1. Decision Support Systems

2. Characteristics and capabilities of DSS Components of DSS DSS hardware
Introduction to DSS
Types of decisions
Characteristics and capabilities of DSS
Components of DSS
DSS hardware
DSS Classification
DSS Classification (Model Based)
Model driven DSS
Data Driven DSS

3. 11. Group Decision Support System
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

4. 1. Decision Support Systems
Decision support systems (DSS)
Offer potential to assist in solving both semi-structured and unstructured problems

5. 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.

6. Decision Making as a Component of Problem Solving(cont.)
Intelligence
Decision making
Design
Problem solving
Choice
Implementation
Monitoring

7. Information Requirements by Management Level
Strategic
Management
Tactical
Management
Information
Decisions
Operational
Management

8. 2. Types of Decisions
Organisational theory classifies decision-making into fundamentally three different types:
Strategic
Management or Tactical
Operational
Strategic decision-making is concerned with long-term goals & policies for resource allocation/management to meet defined objectives

9. What types of Decision-Making ?
Organisational theory classifies decision-making into fundamentally three different types:
Strategic
Management or
Tactical
Operational
Tactical decision-making is concerned with the acquisition & efficient utilization of resources to achieve defined goals

10. 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

11. Types of Decision-Making
Often less detailed data available & so requires good tools for
modeling & forecasting
More Unstructured
Operational
Tactical/Managerial
Strategic
More structured
Requires detailed data & uses tools for analysis & integration

12. Types of Decision-Making
Often less detailed data available & so requires good tools for
modeling & forecasting
More Unstructured
Structured
Unstructured
Semi -Structured
More structured
Requires detailed data & uses tools for analysis & integration

13. Global SDI Regional SDI National SDI State SDI Local SDI Example 1:
Mapping Malaria Risk in Africa
Operational
Management/ Tactical
Strategic
Decision-Making
Decision-Making
Decision-Making
Continental Malaria distribution Maps
Global SDI
Regional SDI
Malaria Data
National SDI
Spatial Models on geographic distribution, seasonality
Malaria Seasonality Data
Used for planning, intervention & prevention by national & international health officials
State SDI
Malaria Occurrence Data
Local SDI

14. Structured vs. Semi-Structured
For each decision you make, the decision will fall into one of the following categories:
Structured Decisions
Unstructured
Semi-Structured

15. 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”

16. 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

17. Semi-structured Decisions
Decision scenarios that have some structured components and some unstructured components.

18. 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

19. 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

20. Characteristics of a DSS
Handles large amounts of data from different sources
Provides report and presentation flexibility
Offers both textual and graphical orientation

21. 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

22. 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

23. You know the desired result You want to know the required input(s)
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

24. Excel
demo

25. Capabilities of a DSS (cont.)
Supports
Problem solving phases
Different decision frequencies
Merge with another company?
How many widgets should I order?
low
high
Frequency

26. 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

27. DSS Characteristics and Capabilities (cont.)

28. 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

29. Components of DSS(cont.)

30. a) Database Management Subsystem Key Data Issues
Data quality
“Garbage in/garbage out" (GIGO)
Data integration
“Creating a single version of the truth”
Scalability
Data Security
Timeliness
Completeness, …

31. b) DSS Components Model Management Subsystem
Model base
MBMS
Modeling language
Model directory
Model execution, integration, and command processor

32. DSS Components Model Management Subsystem
Model base (= database ?)
Model Types
Strategic models
Tactical models
Operational models
Analytic models
Model building blocks
Modeling tools

33. DSS Components Model Management Subsystem
The four (4) functions
Model creation, using programming languages, DSS tools and/or subroutines, and other building blocks
Generation of new routines and reports
Model updating and changing
Model data manipulation
Model directory
Model execution, integration and command

34. Model Base Model Base Models
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

35. Advantages and Disadvantages of Modeling
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

36. c) DSS Components User Interface (Dialog) Subsystem
Application interface
User Interface
Graphical User Interface (GUI)
DSS User Interface
Portal
Graphical icons
Dashboard
Color coding
Interfacing with PDAs, cell phones, etc.

37. 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

38. A Web-Based DSS Architecture

39. 5. DSS Hardware Major Hardware Options
Evolved with computer hardware and
software technologies
Major Hardware Options
Mainframe
Workstation
Personal computer
Web server system
Internet
Intranets
Extranets

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

41. Intranets and Extranets
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
Principles of Information Systems, Seventh Edition

44. 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

45. DSS Classifications(cont.)
Holsapple and Whinston's Classification
The text-oriented DSS
The database-oriented DSS.
The spreadsheet-oriented DSS
The solver-oriented DSS
The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications)
The compound DSS

46. DSS Classifications (cont.)
Alter's Output Classification

47. DSS Classifications (cont.)
Holsapple and Whinston's Classification
The text-oriented DSS
The database-oriented DSS
The spreadsheet-oriented DSS
The solver-oriented DSS
The rule-oriented DSS (include most knowledge-driven DSS, data mining, management, and ES applications)
The compound DSS

48. 7. DSS Classification Model Based
A) Model Based DSS
Data driven DSS
Communication driven DSS
Document Driven
Knowledge driven

49. A model of a DSS External and Internal Data Decision Other Maker
Knowledge
Management
Decision
Maker
Other
Information
Systems
External and
Internal Data
Data Management
Attribute Data
Model Management
Aspatial Models
Dialog Management
Attribute-Based Queries and Reports
Attribute
Data
Object

50. 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

51. 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 ...

52. 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

53. 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.

54. 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.

55. 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.

56. 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.
Once again, different authors identify different components in a DSS

57. 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)

58. 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
TPS
EXTERNAL
DATA
DSS DATA
BASE
DSS SOFTWARE SYSTEM
MODELS
OLAP
TOOLS
DATA MINING
USER
INTERFACE

59. 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.
A growing area of DSS application, concepts, principles, and techniques is in agricultural production, marketing for sustainable development. For example, the DSSAT4 package, developed through financial support of USAID during the 80's and 90's, has allowed rapid assessment of several agricultural production systems around the world to facilitate decision-making at the farm and policy levels.
A specific example concerns the Canadian National Railway system, which tests its equipment on a regular basis using a decision support system. A problem faced by any railroad is worn-out or defective rails, which can result in hundreds of derailments per year. Under a DSS, CN managed to decrease the incidence of derailments at the same time other companies were experiencing an increase.
DSS has many applications that have already been spoken about. However, it can be used in any field where organization is necessary. Additionally, a DSS can be designed to help make decisions on the stock market, or deciding which area or segment to market a product toward.

60. 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

61. 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

62. 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

63. External database access
Databases
Model base
GDSS processor
GDSS software
External databases
Access to the internet and corporate intranet, networks, and other computer system
Dialogue manager
External database access
Users

64. 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

65. Characteristics of a GDSS (2)
Anonymous input
Reduction of negative group behaviour
Parallel communication
Automated record keeping
Cost, control, complexity factors

66. 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

67. GDSS Alternatives high Local area decision network
Wide area decision network
Decision frequency
Decision room
Teleconferencing
low
close
distant
Location of group members

68. 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

70. 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

71. 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 area vice presidents
Function area managers

72. 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