Knowledge management

KNOWLEDGE MANAGEMENT
ASSIGNMENT ON QUESTIONS
SUBMITTED TO-
SHEETAL MAM
SUBMITTED BY-
SAKSHA SHARMA
M.COM II
5810
2016-17

Q1- Discuss the models of knowledge management?
Or
Q2- Discuss Von Krog's Roos and Frid's models?
or
Q3- Explain the SECI value of knowledge creation?
VON KROGH AND ROOS MODEL (1995) - ORGANISATIONAL EPISTEMOLOGY MODEL
This model distinguishes between individual knowledge and social knowledge. Von Krogh and Roos adopt
the connectionist approach and state that knowledge reside both in the individuals of an organization and at
the social level, in the relations between the individuals. Knowledge is said to be embodied, i.e. everything
that is known is known by somebody. Unlike cognitivism which views knowledge as an abstract entity,
connectionism maintains that there is no knowledge without a knower. It also reinforces the strong need to
maintain the link between knowledge objects and those who are knowledgeable about them. They explained
the fragile nature of KM in organization in terms of 5 aspects, which could impede the organizational
knowledge, namely-
 mind set of individual
 communication in organization
 organization structure
 relationship between the members
 management of human resource
According to this model, 4 things need to be analyzed-
1. Why and how the knowledge gets to the employees of a company
2. Why and how the knowledge reaches the organization
3. What does it mean knowledge for the employee/organization
4. What are the barriers for organizational knowledge management
This model has two approaches to understand KM
The cognitive perspective states that a cognitive system, no matter if it’s human or artificial, creates
representations (models) of the reality, and the process of learning appears when this representations are
somehow manipulated (used in different inferences).
A cognitive epistemology sees organizational knowledge as a system with self-organization characteristics,
where people are transparent to the information coming from the exterior. In this perspective, the brain can
be perceived as a machine based on logic and detections, which doesn’t permit opposite declarations. So, the
organization gathers information from its environment, which it process logical. By searches and different
cognitive competencies, possible way of actions will be generated – everything is based on the mobilization
of individual cognitive resources. It is known that the brain is not processing sequential symbols, but rather
it perceives the whole perspective, global properties, models and synergies. Learning rules are those which
can govern how the different components are inter-related. The information it is not just taken from the
exterior environment, it can be generated also internally. The familiarity and practice are leading to learning.
The employees form nodes of an organizations system, with relative weak links – knowledge represents an
emergent phenomenon which comes from the social interaction of these persons.
CONCLUSION
Connectionism approach was further refined to identify a model for 'knowledge activism', before starting
knowledge management program. Activation refers to the general set of organizational activities which
positively affects knowledge creation. These activities can improve human relationship, conversations and

sharing knowledge. This approach seems to be a good base for a theoretical knowledge management model,
mainly because of the fact that the link between knowledge and the ones who possess it, seems to be
permanent.
NONAKA- TAKEUCHI MODEL
This model was obtained after the research regarding the success of some Japanese corporations, about
obtaining creativity and innovation. Ikujiro Nonaka and his co-workers created a consistent body of theory
concerning knowledge creation in organizations based on four main ideas:
a) Knowledge creation at individual level is a direct result of the continuous dialogue between tacit and
explicit knowledge
b) There are four basic knowledge conversion processes: socialization, externalization, combination and
internalization
c) Knowledge creation at the organizational level is based on these four conversion processes and a spiral
driving force
d) There is a shared space Ba for knowledge creation.
The authors point out that there are two types of knowledge: explicit knowledge, contained in manuals and
procedures, and tacit knowledge, learned only by experience, and communicated only indirectly, through
metaphor and analogy. U.S. managers focus on explicit knowledge. The Japanese, on the other hand, focus
on tacit knowledge. And this, the authors argue, is the key to their success - the Japanese have learned how
to transform tacit into explicit knowledge.
They argue that Western culture considers knower and known as separate entities. In contrast, the structural
characteristics of the Japanese language and influences such as Zen Buddhism led the Japanese to consider
that there is an oneness of humanity and nature, body and mind, and self and the other.
Nonaka (1994) considers two dimensions for knowledge creation: epistemological dimension and
ontological dimension. The first dimension is related to the conversion of knowledge from tacit level to
explicit level, and from explicit level to the tacit level. The second dimension is related to the conversion of
knowledge from individuals to groups and further to organization. Combining these two motions Nonaka
gets a spiral model for knowledge creation and processing. Also, he makes a fundamental assumption which
is the core of the SECI model:”The assumption that knowledge is created through conversion between tacit
and explicit knowledge allows us to postulate four different modes‟ of knowledge conversion:
(1) From tacit knowledge to tacit knowledge, {Socialization (tacit to tacit) i.e. Indirect way}
(2) From explicit knowledge to explicit knowledge, {Externalization (tacit to explicit) i.e. Indirect to Direct
way,}
(3) From tacit knowledge to explicit knowledge, and {Combination (explicit to explicit) i.e. Direct way}
(4) From explicit knowledge to tacit knowledge” {Internalization i.e. Direct to indirect way.}
The SECI model describes how explicit and tacit knowledge is generated, transferred, and recreated in
organizations. Making personal knowledge available to others in the company is at the core of this KM
model. This type of knowledge creation process takes place continuously and it occurs at all levels of the
organization. In many cases, the creation of knowledge occurs in an unexpected or unplanned way.

The foundation of these four basic processes is Ba- defined “as a context in which knowledge is shared,
created, and utilized, in recognition of the fact that knowledge needs a context in order to exist”. This
context can be tangible, intangible or any combination of tangible and intangible elements.
SOCIAIZATION- Socialization is the process of sharing tacit knowledge through observation, imitation,
practice, and participation in formal and informal communities. The socialization process is usually pre-
empted by the creation of a physical or virtual space where a given community can interact on a social level.
Tacit knowledge goes beyond the boundary and new knowledge is created by using the process of
interactions, observing, discussing, analyzing, spending time together or living in same environment. This
means to share knowledge by direct social interactions. It implies to obtain a mutual understanding. It is the
simplest form of knowledge exchange, b'coz it's an instinctual state. As the knowledge remains tacit, it will
be rarely written or stored. Prusak emphasized " Complex, tacit knowledge, developed and internalized by
the one who posses it over a long period of time. It is almost impossible to be reproduced in a document or
in a database." Such kind of knowledge incorporates so many details that the rules which define it are
impossible to be separated. This leads to the idea that the process of obtaining new tacit knowledge is not
strictly tied to language but rather to experience and the ability to transmit it.
EXTERNALIZATION- Externalization is the process of articulating tacit knowledge into explicit
concepts. Since tacit knowledge is highly internalized, this process is the key to knowledge sharing and
creation. It helps in creating new knowledge as tacit knowledge comes out of its boundary and became
collective group knowledge. This process we can say that knowledge is crystallized. The process of
externalization is often driven by metaphor analogy and models. The employees are capable to define the
knowledge structures and to use certain methods to apply it in specific circumstances. Once externalized,
knowledge is permanent and tangible. It can be easily shared and spread in the organization.
COMBINATION- Combination is a process where knowledge transforms from explicit knowledge to
explicit knowledge. E.g. The finance department collects all financial reports from each departments and
publics a consolidated annual financial performance report. Creative use of database to get business report,
sorting, adding, categorizing are some examples of combination process.

It means to combine knowledge pieces in order to obtain new knowledge. There is no knowledge which is
created by itself- it represents a combination or a representation of an existing knowledge. It takes place
when concepts are sorted and systematized in a knowledge system.
INTERNALIZATION- By internalization explicit knowledge is created using tacit knowledge and is
shared across the organization. When this tacit knowledge is read or practiced by individuals then it
broadens the learning spiral of knowledge creation. Organization tries to innovate or learn when this new
knowledge is shared in Socialization process. Once internalized, new knowledge is then used by employees
who broaden (expand, extend (develop, and reframe it) within their own existing Tacit Knowledge bases. It
is a process of learning by doing. This implies to convert or integrate our knowledge in mental models
which will extend it, eventually reformulate in the context of a different knowledge base. Knowledge,
experiences, best practices, learned lessons are passing through the conversion process, but it is impossible
to stop at an intermediary phase. Only when knowledge is internalized in the knowledge base, it can be
shared and be useful to the company.
ENABLING CONDITIONS FOR ORGANISATIONAL KNOWLEDGE CREATION-
INTENTION- an organizations aspiration to its goals
AUTONOMY- conditions whereby individuals act autonomously, according to minimum critical
specification principle and are involved in cross functional self organized teams.
FLUCTUATION AND CREATIVE CHAOS- condition that stimulates the interaction between the
organization and the external environment and creates fluctuations and breakdown by means of creative
chaos or strategic equi-vocality.
REDUNDANCY- existence of information that goes beyond the immediate operational requirements of org.
members, competing multiple teams on the same issue and strategic rotation of personnel.
REQUISITE VARIETY- internal diversity to match the variety and complexity of the environment and to
provide everyone in the organization with the fastest access to the broadest variety of necessary information,
flat and flexible org. structure inter linked with effective information networks.
FRID'S KNOWLEDGE MANAGEMENT MODEL
As per frid's framework, the KM maturity assessment levels and knowledge implementation can be
divided into 5 levels.
Level 1- KNOWLEDGE CHAOTIC
It suggests that organizations at this level are in the process of understanding and implementation of frid
framework for knowledge management which encompasses KM vision, KM objectives and KM indices.
Organization should focus on advocating and adapting departmental KM vision and goals as well as
performing Frid's framework Km maturity assessment.
Level 2- KNOWLEDGE AWARE
To implement this framework for KM, advocating and adopting departmental Km vision and goal, and
performing Frid framework maturity assessment, organization at this point should focus on developing a
KM road map and working collaboratively with the KM office.
Level 3- KNOWLEDGE FOCUSED

It indicated that the organizations should have completed their implementation aspects as in the lower 2
levels and start focusing on 5 new activities. They should imbed Km into process engineering, provide initial
Km infrastructure, service and training, support early adopters and knowledge community, monitor and
report on management indices and finally include KM in budgets.
Level 4- KNOWLEDGEMANAGED
By adopting fundamental activities in level 1, 2 and 3 organization should attempt to embed KM in
performance reviews and also in business planes apart.
Level 5- KNOWLEDGE CENTRIC
The distinctive and differentiating activities that organizations should focus on are institutionalizing
successful initiatives and valuing intellectual assets. These activities differentiate knowledge from other
levels.
KOGUT AND ZANDER'S KNOWLEDGE MANAGEMENT MODEL
DIMENSIONS OF THIS KNOWLEDGE

This Model emphasizes on the strategic importance of knowledge as a source of competitive advantage.
Kogut and Zander focused on the idea that "what firms do better than markets is the creation and transfer
of knowledge within the organization. Firms as social communities act as "repository of capabilities"
determined by the social knowledge determined by the social knowledge embedded in enduring individual
relationships structured by organizing principles.
This view is further articulated and empirically tested in Kogut and Zander (1993). They assert that:
1. firms are efficient by which knowledge is created and transferred,
2. a common understanding is developed by individuals and groups in a firm through repeated
interaction to transfer knowledge from ideas into production and markets,
3. what a firm does is not depending on the market’s failure rather the efficiency in the process of
transformation relative to other firms, and
4. the firm’s boundary is determined by the difference in knowledge and the embedded
capabilities between the creator and the users (possessed with complementary skills) and not
market failure.
Kogut and Zander (1996) further extend their discussion on the concept of identity by asserting that
individuals are “unsocial sociality” where they have both a desire to become a member of community
and at the same time also have a desire to retain their own individuality.
SKANDIA INTELLECTUAL CAPITAL MODEL OF KNOWLEDGE MANAGEMENT
It was developed by Swedish firm called Skandia as an approach for measuring intellectual capital. The
model focuses on the importance of equity, human, customer and innovation in managing the flow of
knowledge within and externally across the network of partners. It provides the scientific approach to
knowledge and assumes that intellectual capital can be transformed into assets of organization. It gives
a strong emphasis to measurement associated with each of the decomposed elements (human, customer
and structure) of knowledge management assuming that it can be tightly controlled. This mechanistic
approach to measurement is more consistent with Nonaka's process of externalization and combination.

CHOO SENSE MAKING MODEL
The Choo Sense-Making KM Model (1998) focuses on
 Sense Making
 Knowledge Creation
 Decision making skills
These three highly interconnected processes play a major role in the enfoldment of the organization’s
knowledge vision; it’s potential to knowledge creation and its commitment into taking knowledge creation
to the utmost consequences.
Sense Making − Its long-term aim is the warranty that organizations will adapt and continue to prosper in a
dynamic and complex environment through activities of prospecting and interpretation of suitable
information enabling it to understand changes, trends and scenarios about clients, suppliers, competitors
and other external environment actors. Organizations face issues such as the reduction of uncertainty and
the management of ambiguity.
Knowledge Creation − It is a process that allows a company to create or acquire, organize and process
information in order to generate new knowledge through organizational learning. The new knowledge
obtained, allows company to develop new abilities and capabilities, create new products and new services,
improve the existing ones and redesign its organizational processes.
Decision Making − The Company must choose the best option among those that are plausible and
presented and pursue it based on the organization’s strategy. Decision making process in companies is
constrained by the bounded rationality principle.
This model focuses on how informational elements are selected and fed into organizational actions.

It is based on elements used to create new senses for an optimized decision inference. This action results
from concentration and absorption of information coming from the external environment in every
cycle. this model is important for introducing the concept of sense making, focusing how individuals
make sense or develop understanding, of the knowledge that is available in the organization. This
sense making plays an important role in how the decisions are made within organization. The
absence of sense making will lead to poor decision, so efforts to boost sense- making, through
developing individual's knowledge, skills and talents will be critical to enhanced organizational
performance.
Weick proposed a theory where he tries to describe how chaos can be transformed in clear processes by
sharing individual interpretations in the following situations.
CHANGING THE ENVIRONMENT- It is external for the organization, having the possibility to disturb
information flow between participants.
ADAPTION, SELECTION AND KEEPING- When people tries to interpret what have been observed. This
is the process which refers the creation of organizational memory which will contain those
experiences finalized with success. This memory can be reused in the future for new interpretations
in order to unify them into a coherent organizational vision.
WIIG MODEL
Karl Wiig KM model (1993) highlights the following principle: in order for knowledge to be useful and
valuable, it must be organized. Knowledge should be organized differently depending on what the
knowledge will be used for. Some useful dimensions in the Wiigs KM model are:
 completeness,
 connectedness,
 congruency, and
 perspective and purpose.
Completeness refers to how much relevant knowledge is available from a given source. Sources can vary
from human minds to knowledge bases (i.e., tactic or explicit knowledge).We first need to ascertain that the
knowledge is out there, the knowledge may be complete if all the information available on the subject is
there but if no one knows of its existence, they cannot make use of this knowledge.

Connectedness refers to the well-understood and well-defined relations between the different knowledge
objects. Most knowledge objects are connected to each other, the more connected a knowledge base is then
the more coherent the content and the greater its value.
A knowledge base is said to be congruent when all the facts, concepts, perspectives, values, judgments, and
relational links between the objects are consistent. Most knowledge content will not meet such ideals.
Perspective and purpose is a phenomenon through which we know something but from a particular point
of view for a specific purpose. We organize much of our knowledge using the dual dimensions of
perspective and purpose.
Wiig (1993) also proposes a hierarchy of knowledge that consists of public, shared, and personal knowledge
forms. Each form might include four types of knowledge: factual, conceptual, expectation and
methodological.

Base of this model is 'if we want to have useful and valuable knowledge, these must be organized'. It should
be organized according to the way knowledge will be used. This model addresses the problem of
how relevant can be the knowledge, coming from a specific source. it is very important to now about
knowledge that is discoverable. It also refers to the relation between different objects of knowledge.
We will find elements that are completely disconnected, in other words, independent as a general
rule we can say that content value is higher when we have a greater no. of connections in a
knowledge base.
Wiig has also mentioned about four success factors including:
 Knowledge and Resources
 Opportunities
 Permission (environment)
 Motivation
In a working paper Working Paper KRI #1999-4 Wiig presented a model for Comprehensive
Knowledge Management to support enterprise viability and success. It pursues explicit, systematic, and
enterprise priority-driven approaches to:
 Identify which Intellectual Capital (knowledge) needs to be created and maintained
 Provide and transform the required knowledge and ascertain that it is continually renewed
 Ascertain that all available knowledge assets (Intellectual Capital) are diligently leveraged
wherever appropriate.
 Govern knowledge management-related processes and relationships by providing enterprise-
wide support, infrastructure, and leadership.
Wiig argued that Incremental Knowledge Management (IKM) tends to, almost arbitrarily,
identify and pursue a knowledge-related action, often as an extension of an already occurring activity.
Whereas, CKM pursue broad and systematic knowledge management for an overall success through:
 Fostering Knowledge-Supportive Culture
 Providing Shared Understanding
 Focusing the Knowledge Management Practice to Align with Enterprise Direction
 Practicing Accelerated Learning
 Creating Supportive Infrastructure Capabilities
 Provide Effective Governance for the Knowledge Management Practice
BOISOT I-SPACE KM MODEL
The Boisot KM model is based on the key concept of"information
good“that differs from a physical asset. Boisot distinguishes information from data by emphasizing
that information is what an observer will extract from data as a function
of his or her expectations or prior knowledge. Boisot (1998) proposes the following two key points:
 The more easily data can be structured and converted into information, the more diffusible it
becomes.
 The less data that has been so structured requires a shared context for its diffusion, the more
diffusible it becomes.
Boisot's model can be visualized as three dimensional cube with the following dimensions:
i. from uncodified to codified,
ii. from concrete to abstract,

iii. from undiffused to diffused.
He proposes a Social Learning Cycle (SLC) that uses the I-Space to model the dynamic flow of knowledge
through a series of six phases:
 Scanning: insights are gained from generally available (diffused) data
 Problem-Solving: problems are solved giving structure and coherence to these insights (knowledge
becomes 'codified')
 Abstraction: the newly codified insights are generalized to a wide range of situations (knowledge
becomes more 'abstract')
 Diffusion: the new insights are shared with a target population in a codified and abstract form
(knowledge becomes 'diffused')
 Absorption: the newly codified insights are applied to a variety of situations producing new learning
experiences (knowledge is absorbed and produces learnt behavior and so becomes 'uncodified', or
'tacit')
 Impacting: abstract knowledge becomes embedded in concrete practices, for example in artifacts,
rules or behavior patterns (knowledge becomes 'concrete').
The proposed Social Learning Cycle (SLC) serves to link content, information, and knowledge
management in a very effective way - the codification dimension is linked to categorization and
classification; the abstraction dimension is linked to knowledge creation, and the diffusion dimension
is linked to information access and transfer.
The Information Space or I-Space was developed by Max Boisot as a conceptual framework relating the
degree of structure of knowledge (i.e. its level of codification and abstraction) to its diffusibility as that
knowledge develops.
This results in four different types of knowledge.[1]
 Public knowledge, such as textbooks and newspapers, which is codified and diffused.
 Proprietary knowledge, such as patents and official secrets, which is codified but not diffused. Here
barriers to diffusion have to be set up.
 Personal knowledge, such as biographical knowledge, which is neither codified nor diffused.
 Common sense – i.e. what ‘everybody knows’, which is not codified but widely diffused.
This is based on the concept of "information asset" which is different from a 'physical asset'. The efficiency
of informational asset transfer is largely dependent on senders and receivers, which have to share
same codification scheme or language. Knowledge will also have a context in which it can be

described. Thus, it results that both the sender and receiver will have to share the context besides
codification scheme.
Boisot proposed 3 key points which form together a conceptual framework known as "I- space". It can be
visualized like a cube having 3 dimensions-
CODED-UNCODED- link to categorization and classification
ABSTRACT-CONCRETE- link to knowledge creation through analysis and understanding
DIFFUSED-UNDIFFUSED- link to information access and transfer
His model implies that in many situations the context loss because of the codification can lead to loss of
important knowledge. This content needs to be a shared context in order to be interpreted and it
requires face to face interactions. The activities of codification, abstractization, diffusion, absorption,
impact and analysis contribute to the learning process.
Knowledge assets at the opposite extreme of the I-Space (least abstract, least codified and most diffused)
have the highest level of entropy; therefore, have the least potential for performing value-adding work.
Organization pursuing competitive advantage needs to move their knowledge assets into the region of
minimum entropy. An important aspect of SLC is the recognition of the elusive and dynamic nature of
knowledge. It means that data is filtered to produce meaningful information and this information is then
abstracted and codified to produce useful knowledge. Consequently, knowledge is applied in diverse
situations, hence produces new experiences that produces data for a new cycle of knowledge creation.
Another important conjecture of Boisot's model is that it considers organizations as living organisms. Their
process of growing and developing knowledge assets within organizations is always changing. This means
that organizations need to adopt a dynamic KM strategy which accommodates the dynamicity of the
organizational learning cycle.
COMPLEX ADAPTIVE SYSTEM MODELS OF KM
The ICAS (Intelligent Complex Adaptive Systems) theory sees organization as an adaptive, complex system.
Beer (1981) was a pioneer in the treatment of the organization as a living entity. The theory sees
organization as an adaptive, complex system. The model contains the series of functions which ensure the
viability of any living system in general and of organization, in particular.
Complex adaptive systems consist of many independent agents that interact with one another locally.
Together, their combined behavior gives rise to complex adaptive phenomena. These models contain series
of functions which ensures the viability of any living system in general and of organizations, in particular.
ICAS systems are based on cybernetics principles, which are using communications and control mechanisms
in order to understand, describe and predict what should do a viable organization. Adaptive systems contain
lots of independent agents which are interacting. Their behavior makes possible the appearance of some
complex phenomenons of adaptation. There is no general authority to manage the way in which these agents
should work. A general model of a complex behavior will be the result of all the interactions.
(BENNET MODEL)
Bennet describes an approach of knowledge management, using ICAS systems as a starting point. It is
considered that traditional bureaucracies are not enough to provide the necessary cohesion for the survival of
the organization. It was proposed a new model (Bennet) in which the organization is perceived as a system
found in a symbiotic relation with its environment. Bennet model is based on a number of subsystems which
interacts and evolves in order to generate an advanced and intelligent technological company.

Inside the adaptive model, the intelligent components are made of people which are self organized, but who
can remain as a part of general hierarchies of the organizations. The challenge is to use the advantage given
by the force of the people when they cooperates, keeping a global sense of unity. The organizations solve
problems by creating options, using resources both internal and external which can add value over the initial
input. So, the knowledge became the most valuable resource because it is the only one who can help in the
context where uncertainty exists. This is one of the criteria by which we can distinguish between
information management (predictable reactions to known situations and anticipated situations)
from knowledge management (using new reactions for un-anticipated situations).
We can resume key processes in Bennet model as being the following:
 Understanding
 Creation of new ideas
 Problem solving
 Decision taking
 Following actions to obtain the desired results
Because people can take final decisions and fulfill actions, this model emphasizes the importance of the
person: competencies and learning capacity. According to this model, in order to survive, an organization
needs eight characteristics:
 organizational intelligence,
 shared purposes,
 selectivity,
 optimum complexity,
 open borders,
 knowledge centering,
 optimum streams and
 multidimensionality
There are four ways for ICAS models to describe organizational knowledge management:
 creativity (generation of new ideas, perspectives, understandings, building products, services
definition),
 problem solving,
 taking decisions processes,
 different implementations
THE EUROPEAN FOUNDATION FOR QUALITY MANAGEMENT (EFQM) KM MODEL
Excellence Model was introduced at the beginning of 1992 as the framework for assessing applications for
The European Quality Award. It is a widely used organizational framework in Europe and has become the
basis for a series of national and regional Quality Awards. The EFQM model's is used as a management
system that encourages the discipline of organizational self-assessment.
The EFQM Excellence Model is a practical tool to help organizations to do this by measuring where they are
on the path to Excellence; helping them understand the gaps; and stimulating solutions. It is applicable to
organization irrespective of size and structure, and sector. Self-assessment has wide applicability to
organizations large and small, in the public as well as the private sectors. The outputs from self-assessment
can be used as part of the business planning process and the model itself can be used as a basis for
operational and project review.
The EFQM Model is a non-prescriptive framework that recognizes there are many approaches to achieving
sustainable excellence. Within this approach there are some fundamental concepts which underpin the

EFQM model. However, these concepts are not fixed. It is accepted that they will change overtime as
excellent organizations develop and improve. Current indicative concepts are listed below:
 Results Orientation - Excellence is achieving results that impress all the organization's stakeholders.
 Customer Focus - Excellence is creating sustainable customer value.
 Leadership & Constancy of Purpose - Excellence is visionary and inspirational leadership, coupled
with purpose.
 Management by Processes & Facts - Excellence is managing the organization through a set of
interdependent and interrelated systems, processes and facts.
 People Development & Involvement - Excellence is maximizing the contribution of employees
through their development and involvement.
 Continuous Learning, Innovation & Improvement - Excellence is challenging the status quo and
effecting change by using learning to create innovation and improvement opportunities.
 Partnership Development - Excellence is developing and maintaining value-adding partnerships.
 Corporate Social Responsibility - Excellence is exceeding the minimum regulatory framework in
which the organization operates and to strive to understand and respond to the expectations of their
stakeholders in society.
The framework of the EFQM Excellence Model is based on nine criteria. Five of these are Enablers' and
four are 'Results'. The 'Enabler' criteria cover what an organization does. The 'Results' criteria cover what an
organization achieves. Results' are caused by 'Enablers' and feedback from 'Results' help to improve
'Enablers'. The Model recognizes there are many approaches to achieving sustainable excellence in all
aspects of performance.
Q4 Define Expert System? Explain different types of expert systems?
One of the largest areas of applications of artificial intelligence is in expert systems (ESs), or knowledge
based systems as they are sometimes known. ESs have been successful largely because they restrict the field
of interest to a narrowly defined area that can be naturally described by explicit verbal rules. Expert
Systems manipulate knowledge while conventional programs manipulate data. An expert system is often
defined by its structure. ESs seeks to embed the knowledge of a human expert (e.g. a highly skilled
physician or lawyer) in a 'computerized consulting service'. In artificial intelligence, an expert system is a
computer system that emulates the decision-making ability of a human expert. Expert systems are designed
to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than
through conventional procedural code.
expert sub
system
Knowledge
base
Inference
engine

COMPONENTS OF EXPERT SYSTEM
The components of ES include −
 Knowledge Base
 Inference Engine
 User Interface
KNOWLEDGE BASE
It contains domain-specific and high-quality knowledge. Knowledge is required to exhibit intelligence. The
success of any ES majorly depends upon the collection of highly accurate and precise knowledge. The
knowledge base of an ES is a store of both, factual and heuristic knowledge.
 Factual Knowledge − It is the information widely accepted by the Knowledge Engineers and
scholars in the task domain.
 Heuristic Knowledge − It is about practice, accurate judgment, one’s ability of evaluation, and
guessing.
Knowledge representation
It is the method used to organize and formalize the knowledge in the knowledge base. It is in the form of
IF-THEN-ELSE rules.
Knowledge Acquisition
The success of any expert system majorly depends on the quality, completeness, and accuracy of the
information stored in the knowledge base.
The knowledge base is formed by readings from various experts, scholars, and the Knowledge Engineers.
The knowledge engineer is a person with the qualities of empathy, quick learning, and case analyzing
skills.

He acquires information from subject expert by recording, interviewing, and observing him at work, etc. He
then categorizes and organizes the information in a meaningful way, in the form of IF-THEN-ELSE rules,
to be used by interference machine. The knowledge engineer also monitors the development of the ES.
INFERENCE ENGINE
Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless
solution.
In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the
knowledge base to arrive at a particular solution.
In case of rule based ES, it −
 Applies rules repeatedly to the facts, which are obtained from earlier rule application.
 Adds new knowledge into the knowledge base if required.
 Resolves rules conflict when multiple rules are applicable to a particular case.
To recommend a solution, the Inference Engine uses the following strategies −
 Forward Chaining
 Backward Chaining
This is the 'brain' of the system, and controls how the IFTHEN rules are applied to the facts. In realistic
systems this should allow for the acquisition of further information from the system’s user -- who will be
prompted for further input via the natural language interface -- which can be used to refine a hypothesis or
resolve conflict between currently competing hypotheses.
USER INTERFACE
User interface provides interaction between user of the ES and the ES itself. It is generally Natural
Language Processing so as to be used by the user who is well-versed in the task domain. The user of the ES
need not be necessarily an expert in Artificial Intelligence.
It explains how the ES has arrived at a particular recommendation. The explanation may appear in the
following forms −
 Natural language displayed on screen.
 Verbal narrations in natural language.
 Listing of rule numbers displayed on the screen.
The user interface makes it easy to trace the credibility of the deductions.
Requirements of Efficient ES User Interface
 It should help users to accomplish their goals in shortest possible way.
 It should be designed to work for user’s existing or desired work practices.

 Its technology should be adaptable to user’s requirements; not the other way round.
 It should make efficient use of user input.
STRUCTURE
EXTERNALLY: • Communication with the system is ideally provided by a natural language interface, so
that it can be easily used by a person well-acquainted with the application area but not necessarily
experienced with AI systems.
INTERNALLY there are three major parts to the system:
• The knowledge base:
• The rule base: This holds the set of rules of inference that are used in reasoning. Most of these systems
use IF-THEN rules to represent knowledge. Typically systems can have from a few hundred to a few
thousand rules.
• The inference engine or rule interpreter.
Eliciting and Integrating knowledge with data
When developing expert systems it is common to integrate knowledge with data. Constantinou proposed a
method for eliciting and incorporating expert knowledge in data-driven Bayesian Networks (BNs). The
method addresses the problem whereby the distribution of some variable in a BN is known from data, but
where we wish to explicitly model the impact of some additional knowledge-based variables for which there
is knowledge but no data. The method ensures that the expected value of a data-driven variable is preserved
when a knowledge-based variable is incorporated as a cause (i.e. ancestor/parent) of the data-driven
variable, as long as the knowledge-based variable remains unobserved. This is because the statistical
outcomes of a data-driven variable are already influenced by the causes an expert might identify as variables
missing from the dataset.
OPERATION OF THE SYSTEM
There are three modes to this:
• Knowledge acquisition
• Consultation
• Explanation
KNOWLEDGE ACQUISITION- The designer of the system must liaise with people in order to
gain knowledge and these people must be acknowledged experts in the appropriate area of activity, for
example physicians, lawyers or investment analysts. The knowledge engineer acts as an intermediary
between the human expert and the expert system. Typical of the information that must be gleaned is
vocabulary or jargon, general concepts and facts, problems that commonly arise, the solutions to the
problems that occur, and skills for solving particular problems. This process of picking the brain of an
expert is a specialized form of data capture and makes use of interview techniques. Having acquired the
information the knowledge engineer is also responsible for the self consistency of the data, and a number of

specific tests have to be performed to ensure that the conclusions reached are sensible. The knowledge
engineer may use specialized software systems to help monitor the performance of an ES under
development. When the human expert who is the source of the knowledge spots an error in the program's
performance, in either the program's conclusions or its line of reasoning, such a system assists in finding
the source of the error in the database by explaining the program's conclusions, retracing the reasoning
steps until the faulty (or missing) rule is identified. It may then assist in knowledge acquisition by
modifying faulty rules or adding new rules to the database
CONSULTATION- The system is in this mode when a user is interacting with it. The user
interacts by entering data in English and the system responds using a backward chaining (deductive
reasoning) process to derive an answer to the questions posed by the user. As explained earlier the user may
during this time be asked for information that can be used to support the system’s hypothesis, with
appropriate backtracking if contradictory evidence to this hypothesis is found.
EXPLANATION- This mode allows the system to explain its conclusions and its reasoning
process. This ability comes from the AND/OR trees created during the deduction process. As a result most
expert systems can answer the following 'why' and 'how' questions
• Why was a given fact used?
• Why was a given fact not used?
• How was a given conclusion reached?
• How was it that another conclusion was not reached?
This ability to provide explanations is the big advantage of ESs over neural network based (NN) systems,
and the reason why, despite the notable success of NN systems in many current application areas, ESs are
likely to remain for a long time the AI technique of choice for safety-critical applications such as medical
diagnosis, and ones where for legal reasons a verbal defense of a decision must be available if requested.
Benefits of Expert Systems
 Expert systems allow expert knowledge to be extracted when there is no ready access to a human
expert, and at any time night and day.
 They preserve expert knowledge when a human expert dies.
 Can combine expertise of many human experts.
 Can increase productivity through quick diagnoses.
Limitations/Disadvantages of Expert Systems
 Can only provide knowledge on one restricted domain.
 Can only provide advice on knowledge entered into the system, which may be limited.
 Do not routinely update knowledge, as a human expert does.
 Lack of common sense.
 Have very high development costs (retaining services of an expert, purchasing powerful hardware,
continual programming to keep system up-to-date, and training and maintenance costs).

Rule-based expert systems – most popular type today.
• Knowledge is represented as multiple rules that specify what should/not be concluded from different
situations.
• Forward chaining – start w/facts and use rules do draw conclusions/take actions.
• Backward chaining – start w/hypothesis and look for rules that allow hypothesis to be proven true.

Expert System Architecture
Figure shows the most important modules that make up a rule-based expert system. The user interacts with
the system through a user interface which may use menus, natural language or any other style of
interaction). Then an inference engine is used to reason with both the expert knowledge (extracted from our
friendly expert) and data specific to the particular problem being solved. The expert knowledge will
typically be in the form of a set of IF-THEN rules. The case specific data includes both data provided by the
user and partial conclusions (along with certainty measures) based on this data. In a simple forward chaining
rule-based system the case specific data will be the elements in working memory.
Building an expert system is called knowledge engineering and the practioners are called as knowledge
engineers.
There are three basic types of expert systems:
 ADVISORY - They put forward solutions for further evaluation by humans, if the user isn't satisfied
then an expert system should be able to find another solution.
 DICTATORIAL - This kind of systems are widely used as process controlling systems, where all
actions (or almost all) are taken by an expert system, because there is no way to involve a human or a
human perception is to slow to make proper decisions.
 CRITICIZING - A problem is given with a solution. A system have to analyze them and evaluate
the effectiveness
OTHER
 REPAIR – They define repair strategies. The repair plan typically involves a scheduling structure
and some control structure to validate the repair process.
 DIAGNOSIS – They are used to recommend remedies to illness, troubleshooting electronic or
mechanical problems or debugging.
 PREDICTION – They are used to "guess" the possible outcome of a situation, usually providing a
probability factor. This is used often in weather forecasting.
 INSTRUCTION – They are used for individualized training or instruction in a particular field. The
system presents materials in an order determined by evaluation of the user's ability and current
knowledge and monitors the progress of the student, altering the sequence depending on this
progress.
 INTERPRETATION – They have the ability to analyze data to determine its significance or
usefulness. The knowledge base often contains models of real world situations which it compares to
its data. These are often used in exploration for mineral, gas and oil deposits as well as in
surveillance, image analysis and speech understanding.
 CLASSIFICATION/IDENTIFICATION – They help to classify the goals in the system by the
identification of various features. E.g. various types of animals are classified according to attributes
such as habitat, feeding information, color, relative size etc. They are used by bird watchers, fishing
enthusiasts, animal rescue shelters etc.

 DESIGN AND PLANNING – This allows experts to quickly develop solutions that save time. They
do not replace experts but act as a tool by performing tasks such as costing, building, design, material
ordering and magazine design.
Category Problem addressed Examples
Interpretation
Inferring situation descriptions from
sensor data
Hearsay (speech recognition), PROSPECTOR
Prediction
Inferring likely consequences of given
situations
Preterm Birth Risk Assessment[36]
Diagnosis
Inferring system malfunctions from
observables
CADUCEUS, MYCIN, PUFF, Mistral,[37]
Eydenet,[38]
Kaleidos
Design Configuring objects under constraints
Dendral, Mortgage Loan Advisor, R1 (DEC VAX
Configuration), SID (DEC VAX 9000 CPU)
Planning Designing actions Mission Planning for Autonomous Underwater Vehicle[40]
Monitoring
Comparing observations to plan
vulnerabilities
REACTOR[41]
Debugging
Providing incremental solutions for
complex problems
SAINT, MATHLAB, MACSYMA
Repair
Executing a plan to administer a
prescribed remedy
Toxic Spill Crisis Management
Instruction
Diagnosing, assessing, and repairing
student behavior
SMH.PAL,[42]
Intelligent Clinical Training,[43]
STEAMER[44]
Control
Interpreting, predicting, repairing, and
monitoring system behaviors
Diagnosis and Troubleshooting of Devices and Systems of All Kinds
This class comprises systems that deduce faults and suggest corrective actions for a malfunctioning device
or process. Medical diagnosis was one of the first knowledge areas to which ES technology was applied, but
diagnosis of engineered systems quickly surpassed medical diagnosis. There are probably more diagnostic
applications of ES than any other type. The diagnostic problem can be stated in the abstract as: given the
evidence presenting itself, what is the underlying problem/reason/cause?

Planning and Scheduling
Systems that fall into this class analyze a set of one or more potentially complex and interacting goals in
order to determine a set of actions to achieve those goals, and/or provide a detailed temporal ordering of
those actions, taking into account personnel, materiel, and other constraints. This class has great commercial
potential, which has been recognized. Examples involve airline scheduling of flights, personnel, and gates;
manufacturing job-shop scheduling; and manufacturing process planning.
Configuration of Manufactured Objects from Sub assemblies
Configuration, whereby a solution to a problem is synthesized from a given set of elements related by a set
of constraints, is historically one of the most important of expert system applications. Configuration
applications were pioneered by computer companies as a means of facilitating the manufacture of semi-
custom minicomputers (McDermott 1981). The technique has found its way into use in many different
industries, for example, modular home building, manufacturing, and other problems involving complex
engineering design and manufacturing.
Financial Decision Making
The financial services industry has been a vigorous user of expert system techniques. Advisory programs
have been created to assist bankers in determining whether to make loans to businesses and individuals.
Insurance companies have used expert systems to assess the risk presented by the customer and to determine
a price for the insurance. A typical application in the financial markets is in foreign exchange trading.
Knowledge Publishing
This is a relatively new, but also potentially explosive area. The primary function of the expert system is to
deliver knowledge that is relevant to the user's problem, in the context of the user's problem. The two most
widely distributed expert systems in the world are in this category. The first is an advisor which counsels a
user on appropriate grammatical usage in a text. The second is a tax advisor that accompanies a tax
preparation program and advises the user on tax strategy, tactics, and individual tax policy.
Process Monitoring and Control
Systems falling in this class analyze real-time data from physical devices with the goal of noticing
anomalies, predicting trends, and controlling for both optimality and failure correction. Examples of real-
time systems that actively monitor processes can be found in the steel making and oil refining industries.
They are also used in nuclear energy industry, air traffic control and the stock market.
Design and Manufacturing
These systems assist in the design of physical devices and processes, ranging from high-level conceptual
design of abstract entities all the way to factory floor configuration of manufacturing processes.
Q5 Define knowedge value creation? What are the different ways to create value through
knowledge?
A key to understanding the success and failure of knowledge management efforts within organizations is the
ability to identify the relevant knowledge to manage and to extract value out of this knowledge. Research
indicates that knowledge and knowledge work has infiltrated deep into the value chain of most businesses.
Examples of knowledge-intensive service organizations include consulting, software engineering, law firms,

and health care. Companies create value by instilling knowledge in products and services, by applying new
knowledge to old problems (and in the process displacing discrete kinds of existing knowledge).
A firm’s capacity for sustained and superior value-creating ability may lie in the richness of its knowledge.
The companies should not follow a conventional approach, racing to beat their competition. They should
follow a different strategic logic that is called value innovation. They focus on making competition
irrelevant by creating a leap in value for buyers and their company, thereby opening up a new and
uncontested market space. Value innovation is a new way of thinking about and executing strategy that
results in a break from the competition.
The relationship between value creation and KM has been argued by several scholars and they suggest that
knowledge can be described as a source of value creation.
Løwendahl et al. (2001) propose a framework for the analysis of value and knowledge creation in
professional service firms (PSFs). Knowledge creation in PSFs is identified as a knowledge-intensive
dynamic activity, delivered by highly educated employees who are closely linked with research and
scientific development. The framework integrates the relationship between the domain choice and the
knowledge base and argues that the bridge between the two is best explained as value creation processes
(VCPs) with two interrelated dimensions: direct and indirect value creation for the clients.
Value creation is gradually being established as the next generation of KM. Therefore, once knowledge is
created, it is necessary to study the impact on people in terms of value. Here are 5 major factors that
contribute towrds value creation.
 Human Network
 Social Capital
 Intellectual Capital
 Technology Assets
 Change Processes
HUMAN NETWORK- Human capital can improve value creation in several ways. For example, formal
and informal communication using face-to-face (including scheduled meetings) and virtual
(synchronous/asynchronous) means (e.g. telephone and e-mail) are perceived as effective to promote
knowledge sharing and creation. Intangible assets have the potential to create more value than tangible or
physical assets. Three factors of intangibles, consisting of human capital, external capital, and structure
capital, are expected to generate future benefits and create sustained organizational and societal values.
These also include business relationships, internal structure, human competence, social citizenship,
environment health, and corporate identity. Once created, intangible and tangible value are included as a part
of value networks for creating relationships between people, groups, or organizations.
SOCIAL CAPITAL- The idea of social capital – physical capital, financial capital, and human capital –
can be applied to create value-added for firms. Because of its emphasis on collectivism and co-operation
rather than individualism, distributed community members will be more inclined to connect and use
electronic networks when they are motivated to share knowledge. In terms of socio-technical design, KM
tools to support social capital are aimed to bridge various social communities. The tools may foster social
capital by offering virtual spaces for interaction, providing the context and history of interaction, and
offering a motivational element (e.g. score) to encourage people to share knowledge with each other.
Moreover, in terms of organizational structure, social capital helps people develop trust, respect, and
understanding of others, especially in the context of a strong organizational bureaucratic culture. This
contributes indirectly to value creation.
INTELLECTUAL CAPITAL- It encompasses organizational learning, innovation, skills,
competencies, expertise and capabilities. Value creation is used as a KM metric for measuring intellectual
capital. The value creation metric includes training, R&D investment, employee satisfaction, relationships

development, etc. Nonaka et al. (2000) suggest that learning by doing can embody explicit knowledge into
tacit knowledge through Internalization in the SECI process. Also, training programs can help trainees
understand themselves, and reading documents or manuals can internalize the explicit knowledge written in
such documents to enrich their tacit knowledge base. An interdependent convergence of intellectual assets
and collaboration capabilities, underpinned by the use of collaboration technology, and KM activities is
suggested to increase the potential of an organization to create value.
TECHNOLOGY ASSETS- Managing and enhancing the organizational processes of knowledge
creation, storage/retrieval, transfer, and application have relied on the wide use of Knowledge Management
Systems (KMS). This suggests that technology, including KMS, is an essential ingredient to sustain value
creation. Applications of IT to organizational knowledge management initiatives has focused on three
common applications
(a) The coding and sharing of best practices,
(b) The creation of corporate knowledge directories, and
(c) The creation of knowledge networks.
CHANGE PROCESSES- Organizational change can be divided into two issues: IT and human issues. In
terms of human issues, adapting organizational policies to motivate employees to share and create
knowledge by providing monetary reward or recognition. Study of the change process is necessary to create
the requisite organizational and societal values. A KM maturity roadmap is an important milestone to enable
organizations to assess the effectiveness of their KM implementations in the future.
VALUE CREATION PROCESS-
VALUE CREATION PROCESS
Kaplanand Norton(1996) have developedthe followingframework(seeFigure 1.) todepictthe value creation
process.Followingthisframework,the needof knowledge indifferentphasesinthe value creation processwas
analyzed.

IDENTIFYING CUSTOMER NEEDS- Marketing and Sales departments play a key role in identifying
customer needs. Customer knowledge is very important because identifying customer needs and satisfying
them is not possible without knowing your customers. Gaining knowledge of customers is difficult, and
therefore companies should try to interact with their customers more.
CREATING PRODUCT OR SERVICE OFFERING- This involves requirement and designing/ re-designing
phase. The product and service offerings are to be made as per the needs of the customers. Requirement
phase involves the proper iternal communication of the market analysisto the experts and the
desgin/redesign phase involves developing a prototyoe and putting it to a hit and trial analysis and doing the
feasibility and validity analysis of the product/service.
BUILDING PRODUCT/SERVICE- Companies want to be best in this area and try to develop something
special for their customers. Their promotions are often not about products which they are selling but about
services they can provide. Nowadays the Company is trying to involve customers in the process of
developing services. The customers recognize the importance and appreciate the opportunity to be a part of
the team. Employees can use their knowledge from previous sales situation. The best example of using same
knowledge is when an employee serves the same customer more than once. When a new customer comes,
the seller needs to use knowledge (s)he already has and connect it to the new knowledge from customer
(s)he serves. Using this knowledge is necessary when working with a problematic customer. Many
employees find it difficult dealing with these types of customers. Retails have the possibility to cooperate
together, which enables employees to share key knowledge about processes and help other retails to
improve. Knowledge from customers is usually used for marketing purposes, especially in marketing
strategy. Customers can help to make effective marketing strategies by giving the company insight into how
to market this product. Involving customers can promote the success or cause the failure of a product on the
market. Knowledge from customers and employees is important.
PROVIDING POST SALE SERVICE TO THE CUSTOMER- it is important to include customers In this
phase. The company which easily communicates with the customer and provide the service they need build
loyal customers.

SATISFYING CUSTOMER NEEDS- A good combination of a product portfolio and services promote
success and competitive advantage. Possible problems of dissatisfaction in different companies can be due
to:
- not identifying customer needs precisely,
- making products that are not requested by market, copying competitors,
- not involving customers in the product development process,
- indirect communication with customers,
- ignorance of customers’ suggestions, and
- promising features or outcomes that are not possible.
Ways to create value through knowledge-
It involves 7 levers-
Customer Knowledge
Meeting, or even better, exceeding customers expectations is a key strategic benefit. Customers provide
useful feedback on products and services and how their needs are evolving. They are the major source of
ideas for improved products and services. Most companies know a lot less about their customers and their
markets than they claim.
Strategies and Guidelines
• Broaden the range of contacts with your customers. Buckman Laboratories reckon that over 80 per cent of
its employees now regularly have contact with customers. Make customer visits part of every person’s job.
Send your senior managers to the front line e.g. working on a customer hot-line, one day a month.
• Developing deep knowledge sharing relationships. Create opportunities for in-depth dialogue. Invite
customer representatives to sit on new product steering committees. Encourage engineers to see your
products in use at customer premises. Encourage job swaps and secondments. Use social occasion to deepen
personal relationships, at all levels of the organization.
• Understand the needs of your customers’ customers. Go beyond the immediate customer to the end-user or
their customers. Learn how your products add value throughout the value chain. Look for opportunities that
will help your customers add value to theirs.
• Articulate unmet needs. Facilitate dialogue that gets below surface expression. Run collaborative creativity
sessions to generate new ideas.
• Create collaborative projects. Identify joint development opportunities e.g. to pursue new market
opportunities or on a jointly designed product or service.
• Develop a market intelligence system. Develop systematic processes to collect not just market data, but
information about the wider environment - political, legislative, economic, social etc. Use external experts to
help you assess the impacts for your business.

Example Steelcase, an office products manufacturer, has taken a much broader view of its market -
knowledge worker productivity. It has consciously developed a ‘customer knowledge channel’ from its
product end-users into its R&D. It interviews and observes how end users of their products use them in
practice. Hence it carries out observations of how people work in offices, airports and hotels. Video cameras
are used to observe focus groups using experimental prototypes. Customer knowledge is deepened and new
insights gained. As a result they have developed premium priced products that provide flexible office space
and improve office worker productivity.
Knowledge in Products and Services
‘Intelligent’ or ‘smart’ products can command premium prices and be more beneficial to users. One
example is the ‘intelligent’ oil drill that bends and weaves it way to extract more oil than ever from the
pockets of oil in underground formations. Customization also adds value and creates a more personalized
offering. Hotel chains or car rental companies, for example, can make suitable rooms or cars available by
knowing the customer’s preferences. In creating new products, companies use a lot of knowledge, gleaned
from market research and prototype testing. Yet only a fraction of the knowledge generated makes it into the
final product. Smart organizations will create opportunities out of this knowledge.
• Surround products with knowledge e.g. in user guides or application notes. This will increase the
confidence of uses and create new market opportunities. Provide feedback mechanisms and offer prizes for
user experiences and ideas.
• Surround the products with value added services. Provide advisory and implementation services that take
advantage of the knowledge of the product in use.
• Personalize and customize. Learn about your customers and their personal preferences or needs. Create
ways in which the product or service can be customized e.g. develop a modular platform approach which
allows for multiple combinations from a basic set of modules.
• Add intelligence to products and services. Substitute hard functions with software controlled functions that
can be adapted as needs change. Provide upgrades and optional enhancements in this way.
• Integrate knowledge and information from several sources. Combine user information, information on the
products in use, and other environmental information in novel ways that improve the effectiveness of your
product.
• Use feedback to enhance your product. Ensure that customer inputs, problems, and helpline dialogue is
properly managed and used when making problem enhancements.
• Turn information obtained as a by-product into a paid-for service in its own right.
Examples Campbell Soup's "Intelligent Quisine" (IQ) delivers weekly packages of nutritionally designed,
portion controlled meals to those suffering hypertension or high cholesterol. A service is provided to farmers
by integrating information about the chemical effect prevailing weather conditions and state of growth from
satellite photographs, and hence advise on time and level of dosage. The same effect can be obtained with 40
per cent less chemical. Car rental and fleet hire companies gain significant information about the reliability
of different makes of car, that can then be sold back to manufacturers. American Airlines created a whole
new market opportunity by marketing its SABRE reservation system.
Knowledge in People
“Our most valuable asset”, according to many company reports, although the actual way they are treated
and managed often belies this claim. The challenge is to turn individual knowledge into organizational

knowledge. Many organizations apply this lever through a ‘learning organization’ programme that
stimulates personal development and organizational learning. Another aspect of this lever is to understand
what motivates knowledge workers and reward them accordingly.
• Build learning into doing. Allow time at the end of every meeting for participants to reflect on what they
have learnt and make suggestions for improving meeting processes. Use decision diaries, in which the
rationale for decisions is recorded, and the results are reviewed some time later. Encourage individuals to
turn their daily notebooks into learning diaries, by adding a column to note learning needs and add reflective
notes later.
• Create knowledge sharing opportunities. Hold knowledge share fairs, where individuals and teams display
their portfolio of work in exhibition like booths in a marketable form. Provide knowledge trails for visitors,
to help them identify useful sources of knowledge. Provide informal environments for casual meetings.
• Create an experts directory. Rather than a directory sorted by department or name, develop a directory
where people are grouped by expertise. This works best as a knowledge database.
• Nurture Communities of Practice (CoPs). These are forums that meet informally or through online
computer networks to share learning and experience. Avoid managing them. Provide them with facilities and
the mechanisms to communicate better.
• Embed learning objectives into performance appraisals. Each person’s job review should have explicit
learning goals.
• Use relevant recognition and reward systems. Allocate a proportion of pay according to level of skills.
However, knowledge workers are motivated by factors other than money. Celebrate their achievement and
learnings through public recognition, annual challenges and awards, opportunities for special visits or
assignment, and other relevant motivators. But first, make sure you know what each individual personally
values.
Example Shell has put considerable investment into nurturing and developing its people and making “better
use of this intellectual capital”. It has open learning centres and databases of learning resources on the
company’s intranet. However, the real breakthroughs have come through the establishment of knowledge
communities and developing skills for quality person-to-person dialogue and personal reflection.
Accompanying changes include delayering (it is not untypical for 50 people to report to one person), self-
managed teams, and a focus on empowerment and teamworking. The greatest change has been the removal
of formal lines of communication and hierarchy, relying instead on intensive informal communications.
Knowledge in Processes
In many companies there are often differences in performance levels of 3:1 or more between different
groups performing the same process. The detail and knowledge used is different. If this knowledge of best
practice can be diffused and learned, then overall performance will improve. Every business process
contains embedded knowledge. It is the result of thinking and codifying what was formerly a series of ad-
hoc tasks into something that is systematic and routine. The processes are also surrounded by the skills and
knowledge of the person applying them and the experts who developed them. Making this knowledge more
widely accessible is part of exploiting this lever.
• Turn repeated ad-hoc activities into processes. Document how activities are done and what information or
knowledge is needed to carry them out effectively. Use visual tools such as Visio to help communicate
them. Automate them by codifying them into computerised procedures. In particular use workflow

packages, that separate out routine that can be automated and those parts that will need expert human
intervention.
• Embed help into computerized processes. At a basic level this is simply context sensitive help. More
advanced is immediate access to other resources and even a ‘click to email’ or click to talk’ to an expert.
• Create a best practices database. Develop a taxonomy of business processes that can map similar
operations in different parts of your organization. The database should contain key descriptions of the
process, with special reference to the ‘tricky’ bits or learnings form experience. Above all, it must provide
pointers to people, those who have the in depth knowledge.
• Learn from the experts. Identify the star performers, individuals or teams, of given processes. Provide
opportunities for other individuals or groups to spend time with these people to learn from them. Goal your
star performer to share their expertise, such as through creating learning resources or acting as mentors.
• Glean extra knowledge from modelling. Use simulation and business process modelling to validate new
processes before they are put into practice. Build models of existing critical processes to gain insights on
how they actually work and might be improved. Use a multi-disciplinary team to carry out ‘what if’
analyses.
Examples Texas Instruments TI-BEST programme saved the cost of one new semiconductor fabrication
plant (a $500 million investment). A key element of the programme is an office of best practice. It identified
best practices, created a database and acted as a clearing house to link those with knowledge to those who
needed it. A network of facilitators throughout the business enabled the prices and accelerated the necessary
culture change. CIGNA made its best underwriting knowledge available as guidance screens in their
computerized underwriting processes. Contact details of experts are also given should further guidance be
needed. This helped it turn a loss into a profit.
Organizational Memory
Much knowledge flow in organizations is transitory. It occurs in conversations, meetings, emails. This
strategic lever is a way of addressing the issue of knowing “what we know” or once knew. It helps avoid
repeating the mistakes of the past, and in drawing lessons from similar situations elsewhere. Organization
memory exists in many forms - processes, databases, artefacts, documents, but above all the minds of
people. Sometimes overlooked are archives owned by outsiders, such as researchers, customers or former
employees. They may have retained detail that your own company lost as people left.
• Capture while fresh and before losing. Take note taking seriously. Using mind maps or other devices helps
embed discussion not personal memory. Recording and publishing not just decisions, but some elements of
discussion also helps. Have people about to retire impart their knowledge to others, such as through video
recordings.
• Post project reviews. A project is not completed until participants have reviewed it and extracted the
lessons to record. Also index and make available project documents
• Customer histories. In your customer databases, make space to record details of interactions with a given
customer - products bought, sales visit reports etc.
• Make documents easily accessible. Documents provide a crucial part of organization memory, but are
often poorly managed. Introduce a document management system that also abstracts, indexes and records
availability of printed document not held in computer format.

• Develop and maintain boilerplates. ICL’s Cafe Vik holds information used in previous project proposals.
In a typical situation 80 per cent of information for a project bid is quickly assembled from existing material
leaving a bid team to concentrate on the 20 per cent new material that could clinch the sale.
• Edit and refine knowledge. On a regular basis, have subject experts review postings to conferences and
email conversations, to extract knowledge nuggets that can be turned into more formal documents or
database records.
• Enrich database entries with tacit knowledge. Add details of where used, how useful it was, other
contextual information, and contact details of the people involved. Add multimedia clips to explain and
show how certain activities were carried out.
Examples Price Waterhouse Coopers is typical of several consultancies who have knowledge databases to
allow recording and sharing of company knowledge. Alongside many structured databases are knowledge
centres that provide human analysts and navigators. It helps them solve customer problems faster.
Schlumberger has two important databases - a needs/problems database and an ideas bank. Even if an idea
or the result of research is not immediately used, it is retained for future matching against new customer
requirements or problems.
Knowledge in Relationships
Such depth of knowledge is not easily replaced overnight. Companies have many relationship webs
involving customers, suppliers, employees, business partners, shareholders etc. These relationships involve
sharing knowledge and understanding - not just of needs and factual information, but of deeper knowledge
such as behaviours, motivations, personal characteristics, ambitions and feelings. Such knowledge is often
highly personal, but is easily lost during restructuring.
• Broaden the scope of relationships. Make sure that any important relationship is not dependent on just one
person. Find opportunities to interact through several levels and functions from organization to organization.
• Understand your relationship partners. Get on their mailing lists for product and corporate affairs
information. Sing up for daily newsfeeds that feature them. Create a database of key information about the
relationship.
• Deepen relationships. Involve stakeholders such as customers and suppliers in activities that might
previously have been considered in-company. These include product planning meetings and marketing
campaign planning. Include external organizational representatives on strategic task forces, even when the
matter discussed might be normally considered confidential. Confidentiality is generally respected and the
degree of trust deepens. Use social events to deepen personal relationships - corporate hospitality does have
its benefits!
• Create online connections. Make certain databases or communications networks open on a selected basis to
different external organizations. An example of this is the inclusion of external public relations companies
on the companies marketing communications databases and electronic bulletin boards.
• Enhance knowledge flows. Include suppliers and customers on some of your in-house mailings and
briefings. Send them your press releases, before they read them in the press.
• Create collocation spaces. Assign certain areas for visitor hospitality, and make sure your staff, perhaps on
a rotation basis, are three to help them find their way around your organization. Give suppliers desks and
other facilities within departments.

Examples Toshiba collects comparative data on suppliers ranking 200 quantitative and qualitative factors. It
has an active suppliers network and association where knowledge is shared and suppliers are integrated into
future strategies.
Dell has built strong knowledge relationships into its whole supply chain. Through its web site, customers
can configure their own systems freeing up technical advisors to deal with those who have specific
questions. Suppliers are informed on a daily basis of Dell’s manufacturing requirements. It shares future
plans, design databases and methodologies with them. This knowledge flow creates faster time to market
and creates benefits and incentives for those with a business relationship with Dell.
Knowledge Assets
Knowledge is one of the intangible assets of a company that do not appear in its balance sheet. The core of
this lever is the adage “what you can measure you can manage”. Many economists have argued that
knowledge is now a critical resource that needs such an approach. However, most business managers have
not turned this concept into practice. While accountants and auditors pore over detailed figures about every
piece of physical plant and machinery, the major contributor to the value of their business, intellectual
capital, gets scant attention. It so happens that changes in intellectual capital are usually lead indicators of
future financial performance - an important reasons for taking their measurement and management seriously.
• Identify your intellectual and knowledge assets. At a broad level this involves classifying them into
different categories such as human capital (knowledge, competencies, experience, know-how etc.), structural
capital (processes, information systems, databases etc.), and customer capital (customer relationships,
brands, trademarks etc.). In other schemes, intellectual property is separated out as a distinct category. This
is assets that are protected by law and include trade-marks, patents, copyrights, licences, design rights etc.
• Conduct a knowledge inventory. Use structured interviews and questionnaires. Find out the key activities
in people’s job, what information and knowledge is crucial to good performance, and where this knowledge
resides. Find out what information and knowledge they generate. Develop a suppliers/users matrix to
identify gaps and overlaps.
• Develop a set of key indicators. These should flow directly from strategy and objectives. However, there
should be a balanced set, such as those in a Balanced Scorecard, giving particular emphasis to intellectual
capital measures. These should include not just efficiency measures, but also measures of stability and
development. For example, Sveiby suggests measures of customer longevity and ‘rookie ratios’, the
proportion of new people in a team. Make sure you distinguish inputs, output and outcome measures.
• Create a measurement system linked to objectives. A comprehensive system will have in place
mechanisms to collect and aggregate data and to use this to measure progress against objectives and to
adjust strategies. Ideally managers should be able to monitor in
• Review and pilot an IC measurement system. There are new systems, such as IC IndexTM and Inclusive
Valuation Methodology (IVMTM), that are specifically geared to measurement of intangibles and
intellectual capital.
Examples Dow Chemical Intellectual Assets Management team takes an active role in managing patents
and other intellectual assets in order to develop their value. They have generated over $125 million in
additional revenues from this activity. Skandia has developed a range of measures to monitor its
development of intellectual capital. It published results in an Intellectual Capital Supplement to its financial
reports. A PC-based system Dolphin, provides managers with real-time monitoring of these measures.

Knowledge management

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