This document provides an overview of decision support systems (DSS) and knowledge management. It discusses the types of DSS including status inquiry systems, data analysis systems, and model-based systems. It also covers knowledge management topics such as tacit vs explicit knowledge, knowledge building processes, and knowledge-based expert systems. Knowledge management systems aim to identify, define, generate, deliver, and store organizational knowledge to support decision making. DSS and knowledge management tools can help decision makers understand problems, identify alternatives, and make better, more informed decisions.

1. CHAPTER 14: DSS & KNOWLEDGE
MANAGEMENT

2. Page  2
LEARNING OBJECTIVES
 Understanding of DSS for MIS design
 Types of DSS
 Operational Research Models
 Knowledge and Knowledge management
 Knowledge building process
 Tacit and explicit knowledge
 Knowledge based expert system.

3. Page  3
DSS:Concets and philosophy
 DSS are an application of Herbert Simon model(intelligence,design and
choice)
 It is help the information system to identify problem and then provide
solution
 Helps in decision making process for management
 Provide effectiveness so that performance evaluation take place using
DSS
 It generally focused on class of system
 Using dss decision can be classified in 2 ways programmable and
nonprogrammable decisions
 Programmable decisions are those which has particular structure and
follow certain rules and regulation
 Non programmable decisions are assumed decision which is unstructured
and can not follow any rules.

4. Page  4
Types OF DSS
 Status inquiry systems:
in this systems decisions comes on basic of status if the status is
known the decision is automatic
• Data Analysis Systems:
These decision systems are based on corporative analysis, this
processes are not structured and therefore it is vary. the use of simple
data processing tools and business rules are required to develop this
system.
• Information and Analysis Systems:
in this system data is analyzed and information reports are
generated. The reports might be having exception as feature. the decision
maker use this reports for assessment of situation.

5. Page  5
Types OF DSS
 Accounting Systems:
These systems are not necessarily for decision making but they are
desirable to keep track of the major aspects of the business or functions.
It is based on data processing systems. This system is specially related
with accounting application like cash, inventory etc
• Model Based Systems:
These systems are simulations models or optimizations models for
decision making.

6. Page  6
Types OF DSS
 In order to illustrate these DSS let us take example of material
management functions and the variety of decision and type of systems
are used to support and evaluate the decision
Decision Types of Systems requied
Finding and selection of vendor Inquiry system
Procurement Performance analysis system
Pricing Data analysis
Selection of vendor based on price and
quality performance
Information analysis system
Selection of order quantity Model based system
Inventory rationalization Valuation of inventory and accounting
system
Management of inventory within various
financial and stocking constraints
Inventory optimization model

7. Page  7
DSS
 Facts OF DSS
- The dss are developed by users and system analyst jointly.
- The dss uses the principles of economics, science and engineering and
tools of management
- The data uses in dss is drawn from the information systems developed
from company
- It is isolated from independenent system of MIS
- The most common uses of dss is to test the decision alternatives and also
test the sensitivity of the result to change in the system assumptions.
- The data and information for the dss are used as internal sources such as
database and conventional files

8. Page  8
DSS Models
 The DSS uses three approaches which are as given
DSS
Behavior
Models
Management
Science model
OR Models

9. Page  9
DSS: Models
 Behavior Models:
- These models are useful in understanding the behavior amongst the
business variables
- The decision maker can make decisions giving regards to such behavior
relationships.
- The trend analysis, forecasting and the stastical analysis models are
example of this model
- A trend analysis indicates how different variables behave in trend setting
in the past and hence in the future.
- The regression model is example of stastical approaches and generally it
is used to count correlation between one or more variables
- These types of models are largerly used in process control, marketing etc.

10. Page  10
DSS: Models
 Management science models:
- These models are developed on the business management accounting
and economics.
- These are some management which can be converted into for dss
models
- For examples the cost accounting systems, the system of capital
budgeting for better return on investment.

11. Page  11
DSS: Models
 Operational Research (OR) models:
- It is mathematical model
- These models represent a real life problem situation in terms of variables,
constants and parameters expressed in algebraic equations.
- It is generally used to compare 2 variables and f aspects.ind conclusion
from this
- OR models generally try to find a solution which maximizes certain
aspects of business under conditions of constraints

12. Page  12
GROUP DECISION SUPPORT SYSTEMS(GDSS)
 It is part of DSS
 Main difference is in GDSS there are number of people involve compare
to DSS
 Same characteristics of DSS like database,query,olap,stastical analysis
and others which a group of people need to take decisions
 The main objective is to take decision with take suggestions from all the
members of group and implement this suggestions into decisions.
 In GDSS group members intrect,debate,communicate and conclude using
different tool and technique.
 GDSS is process that can be run online to conclude important decisions.

13. Page  13
GROUP DECISION SUPPORT SYSTEMS(GDSS)
 The group members have some configuration which are as mention
beloved:
1)Group members in one room operating on network with common display
screen to share display for all members.GDSS process is transparent
2)Group members sit in their respective locations and use their desktop
and LAN to interact with other members.GDSS process is not as
transparent as ‘1’
3)Group members are in different cities and they come together threw
teleconferencing or video conferencing with prior planning
4)Group members are at remote locations may be in different countries and
they come together through long distance telecommunication network.

14. Page  14
GROUP DECISION SUPPORT SYSTEMS(GDSS)
 In all 4 configurations,GDSS support software is available on server for
members to use. there are some common activities which are as mention
beloved:
- Sending and receiving information in all forms, type across the network
- Display of notes,graphic,drawings,pictures
- Sharing's ideas choice and indicating preferences
- Participate in decision making process with input, help and so on.

15. Page  15
Artificial intelligence system(AI)
 Intelligence supports knowledge and reasoning ability of persons it
becomes artificial intelligence
 When some AI is picked into a database as a system, then we have AI
system
 AI System fall three basic category which are:
- Expert systems(Knowledge based)
- Natural language(Native languages)
- Perception systems(vision,speech,touch)
• AI is a software technique which applied on the non numerical data
expressed in terms of symbols, statements and patterns
• Ai uses in analysis,planning,training and forecasting.

16. Page  16
Artificial intelligence system(AI)
 AI do not replace people
 The best example of Ai is knowledge based expert systems
 Combinative science application uses knowledge and human information
processing capabilities to produce major application as expert systems.
 Natural interface application uses AI to build natural,realistic,multi sensory
human computer interface.
 Generally AI systems is related with virtual world in short it is related with
real world.

17. Page  17
DSS Application in E-enterprise
 DSS is data driven and model driven.
 They are used for solving problem requiring a systematic approach.
 The decision is applied on supply chain management
 It is depend on structural decision are:
- Deciding number of warehouses, service centres,manufacturing units etc
Use of mechanized and automated material handling system in warehouse
Use of inventory models to decide decisions.

18. Page  18
DSS Application in E-enterprise
 The application areas of AI
AI Application
HR Information
Processing
Capability
Computer
Uses for
production
Computer
Uses for
interfacing
AI Applicatins
Robotics
application
Natural interface
Application

19. Page  19
Knowledge management
 Knowledge is the ability of a person to understand the situation and act
effectively
 Knowledgeable persons should have ability to abstract, understand,
speculate and act of subject.
 Knowledge is a set of information which provides capability to understand
different situations , enables to anticipate implications and judge their
effects, suggest ways or clues to handle situations
 Knowledge is provide a complete platform to handle complex situation
and it has capability to provide complete solution to decision maker.
 Knowledge is best illustrated and applicable to resolve complex problem
situations.

20. Page  20
Structure and Architecture of Knowledge
Customer
Intelligence
Database
Knowledge
Database
Information
Database
DSS Software Solutions
Model based System
Business Forcasting
Business planning
Stastical Analysis ROI
Systems
Data Driven
Systems
Pay off
Analysis
Decision
Tree

21. Page  21
Knowledge Management
 It is the systematic and explicit management of knowledge related
activities.
 KM is comprehensive towards focusing on three perspectives of business
operational, tactical and strategic
 KM dispels some myths which must be mentioned for correction
- KM initiatives and activities lead to more work. Instead improved
knowledge and usage.
- KM initiatives and activities is an additional function. Instead it is an
extension to existing technology driven information management function.
- People are often afraid to share their knowledge.

22. Page  22
Knowledge Management
 KM has following processes
- Define,capture,manipulate,store and develop
- Develop information systems for knowledge creation
- Design applications for improving organization’s effectiveness
- Create knowledge set for example intellectual capital to increase
economics.
- Keep IC continuously on upgrade to use it is a central resource
- Distribute and share to concerned

23. Page  23
Knowledge Management- Driving forces
Driving Force
External Internal
Competitors Analysis
Customization
Continuous evaluation
Business partner
Analysis
Effectiveness
Behavior analysis
Knowledge intensive
work
Intelligence

24. Page  24
Knowledge Management Systems
 Some facts about knowledge management
Facts Comments
Km leads more additional work Reduce problem solving time in routine
and non-routine situation
Km is an additional function and a high
overhead
Though it is additional function but not
provide any benefit
Requires investment in hardware and
software
Operational and tacit knowledge
doesn’t need any investment
People doesn’t like to share knowledge Yes, But it is managed
Knowledge is kept secret No today’s knowledge is a general
knowledge of tomorrow
Km is a static system No it is dynamic
Knowledge is an analytical information,
processed for specific goal
Yes it is provide a perfect problem
solving mechanism

25. Page  25
Knowledge Management Systems architecture
KMS
Identification
Definition
Survey
Build Structure
Knowledge
Generation
Process
Manipulate
Create DB
Knowledge
Delivery
Access
Control
Application
Method
Storage &
Security

26. Page  26
Knowledge Management Systems architecture
 Identification:
in this phase the knowledge definition, scope and category has
been defined then surveys and knowledge structure has been build.
• Knowledge generation:
In this step the knowledge manipulation, process and knowledge
database has been generated.
• Knowledge delivery:
this step involves knowledge sharing with proper access control
with authorization and authentication process.

27. Page  27
Knowledge management
 Tools of KM:
- Database management tools
- DW,Data mining and Data mart
- Process modeling and management tools
- Workflow management tools
- Search engine tools
- Web based tools

28. Page  28
Knowledge based expert system(KBES)
 KBES is one kind of problem solving mechanism which generally deals
with uncertain conditions
 It is helpful in open decision making process where the situation is full of
uncertainty.
 It deals with applicable constriants,examines all possible alternatives and
selects one from this which is near from its goal.
 This system is work as source of knowledge
 It is developed by experts so this system has ability deal with any kind of
uncertain condition

29. Page  29
Knowledge based expert system(KBES)
 KBES MODEL
USER CONTROL
MECHANISM
KNOWLEDGE
BASE
INTERFACE
MECHANISM

30. Page  30
Knowledge based expert system(KBES)
 Knowledge base:
It is a database of knowledge consisting of the theoretical
foundation, facts, rules, formulas and experience. It is a structural storage
with facilities of easy access.
• Interface mechanism:
It is a tool to intercept the knowledge available and to perform
logical deductions in a given situations.
• User Control Mechanism:
it is a tool applied to the inference mechanism to select, interpret
and deduct or intert.this mechanism uses knowledge base in guiding the
inference process.

31. Page  31
The benefits of DSS
 Ability to deal with data, information in different dimensions and sensing
the problem, trend, pattern threw different views
 Ability to understand business performance threw evaluations
 Ability to identify problem and understand its impact on business.
 Ability identify negative Areas of business where the impact starts from.
 Ability view a complex scenarios
 Ability to make better decisions due to quick
analysis,modeling,developing alternatives and testing for selections
 Ability to control risk exposure in decisions.