Decision Support Systems

1. Introduction to DSS

2. Types of decisions

3. Characteristics and capabilities of DSS

4. Components of DSS

5. DSS hardware

6. DSS Classification

7. DSS Classification (Model Based)a) Model driven DSS

b) Data Driven DSS

b) Knowledge driven

c) Communication driven

d) Document driven

8. Architecture of DSS

9. Relation To MIS

10. Relation to BI

11. Group Decision Support System characterstics GDSS Components of GDSS

12. Executive Support System

1. Decision Support Systems

Decision support systems (DSS) Offer potential to assist in solving both semi-

structured and unstructured problems

Decision Support System (cont.) A Decision Support System (DSS) is an interactive

computer-based system or subsystem intended to help decision makers use communications technologies, data, documents, knowledge and/or models to identify and solve problems, complete decision process tasks, and make decisions.

Decision Support System is a general term for any computer application that enhances a person or group’s ability to make decisions.

Decision Making as a Component of Problem Solving(cont.)

Intelligence

Design

Choice

Implementation

Monitoring

Problemsolving

Decisionmaking

Information Requirements by Management LevelInformation Requirements by Management Level

StrategicManagement

TacticalManagemen

t

OperationalManagemen

t

Decis

ions

Information

2. Types of Decisions

Organisational theory classifies decision-making into fundamentally three different types:

StrategicManagement or TacticalOperational Strategic decision-making is

concerned with long-term goals & policies for resource allocation/management to meet defined objectives

What types of Decision-Making ?

Organisational theory classifies decision-making into fundamentally three different types:

StrategicManagement or TacticalOperational

Tactical decision-making is concerned with the acquisition & efficient utilization of resources to achieve defined goals

What types of Decision-Making ?

Organisational theory classifies decision-making into fundamentally three different types:

•Strategic•Management or Tactical•Operational

Operational decision-making is concerned with the effective & efficient use of resources for execution of specific tasks

Types of Decision-Making

More structuredMore structured

More UnstructuredMore Unstructured

Tactical/Managerial Strategic Operational

Requires detailed data & uses tools for analysis & integrationRequires detailed data & uses tools for analysis & integration

Often less detailed data available & so requires good tools for

modeling & forecasting

Often less detailed data available & so requires good tools for

modeling & forecasting

Types of Decision-Making

More structuredMore structured

More UnstructuredMore Unstructured

Requires detailed data & uses tools for analysis & integrationRequires detailed data & uses tools for analysis & integration

Often less detailed data available & so requires good tools for

modeling & forecasting

Often less detailed data available & so requires good tools for

modeling & forecasting

StructuredStructured

UnstructuredUnstructured

Semi -Structured

Semi -Structured

Example 1:

Global SDI

Regional SDI

National SDI

State SDI

Local SDI

Strategic

Decision-MakingDecision-Making

Management/ Tactical

Decision-MakingDecision-Making

Operational

Decision-MakingDecision-Making

Malaria Occurrence

Data

Continental Malaria

distribution Maps

Used for planning, intervention & prevention by

national & international health

officials

Malaria Seasonality

Data

Malaria Data

Spatial Models on geographic distribution, seasonality

Mapping Malaria Risk in Africa

Structured vs. Semi-Structured For each decision you make, the

decision will fall into one of the following categories: Structured Decisions Unstructured Semi-Structured

Structured Decisions

Often called “programmed decisions” because they are routine and there are usually specific policies, procedures, or actions that can be identified to help make the decision “This is how we usually solve this type of

problem”

Unstructured Decisions

Decision scenarios that often involve new or unique problems and the individual has little or no programmatic or routine procedure for addressing the problem or making a decision

Semi-structured Decisions

Decision scenarios that have some structured components and some unstructured components.

The Role of the Decision Maker Decision makers can be

Individuals Teams Groups Organizations

All of these types of decision makers will differ in their knowledge and experience; therefore, there will be differences in how they will react to a given problem scenario

3. DSS Characteristics and Capabilities

Business analytics implies the use of models and data to improve an organization's performance and/or competitive posture

Web analytics implies using business analytics on real-time Web information to assist in decision making; often related to e-Commerce

Predictive analytics describes the business analytics method of forecasting problems and opportunities rather than simply reporting them as they occur

Characteristics of a DSS

Handles large amounts of data from different sources

Provides report and presentation flexibility Offers both textual and graphical orientation

Characteristics of a DSS (cont)

Supports drill down analysis Performs complex, sophisticated analysis

and comparisons using advanced software packages

Supports optimization, satisficing, and heuristic approaches

Characteristics of a DSS (cont)

Performs different types of analyses “What-if” analysis

Makes hypothetical changes to problem and observes impact on the results

Simulation Duplicates features of a real system

Goal-seeking analysis Determines problem data required for a given result

Goal Seeking Example

You know the desired result You want to know the required input(s) Example:

Microsoft Excel’s “Goal Seek” and “Solver” functions

Exceldemo

Capabilities of a DSS (cont.)

Supports Problem solving phases Different decision frequencies

Frequencylow high

Merge withanother

company?

How many widgets

should I order?

Capabilities of a DSS (cont.)

Highly structured problems Straightforward problems, requiring known facts

and relationships. Semi-structured or unstructured problems

Complex problems wherein relationships among data are not always clear, the data may be in a variety of formats, and are often difficult to manipulate or obtain

DSS Characteristics and Capabilities (cont.)

4. Components of DSS Data Management Subsystem

Includes the database that contains the data Database management system (DBMS) Can be connected to a data warehouse

Model Management Subsystem Model base management system (MBMS)

User Interface Subsystem Knowledgebase Management Subsystem

Organizational knowledge base

Components of DSS(cont.)

a) Database Management SubsystemKey Data Issues

Data quality “Garbage in/garbage out" (GIGO)

Data integration “Creating a single version of the truth”

Scalability Data Security Timeliness Completeness, …

b) DSS ComponentsModel Management Subsystem

Model base MBMS Modeling

language Model directory Model execution,

integration, and command processor

DSS ComponentsModel Management Subsystem

Model base (= database ?) Model Types

Strategic models Tactical models Operational models

Analytic models Model building blocks Modeling tools

DSS Components Model Management Subsystem

The four (4) functions1. Model creation, using programming

languages, DSS tools and/or subroutines, and other building blocks

2. Generation of new routines and reports

3. Model updating and changing

4. Model data manipulation Model directory Model execution, integration and command

Model Base

Model Base Provides decision makers with

access to a variety of models and assists them in decision making

Models Financial models Statistical analysis models Graphical models Project management models

Advantages and Disadvantagesof Modeling Advantages

Less expensive than custom approaches or real systems. Faster to construct than real systems Less risky than real systems Provides learning experience (trial and error) Future projections are possible Can test assumptions

Disadvantages Assumptions about reality may be incorrect Accuracy of predications often unreliable Requires abstract thinking

c) DSS ComponentsUser Interface (Dialog) Subsystem

Interface Application interface User Interface

Graphical User Interface (GUI)

DSS User Interface Portal Graphical icons

Dashboard

Color coding

Interfacing with PDAs, cell phones, etc.

d) DSS Components Knowledgebase Management System Incorporation of intelligence and expertise Knowledge components:

Expert systems, Knowledge management systems, Neural networks, Intelligent agents, Fuzzy logic, Case-based reasoning systems, and so on

Often used to better manage the other DSS components

A Web-Based DSS Architecture

39

5. DSS Hardware Evolved with computer hardware and

software technologies

Major Hardware Options Mainframe Workstation Personal computer Web server system

Internet Intranets Extranets

Internet: a collection of interconnected networks, all freely exchanging information.

Principles of Information Systems, Seventh Edition

41

Intranets and Extranets

Intranet

Internal corporate network built using Internet and World Wide Web standards and products

Slashes the need for paper

Provides employees with an easy and intuitive approach to access information that was previously difficult to obtain

Principles of Information Systems, Seventh Edition

42

Intranets and Extranets (continued)

Extranet: a network based on Web technologies that links selected resources of a company’s intranet with its customers, suppliers, or other business partners

Virtual private network (VPN): a secure connection between two points across the Internet

Tunneling: the process by which VPNs transfer information by encapsulating traffic in IP packets over the Internet

Principles of Information Systems, Seventh Edition 43

Table: Summary of Internet, Intranet, and Extranet Users

6. DSS Classifications

Other DSS Categories Institutional and ad-hoc DSS Personal, group, and organizational support Individual support system versus group support

system (GSS) Custom-made systems versus ready-made

systems

DSS Classifications(cont.)

Holsapple and Whinston's Classification1. The text-oriented DSS

2. The database-oriented DSS.

3. The spreadsheet-oriented DSS

4. The solver-oriented DSS

5. The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications)

6. The compound DSS

DSS Classifications (cont.) Alter's Output Classification

Orientation Category Type of Operation

Data File drawer systems Access data items

Data analysis systems Ad hoc analysis of data files

Data or models

Analysis information systems

Ad hoc analysis involving multiple databases and small models

Models Accounting models Standard calculations that estimate future results on the basis of accounting definitions

Optimization models Calculating an optimal solution to a combinatorial problem

DSS Classifications (cont.)

Holsapple and Whinston's Classification1. The text-oriented DSS

2. The database-oriented DSS

3. The spreadsheet-oriented DSS

4. The solver-oriented DSS

5. The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications)

6. The compound DSS

7. DSS Classification Model Based

a) A) Model Based DSS

b) Data driven DSS

c) Communication driven DSS

d) Document Driven

e) Knowledge driven

A model of a DSS

KnowledgeManagement

DecisionMaker

OtherInformation

Systems

External andInternal Data

Data ManagementAttribute Data

Model ManagementAspatial Models

Dialog ManagementAttribute-Based Queries and Reports

AttributeData

ObjectData

a) Model-driven DSS A model-driven DSS emphasizes access to and manipulation of

a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analyzing a situation; they are not necessarily data intensive. Dicodess is an example of an open source model-driven DSS generator .

Other examples: A spread-sheet with formulas in

A statistical forecasting model

An optimum routing model

b) Data-driven (retrieving) DSS A data-driven DSS or data-oriented DSS emphasizes access to

and manipulation of a time series of internal company data and, sometimes, external data.

Simple file systems accessed by query and retrieval tools provides the elementary level of functionality. Data warehouses provide additional functionality. OLAP provides highest level of functionality.

Examples: Accessing AMMIS data base for all maintenance Jan89-Jul94 for

CH124

Accessing INTERPOL database for crimes by …….

Accessing border patrol database for all incidents in Sector ...

Model and data-retrieving DSS

Examples: Collect weather observations at all stations and

forecast tomorrow’s weather

Collect data on all civilian casualties to predict casualties over the next month

c) Communication-driven DSS

A communication-driven DSS use network and comminication technologies to faciliate collaboartion on decision making. It supports more than one person working on a shared task.

examples include integrated tools like Microsoft's NetMeeting or Groove (Stanhope 2002), Vide conferencing.

It is related to group decision support systems.

d) Document-driven DSS

A document-driven DSS uses storage and processing technologies to document retrieval and analysis. It manages, retrieves and manipulates unstructured information in a variety of electronic formats.

Document database may include: Scanned documents, hypertext documents, images, sound and video.

A search engine is a primary tool associated with document drivel DSS.

e)Knowledge-driven DSS

A knowledge-driven DSS provides specialized problem solving expertise stored as facts, rules, procedures, or in similar structures. It suggest or recommend actions to managers.

MYCIN: A rule based reasoning program which help physicians diagnose blood disease.

8. Architecture Three fundamental components of DSS:

the database management system (DBMS), the model management system (MBMS), and the dialog generation and management system (DGMS).

the Data Management Component stores information (which can be further subdivided into that derived from an organization's traditional data repositories, from external sources such as the Internet, or from the personal insights and experiences of individual users);

the Model Management Component handles representations of events, facts, or situations (using various kinds of models, two examples being optimization models and goal-seeking models); and

the User Interface Management Component is of course the component that allows a user to interact with the system.

A Detailed Architecture

Even though different authors identify different components in a DSS, academics and practitioners have come up with a generalized architecture made of six distinct parts: the data management system, the model management system, the knowledge engine, The user interface, the DSS architecture and network, and the user(s)

Typical Architecture TPS: transaction

processing system MODEL:

representation of a problem

OLAP: on-line analytical processing

USER INTERFACE: how user enters problem & receives answers

DSS DATABASE: current data from applications or groups

DATA MINING: technology for finding relationships in large data bases for prediction

TPSEXTERNAL

DATADSS DATA

BASE

DSS SOFTWARE SYSTEMMODELS

OLAP TOOLS

DATA MINING TOOLS

USERINTERFACE

USER

Applications There are theoretical possibilities of building such systems in any

knowledge domain. Clinical decision support system for medical diagnosis. a bank loan officer verifying the credit of a loan applicant an engineering firm that has bids on several projects and wants

to know if they can be competitive with their costs. DSS is extensively used in business and management.

Executive dashboards and other business performance software allow faster decision making, identification of negative trends, and better allocation of business resources.

A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development.

A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system.

A DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.

9. Information Systems to support decisions

Management Information Systems

Decision Support Systems

Decision support provided

Provide information about the performance of the organization

Provide information and techniques to analyze specific problems

Information form and frequency

Periodic, exception, demand, and push reports and responses

Interactive inquiries and responses

Information format

Prespecified, fixed format Ad hoc, flexible, and adaptable format

Information processing methodology

Information produced by extraction and manipulation of business data

Information produced by analytical modeling of business data

10. DSS and BI DSS is not quite synonymous with BI

DSS are generally built to solve a specific problem and include their own database(s)

BI applications focus on reporting and identifying problems by scanning data stored in data warehouses

Both systems generally include analytical tools (BI called business analytics systems)

Although some may run locally as a spreadsheet, both DSS and BI uses Web

11. Group Decision Support System

Group Decision Support System (GDSS) Contains most of the elements of DSS plus

software to provide effective support in group decision-making settings

Databases

Model base GDSS processor GDSS software

Dialoguemanager

External databaseaccess

Users

Access to the internetand corporate intranet,

networks, and othercomputer system

Externaldatabases

Characteristics of a GDSS (1)

Special design Ease of use Flexibility Decision-making support

Delphi approach (decision makers are geographically dispersed)

Brainstorming Group consensus Nominal group technique

Characteristics of a GDSS (2)

Anonymous input Reduction of negative group behaviour Parallel communication Automated record keeping Cost, control, complexity factors

Components of a GDSS and GDSS Software

Database Model base Dialogue manager Communication capability Special software (also called GroupWare) E.g., Lotus Notes

people located around the world work on the same project, documents, and files, efficiently and at the same time

GDSS Alternatives

Local areadecision network

Wide areadecision network

Decisionroom

Teleconferencing

Location of group members

close distant

high

low

Dec

isio

n fr

eque

ncy

Decision Room

Decision Room For decision makers located in the same geographic

area or building Use of computing devices, special software,

networking capabilities, display equipment, and a session leader

Collect, coordinate, and feed back organized information to help a group make a decision

Combines face-to-face verbal interaction with technology-aided formalization

Wide Area Decision Network

Characteristics Location of group members is distant Decision frequency is high Virtual workgroups

Groups of workers located around the world working on common problems via a GDSS

12. Executive Support System

Characteristics A specialized DSS that

includes all the hardware, software, data, procedures, and people used to assist senior-level executives within the organization

Board of directors

President

Function areavice presidents

Function areamanagers

Characteristics of ESSs

Tailored to individual executives Easy to use Drill down capabilities Support the need for external data Help with situations with high degree of

uncertainty Futures orientation (predictions, forecasting) Linked with value-added business processes