greater optimization of network and system
resources
Automation of management processes
Network and data security
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ



Enterprise client/server architecture
provides total integration of departmental
and corporate IS resources
Provides better control and security over
data in a distributed environment
IS organizations can maximize the value of
information by increasing its availability.
Enterprise client/server computing
empowers organizations to
– Reengineer business processes
– Distribute transactions to streamline operations
– Provide better and newer services to customers
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Chapter 11: Executive Information and Support Systems