Primary Decision Support
Technologies
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Management Support Systems (MSS)
Decision Support Systems (DSS)
Group Support Systems (GSS),
including Group DSS (GDSS)
Executive Information Systems (EIS)
Expert Systems (ES)
Artificial Neural Networks (ANN)
Hybrid Support Systems
Cutting Edge Intelligent Systems
(Genetic Algorithms, Fuzzy Logic,
Intelligent Agents, ...)
A Classic Framework for
Decision Support
[Figure 1.2--Proposed by Gorry and Scott Morton [1971]]
Combination of
 Simon [1977] Taxonomy and
– Highly structured (programmed) decisions to
– Highly unstructured (nonprogrammed) decisions
Unstructured problem often solved with human intuition
Semistructured problems fall in between.
Solve with both standard solution procedures and human
judgment

Anthony [1965] Taxonomy
– Broad Categories encompass ALL managerial activities
Strategic planning, Management control, Operational
control
O p e ra tio n a l
C o n tro l
M a n a g e ria l
C o n tro l
S tra te g ic
P la n n in g
T e c h n o lo g y
s u p p o rt
S tru c tu re d
A cc ts . R e c via b le
O rd e r E n try
B u d g e t a n a lys is
S h o rt-te rm
fo re c a stin g
P e rs o n n e l R e p o rts
In ve s tm e n t a n a lys is
W a re h o u s e lo c a tio n
M IS , O p e ra tio n s
re s e a rc h m o d e ls ,
T ra n s a c tio n
p ro c e s sin g s ys te m s
Sem i
S tru c tu re d
P ro d u ctio n
s c h e d u lin g
In ve n to ry c o n tro l
C re d it e va lu a tio n
P la n t la yo u t
P ro je ct sc h e d u lin g
DSS
U n s tru c tu re d
S e le ctin g
m a g a z in e c o ve r
B u yin g s o ftw a re ,
A p p ro vin g lo a n s
N e g o tia tio n ,
e xe c u tive re c ru itin g
B u ild in g n e w p la n ts
M e rg e rs a n d
a c q u is itio n s
N e w p ro d u c t
p la n n in g
R & D p la n n in g
N e w te c h n o lo g y
d e ve lo p m e n t
M IS
M anagem ent
s c ie n c e
M g t. S c ie n ce
DSS
E IS
ES
T e c h n o lo g y
s u p p o rt
DSS
E IS
M a c h in e le a rn in g
E IS
ES
M a c h in e le a rn in g
Decision Support Framework (Gary and Scott-Morton)
Concept of Decision Support
Systems (DSS)
Scott Morton [1971]
 DSS are interactive computer-based systems, which help
decision makers utilize data and models to solve
unstructured problems [1971]
Keen and Scott Morton [1978]
 Decision support systems couple the intellectual resources
of individuals with the capabilities of the computer to improve
the quality of decisions. It is a computer-based support
system for management decision makers who deal with
semi-structured problems.
DSS: means different things to different people
There is no universally accepted definition of DSS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS Definitions

Little [1970]
“model-based set of procedures for
processing data and judgments to assist a
manager in his decision making”
Assumption: that the system is computerbased and extends the user’s capabilities.

Alter [1980]
Contrasts DSS with traditional EDP
systems (Table 3.1)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A BL E 3.1 D SS ver sus E D P.
D imension
D SS
EDP
U se
A ctive
Passive
U ser
L ine and staff
management
C ler ical
G oal
E ffectiveness
M echanical
efficiency
T ime
H or izon
Pr esent and futur e
Past
O bj ective
F lexibility
C onsistency
S ou rce: A lter [1980].
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Moore and Chang [1980]
1.extendible systems
2.capable of supporting ad hoc data analysis
and decision modeling
3.oriented toward future planning
4.used at irregular, unplanned intervals

Bonczek et al. [1980]
A computer-based system consisting of
1. a language system -- communication
between the user and DSS components
2. a knowledge system
3. a problem-processing system--the link
between the other two components
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ

Keen [1980]
DSS apply “to situations where a `final’
system can be developed only through an
adaptive process of learning and
evolution”

Central Issue in DSS
support and improvement of decision
making
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A BL E 3.2 C oncepts U nder lying D SS D efinitions.
Sour ce
D SS D efined in T er ms of
G or r y and Scott M or ton [1971]
Pr oblem type, system function (suppor t)
L ittle [1970]
System function, inter face
char acter istics
A lter [1980]
U sage patter n, system obj ectives
M oor e and C hang [1980]
U sage patter n, system capabilities
Bonczek, et al. [1996]
System components
K een [1980]
D evelopment pr ocess
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Working Definition of DSS

A DSS is an interactive, flexible, and adaptable
CBIS, specially developed for supporting the
solution of a non-structured management
problem for improved decision making. It
utilizes data, it provides easy user interface, and
it allows for the decision maker’s own insights

DSS may utilize models, is built by an
interactive process (frequently by end-users),
supports all the phases of the decision making,
and may include a knowledge component
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Characteristics and
Capabilities of DSS

DSS (Figure 3.1)
1. Provide support in semi-structured and
unstructured situations
2. Support for various managerial levels
3. Support to individuals and groups
4. Support to interdependent and/or sequential
decisions
5. Support all phases of the decision-making
process
6. Support a variety of decision-making
processes and styles
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
7. Are adaptive
8. Have user friendly interfaces
9. Goal is to improve the effectiveness of
decision making
10. The decision maker controls the decisionmaking process
11. End-users can build simple systems
12. Utilizes models for analysis
13. Provides access to a variety of data
sources, formats, and types
Decision makers can make better, more
consistent decisions in a timely manner
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS Components
1. Data Management Subsystem
2. Model Management Subsystem
3. Knowledge Management Subsystem
4. User Interface Subsystem
5. The User
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Data
Management
Model
Management
Knowledge
Management
User Interface
User
DSS Architecture
Other
Systems
3.6 The Data Management
Subsystem
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DSS database
Database management system
Data directory
Query facility
(Figure 3.3)
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D SS I n F ocus 3.2: T he C apabilities of D BM S in a D SS
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C aptur es/extr acts data for inclusion in a D SS database

U pdates (adds, deletes, edits, changes) data r ecor ds and files
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I nter r elates data fr om differ ent sour ces

R etr ieves data fr om the database for quer ies and r epor ts

Pr ovides compr ehensive data secur ity (pr otection fr om unauthor ized access, r ecover y
capabilities, etc.)

H andles per sonal and unofficial data so that user s can exper iment w ith alter native
solutions based on their ow n j udgment

Per for ms complex data manipulation task s based on quer ies

T r ack s data use w ithin the D SS

M anages data thr ough a data dictionar y
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
DSS Database Issues
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Data warehouse
Special independent DSS databases
Extraction of data from internal, external and
private sources
Web browser access of data
Multimedia databases
Object-oriented databases
Commercial database management systems
(DBMS)
The Model Management
Subsystem

Mirrors the database management
subsystem
(Figure 3.4)
Model Management Issues
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Model level: Strategic, managerial (tactical)
and operational, model building blocks
Modeling languages
Model execution, integration
Use of AI and Fuzzy logic in MBMS
D SS I n F ocus 3.3: M aj or F unctions (C apabilities) of the M BM S

C r eates models easily and quickly, either fr om scr atch or fr om
existing models or fr om the building blocks.

A llow s user s to manipulate the models so they can conduct
exper iments and sensitivity analyses r anging fr om “ w hat-if” to goal
seeking.

Stor es, r etr ieves, and manages a w ide var iety of differ ent types of
models in a logical and integr ated manner .

A ccesses and integr ates the model building blocks.

C atalogs and displays the dir ector y of models for use by sever al
individuals in the or ganization.

T r acks models data and application use.

I nter r elates models w ith appr opr iate linkages w ith the database and
integr ates them w ithin the D SS.

M anages and maintains the model base w ith management functions
analogous to database management: stor e, access, r un, update, link,
catalog, and quer y.

U ses multiple models to suppor t pr oblem solving.
The Knowledge Management
Subsystem
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Provides expertise in solving complex
unstructured and semi-structured problems
What models to use, how, interpreting results
Reasoning, handling uncertainty, learning
from data
Expertise provided by an expert system or
other intelligent system (AI techniques)
Leads to intelligent DSS
Example: Data mining
The User Interface (Dialog)
Subsystem


Includes all communication between a
user and the MSS
To most users, the user interface is the
system
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
D SS I n F ocus 3.5: M aj or C apabilities of the U I M S
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
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Pr ovides gr aphical user inter face.
A ccommodates the user w ith a var iety of input devices.
Pr esents data w ith a var iety of for mats and output devices.

G ives user s “ help” capabilities, pr ompting, diagnostic and
suggestion r outines, or any other flexible suppor t.

Pr ovides inter actions w ith the database and the model base.

Stor es input and output data.

Pr ovides color gr aphics, thr ee-dimensional gr aphics, and data
plotting.

H as w indow s to allow multiple functions to be displayed
concur r ently.

C an suppor t communication among and betw een user s and
builder s of M SS.

Pr ovides tr aining by examples (guiding user s thr ough the input
and modeling pr ocess).

Pr ovides flexibility and adaptiveness so the M SS w ill be able to
accommodate differ ent pr oblems and technologies.

I nter acts in multiple, differ ent dialog styles.

C aptur es, stor es, and analyzes dialog usage (tr acking), to
impr ove the dialog system. T r acking by the user is also available.
The User


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Managers
Staff specialists
Intermediary:
1.Staff assistant
2.Expert tool user
3.Business (system) analyst
4.Group DSS Facilitator
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
Distinguishing DSS from
Management Science and MIS
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DSS is a problem solving tool and is
frequently used to address ad hoc and
unexpected problems
Different than MIS
DSS evolve as they develop
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T able 3.4 T he M aj or C har acter istics of M I S, M S /O R , and D SS
M anagem ent I nfor m ation System s

T he m ain im pact has been on str uctur ed tasks, wher e standar d oper ating pr ocedur es,
decision r ules and infor m ation flows can be r eliable pr edefined.

T he m ain payoff has been in im pr oving efficiency by r educing costs, tur nar ound tim e, and
so on, and by r eplacing cler ical per sonnel.

T he r elevance for m anager s’ decision m aking has m ainly been indir ect; for exam ple, by
pr oviding r epor ts and access to data.
M anagem ent Science/O per ations R esear ch

T he im pact has m ostly been on str uctur ed pr oblem s (r ather than tasks), wher e the
obj ective, data, and constr aints can be pr especified.

T he payoff has been in gener ating better solutions for given types of pr oblem s.

T he r elevance for m anager s has been the pr ovision of detailed r ecom m endations and new
m ethodologies for handling com plex pr oblem s.
D ecision Suppor t System s

T he im pact is on decisions in which ther e is sufficient str uctur e for com puter and analytic
aids to be of value but wher e the m anager ’ s j udgm ent is essential.

T he payoff is in extending the r ange and capability of com puter ized m anager s’ decision
pr ocesses to help them im pr ove their effectiveness.

T he r elevance for m anager s is the cr eation of a suppor tive tool, under their own contr ol,
that does not attem pt to autom ate the decision pr ocess, pr edefine obj ectives, or im pose
solutions.
Source: K een and Scott M or ton [1978], p. 1.
DSS Classifications
Alter’s Output Classification [1980]
 Degree of action implication of system
outputs (supporting decision) (Table 3.3)

Holsapple and Whinston’s Classification
1.Text-oriented DSS
2.Database-oriented DSS
3.Spreadsheet-oriented DSS
4.Solver-oriented DSS
5.Rule-oriented DSS
6.Compound DSS
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
T A B LE 3 . 4 C h ar act er ist ics o f D if f er en t C lasses o f D ecisio n Su p p o r t Sy st em s.
O r ien t at io n
D at a
D at a o r
M o d els
M o d els
C at ego r y
T ype o f
O p er at io n
T y p e o f T ask
U ser
U sage
P at t er n
T im e Fr am e
File d r aw er
sy st em s
A ccess d at a
it em s
O p er at io n al
N o n m an ager ial lin e
p er so n n el
Sim p le
in q u ir ies
I r r egu lar
D at a an aly sis
sy st em s
A d hoc
an aly sis o f f iles
o f d at a
O p er at io n al,
an aly sis
St af f an aly st
o r m an ager ial
lin e p er so n n el
M an ip u lat io n
an d d isp lay o f
d at a
I r r egu lar o r
p er io d ic
A n aly sis
in f o r m at io n
sy st em s
A d hoc
an aly sis
in vo lvin g
m u lt ip le
d at ab ases an d
sm all m o d els
A n aly sis,
p lan n in g
St af f an aly st
Pr o gr am m in g
sp ecial
r ep o r t s,
d evelo p in g
sm all m o d els
I r r egu lar , o n
r eq u est
A cco u n t in g
m o d els
St an d ar d
calcu lat io n s
t h at est im at e
f u t u r e r esu lt s
o n t h e b asis o f
acco u n t in g
d ef in it io n s
Plan n in g,
b u d get in g
St af f an aly st
o r m an ager
Input
est im at es o f
act ivit y ;
r eceive
est im at ed
m o n et ar y
r esu lt s as
out put
Per io d ic ( e. g. ,
w eekly ,
m o n t h ly ,
y ear ly )
Rep r esen t at io n al m o d els
Est im at in g
co n seq u en ces
o f p ar t icu lar
act io n s
Plan n in g,
b u d get in g
St af f an aly st
I n p u t p o ssib le
d ecisio n s;
r eceive
est im at ed
r esu lt s as
out put
Per io d ic o r
ir r egu lar ( ad
h o c an aly sis)
O p t im izat io n
m o d els
C alcu lat in g an
o p t im al
so lu t io n t o a
co m b in at o r ial
p r o b lem
Plan n in g,
r eso u r ce
allo cat io n
St af f an aly st
Input
co n st r ain t s
an d
o b ject ives;
r eceive an sw er
Per io d ic o r
ir r egu lar ( ad
h o c) an aly sis
Su ggest io n
m o d els
Per f o r m in g
calcu lat io n s
t h at gen er at e
a su ggest ed
d ecisio n
O p er at io n al
N o n m an ager ial lin e
p er so n n el
Input a
st r u ct u r ed
d escr ip t io n o f
t h e d ecisio n
sit u at io n ;
r eceive a
su ggest ed
d ecisio n as
out put
D aily o r
p er io d ic
So u r c e : C o n d en sed f r o m A lt er [ 1 9 8 0 ] , p p . 9 0 -9 1 .
Other Classifications

Degree of Nonprocedurality (Bonczek, et al.
[1980])

Personal, Group, and Organizational Support
(Hackathorn and Keen [1981])

Individual versus Group DSS

Custom-made versus Ready-made Systems

DSS Tools, DSS Generators, Specific DSS
Summary

Fundamentals of DSS

GLSC Case

Components of DSS

Major Capabilities of the DSS
Components
Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson
Copyright 1998, Prentice Hall, Upper Saddle River, NJ
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Part 2: Decision Support Systems