Class Notes of Batch 2004 - 2007 (3rd Year)

Decision Support System – Prof. Chandan Bhattacharya – 17th July, 2006.

GENETIC ALGORITHM

Genetic Algorithm is a way of solving problems by mimicking the same process or processes that the Mother Nature uses.

It is basically a searching process with approach to optimization of the problem based on same sample data. They use the same combination of selection, recombination & mutation to evolve a solution to a given problem.

At the beginning of a run of a genetic algorithm, a large population of random chromosomes (a string of real numbers / a binary string) is created. Each one, when decoded with represents a different solution to the problem. Let there are ‘M’ no of Chromosomes in the initial composition. The following steps are repeated until a solution is found.

1) Test each chromosome to see how good it is at solving the problem & assign a fitness score accordingly. The fitness score is a measure of how good that chromosome is to solve the given problem.

2) Select 2 members from the current population. The chance of being selected is proportional to the chromosome fitness.

3) Dependent on the crossover rate, crossover the bits from each chosen chromosome at a randomly chosen point.

4) State through the chosen chromosomes bits & flip dependent on the mutation rate.

5) Repeat step 2,3 & 4 until a new population of n number has been created.


CROSSOVER RATE

This is simply the chance that 2 chromosomes will swap their bits. Crossover is performed by selecting the random gene along the length of the chromosomes & swapping all the genes after that point.

X 10111001 011101
Y 11001011 101000
Becomes: -
X 10111001 101000 (final output)
Y 11001011 011101

Informative part is called MOTIF & the rest is called CROSSOVER.

Diagram

MUTATION

This is the chance that a bit within a chromosome will be fit (0 changes to 1 & 1 becomes 0). This is usually a very low chance. Whenever chromosomes are chosen from the chains the algorithm first checks to see if crossover should be applied & then the algorithm iterates down the length to each chromosome mutating the bits if applicable.

Given the digits 0 through 9 & operators +, -, X, / find a sequence that will represent a given target number. The operators will be applied sequentially from left to right as you read.

Eg: 4 – 3 * 6 = 1 * 6 = 6

23 => 6 + 5 * 4 / 2 + 1 (9 genes)
= 0110 1010 0101 1100 0100 1101 0010 1010 0001
= 011010100101110001001101001010100001 (Chromosome)

This has been obtained from the chart below: -

0 - 0000
1 - 0001
2 - 0010
3 - 0011
4 - 0100
5 - 0101
6 - 0110
7 - 0111
8 - 1000
9 - 1001
+ - 1010
- - 1011
* - 1100
/ - 1101
blank - 1110
blank - 1111

Steps: -

1) First we need to encode a possible solution as a string of bits to represent all the different characters available to the solution. This will represent a gene.

Each chromosome will be made up of several genes. The above, show all the different genes required to encode the problem.

The possible genes 1100 1111 will remain unused & will be ignored by the algorithm if encountered. Hence the target no 23 would represent 9 genes as follows. These genes all together form the chromosome.

FITNESS FUNCTION

This can be the most difficult part of the algorithm to figure out. It really depends on the problem to solve. The general idea is to give a higher fitness score; the closer a chromosome comes to solving the problem.

A fitness score can be assigned that is universally proportional to the difference between the solution & the value, a decoded chromosome represents.

Assuming solution target no is 42, the fitness score of the chromosome mentioned above
1 / (42 – 23) = 1 / 19

If a solution is found, a divide by zero, error would occur, as the fitness would be 1/ 42 – 42. This is not a problem, as we are looking for a solution and not a mathematical data.


Decision Support System – Prof. Chandan Bhattacharya – 25th July, 2006.

NEURAL NETWORK

A Neural Network is a massively parallel-distributed processor made up of simple processing unit, which has a natural characteristic for storing experimental knowledge and making it available for use. It resembles the brain in 2 respects: -

1) Knowledge is acquired by the network from its environment through a learning process.
2) Inter-neuron connection strengths, known as synaptic weight, are used to store the acquired knowledge.

BENEFITS OF NEURAL NETWORKS

1) Non-linearity – An artificial neuron can be linear or non linear. A neural network made up of an interconnection of nonlinear neurons is itself a non linear.

2) Input / Output Mapping – A popular technique of learning, called supervised learning involves modification of the synaptic weights of a neural network by applying a set of labelled training samples or task examples.

3) Adaptability – Neural Networks have a built in capability to adopt their synaptic weights, to changes in the surrounding environment.

A neural network trained to operate in a specific environment can easily be retrained to deal with minor changes in the operating environmental conditions.


4) Evidential Response – In the context of pattern classification, a neural network can be designed to provide information not only about which particular pattern to select but also about the confidence of the decision made.

5) Contextual Information – Knowledge is represented by the very structure 7 activation state of a neural network. Every neuron in a network is potentially affected by the global activity of all other neurons in the network.

6) Neurobiological analogy – The design of a neural network is motivated by analogy of the brain, which is a living proof of fault tolerant parallel processing.


Diagram 1

Diagram 2

(Mapping how should work if we make analogy to a human brain)
Nonlinear Model of Neuron

A neuron is an information-processing unit that is fundamental to the operation of a neural network.

The diagram above shows the model of a neuron, which forms the basis of designing artificial neural network. The model comprises of the following key elements.

1) A set of synapse or connecting links, each of which is characterised by a weight or strength of its own. A signal ‘xj’ (1 <= j <= n) Specially ‘xj‘ at the input of synapse j connected to neuron k is multiplied by the synaptic weight ‘wkj’. Unlike a synapse in the brain, the synaptic weight of an artificial neuron may be negative as well as positive values.

2) An adder for summing the input signals, weighted by the respective synapse of the neuron forms a linear combiner.

3) An activation function for limiting the amplitude of the output of a neuron is called squashing function.

The neural model includes an externally applied bias denoted by ‘bk’ which has the effect of increasing or lowering the net input of the activation function.

In mathematical terms we now may describe a neuron k by the following equation: -
m
Uk = Σ Wkj xj
j=1
Yk = Φ (uk + bk )

Where,
x1, x2 …….xn are the input signals,
wk1, wk2 ………xkn are the synaptic weights of neuron k
uk is the linear combiner output
bk is the bias
Φ() is the activation function &
yk is the output signal of neuron ‘k’.

Inparticular depending on whether the bias bk is +ve or –ve the relationship between the induced local field for activation potential vk of the neuron k and the linear combined output uk is modified by induced local field.

DIFFERNECE BETWEEN NEURAL NETWORK AND EXPERT SYSTEM

Expert system like post-traditional systems seek to emulate or model, human experts way of solving a set of problems. The Knowledge Engineers observe humans, builds a model of their expertise and writes a computer programs or algorithm that implements the model. The program incorporates specific rules, which are derived from human experts & their experience in a limited problem domain. Neural Networks do not model human intelligence nor to use knowledge engineers to solve a specific problem. Instead of putting intelligence into program neural network designers.


Others:-

FUZZY LOGIC

Professor Zadeh reasoned that people do not require precise numerical information input & yet they are capable of highly adoptive control. If feedback controllers could be programmed to accept noisy, imprecise input they could be much more effective and perhaps easier to implement.

Fuzzy Logic is a problem solving Control System methodology that lend itself to implement in systems ranging from simple, small embedded micro-controllers to large, networked, multi-channel PC or Work-Stations based data acquisition and Control Systems. It can be implemented in Hardware, Software or a combination of both. If fuzzy logic provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy or even missing input information.

Fuzzy Logic incorporates a simple, rule based IF X AND Y THEN Z approach to a solving control problem rather than attending to model a system mathematically. The fuzzy logic model is empirically based and relies on operator’s experience rather than technical understanding of the system.

Fuzzy Logic requires some numerical parameters in order to operate such as what is considered to be significant error and significant rate of change of error, but exact values of these numbers are usually not critical unless very responsive performance is required.

WHY USE FUZZY LOGIC

1) It is inherently robust since it does not require precise, noise free inputs. The output control is a smooth control function despite a wide range of input variations.

2) Since the Fuzzy logic Controllers processes user defined rules, it can be modified easily to improve or alter system performances.

3) Fuzzy Logic is not limited to a few feedback inputs nor is it necessary to measure or compute rate of change parameters.

4) Because of the rule based operation any reasonable number of inputs can be processed and numerous output can be generated with more limited responsibilities.

5) Fuzzy Logic can control non-linear system that would be difficult or impossible to model mathematically.


Suppose we want to design a simple proportional temperature controller with an electric healing element and a variable speed cooling fan. A positive element output calls for 0 – 100 % heat while a negative signal output calls for 0 – 100 % cooling. Control is achieved through proper balance and control of these two active devices.


Diag DSS 4


Cmd: Target Temperature
Temp: Feedback sensor in Controlled environment.
Error: Cmd – Temp ( + = too cold ; - = too hot )
Cmd-dot : Time derivative or Error ( + = getting hotter ; - = getting cooler )
Output: HEAT or NO CHANGE or COOL

A simple block diagram of the Control System

Notations:
N – Negative error or error dot in input level
Z – Zero(0) error or error dot in input level
P – Positive error or error dot in input level
H – Heat output response
- - No change
C – Cool

Rule Structure & Rule Matrix: (assuming 3 X 3 matrix)

Diag DSS 5

Rule Structure:
1) If Cmd - Temp – N and d/dt [Cmd – Temp)] = N then output = C
2) If Cmd - Temp – Z and d/dt [Cmd – Temp)] = N then output = H
3) If Cmd - Temp – P and d/dt [Cmd – Temp)] = N then output = H
4) If Cmd - Temp – N and d/dt [Cmd – Temp)] = Z then output = C
5) If Cmd - Temp – Z and d/dt [Cmd – Temp)] = Z then output = NC
6) If Cmd - Temp – P and d/dt [Cmd – Temp)] = Z then output = H
7) If Cmd - Temp – N and d/dt [Cmd – Temp)] = P then output = C
8) If Cmd - Temp – Z and d/dt [Cmd – Temp)] = P then output = C
9) If Cmd - Temp – P and d/dt [Cmd – Temp)] = P then output = H








Definition:

System Status Input:

[Error] – P – Too Cold
Z – Just Right
N – Too Hot

[Error dot] – P – Getting Hotter
Z – Not Changing
N – Getting Colder

System Status Output:

H – Call for heating
C – Call for Cooling
NC – No Change


GENETIC ALGORITHMS

Genetic Algorithms are a way of solving problems by mimicking the same process that the nature uses. They use the same combination of selection recombination and mutation to evolve a solution to a problem. Every organism has a set of rules. These rules are encoded in the genes of an organism, which in turn are connected together into long strings called chromosomes. A typical chromosome may look like follows when it is coded in binary sequences: -

10010010110010………………..

At the beginning of run of genetic algorithm, a large population of random chromosomes is created. Each one, when decoded will represent a different solution to the problem. Let us assume that there are N chromosomes in the initial population. The following steps are repeated until a solution is found: -

1) Test each chromosome to see how good it is at solving the problems and assign a fitness score accordingly. The fitness score is a measure of how good that chromosome is at solving the problem.
2) Select two members from the current population. The chance of being selected is proportional to the chromosome fitness.
3) Dependent on the crossover rate, crossover the bits from each chosen chromosomes at a randomly chosen point.
4) Step through the chosen chromosome bits and flip dependent on the mutation rate.
5) Repeat step II, III, IV until a new population of N members has been created.



Soln: - max[min[(A,B),(B,D)],min{A,C),(C,D)]]
So, max[min[0.8,.05],min[9,6]]
= max[0.5,0.6]
= 0.6

Decision Support System – Prof. Chandan Bhattacharya – 29th Jan, 2006.

MANAGEMENT INFORMATION SYSTEM

The study of Management Information System started in 1960 to focus on computer based information system aimed at managers. MIS combines theoretical work of computer science, management science and operations research. It is a practical orientation towards building systems and applications. This is shown in the following figure.

Diag DSS 1

TECHNICAL APPROACH

The technical approach of information system emphasizes mathematically based models as well as the physical technologies to study information systems. The discipline that contributes the technical approach are computer science, management science and operation research computer science is concerned with establishing theories of compatibility, methods of computations and methods of efficient data storage and accesses Management Science emphasizes the development of models for decision-making and management practices. Operation Research focuses on mathematical techniques for optimizing selected parameters of organizations such as transportations inventory control.

BEHAVIOURAL APPROACH

The growing part of the information system is concerned with behavioral problems and issues. Sociologists focused on the impact of the information system on organizations and society.

Political Science investigates the political impact and uses of information system and technology psychology concern with individual responses and information systems and cognitive model of human reasons.

DIFFERENT KINDS OF INFORMATION SYSTEMS

There are 4 main kinds of information system serving different organisation levels: -

1. Operations Level
2. Knowledge Level
3. Management Level
4. Strategic Level

This is shown in following diagram: -

Diag DSS 2

Organisations and information systems can be divided into strategic management, knowledge and operational levels. They can be divided further into five functional areas: -

1. Sales & Marketing
2. Manufacturing
3. Finance
4. Accounting
5. Human Resources

1) STRATEGIC LEVEL SYSTEM help senior managers for long term planning.
2) MANAGEMENT LEVEL SYSTEM helps middle managers monitor and control.
3) KNOWLEDGE LEVEL SYSTEM help knowledge and data workers design product distribute information and cope with papers work.
4) OPERATIONAL LEVEL SYSTEM help operational managers to keep track of organisations day to day activities.

DIFFERENT TYPES OF INFORMATIONS SYSTEM

6 major types of information system needed for the 4 levels of an organisation as shown in the following table. Information systems are built from each of the four levels of an organization.



A Table Chart



TRANSACTION PROCESSING SYSTEM
Computerised systems that perform and record the daily routine transaction to necessary conduct the business. It serves the operational level of an organisation.

KNOWLEDGE WORK SYSTEMS
Information Systems that aid knowledge workers in the creation and investigation of new knowledge of the organisation. It serves the knowledge level of an organisation.

OFFICE AUTOMATION SYSTEM
Computer Systems, such as word processing, electronic mail etc. are designed to increase the productivity of data workers in the office. It serves the knowledge level of an organisation.

DECISION SUPPORT SYSTEM
Information Systems at the management level of an organisation that combine data and sophisticated analytical model to support semi-structured or un-structured decision-making. It serves the management level of an organisation.

MANAGEMENT INFORMATION SYSTEM
Information System at the management level of an organisation that serves the function of planning, controlling and decision making by providing routine summary and exception reports. It serves management level of organisation.

EXECUTIVE SUPPORT SYSTEM
Information Systems at the strategic level of an organisation designed to address unstructured decision making to advanced graphic and communication.


CHARACTERTISICS OF MANAGEMENT INFORMATION SYSTEM

1. MIS support structured and semi-structured decision at the operational and management control useful for planning purpose of senior management stuff.
2. MIS are generally reporting and control oriented. They are designed to report on existing operations and therefore to help to provide day-to-day control of operations.
3. MIS relay on existing corporate date and data flow.
4. MIS have little analytical capabilities.
5. MIS generally aid in decision making using past and present data.
6. MIS are relatively flexible.
7. MIS have an internal rather than external orientation.
8. Information requires are known and stable.
9. MIS requires a lengthy analysis and design process.

CHARACTERISTICS of DECISION SUPPORT SYSTEM

1. DSS offer users flexibility and adoptability and a quick response.
2. DSS allow users to initiate and control the input and output.
3. DSS operate with little or no assistance from professional programmers.
4. DSS provide support for decisions and problems whose solutions cannot be specified in advance.
5. DSS use sophisticated analysis and no modeling.


Diag DSS 3


The figure above illustrates how various types of system in the organisation are related to each other – TPS are the major producer of information that is required by the other systems which in turn produces information for other systems. These different types of systems are loosely coupled in most organisations.


Decision Support System – Prof. Chandan Bhattacharya – 20th June, 2006.

DATA MINING

The non-trivial extraction of implicit, previously unknown & potentially useful information from network.

TECHNIQUES USED

1) Clustering
2) Data summarisation
3) Learning classification rules
4) Analyzing changes
5) Detecting anomalies

DEFINITION

1) Data Mining is the analysis of data & the use of software techniques for finding patterns & regularities, in sets of data.

2) It is responsible for finding the patterns by identifying & underlying rules & features in the data.

COMPARISON BETWEEN DATA MINING & DBMS

DATA: Queries (SQL) based on the data held.
DATA MINING: Infer (knowledge required) from the data held to answer queries.

CHARACTERISTICS OF DATA MINING

1) Large quantities of data.
2) Noisy, incomplete data.
3) Complex data structure.
4) Heterogeneous data stored in legacy systems.


Decision Support System – Prof. Chandan Bhattacharya – 27th June, 2006.

DIFFERENCE BETWEEN DATA MINING AND MACHINE LEARNING

Data Mining or Knowledge Discovery in database is about finding understandable knowledge where as Machine Learning is concerned with improving performance of an agent like training a Neural Network.

Data Mining is concerned with very large & real world databases whereas Machine Learning typically looks at smaller data sets.


DATA WAREHOUSING

A data warehouse can be defined as any centralized data repository, which can be required for business benefit.

Warehousing makes it possible to: -
(i) Extract achieved operational data.
(ii) Overcome inconsistencies between different legacy data format.
(iii) Integrate data through put throughout an enterprise regardless of location, format or communication requirements.
(iv) Incorporate additional or expert information.

CHARACTERISTICS OF DATA WAREHOUSE SUBJECT ORIENTED

Data organized by subject instead of application. It contains only the information necessary for decision support process.

1) Integrated – Encoding of data is often inconsistent.
2) Mass user scalability – Access to warehouse to support concurrent users while maintaining acceptable query performance.
3) Network data-warehouse – Data warehouse rarely exist in isolation. Users must be able to look at and work with multiple warehouses from a single client workstation.
4) Warehouse administration – Large scale and time cyclic nature of the data warehouse demands administrative flexibility
5) The RDBMS must integrate dimentioned analysis.
6) Advanced query functionality.
7) End users require advanced analysis calculations sequential comparative analysis to detailed and summarized date.

DATAWAREHOUSING & ONLINE TRANSACTION PROCESSING (OLTP)

Table Chart

Decision Support System – Prof. Chandan Bhattacharya – 4th July, 2006.

DATA WAREHOUSING SYSTEMS

Data Warehouses are interested in query processing as opposed to transaction processing. It contains a place for storing data that are 5-10 years old. These data is used for consequences, trends & forecasting.

PROCESS

INSULATE DATA

1) Preserves the security 7 integrity of mission critical OLTP applications.
2) Give access to the broadest possible base of data.

RETRIEVE DATA

1) Data is transformed 7 delivered to the data warehouse on a selected model (of mapping definition)

META DATA

1) Information describing the model & definition of the source data element.

Removal of certain aspect of operational data such as low level transaction information, which slow down the query time.

TRANSFER

Processed data transferred to the data warehouse, a large database on a high performance box.


USE OF DATA WAREHOUSE

A central store against which the queries are own, it uses very simple data structures with very little assumptions about the relationships between data.

A data mart is a small warehouse, which provides subsets of the main store, depending on the requirement of a specific group or department. Data marts often use multidimensional databases, which can speed up query processing as they can have data structures, which reflects the most likely queries.

CRITERION FOR A DATA WAREHOUSE

1) Load performance – require incremental loading of new data on a periodic basis.
2) Load processing – data conversion, filtering, reformatting, integrity checks, physical storage, indexing, metadata update.
3) Data quality management – Ensure consistency & referential integrity in massive database size.
4) Query performance – must not be slow b the query of data warehouse RDBMS. It must support modular parallel management.

4 diagrams

ARTIFICIAL INTELLIGENCE FAMILY

Artificial Intelligence is commonly defined as the effort to develop computer based systems that behave as human. Such systems would be able to learn natural languages, accomplish coordinated physical task (robotics) & emulate human expertise & decision making (expert system) such systems will also exhibit logic, reasoning, intuition & common sense that is associated with human being.

The figure above illustrates the major branches of artificial intelligence family.

The birth of AI is based on to different approaches
1) Top down
2) Bottom up

Bottom up approach is an effort to build a physical analogy to the human brain while top down approach is the effort to develop analogy to how brain works.


Decision Support System – Prof. Chandan Bhattacharya – 11th July, 2006.

DIAGRAM

EVOLUTION OF ARTIFICIAL INTELLIGENCE

TOP DOWN APPROACH

One of the first top down efforts was Logic Theory in 1950 applying which a software was developed named Logic Theories that mimicked deductive logic:

Selecting the correct rules and postulates so as to create a coherent logical chain. From these developments emerged expert system, which consist of a limited number of rules for a very specific and limited domain of human expertise.

BOTTOM UP APPROACH

The beginning of contemporary AI started with the concept of feedback to develop a theory of how brain works. According to this theory a brain is composed of millions of neuron cells which processed binary numbers those were connected into a network that took feedback or information from the environment. Learning was simply a matter of teaching the neuron in a brain how to response to the environment.

SYMBOLIC ARTIFICIAL INTELLIGENCE

Symbolic AI (classical AI) is the branch of AI that concerns itself with attempting to explicitly represent human knowledge in a declarative form (i.e. with facts and rules). If such an approach is to be successful in producing human life intelligent, then it is necessary to translate procedure knowledge possessed by human into an explicit form. Game playing programs are the best human experts. One difficult problem encountered by symbolic AI, is Common Sense Knowledge Problem.

STRONG AI AND WEAK AI

Strong AI is the view that a sufficiently programmed computer would actually be intelligent and would think in the same way that a human does where as weak AI is the use of methods modeled on intelligent behaviour to make computers more efficient at solving problems.

HUMAN v/s ARTIFICIAL INTELLIGENCE

1) Human intelligence is a way of reasoning
2) One part of human intelligence can be described as the applications of rules, based on human experience and genetics.
3) Human intelligence is the way of behaving. Even if human do not actually invoke rules, they are obligated to act as if they did by a culture and a society that values reasonable and intelligent behaviour.
4) Human intelligence includes the development and use of metaphors and analogies. Using metaphors and analogy human creates new rules, apply old rules to an unknown/new situation and at times instinctively apply without rules.
5) Human intelligence includes the creation and use of concept. Humans have a unique ability to improve a conceptual apparatus on world around them.
6) ***************************************************************************** Artificial intelligence refers to an effort to develop machines that can reason, behave compare and conceptualise like human being.

Questions
How brain works?
(Find out from google.com + howstuffworks.com + wikipedia.org + techtutorials.com )

EXPERT SYSTEMS

An expert system is knowledge intensive program that solves a program by capturing the expertise of a human in limited domains of knowledge and experience . An expert system can assist decision making by asking relevant questions and explaining the reasons for adopting certain actions. Some of the common characteristics of expert systems are the following:
1) Perform some of the problem solving work of humans.
2) Represent knowledge informs such as rules or frames.
3) Interact with human.
4) Can consider multiple hypotheses simultaneously.


DIAGRAM

How expert systems work?

COMPONENT OF EXPERT SYSTEM

4 BASIC ELEMTS OF EXPERT SYSTEM
1) Knowledge base
2) Development Team
3) At Cell
4) The User

KNOWLEDGE BASE

This is the modeling of human knowledge that a computer can deal. This model of human knowledge used by expert system is called knowledge base. A standard structured programming construct is the If Then construct in which a condition is evaluated. The difference between a traditional program and a rule base expert system program is the degree and magnitude.

Symantec Nets can be used to represent Knowledge when the knowledge base is composed of easily identified objects of interrelated characteristic. Symmetric net can be much more efficient than rules.

Frames also recognised knowledge into chunks but the relationship is based on shared characteristic rather than hierarchy. This approach is grounded as human use frames a concept to make rapid sense out of perception.


DEVELOPMENT TEAM

An AI development team is composed of one or several experts who have command over knowledge base and one or more knowledge engineers who can translate the knowledge as described by the expert with a set of rules, frames of Symantec Nets.

A knowledge engineer is similar to a traditional system analyst but has special expertise in collecting information and expertise from other professionals.

AI SHELL

The AI shell is the programming environment of an expert system. They are user friendly development environment that can quickly generate user interface screen to capture the knowledge base and manage the strategies of searching the rule base.

THE USER

The role of the user is both to pose questions of the system and to enter relevant date to guide a system alone. The user may employ the expert system as a source of advice or to perform tedious and routine analysis tasks.

PROBLEMS WITH EXPERT SYSTEMS

1) Expert systems are not applicable for complex managerial problems.
2) Many experts cannot express their knowledge using an IF THEN format.
3) Expertise is collective
4) It may be distributed throughout an organisation
5) Expert systems are extensive to maintain.
6) A more limited role of an expert system.

7th July, 2006 – Organisation Effectiveness & Change – Prof Satyajyoti De

STRATEGIES FOR MERGING DIFFERENT ORGANISATIONAL CULTURE

TABLE

DIAGRAM

CONFLICT: Conflict in any situation in which two or more parties feel themselves in opposition. It is an interpersonal process that arises from disagreements over the goal to attain or methods to be used to accomplish.
-KEITH DAVIS

CONFLICT: It is a process that begins with one party perceives that another party has negatively affected or is about to negatively affect something that the first party cares about.
-STEPHEN. P. ROBBINS

TRANSITION IN CONFLICT THOUGHTS

A) TRADITIONAL VIEW- Believes that all conflicts are harmful and must be avoided.

B) HUMAN RELATIONS- Believes that conflicts are a natural and inevitable outcome in any group.

C) INTERACTIONIST VIEW- Believes that conflict is not a positive in a group but that it is absolutely necessary for a group to perform effectively.

D) FUNCTIONAL v/s DYSFUNCTIONAL VIEW- The criterion that differentiates a functional view from a dysfunctional conflict is a group performance. Functional conflict supports the goal of the group and improves its performance. Dysfunctional conflict hinders group performances.

STAGES OF CONFLICT PROCESS

Stage 1 – May arise because of personal variables.
Stage 2 – Cognition or personalization.
Stage 3 – Decision to act in a given way.
Stage 4 – Behavioral pattern
Stage 5 – Outcome

CONFLICT RESOLUTION PROCESS, CAUSE OF CONFLICT

a) Organisational change
b) Personality clash
c) Different sets of values
d) Threat to status
e) Contrasting perception
f) Lack of trust

CONFLICT PERCEPTION
(perception of other parties, how they will behave)

Functional or Dysfunctional

PARTICIPANT INTENTION
(2 parties who are in conflict)
Winning v/s Loosing – Every party wants to win. Boss is the sufferer as he is deprived of certain situation.)

RESOLUTION STRATEGIES

Avoiding
Smoothing
Forcing
Compromising
Confronting (There is no organisation that do not have a conflict. Even RK Mission has conflicts.)

12th July, 2006 – Organisation Effectiveness & Change – Prof Satyajyoti De

Stakeholder = shareholder + customer + others

Stakeholders help in effective running of the organisation. All the stakeholders have different interest, claim from the company.

STAKEHOLDERS
(In what way they are connected to the organisation)

SHAREHOLDERS – Profit + ROI
CUSTOMER – Price + Quality + Service + Market Share
SUPPLIERS – Terms of Contract + Payment
CREDITORS – Credit worthiness
EMPLOYEES – Job satisfaction + Remuneration + Work Environment + Quality of Work Life
GOVERNMENT – Law Abiding + Tax + Welfare to employees the family members
SOCIETY – Social Responsibility + Awareness towards Ecology & Pollution.



O C T A PA C E

These are 8 different activities on which the organisation works & success of the organisation depends on the OCTAPACE.

O = OPENNESS & RISK TAKING

Openness – Works with open mind. Eg: Connection to Internet & information accessibility for all.

C = CONFRONTATION

Confrontation – Whenever there is a problem – don’t shy way from it – simply try to resolve it – this is not a clash between two persons.

Trust – When somebody tells you something in the organisation you need not necessarily suspect that man. Trust that man. So we repose trust on the other person’s behaviour.

A = AUTHENTICITY

Authenticity – While communicating, don’t think whether it has some authenticity or not. When you give information to other, say something factual.

P = PROACTIVE

Proactive – React to a situation in response to a stimulus is being Reactive. Doing something out of initiative is being Proactive.

A = AUTONOMY

Autonomy - You ask the people in your organisation to perform som
e task without too much supervision, freedom, independence, giving them job satisfaction.

C = COLLABORATION

Collaboration – Every one of us are dependent on someone or other.
People have 2 notions
1) Omnipotence – I can do everything. Independence. It is a wrong concept. No one can be fully independent.
2) Impotence – I cannot do anything.

Between the above 2 there is a concept called MUTUAL INTERDEPENDENCE. Say small children are dependent on parents. Similarly mother is also dependent on the child for love & affection.

So collaboration comes from concept of mutual interdependence. Say you are a HR Manager. But you cannot function in organisation unless you now technical functioning, and so on. So in certain cases technical manager can collaborate with financial manager & vice versa. Thus collaboration occurs.

E = EXPERIMENTATION

Experimentation – Autonomy & Proactive & Experimentation are connected, make experiment about new aspects of functioning of organisation & carry the organisation forward. We may not be aware but everyday something is happening in the organisation that carries the organisation forward.

These are the 8 pillars of Organisation Culture.

Questions
1) Write short notes on OCTAPACE.
2) How organisation effectiveness & culture can develop through this OCTAPACE?

DIAGRAM

DIMENSION OF CONFLICT HANDLING INTENTIONS

2 persons in conflict – perception of situation. I may not know what has been perceived by the Manager. So this is perception of conflict. I have been perceived as an assertive person but I see opponent is more collaborative.


14th July, 2006 – Organisation Effectiveness & Change – Prof Satyajyoti De

ORGANISATION CHANGE

Any organisation + it is dynamic + objective of the organisation goal does not change + process of achieving the objective may change

2 REASONS FOR ORGANISATION CHANGE

1) REACTIVE CHANGE – because ofMacro Environmental condition (changes taking place outside borders of organisation) like merger, takeover, but organisation prepares to cope the change.

2) PROACTIVE CHANGE - Management Initiative – Changes the company wants to make within an organisation eg: procedure for Financial Management, Material Management, Human Resource Management.

So changes are made either in reactive or proactive way.
Some changes
1) psychological
2) system
3) one way

1) PSYCHOLOGICAL – Changes a) Macro level b) Micro level
In Micro level changes people are affected because of
a) Insecurity
b) Opportunity

2) SYSTEMS – Indications in the Management
Eg: Study by scientists that evening birds flock together & fly together in a certain direction. They change their direction and move in another direction. They have noticed that the sense of direction of individual bird is much faster than a group of birds.

3) ONE WAY – As a whole, like quantum theory of physics, concept of change of the whole thing. When change takes place, all people try to accommodate each other in this situation.
Eg: Say a balloon is filled with air means it has air particles inside it. They are all arrested inside the boundary of the balloon, like the boundary of the organisation. If a pressure is applied on the surface of the balloon, the particles inside the balloon want to adjust themselves quickly. Thus all try to accommodate each other. This is the process of pressure change. It is the case of an organisation.

Macro Environmental Condition – Employees start adjusting them to the change in condition. Management tries to cope up with the pressure. It is called Macro Level Pressure.

FACTORS OF CHANGE
Organisation is dynamic. In an organisation whether we are putting a pressure from inside – organisation is established – goes through a life cycle- certain changes are seen – those changes are used in a 1) positive manner – they can be handled carefully.

Changes are of organisation: -
1) Technological
2) Structural
3) Workforce

Change Management - approach to change
Gradual improvement of the organisation.

Change Agent – When change takes place in an organisation, a person or group of persons who implement in a manner that makes the change effective.
Change agent does not mean outsourcing.
It may be
1) External
2) Internal



21st July, 2006 – Organisation Effectiveness & Change – Prof Satyajyoti De

OPERATIONAL DEVELOPMENT

1) Richmond Beckhand
managed from top
1) Top Management – To increase organisational effectiveness through intervention.

2) Wendell French & Cecil Bell – tells everything about OD

Culture Intervention through change Agent (like catalyst)
Action Research – is a process by which when any change is made in an organisation certain exercises are followed like a research type of work.

3) Increasing organisation performance change attitude values, organisation structure, practices to improve organisation performance.

4) OD intervention is a scientific process through which we try to identify & diagnose the problem, find a solution to that. It is done by: -
1) Internal Change Agent –
2) External Change Agent –

DIAGRAM 1

DIAGRAM 2

FOUNDATION OF ORGANISATION DEVELOPMENT PROCESS

ORGANISATIONAL DEVELOPMENT INTERVENTION PROCESS
Eg: Family business with father & 2 sons. Disputes between the 2 brothers due to family influence.

VALUES & ASSUMPTIONS
Mechanical – Operational Pattern
Organic – Behavioral Pattern


28th July, 2006 – Organisation Effectiveness & Change – Prof Satyajyoti De

ORGANISATON LEARNING (learn from OB book) (VVVV Imp)
Definition
Interaction with people – continuous learning organisation has some goals & objectives – we come into close contact with them & judge whether they are correct or not –

We learn certain values & systems – double loop (Imp) + we change the values & system

Values, ethics – governing variable (these govern us)
1) I may follow the governing variables whatever may be the consequence. (single loop learning)
2) I may challenge them. (double loop learning)

Because of learning we can react to a situation in a different manner.
We act according to the Brain Map (Brain Theory) but we normally act according to our decisions.

Espoused Theory - If I do something other than my normal doings then it is a deliberate acting to make others know that I am acting in a different manner.
Eg: of Creativity (eg given in Luthans book)

Qs) How many different things you can do with a newspaper?

Thinking
Cognitive – what is within me.
Divergent – thinking on different plains.

Question
Individual Behaviour in organisational learning context.

Transference of knowledge which generates by creative thinking started from F.W.Taylor.

DIVERSIFICATION WORKFORCE

Change in workforce – by age, culture
Influx of people from different countries due to increase in investments.
Techno specific diversity – 7 specific diversity eg: agro product – fruits, (particular fruits in particular place)
Organisation is from some definite culture & they make certain diversity in workforce.
Gender Discrimination
Colour Discrimination
Age Discrimination

These may increase in future. So how can there be cultural convergence.

CASE OF DISCRIMINATION BY A MANAGER
Discrimination against the women – It is called the glass ceiling effect. It was coined in USA.

1) She cannot be placed to a place where there are only 5/6 employees.
2) She cannot be placed in a factory
3) She cannot be made to work at night
4) She cannot be raised after certain position.

DIAGRAM