Decision Support Systems 1. Introduction to DSS 2. Types of decisions 3. Characteristics and...
Transcript of Decision Support Systems 1. Introduction to DSS 2. Types of decisions 3. Characteristics and...
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