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Decision Making2011
04/19/23 2
DECISION MAKING
System is a collection of objects such as people, resources,concepts, and procedures intended to perform a function orto serve a goal.
• Closed systems are totally independent.• Open systems dependent on their environment.
• System effectiveness is the degree to which goals are achieved.• System efficiency is a measure of the use of inputs (or resources)
to achieve outputs.
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Decision making is a process of choosing among alternativecourses of action for the purpose of attaining a goal or goals.
(1) intelligence
(2) design
(3) choice
(4) implementation
problem solving
decision making
decision making
problem solving
Simon’s 4 Phases of Decision Making
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INTELLIGENCE PHASE
Organizational objectivesSearch and scanningData collectionProblem identificationProblem ownershipProblem classificationProblem statement
DESIGN PHASEFormulate a modelSet criteria for choiceSearch for alternativesPredict and measure outcomes
Reality
Implementation of solution
Failure
Solution
Alternatives
Problem statement
Validation of the model
Verification, testing of proposed solution SuccessCHOICE PHASE
Solution to the modelSensitivity analysisSelection of best alternative (s)Plan for implementation
Simplification/Assumption
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1. Intelligence phase
Scan the environment Analyze organizational goals (e.g. Inventory Management,
Job Selection, lack or an incorrect web presence) Collect data (Monitoring & analyzing) Identify problem Categorize problem
– Programmed (repetitive & routine) ---Scheduling of employees, inventory level etc
– Non-programmed (Unstructured) --- Merger & Acquisitions
– Decomposed into smaller parts Assess ownership and responsibility for problem resolution
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2. Design phase
• Formulate a model
• Set criteria for choice (Are we willing to take High risk or we prefer low risk approach)
• Search for alternatives
• Predict and measure outcomes (E.g. Profit Maximization)
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3. Choice phase
•Each alternative must be evaluated
•Sensitivity analysis (determines robustness of any given alternative)
•Selection of best alternative (s)
•Plan for implementation
solution - set of values for the decision variables in a selected alternative
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4. Implementation phase
•Putting a recommended solution to work
• Vague boundaries which include:–Dealing with resistance to change–User training–Upper management support
•The problem is considered solved after the recommended solution to the model is successfully implemented.
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Source: Based on Sprague, R.H., Jr., “A Framework for the Development of DSS.” MIS Quarterly, Dec. 1980, Fig. 5, p. 13.
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Decision Support Systems
Intelligence Phase
– Automatic
• Data Mining
– Expert systems, CRM, neural networks
– Manual
• OLAP
• KMS
– Reporting
• Routine and ad hoc
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Decision Support Systems
Design Phase
– Financial and forecasting models
– Generation of alternatives by expert system
– Relationship identification through OLAP and data mining
– Use of KMS
– Business process models from CRM, RMS, ERP, and SCM
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Decision Support Systems
Choice Phase
– Identification of best alternative
– Identification of good enough alternative
– What-if analysis
– Goal-seeking analysis
– May use KMS, GSS, CRM, ERP, and SCM systems
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Decision Support Systems
Implementation Phase
– Improved communications
– Collaboration
– Training
– Supported by KMS, expert systems, GSS
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TYPES OF DECISIONS
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TYPES OF DECISIONS
Decisions are categorized along two dimensions:-
The nature of the decision to be made
The scope of the decision itself
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TYPES OF DECISION
On the basis of the nature of the decision:-
1)Structured decision:-It’s the one for which a well defined decision
making procedure exists.
2)Unstructured decision:- it is the one for which all the three decision
phases are unstructured.
3)Semi structured decision:- In this type one or two phases are
structured and the others are not.
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On the basis of scope of the decision itself.
1. Strategic Decision:- It is the one which effects the entire
organization or a major part of it for a long period of time
2. Tactical Decision:- It effects how a part of the organization does
business for a limited time in the future.
3. Operational Decision:- It is the one which effects a particular
activity currently taking place in an organization but either has a
little impact on the future.
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Combination of various types of Decisions
Structured /operational
Structured / tactical
Structured/ strategic
Semi-structured/ operational.
Semi-structured/ tactical
Semi-structured / strategic
Unstructured/ operational
Unstructured/ tactical
Unstructured/ strategic
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Structured/Operational: Decide how to cut a log into
boards in order to minimize wastage.
The intelligence phase is trivial; if a log arrives at mill, it must
be cut .
The design phases likewise fixed; the products that the mill
produces and hence the acceptable types of cuts.
The choice phase can be optimized mathematically because
the value of each potential board is known from business
consideration and the number of boards that can be operated
via each communication of cuts is a problem.
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Structured /Tactical: Choosing the way in which to
depreciate corporate assets.
Resource allocation problems that can be solved by linear
programming methods are also in this category.
Structured /Strategic: Deciding weather or not to proceed
with an R&D project on the bases of projected ROI
A plant location decision could be in this category if the only
factors in decisions are quantifiable, such as transportation
costs of known raw materials from known locations and of
known products to known markets.
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Semistructured/Operational: Deciding to accept or reject
an applicant to a selective collage.
Semitructured /Tactical: Choosing an insurance company
for an employee health program. Cost per employee is an
important and objective factor in this decision. Intangible
factors include acceptability of a company to the employee
population and the relative importance of different benefits: is
100 percent hospitalization coverage with Rs. 500 deductible
amount better or worse than 80 percent coverage with no
deductible?
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Semitructured /Strategic: Deciding whether or not to enter
a new market. Sales projections, marketplace growth data,
development cost estimates and marketing expenses
forecasts can combine to provide a profit-and-loss forecast.
However there are countless factors that could make it totally
worthless. Judgment of experienced managers is needed for
the final step.
Unstructured/Operational: Dealing with a machine
breakdown. There is no set procedure what to do while
awaiting repairs. The decision is operational because the way
a company deals with one machine failure need not set a
precedent for the next.
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Unstructured /Tactical: Hiring decisions typically fall into
this area, especially if the job to be filled is above level where
aptitude and ability tests can be relied on as performance
indicators.
Unstructured/Strategic: Deciding how to respond to an
unfriendly takeover proposal made by a competitor. The
action can have a long term impact on the entire firm.
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Decision Support Frameworks
Type of Control
Type of
Decision:
Operational Control Managerial Control Strategic Planning
Structured
(Programmed)
Accounts receivable,
accounts payable,
order entry
Budget analysis,
short-term
forecasting, personnel
reports
Investments,
warehouse locations,
distribution centers
Semistructured Production
scheduling,
inventory control
Credit evaluation,
budget preparation,
project scheduling,
rewards systems
Mergers and
acquisitions, new
product planning,
compensation, QA,
HR policy planning
Unstructured
(Unprogrammed
)
Buying software,
approving loans,
help desk
Negotiations,
recruitment,
hardware purchasing
R&D planning,
technology
development, social
responsibility plans
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The components of the quantitative model– result variable indicate how well the system performs– decision variables describe the alternative course of action– uncontrollable variables or parameters are not under the control of the decision maker
Uncontrollablevariables
Mathematicalrelationships
Result variablesDecision variables
– intermediate result variables reflect intermediate outcomes
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Examples of the Components of Models.
Area
Decision
Variables
Result
Variables
Uncontrollable
Variables and Parameters
Financial investment Investment alternatives and
amounts
How long to invest
When to invest
Total profit
Rate of return (ROI)
Earnings per share
Liquidity level
Inflation rate
Prime rate
Competition
Marketing Advertising budget
Where to advertise
Market share
Customer satisfaction
Customers' income
Competitors' actions
Manufacturing What and how much to
produce
Inventory levels
Compensation programs
Total cost
Quality level
Employee satisfaction
Machine capacity
Technology
Materials prices
Accounting Use of computers
Audit schedule
Data processing cost
Error rate
Computer technology
Tax rates
Legal requirements
Transportation Shipments schedule Total transport cost Delivery distance
Regulations
Services Staffing levels Customer satisfaction Demand for services
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Example
Company makes special purpose computers.Decision to be made: how many computers should be produced next month?Two types of computers are considered: T1, T2.They require different days of labour, different costs for material.
Uncontrollablevariables
constraints on labourand budget
Mathematicalrelationships
Maximise profit subject to constraints
Result variables
Total profit
Decision variables
X1 = NofT1X2 = NofT2
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Principle of choice is a decision regarding the acceptability of a solution approach.
• Normative models – chosen alternative is the best of all possible alternatives– suboptimisation– optimisation models
• Descriptive models describe things as they are, or as they arebelieved to be.– no guarantee a solution is optimal– simulation
Generating alternatives– automatically by the model– by using heuristics
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Predicting the outcomes of alternatives
1. Decision making under certaintyDecision maker knows exactly what the outcome ofeach course of action will be - deterministic environment.
2. Decision making under riskEach alternative has several possible outcomes,each with a given probability of occurrence - probabilistic or stochastic decision situation.
3. Decision making under uncertaintySeveral outcomes are possible for each course of action,their probabilities are not known.
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Measuring outcomesThe value of the an alternative is judged in terms ofgoal attainment.
Scenario describes the decision and uncontrollable variablesand parameters for a specific modelling situation.
Of special interest are:– the worst possible scenario– the best possible scenario– the most likely scenario
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Search• Analytical techniques
– mathematical formulae– algorithm: step-by-step search process
• Blind search– complete enumeration– incomplete search
• Heuristic search (derived from the Greek word for discovery)rules guide the search process
Normative models: – analytical techniques– complete, exhaustive enumeration
Descriptive models:– blind search– using heuristics
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Evaluation• Multiple goals
– Today's management systems want to achievemultiple goals simultaneously.
– Goals are usually partially or totally conflicting.
• Sensitivity analysis
Checks the impact of a change in the input data or parameterson the proposed solution (the result variable)
1. Automatic sensitivity analysistells the range within which an input variable or parametercan vary without impact on the proposed solutionone change at a time
2. Trial and errorsome input data are changed
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• What-if-analysis
What will happen to the solution if an input variable ora parameter is changed?e.g. what will happen to the total inventory cost if the cost of carrying inventories increases by 10%?
• Goal seeking analysis
Computes the amount of inputs necessary to achieve a desired levelof an input (goal).
e.g. How many nurses are needed to reduce the average waiting timeof a patient in the emergency room to less than 10 minutes.
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Literature:
1. (a) Decision Support Systems and Intelligent Systems, Fifth EditionE.Turban, Jay Aronson,Prentice Hall, 1998. (b) Decision Support Systems and Expert Systems, Management Support Systems, E.Turban, Fourth Edition,Prentice Hall, 1995.
2. Knowledge-based Decision Support Systems, With Applicationsin Business, 2nd Edition, M. Klein, L. Methlie, Wiley, 1995.
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Systems are composed of inputs, outputs, processes, anddecision makers.
A model is simplified representation or abstraction of reality.They can be iconic, analog, or mathematical.
Decision making involves four major phases: intelligence, design,choice, and implementation.
SUMMARY
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Models
A model is a simplified representation or abstraction of reality.
1. Iconic model is a physical replica of a system.
2. Analog model gives a symbolic representation of reality, behaves like the real system but does not look like it.
3. Mathematical (quantitative) models use mathematical relationships
Benefits:– compression of time– easy model manipulation– low cost of the analysis– cost of making mistakes is less than mistakes on real system– can model risk and uncertainty– a very large number of solutions can be analysed– enhance learning and training
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3. Optimisationmodel generates an optimal solution
Limitations:– works if the problem is structured and deterministic
4. HeuristicsInformal knowledge of how to solve problems efficiently andeffectively, how to plan steps in solving a complex problem,how to improve performance, and so forth.
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Modelling Process
Example: How much to order for the grocery?The Question: How much bread to stock each day?
1. Trial-and-errorexperimentation on the real system
Not appropriate if:– too many alternatives to explore– the cost of making errors is very high– the environment keeps changing
2. Simulationassume the appearance of the characteristics of reality
Problems:– no guarantee that the solution is optimal one– professional development
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Definition of DSS
DSS is an interactive computer-based systems, which help
decision makers utilize data and models to solve unstructured
problems.
DSS is an interactive computer-based systems, which help
decision makers utilize data and models to solve unstructured
problems.
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Definition of DSS
Decision Support Systems (DSS) are a class of computerized
information systems that support decision-making activities. DSS
are interactive computer-based systems and subsystems intended
to help decision makers use communications technologies, data,
documents, knowledge and/or models to successfully complete
decision process tasks.
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Components of DSS
Other computer based systems
Internet, intranet, extranet.
Data management
Model management
External models
Knowledge-based subsystems
User interface
Manager (user)Organizational KB
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Components of DSS
Data management subsystem
The data management subsystem includes a data base, which contains relevant data for the situation and is managed by software call the database management system (DBMS) .the data management subsystem can be interconnected with the corporate data warehouse, a repository for corporate relevant decision making data.
Model management subsystem
This is software package that includes financial, statistical, management science, or other quantitative models that provide the system analytical capabilities and appropriate software management. Modeling languages in building custom models are also included, this software is often called a model base management system (MBMS). This component can be connected to corporate or external storage of models.
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Components of DSS
Knowledge based management subsystem
This subsystem can support any of the other subsystems or act as
an independent component. It provides intelligence to augment
the decision maker’s own. It can be interconnected with the
organization’s knowledge depository, which is called the
organizational knowledge base.
User interface subsystem
The user communicates with and commands the DSS through this
subsystem. The user is considered part of the system. Researchers
assert that some of unique contributions of DSS are derived from
the intensive interaction between the computer and the decision
maker.
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TH
E D
ATA
MA
NA
GEM
EN
T S
UB
SY
STEM
External data Source
Internal data sources
Finance Marketing Production
Personal Other
Extraction
Organizational knowledge base
Private personal data
Decision support database
QueryFacility
Corporate data warehouse
Database management System
oRetrievaloInquiryoUpdateoReport generationoDelete Data directory
Interface management
Model management
Knowledge-based subsystem
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THE DATA MANAGEMENT SUBSYSTEM
The data management subsystem is composed of the following
elements:
DSS database
Database management system
Data directory.
Query facility.
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THE DATABASE
A database is a collection of interrelated data organized to meet
the needs and struc ture of an organization and can be used by
more than one person for more than one ap plication
The data in the DSS database are extracted from internal and
external data sources, as well as from personal data belonging to
one or more Users.
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DATA ORGANISATION
In small ad hoc DSS, data can be entered directly into models
some times extracted directly from larger databases.
In large organizations that use extensive data ,such as Wal-Mart,
AT&T, and United Air Lines data are organized in a data warehouse
and used when needed .
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EXTRACTION
To create a DSS database or a data warehouse it is often necessary
to capture data from several sources. This operation is called
extraction.
It basically consists of importing of files, summarization,
standardization, filtration, and condensation of data.
The data for the warehouse are extracted from internal and
external sources. The extraction process is frequently managed by
a DBMS.
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DATABASE MANAGEMENT SYSTEM
A database is created, accessed, and updated by a DBMS.
Most DSS are built with a standard commercial relational DBMS
that provides capabilities such as it captures or extracts data for
inclusion in a DSS database ,it updates (adds, deletes, edits,
changes) data records and files, retrieves data ,provides data
security etc.
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THE QUERY FACILITY
Query facility is necessary to access, manipulate, and query data.
The query facility includes a special query language.
Important functions of DSS query system are selection and
manipulation operation (e.g., the ability to follow a computer
instruction such as "Search for a sales in zone B during June 2000
and summarize sales by salesperson").
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THE DIRECTORY
The data directory is a catalog of all the data in the database.
It contains data definitions and its main function is to answer
questions about the availability of data items, their source, and
their exact meaning.
The directory is especially appropriate for supporting the
intelligence phase of the decision-making process by helping to
scan data and identify problem areas or opportunities.
It supports the addition of new entries, deletion of entries, and
retrieval of information on specific objects.
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General Functions of the DBMS
Data Definition
Provides a data definition language (DDL) that allows users to
describe the data entities and their associated attributes and
relationships
Allows for the interrelation of data from multiple sources
Data Manipulation
Provides the user with a query language to interact with the database
Allows for capture and extraction of data
Provides rapid retrieval of data for ad hoc queries and reports
Allows for the construction of complex queries for retrieval and data
manipulation
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Data Integrity
Allows the user to describe rules (integrity constraints) to maintain
the integrity of the database
Assists in the control of erroneous data entry based on the defined
integrity constraints
Access Control
Allows identification of authorized users
Controls access to data various elements and data manipulation
activities within the database
Tracks usage and access to data by authorized users
Concurrency Control
Provides procedures for controlling simultaneous access to the
same data by more than one user
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Improved data sharing.
The DBMS helps create an environment in which end users have better access to more and better-managed data. Such access takes it possible for end users to respond quickly to changes in their environment.
Transaction Recovery
Provides a mechanism for restart and reconciliation of the database in the event of hardware failure
Records information on all transactions at certain points to enable satisfactory database restart
Minimized data inconsistency.
Data inconsistency exists when different versions of the same data appear in different places.
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Improved decision making.
Better-managed data and improved data access make it
possible to generate better quality information, on which better
decisions are based.
Increased end-user productivity.
The availability of data, combined with the tools that transform
data into usable information, empowers end users to make
quick, informed decisions that can make the difference between
success and failure in the global economy.
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Models (model base)•Strategic, tactical, operational•Statistical, financial, marketing, mgt. science, accounting etc•Model building blocks
Model directory
Model base management•Modeling commands : creation•Maintenance: update•Database interface•Modeling language
Model execution, integration, and command processor
Data management Interface management
Knowledge –based subsystem
Structure of Model Management System
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Functions of the MBMS
Creates models easily and quickly, either from scratch or from the
building blocks
Allows users to manipulate models so that they can conduct
experiments and sensitivity analyses ranging from what-if to goal
seeking
Stores, retrieves and manages a wide variety of different types of
models in a logical and integrated manner
Accesses and integrates the model building blocks
Catalogs and displays the directory of models for use by several
individuals in the organization
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Functions of the MBMS
Tracks model data and application use
Interrelates model with appropriate linkages with the database and
integrates them within the DSS
Manages and maintains the model base with management
functions analogous to database management: store, access, run,
update, link, catalog, and query
Use multiple models to support problem solving
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US
ER
INTER
FAC
E M
AN
AG
EM
EN
T
SY
STEM
Data management
and DBMSKnowledge-
based subsystemModel
management and MBMS
User Interface Management
System (UIMS)
Language Processor
Printers, plotters
Users
Input Output
Action DisplayLanguages Languages
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General Functions of the DSS Interface
Allows for interaction with the DSS in a variety of dialog styles
Accommodates the user with a variety of input devices
Presents data with a variety of formats and output devices
Gives user help capabilities, prompting, diagnostic and suggestion
routines, or any other flexible support.
Stores input and output data.
Provides support for communication among and between multiple
DSS users
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General Functions of the DSS Interface
variety of formats included menu driven, question/answer,
procedural command language, or natural command language
Provides for the presentation of data in a variety of formats
Allows for detailed report definition and generation by the DSS
user
Allows for the creation of forms, tables, and graphics for data
output
Can provide multiple “windows” or views of the data to be
available simultaneously
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CHARACTERISTICS OF DSS
DSS provides support for decision makers mainly in semi-
structured and unstructured situations by bringing together
human judgment and computerized information.
Support is provided for various managerial levels, ranging from top
executives to line managers.
Support is provided to individuals as well as to groups.
DSS provides support to several interdependent or sequential
decisions. The decisions may be made once, several times or
repeatedly.
DSS supports all phases of decision making process; intelligence,
design, choice and implementation.
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CHARACTERISTICS OF DSS
DSS are adaptive over time. DSS are flexible and so users can add,
delete, combine, change or rearrange basic elements.
User Interface – Interactive and friendly.
DSS attempt to prove the effectiveness of decision making rather
than its efficiency.
The decision maker has complete control over all steps of the
decision making process in solving a problem. A DSS specifically
aims to support and not to replace the decision maker.
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CHARACTERISTICS OF DSS
End users should be able to construct and modify simple systems
by themselves. Larger systems can be built with assistance from
information system (IS) specialists.
A DSS usually utilizes models for analyzing decision making
situations. The modeling capability enables experimenting with
different strategies under different configurations.
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Benefits of DSS Use
Extend the decision-maker’s ability to process information and knowledge
Extend the decision-maker’s ability to tackle large-scale, time-consuming, complex problems
Improve the time associated with making a particular decision
Improve the reliability of a particular decision process or outcome
Encourage exploration and discovery on the part of the decision-maker
Reveal new approaches to thinking about a particular problem space or decision context
Generate new evidence in support of a particular decision or confirmation of existing assumptions
Create a strategic or competitive advantage over competing organizations
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Limitations of DSS Use
DSSs cannot yet be designed to contain distinctly human decision-
making talents such as creativity, imaginativeness, or intuition
The power of a DSS is limited by the computer system upon which it
is running, its design, and the knowledge it possesses at the time of
its use
Language and command interfaces are not yet sophisticated enough
to allow for natural language processing of user directives and
inquiries
DSSs are normally designed to be narrow in scope of application thus
limiting their generalizability to multiple decision-making contexts
68
DSS Classification
1. Alter’s Output Classification (1980)
2. 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
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Alters' Classification of DSS
Optimization Models
Accounting Models
Suggestion Models
Analysis Information
Systems
File Drawer Systems
Data Analysis Systems
Representational Models
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Alter’s Classification of DSS
Data-Driven DSS
Data-Driven DSS take the massive amounts of data available
through the company's TPS and MIS systems and cull from it useful
information which executives can use to make more informed
decisions.
Data- Driven DSS emphasize access to and manipulation of large
databases of structured data
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Alter’s Classification of DSS
Model-Driven DSS
A second category, Model-Driven DSS (accounting and financial
models, representational models, and optimization models).
Model-Driven DSS emphasize access to and manipulation of a model.
Model-Driven DSS use data and parameters provided by decision-
makers to aid them in analyzing a situation, but they are not usually
data intensive.
Very large databases are usually not needed for Model-Driven DSS.
Primarily used for the typical "what-if" analysis. That is, "What
if we increase production of our products and decrease the
shipment time?"
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DSS Classifications
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
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Holsapple and Winston Classification
TEXT ORIENTED DSS
Textually represented information that could have a bearing on decision.
Documents to be electronically created, revised and viewed as needed.
Information Technologies such as documents imaging, hypertext etc can
be incorporated into this type.
DMS, KMS, Content Mgt System, Business rule system
DATABASE ORIENTED DSS
In this type of DSS the database plays a major role in the DSS structure.
Strong report generation and query capabilities.
Data are organized in a highly structured format.
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Holsapple and Winston Classification
SPREADSHEET ORIENTED DSS
Spreadsheet is a modeling language that allows the user to write models to execute DSS analysis.
Tools- Statistical packages, linear programming package (Solver), financial and management science models.
The most popular tools used are Excel and Lotus 1-2-3.
SOLVER ORIENTED DSS
A solver is an algorithmic or procedure written as a computer program for performing certain computations for solving a particular problem type.
EOQ for calculating optimal ordering quantity or a linear regression routine for calculating trend.
Excel, Lotus 1-2-3 and quatro pro can be used to develop such a system.
C++, Lingo etc
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Holsapple and Winston Classification
RULE ORIENTED DSS
The knowledge component of DSS includes both procedural and
inferential (Reasoning) rules, often in an expert system, format.
Assignment Algorithm for Flight Scheduling
COMPOUND DSS
It is a hybrid system that includes two or more of the fine basic
structures explained above. It can be built by using a set of
independent DSS, each specializing in one area.
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Other DSS Classification
Personal
Group
Organizational
Custom VS Readymade
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DSS Classification
OTHER CLASSIFICATIONS OF DSS
INSTITUTIONAL DSS
Deal with decisions of a recurring nature. An institutionalized DSS
can be developed and refined as it evolves over a number of years
because the DSS is used repeatedly to solve identical or similar
problems.
Portfolio Management
ADHOC DSS
Deals with specific problems that are usually neither anticipated
nor recurring. Adhoc decisions often involve strategic planning
issues sometimes management control problems.
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Knowledge-Driven DSS
Knowledge-Driven DSS
It suggest or recommend actions to managers.
These DSS are computer systems with specialized problem-solving
expertise.
The "expertise" consists of knowledge about a particular domain,
understanding of problems within that domain, and "skill" at
solving some of these problems.
A related concept is Data Mining. It refers to a class of analytical
applications that search for hidden patterns in a database.
Data mining is the process of searching through large amounts of
data to produce data content relationships.
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Document-Driven DSS
A new type of DSS, a Document-Driven DSS is evolving to help
managers retrieve and manage unstructured documents and Web
pages.
The Web provides access to large document databases including
databases of hypertext documents, images, sounds and video.
Examples of documents that would be accessed by a Document-
Based DSS are policies and procedures, product specifications,
catalogs, and corporate historical documents, including minutes of
meetings, corporate records, and important correspondence.
A search engine is a powerful decision aiding tool associated with a
Document-Driven DSS.
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Communications-Driven and Group DSS
Group Decision Support Systems (GDSS) came first, but now a
broader category of Communications-Driven DSS or groupware can
be identified.
It includes communication, collaboration and decision support
technologies that do not fit within those DSS types identified.
A Group DSS is a hybrid Decision Support System that emphasizes
both the use of communications and decision models.
A Group Decision Support System is an interactive computer-based
system intended to facilitate the solution of problems by decision-
makers working together as a group.
Groupware supports electronic communication, scheduling,
document sharing, two-way interactive video, White Boards,
Bulletin Boards, and Email.
