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A Thesis on
SELECTION OF ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KNOWLEDGE MANAGEMENT IN INDUSTIRES: AN ANP
APPROACH
A DISSERTATION Submitted in partial fulfillment of the requirements for the award of the degree
of
MASTER OF TECHNOLOGY
In
INFORMATION TECHNOLOGY (Specialization: SOFTWARE ENGINEERING)
Submitted by
ADISH KUMAR
(Enrol. No. – MS200501)
Under the Guidance of:
Mr. Manish Kumar IIIT-Allahabad
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY, ALLAHABAD
(A University Established under sec.3 of UGC Act, 1956 vide Notification No.
F.9-4/99-U.3 Dated 04.08.2000 of the Govt. of India)
(A Centre of Excellence in Information Technology Established by Govt. of India)
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY
AALLLLAAHHAABBAADD (A University Established under sec.3 of UGC Act, 1956 vide Notification No. F.9-4/99-U.3 Dated 04.08.2000
of the Govt. of India )
(A Centre of Excellence in Information Technology Established by Govt. of India)
Date: ______________
I/WE DO HEREBY RECOMMEND THAT THE THESIS WORK PREPARED UNDER
MY/OUR SUPERVISION BY ADISH KUMAR ENTITLED “SELECTION OF
ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KNOWLEDGE
MANAGEMENT IN INDUSTRIES: AN ANP APPROACH” BE ACCEPTED IN
PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF TECHNOLOGY IN INFORMATION TECHNOLOGY (SOFTWARE
ENGINEERING) FOR EXAMINATION.
COUNTERSIGNED
MR. MANISH KUMAR ______________________________ (THESIS ADVISER)
i
DR. U. S. TIWARY DEAN (ACADEMICS)
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY
AALLLLAAHHAABBAADD (A University Established under sec.3 of UGC Act, 1956 vide Notification No. F.9-4/99-U.3 Dated 04.08.2000
of the Govt. of India )
(A Centre of Excellence in Information Technology Established by Govt. of India)
CERTIFICATE OF APPROVAL*
The foregoing thesis is hereby approved as a creditable study in the area of
knowledge management carried out and presented in a manner satisfactory to
warrant its acceptance as a pre-requisite to the degree for which it has been
submitted. It is understood that by this approval the undersigned do not
necessarily endorse or approve any statement made, opinion expressed or
conclusion drawn therein but approve the thesis only for the purpose for which it
is submitted.
COMMITTEE ON
FINAL EXAMINATION
FOR EVALUATION
OF THE THESIS
* Only in case the recommendation is concurred in
i
ii
IINNDDIIAANN IINNSSTTIITTUUTTEE OOFF IINNFFOORRMMAATTIIOONN TTEECCHHNNOOLLOOGGYY
AALLLLAAHHAABBAADD (A University Established under sec.3 of UGC Act, 1956 vide Notification No. F.9-4/99-U.3 Dated 04.08.2000
of the Govt. of India )
(A Centre of Excellence in Information Technology Established by Govt. of India)
CANDIDATE DECLARATION
This is to certify that Report entitled “Selection of Architectural Model
for the Implementation of Knowledge Management in Industries: An ANP
Approach” which is submitted by me in partial fulfillment of the requirement
for the completion of M.Tech. in Information Technology (with specialization in
Software Engineering) to Indian Institute of Information Technology, Allahabad
comprises only my original work and due acknowledgement has been made in
the text to all other material used.
Adish Kumar
(Enrol No. : MS200501)
Selection of the Architectural Model for KM Implementation: An ANP Approach
Indian Institute of Information Technology - Allahabad
iii
ACKNOWLEDGEMENT
It was a pleasure doing the thesis work which helped me
learn new things, coming out with solutions to the toughest problems and
develop my programming skills. First and foremost, I would like to thank
INDIAN INSTITUTE OF INFORMATION TECHNOLOGY,
ALLAHABAD for providing me such opportunity to carry out the
dissertation work.
I would like to express my sincere gratitude to my thesis
supervisor Mr. Manish Kumar, IIIT Allahabad for providing his precious
advices and suggestions. I would like to thank him for spending his
valuable time in correcting my mistakes and posing new challenges which
really made me work hard on it. With his valuable guidance, the work was
finally completed.
I would also like to express my sincere gratitude to Prof.
Rakesh Narain and Mr. Ravi Kant, M. N. N. I. T. Allahabad, for their
valuable suggestions and advices in carrying out this work.
I would like to thank my friends who always supported me
and encouraged me in my studies. I would like to pay my special thanks to
Mr. Vineet Chauhan and Mr. Prateek Dayal with whom I used to discuss
about the problems in my thesis and they were always there to listen to
me and give their suggestions. Mr. Nilesh Shukla, who was sort of a
teacher to me, for giving clues about how to solve problems; Mr. Anand
Arun Atre, whose hard working always left an impression on me; Mr.
Abhay Pawane, who taught me how to enjoy life and make maximum of it;
Mr. Kamal Sawan, whose working setup has always astonished me; Mr.
Prabhat Saheja, who taught me how to admire the beauty; Mr. Imran
Khan, who inspired me always to be perfect; Mr. Sampath Kumar, who
Selection of the Architectural Model for KM Implementation: An ANP Approach
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iv
taught me to how to enjoy things which you don’t understand; Mr. Pankaj
Kandpal, from whom I came to know how to live life to the fullest. I would
like to thank Mr. Kamal Singh, Mr. Anil Pandey and Mr. Dinh Ngoc Lan
who were always there whenever I needed them. I would also like to thank
my batch mates from M. Tech (WCC), M. Tech (IS) and M, Tech (BI) for
giving me such a beautiful time here in IIITA and make my this two years
a memorable journey.
I would like to thank my parents Dr. M. D. Singh and Smt.
Mala Singh, and my sister Ms. Aditi Singh, who always supported me and
encouraged me in my studies. They have been my strength and
encouragement in my life. I would not be writing this thesis today without
their constant support and devotion. I dedicate this thesis to them.
Thank you,
Adish Kumar
Selection of the Architectural Model for KM Implementation: An ANP Approach
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ABSTRACT
Globalization is generating product synergies, high
purchasing power, and access to world markets, which is ultimately
increasing the competition. To sustain in the today’s global business
environment, knowledge plays an important role in increasing the
performance and responsiveness of an organization. Due to the role of
knowledge, Knowledge Management (KM) has come into light. KM is
basically concerned with the creation, capture, storage, application and
reuse of knowledge. Keeping knowledge in mind, in this thesis, three
architectures have been proposed. These architectures are based on
knowledge extensive, knowledge intensive and the existing system, from
these one of the architectures are to be selected for the implementation of
KM in industries.
However, selecting a proper architecture for the
implementation of KM is a kind of multiple criteria decision making
(MCDM) problem required to consider a large number of complex factors.
The analytic network process (ANP) is a new MCDM method which can
deal with all kinds of dependencies systematically. Since the ANP has
these advantages, in this thesis work, we develop an effective framework
based on the ANP to help industries to evaluate and select the
architecture. ANP framework presented in this thesis work consists of
Cost, Competitiveness and Responsiveness as determinants for evaluation
of the architectures. The framework explores relationships among
Organizational Structure, Process Integration and Innovation, and also
among Knowledge Extensive, Knowledge Intensive and Existing system
architectures for implementation of KM. Additionally, evaluation matrices
are presented to illustrate the application of the proposed framework.
Also, sensitivity analysis is conducted for incorporating the variation’s in
expert’s opinion for relative importance of one determinant over the other
in relation to implementation of KM.
Selection of the Architectural Model for KM Implementation: An ANP Approach
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TABLE OF CONTENTS Certificate …………………………………………………………………………… i
Declaration …………………………………………………………………………. ii
Acknowledgement …………………………………………………………………. iii
Abstract ……………………………………………………………………………… v
Table of Contents ………………………………..………………………………... vi
List of Figures ……………………………………………………………………… viii
List of Tables ……………………………………………………………………….. ix
List of Abbreviations ………………………………………………………………. x
1 INTRODUCTION ………………………………………………………………. 1
1.1 In Context of Knowledge Management in Industries …………………….. 2
1.2 In Context of Need of Architectural Model for Implementation of KM … 4
1.3 In Context of Need of Analytic Network Process (ANP) Approach in KM 5
2 LITERATURE REVIEW ……………………………………………………… 7
2.1 Literature Review for Knowledge and Knowledge Management ……….. 7
2.1.1 Data ………………………………………………………………………. 8
2.1.2 Information ……………………………………………………………… 9
2.1.3 Knowledge ………………………………………………………………. 11
2.1.3.1 Definition of Knowledge …………………………………... 12
2.1.3.2 Elements of Knowledge …………………………………… 13
2.2 What is Knowledge Management? …………………………………………… 15
2.3 Literature Review for Knowledge Management Enablers ……………….. 18
2.4 Literature Review for Models / Frameworks / Architectures for Implementation of KM ………………………………………………………….
23
2.5 Literature Review for Analytic Network Process (ANP) Approach for Strategic Decision Making ……………………………………………………..
33
2.6 Problem Discussion …………………………………………………………….. 37
3 RESEARCH MODEL FRAMEWORK............................................................... 39
3.1 Determinants ……………………………………………………………………. 39
3.1.1 Cost ………………………………………………………………………. 40
3.1.2 Competitiveness ………………………………………………………... 40
3.1.3 Responsiveness …………………………………………………………. 41
3.2 Dimensions & Attributes ……………………………………………………… 42
3.2.1 Organizational Structure ……………………………………………... 43
3.2.2 Process Integration …………………………………………………….. 45
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3.2.3 Innovation ……………………………………………………………….. 47
3.3 Alternatives ……………………………………………………………………… 48
3.3.1 Knowledge Extensive Architectural Model ………………………… 48
3.3.2 Knowledge Intensive Architectural Model …………………………. 52
3.3.3 The Existing System Architectural Model …………………………. 55
3.4 Comparison of the Three Architectural Models ……………………………. 59
4 METHODOLOGY: THE ANALYTIC NETWORK PROCESS (ANP) ……. 65
4.1 Analytic Network Process (ANP) …………………………………………….. 65
4.2 Advantages of ANP …………………………………………………………….. 67
4.3 Disadvantages of ANP …………………………………………………………. 68
4.4 Outline of the Steps of ANP useful for Calculation ……………………….. 69
5 APPLICATION OF ANP FRAMEWORK MODEL …………………………. 71
5.1 Model Development and Problem Formulation ...…………………………….. 71
5.2 Decision Making ………………….……………………………………………………. 72
5.2.1 Pair wise Comparison Matrices for Determinants …………………. 73
5.2.2 Pair wise Comparison Matrices for Dimensions …………………….. 74
5.2.3 Pair wise Comparison Matrices between Component / Attribute Level ………………………………………………………………………
75
5.2.4 Pair wise Comparison Matrices for Interdependencies................. 76
5.3 Supermatrix Formation and Analysis ...………………………………................. 78
5.4 Evaluation of Alternatives…….……………………………...................................... 80
5.5 Desirability Index for a Determinant …………………………………................. 81
5.6 Calculation of Architectural Weighted Index (AWI) ………………………… 84
6 RESULTS & DISCUSSIONS …………………………………………………... 85
6.1 Results …..……………………………………………………………………….. 85
6.2 Sensitivity Analysis …………………………………………………………….. 88
6.3 Future Scope of Work …………………………………………………………... 94
7 CONCLUSIONS ………………………………………………………………… 95
REFERENCES ……………………………………………………………………... 97
APPENDIX A (Consistency Ratio) …………………………...…………………. 109
APPENDIX B (Tables and Calculations) …………….………………………… 112
Selection of the Architectural Model for KM Implementation: An ANP Approach
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LIST OF FIGURES
2.1 The relationship between Data, Information and Knowledge. 8 2.2 Conversion of Data to Knowledge……………………………….. 122.3 Pillars of Knowledge Management……………………………… 252.4 Core Capabilities and Knowledge Building Activities……….. 262.5 Organizational Knowledge Management (KM) Model……….. 272.6 Model of the Knowing Organization…………………………….. 282.7 A Framework for Knowledge Management……………………. 292.8 Intangible Assets Framework……………………………………. 302.9 Intellectual and Capital Model…………………………………… 31
2.10 KPMG Knowledge Management Process………………………. 312.11 Spiral of Organizational Knowledge Creation…………………. 323.1 ANP Framework Model…………………………………………… 463.2 Alternative 1: The Knowledge Extensive Architectural
Model…………………………………………………………………. 513.3 Alternative 2: The Knowledge Intensive Architectural
Model…………………………………………………………………. 543.4 Alternative 3: The Existing System Architectural Model……. 566.1 Variation in priority of KM Architectural Models with the
Changes in the Weights assigned to Cost with respect to Competitiveness…………………………………………………….. 89
6.2 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Cost with respect to Responsiveness……………………………………………………… 91
6.3 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Competitiveness with respect to Responsiveness………………………………………… 93
Selection of the Architectural Model for KM Implementation: An ANP Approach
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LIST OF TABLES
2.1 Comparison of properties of Tacit vs Explicit Knowledge……….... 143.1 Comparison of the three architectural models……………………… 605.1 Pair wise Comparison Matrix for relative importance of
Determinants……………………………………………………………. 735.2 Pair wise Comparison Matrix for relative importance of
Dimensions on Determinant / Responsiveness…………………….. 755.3 Pair wise Comparison Matrix for attribute Enablers under
Determinant / Responsiveness and Dimension / Organizational Structure…………………………………………………………………. 76
5.4 Pair wise Comparison Matrix for attribute Enabler Culture under Determinant / Responsiveness and Dimension / Organizational Structure, OS/RESP/CU…………………………….. 77
5.5 Super Matrix formation for Responsiveness before Convergence.. 795.6 Super Matrix formation for Responsiveness after Convergence.... 795.7 Pair wise Comparison Matrix for the relative importance of
Alternatives on Enablers for RESP/OS/CU….……………………... 805.8 Architectural Weighted Desirability Index for Responsiveness…. 836.1 Architectural Weighted Index (AWI) for various alternative KM
Architectural Models under selection of the Architectural Model……………………………………………………………………… 87
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LIST OF ABBREVIATIONS
AHP: Analytic Hierarchy Process ANP: Analytic Network Process
APQC: American Productivity and Quality Center AWI: Architectural Weighted Index COL: Collaboration
COMP: Competitiveness CSKB: Central Single Knowledge Base
CU: Culture HRD: Human Resource Department HRM: Human Resource Management
ICT: Information and Communication Technology INN: Innovation
IT: Information Technology ITW: Information Technology and Web KC: Knowledge Creation KF: Knowledge Flow KM: Knowledge Management
KPMG: Knowledge Process Management Group KS: Knowledge Sharing
KW: Knowledge Worker MCDM: Multi – Criteria Decision – Making
OS: Organizational Structure PI: Process Integration
PPA: Product & Process Architectures R&D: Research & Development
RESP: Responsiveness SC: Supply Chain SP: Strategic Planning TE: Training & Education
TMC: Top Management Commitment TQC: Total Quality Control
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CHAPTER – 1
INTRODUCTION
Globalization is the interlocking of different countries in the
world in a political, economic, social and technological sense [1]. It has
become the most important phenomenon in our time. This process effects the
environment, culture, political systems, economic development and
prosperity, and human physical well – being in societies around the world.
Global reach brings many benefits. Operations support one another by
generating product synergies and sharing know – how. All benefit from the
improved purchasing power that scale offers. All enjoy access to world
markets through a sales and marketing network that touches every corner of
the globe so it increases the competition at both domestic and international
level. To sustain in the global market continuous innovation is required.
Knowledge is the core component of innovation not technology or finance [2].
It is the new core competence of organization as resource based economy has
been moved to the knowledge based economy. The globalization shifts the
business from production – based to knowledge – based economy [3].
In today’s competitive world, organizations need capacity to
retain, develop, organize and utilize their employee’s capabilities, in order to
remain competitive. It has become more difficult to survive in the market if
an organization is not willing to understand that technology based
competitive advantages are temporary and that the only sustainable
competitive advantage that they have is their employees. Drucker [4]
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maintains that ‘‘Knowledge has become the key economic resource and the
dominant – and perhaps even the only source of competitive advantage’’.
Knowledge management (KM) is helpful in creating a competitive edge in
today’s global business environment. Thus, knowledge (explicit or tacit) and
knowledge management have emerged as increasingly important features for
organizational survival.
As quoted by Benjamin Franklin about knowledge... “If a man empties his purse into his head, no man can take it away
from him; an investment in knowledge always pays the best interest”.
As quoted by Ikujiro Nonaka about knowledge...
“In an organization where the only certainty is uncertainty, the one source of lasting competitive advantage is knowledge...successful companies are those that consistently create new knowledge, disseminate it widely throughout the organization, and quickly embody it in new technology and products”.
1.1 IN CONTEXT OF KNOWLEDGE MANAGEMENT (KM) IN INDUSTRIES
Knowledge Management (KM) helps management of the
information, knowledge and experiences available to an organization – its
creation, capture, storage, availability and utilization – in order that
organizational activities build on what is already known and extend it
further [5]. KM is also considered as an integrated systematic approach for
identifying, managing, and sharing all the information assets of the
organization, including databases, documents, policies and procedures as well
Selection of Architectural Model for KM Implementation: An ANP Approach
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as previously unarticulated expertise and experience held by individuals,
groups and departments [6]. KM helps organizations to identify, select,
organize, disseminate, reuse and transfer important information and
expertise. These are necessary for problem – solving, dynamic learning,
strategic planning, and decision – making [7, 8].
Different industries are implementing KM, such industry types
are – banking, manufacturing, construction, electronics, textiles, mining, oil
& gas sector, pharmaceutical industry, aerospace and food/beverages. KM is
applied in manufacturing industries such as automobile, machine tools,
electronics–telecommunication–IT, and chemicals. Manufacturing
organization can apply KM to their planning and organizing, shop floor and
operations, R&D, marketing and finance, logistics and supply chain, and
human resource development. The reasons for using KM are:
Ensuring competitive advantage,
Creating new knowledge for the organization,
Managing resources effectively, and
Developing new technologies and products.
The use of KM may deliver strong benefits by way of improved
efficiency, decision – making, responsiveness, innovation, sharing of best
practices, lower cost and better judgments on task. KM also imparts other
benefits by way of providing faster information to professionals, avoiding
duplications, reducing cycle time, reusing information and knowledge,
increased sales, improved flexibility, brainstorming tools, reduced time – to –
market, and improved customization.
Selection of Architectural Model for KM Implementation: An ANP Approach
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A chief concern in industries is that experts are leaving the
industries and new recruits are taking long time to acquire professionalism.
Also KM is necessary to enhance internal collaboration among employees, to
capture and share best practices, to manage legal property (patents, brands,
etc) and to maintain long term customer relationships.
Thus KM in industries is all about coming together, keeping
together and working together. Hence application of KM in industries will
lead to the ability to be flexible and more innovative as well as improving
decision making which ultimately leads to customer satisfaction.
1.2 IN CONTEXT OF NEED OF ARCHITECTURAL MODEL FOR THE IMPLEMENTATION OF KM
In the knowledge economy, a key source of sustainable
competitive advantage relies on the way to create, share, and utilize
knowledge [9]. Many models and frameworks have been proposed by many
researchers for the implementation of KM by considering different
approaches [10]. Three broad categories of Knowledge Management (KM)
models have been identified by McAdam and McCreedy [11], they are
namely:
Knowledge Category Models.
Intellectual Category Models.
Socially Constructed Models.
In this thesis, three architectural models are being proposed for
the implementation of Knowledge Management in the industries. They are
namely:
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Knowledge Extensive System.
Knowledge Intensive System.
The Existing System.
These systems have been proposed keeping in mind the places
from where we can extract knowledge i.e. the architectures are knowledge –
centric. This will help the industry people in tracing out the places from
where they can extract maximum knowledge. This will also help the experts
who are working for the implementation of KM in getting the idea about
what are the factors that are to be taken care of while implementing KM in
an industry. It will also give them an idea about the factors which are to be
taken care of most and what are the factors which are to be considered later
while implementing KM. By using these architectures, we can easily get to
know the present scenario of the industry and also for saving time and cost
both, in the implementation of KM. Thus there is a need of architectural
model for the implementation of KM.
1.3 IN CONTEXT OF USE OF ANALYTIC NETWORK
PROCESS (ANP) APPROACH IN KM
ANP is a coupling of two parts, where the first consists of a
control hierarchy or network of criteria and sub – criteria that controls the
interactions, while the second part is a network of influences among the
elements and clusters [12]. Analytical Network Process (ANP) is a more
general form of AHP, incorporating feedback and interdependent
relationships among decision attributes and alternatives. ANP is a decision
making tool and it has been used in various fields for decision making. It has
been used by researchers in selection of R & D projects [13], in supply chain
management [14], in selection of process in chemical industry [15], in
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accessing the performance factors [16], in decisions related to vendor
selection [17], in selection of strategies [18] for various purposes and many
more uses are there.
ANP is being used in the field of KM but not on a large scale. It
is used for fulfilling partial goals in the industries. The researchers have used
ANP to implement KM partially in industries but not considering the
industry as a whole. No one has ever given any decision making framework
for the selection of the architectural models and also the architectural models
for the implementation of KM in industries. In this thesis, ANP is being used
with KM for giving a decision making framework for the selection of the most
suitable proposed architecture for the implementation of KM in industries.
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CHAPTER – 2
LITERATURE REVIEW
The critical review of the literature gives an idea that most of
the research work carried out has been concentrated on KM and its enablers
in industries. The literature review regarding Analytic Network Process
(ANP) describes it as a multi – criteria decision making tool which
incorporates interdependencies and feedback both. Thus the literature review
can be classified into following main areas:
Literature review for knowledge and KM
Literature review for KM enablers in industries.
Literature review for models / frameworks / architectures for
implementation of KM.
Literature review for Analytic Network Process (ANP) application for
strategic decision – making.
2.1 LITERATURE REVIEW FOR KNOWLEDGE & KNOWLEDGE MANAGEMENT
The basic building block of knowledge is data, the processing of
data resulting in information, and as a consequence of processing information
knowledge is derived. Knowledge is the next natural progression after
information; that is, a higher order than information. Fig.2.1 shows the
relationship between data, information and knowledge [19].
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Figure 2.1 The relationships between Data, Information & Knowledge
2.1.1 DATA Data are discrete, objective, non – contextual facts about events.
In an organizational context, data is most usefully described as structured
records of transactions [20]. Data are used in and created by all daily
operations, from serving a customer, manufacturing a product, tracking
inventory, down times of machines, cycle times of production, turnover etc.
Modern organizations usually store data in some sort of technology system. It
is entered into the system by departments such as finance, accounting, R&D,
HRM, quality and marketing. Until recently, it has been managed by central
information systems department that respond to requests for data from
management and other parts of the organization. The company does not
know what data it has or understand the processes that create data and
transform them into useful information. Quantitatively there is too much
data of little or no value. Redundancy is high and unmanaged. Obsolete data
are not retired, diverting management attention from important data.
DATA
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Organizations need data and some organizations are heavily
dependent on it. Banks, insurance companies, utilities, and government
agencies are obvious examples. Firms sometimes pile up data because it is
factual and therefore creates an illusion of scientific accuracy. Gather enough
data, the argument goes, and objectively correct decisions will automatically
suggest themselves. This is false on two counts. First, too much data can
make it harder to identify and make sense of the data that matters. Second,
and most fundamentally, there is no inherent meaning in data. Data
describes only a part of what happened. It provides no judgment or
interpretation and no sustainable basis of action. While the raw material of
decision – making may include data, it cannot tell you what to do. Data says
nothing about its own importance or irrelevance. But data is important to
organizations largely, of course, because it is essential raw material for
information.
2.1.2 INFORMATION The basic building block of knowledge is data, the processing of
data results in information. That is, adding context by organizing, or
categorizing it with a particular purpose in mind. Information requires a
sender and receiver. For example for any message, it has a sender and a
receiver. Information is meant to change the way the receiver perceives
something, to have an impact on his judgment and behavior. Information
moves around organization through hard and soft networks. A hard network
has a visible and definite infrastructure: wires, delivery vans, satellite dishes,
post offices, addresses, electronic mailboxes. The messages these networks
deliver include e – mail, traditional or "snail" mail, delivery – service
packages, and Internet transmissions. A soft network is less formal and
visible. It is ad hoc. Someone's handing you a note or a copy of an article
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marked "FYI" (For Your Information) is an example of information
transmission via soft network. Unlike data, information has meaning the
"relevance and purpose". Not only does it potentially shape the receiver, it
has a shape: it is organized to some purpose. Data becomes information when
its creator adds meaning. We transform data into information by adding
value in various ways. Let's consider several important methods, all
beginning with the letter C [21]:
Calculated – the data may have been analyzed mathematically or
statistically.
Condensed – the data may have been summarized in a more concise
form.
Corrected – errors have been removed from the data.
Contextualized – we know for what purpose the data was gathered.
Categorized – we know the units of analysis or key components of the
data.
Note that computers can help to add these values and transform
data into information, but they can rarely help with context, and humans
must usually help with categorization, calculation, and condensing. In
general, data are considered as row facts, information is regarded as an
organized set of data, and knowledge is perceived as meaningful information
[22]. Information is only valuable to the extent that is structured. Because of
a lack of structure in its creation, distribution, and reception of information,
the information often does not arrive where it is needed and, therefore, is
useless [23]. Information has little value and will not become knowledge until
it is processed by the human mind [24].
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2.1.3 KNOWLEDGE
A dictionary definition of knowledge is “the facts, feelings or
experience known by person or group of people”. Knowledge is derived from
information but it is richer and more meaningful than information. It
includes familiarity, awareness and understanding gained through
experience or study, and results from making comparisons, identifying
consequences and making connection. In organizational term, knowledge is
generally thought of as being “know how” or “applied action”.
Knowledge is a fluid mix of framed experiences, values,
contextual information, and expert insight that provides a framework for
evaluating and incorporating new experiences and information. It originates
and is applied in the minds of knower. In organizations, it often becomes
embedded not only in documents or repositories but also in organizations
routines, process, practices and norms. It is a mixture of various elements; it
is fluid as well as formally structured; it is intuitive and therefore hard to
capture in words or understand completely in logical terms. Knowledge exists
within people, part and parcel of human complexity and unpredictability. If
information is to become knowledge, humans must do virtually all the work.
This transformation happens through such C words [21].
Comparison – how does information about this situation compare to
other situations we have known?
Consequences – what implications does the information have for
decisions and actions?
Connections – how does this bit of knowledge relate to others?
Conversation – what do other people think about this information?
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Thus from the fig 2.2 we can have an idea of how a data is
converted into knowledge [25],
Figure 2.2 Conversion of Data to Knowledge.
2.1.3.1 DEFINITION OF KNOWLEDGE Grey [26] noted that knowledge is the full utilization of
information and data, coupled with the potential of people’s skills,
competencies, ideas, intuitions, commitments and motivations. Knowledge is
people, money, leverage, learning, flexibility, power, and competitive
advantage; it is stored in the individual brain or encoded in organizational
processes, documents, products, services, facilities and systems. It is the
result of learning which provides the sustainable competitive advantage.
On the other hand, Zack [27] added that knowledge is that
which we come to believe and value, based on the meaningfully organized
accumulation of information (messages) through experience, communication
or inference. Davenport and Pursak [21] defined knowledge as “information
combined with experience, context interpretation and reflection. It is ‘high-
value’ from information that is ready to apply decisions and actions”.
DATA Unrecognized
numbers, words or images
INFORMATIONData processed into meaningful
patterns
KNOWLEDGE Information made into productive use
and made actionable.
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Beijerse [28] defines knowledge here as follows: Knowledge is
seen here as the capability to interpret data and information through a
process of giving meaning to these data and information; and an attitude
aimed at wanting to do so. New information and knowledge are thus being
created, and tasks can be executed. The capability and the attitude are of
course the result of available sources of information, experience, skills,
culture, character, personality, feelings, etc.
2.1.3.2 ELEMENTS OF KNOWLEDGE
Knowledge can be categorized in two ways – Explicit and Tacit
knowledge [29]:
Explicit knowledge: It can be relatively easily to formulate and
transmit in formal languages, document forms, mathematical
equations or symbols. It can be expressed in form of manuals,
computer codes, verbal languages, etc. This kind of knowledge can be
readily transmitted between individuals formally and systematically.
It is easier to identify. It is reusable in a consistent and repeatable
manner. it is usually contained within tangible or concrete media.
Tacit knowledge: It includes subjective insights, intuitions and
hunches that are highly personal and hard to formalize [30]. Tacitness
is the property of the knower; that is very difficult to articulate and
express to others. It is deeply rooted in an individual’s actions and
experience, as well as in the ideals, values or emotions he or she
embraces. It encompasses the kind of informal personal skills or crafts
often referred to as “know – how.” It consists of beliefs, ideals, values
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and mental models, which are deeply ingrained in us and which we
often take for granted.
The comparison of properties of Tacit vs. Explicit Knowledge is shown in table 2.1.
Table 2.1
Comparison of the properties of Tacit vs. Explicit Knowledge
S. No. Properties of Tacit Knowledge Properties of Explicit Knowledge
1. Ability to adapt, to deal
with new and exceptional situation.
Ability to disseminate, to reproduce, to access and to
reapply throughout the organization.
2. Expertise know - how, know - why and care - why. Ability to teach, to train.
3. Ability to collaborate, to
share a vision, to transmit a culture.
Ability to organize, to systematize, to translate a
vision into mission statement, into operational guidelines.
4. Coaching and mentoring to
transfer experimental knowledge.
Transfer of knowledge via product, services and
documented processes.
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2.2 WHAT IS KNOWLEDGE MANAGEMENT?
Fundamentally, KM is about applying the collective knowledge
of the entire workforce to achieve specific goals of the organization. The aim
of knowledge management is not necessarily to manage all knowledge, just
the knowledge that is most important to the organization. It is about
ensuring that people have the knowledge they need, where they need it, when
they need it – right knowledge, in right place, at the right time. As knowledge
reside in mind, managing knowledge is difficult so the work of knowledge
management is to establish an environment in which people are encouraged
to create, learn, share and use knowledge together for the benefit of the
organization. KM is the systematic effort to capture, store, retrieve, reuse,
create, transfer and share knowledge assets within an organization, in a
measurable way completely integrated in its operational and business goals,
in order to maximize innovation and competitive advantage [31]. KM is the
spark that will ignite an organization’s ability to get the most from the
investments it has made in workforce and information technology, and to
harness the considerable intellectual capital within a company and its
partners [32]. "Knowledge Management caters to the critical issues of
organizational adaptation, survival and competence in face of increasingly
discontinuous environmental change. Essentially, it embodies organizational
processes that seek synergistic combination of data and information
processing capacity of information technologies and the creative and
innovative capacity of human beings" [33].
KM thus is about many different things like:
Gathering information and transforming them into knowledge.
Collecting knowledge and making it available to others.
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Increasing the amount of knowledge by sharing and leveraging the
existing knowledge.
Making knowledge a tangible asset owned by the company.
Producing tangible results by managing this whole process.
There are different definitions of KM. Unfortunately, the term
KM is not easy to define because it contains multiple representations and
concepts. Many authors agree that KM requires a total organizational
transformation, including organizational culture, structure, and management
style [21]. The present research examines some of the definitions, for example
Snowden [34] defines KM as “the identification, optimization,
and active management of intellectual assets, either in the form of explicit
knowledge held in artifacts or as tacit knowledge possessed by individuals or
Communities”
Poynder [35] suggests that there are currently three major
schools of thought on what KM is. One such school recommends that KM is
mainly an IT issue, with networks of computers and groupware being the
keys. If one constructs widespread computer networks and adds
communication tools that allow group collaboration, people will be more
disposed to sharing information and knowledge
Grey [26] defines KM as “an audit of ‘intellectual assets’ that
highlights unique sources, critical functions and potential bottlenecks which
hinder knowledge flows to the point of use. It protects intellectual assets from
decay, seeks opportunities to enhance decisions, services and products
through adding intelligence, increasing value and providing flexibility”
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Bertels [36] defines KM as “the management of the organization
towards the continuous renewal of the organizational knowledge base - this
means, e.g. creation of supportive organizational structures, facilitation of
organizational members, putting IT-instruments with emphasis on teamwork
and diffusion of knowledge (e.g. groupware) into place.”
Finneran [37] regards KM as a discipline that assists the spread
of knowledge of individuals or groups across companies in ways that directly
affect performance. KM envisions getting the Right Information within the
Right Context to the Right Person at the Right Time for the Right Business
Purpose.
According to Macintosh [38], “Knowledge management involves
the recognition and analysis of obtainable and required knowledge assets and
knowledge asset – related processes, and the ensuing planning and control of
actions to develop both the assets and the processes so as to fulfill
organizational objectives.”
Starr [39] states that KM is information or data management
with the added process of capturing the tacit experience of the individual to
be shared, used and built upon by the organization, leading to increased
productivity.
Liebowitz [40] gave a short definition of KM as the process of
creating value from an organization’s intangible assets.
Gupta and Iyer [7] define KM as process that assists
organizations to find, select, arrange, distribute, and transfer important
information and expertise essential for activities such as problem solving,
lively learning, strategic planning and decision making.
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Morse [41] has stated that KM focuses on understanding how
knowledge is obtained, created, stored, and utilized within an organization.
Maier [42] defined Knowledge Management as the management
function responsible for the regular selection, implementation and evaluation
of goal oriented knowledge strategies that aim at improving an organization’s
way of handling knowledge internal and external to the organization in order
to improve organizational performance. The implementation of knowledge
strategies comprises all person oriented, organizational and technological
instruments suitable to dynamically optimize the organization wide level of
competencies, education and ability to learn of the members of the
organization as well as to develop collective intelligence.
2.3 LITERATURE REVIEW FOR KM ENABLERS
It was in mid 1980’s, when knowledge was appreciated among
the individuals and organizations as having role to play in the emerging
competitive environment [43]. Up till now there was no clear understanding
developed among the organizations as how to manage the knowledge, which
certainly is of two types: “explicit” knowledge and “tacit” knowledge. Explicit
knowledge is the knowledge that can be expressed externally through
codification and documentation and is later stored in the organizational
computer memories. Tacit knowledge on the other hand cannot be expressed
externally as it resides in the heads of the employees working in the
organization.
In regard to knowledge Alfred Marshall says...
“Knowledge is our most powerful engine for production”.
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In organizations, the concept of documenting knowledge and
sharing knowledge is not new as the organizational policies, producers,
employee training and personality development programs, reports and
manuals are serving the same purpose for years [44].
As per Nonka and Huber [44]...
“Knowledge is a justified personal belief that increases an individual’s
capacity to take effective action”.
As organizations strive to improve their business performance
and capacity for innovation, their attention is increasingly focused on how
they manage knowledge effectively. Experience has shown that successful
KM implementations in business settings prioritize attention on soft issues –
including human and cultural aspects, personal motivations, change
management methodologies, new and improved business processes enabling
multidisciplinary knowledge sharing, communication and collaboration, and
sees technology as an enabler. There are some basic steps in the management
of knowledge as knowledge capture, knowledge development, knowledge
sharing and knowledge utilization without which any organization will not be
able to effectively manage their knowledge [45].
Many scholars and practitioners viewed that a supportive
organizational culture can enable the successful implementation of
knowledge management. Organizational culture can be more simply defined
as the character or the personality of an organization. It is often described as
“the ways things are done in an organization”. Routinized ways of doing
things that people accept and line by. Organization have norms and values
that influence how members effort or may encourage them to do so” [46].
People’s willingness to ask questions that reveal their ‘‘ignorance’’, disagree
with others in public, contradict known experts, discuss their problems,
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follow others in the thread of conversation – all these behaviors vary greatly
across cultures [47]. Organizational culture is the way of organizational life
that enables and motivates people to create, share and utilize knowledge for
the benefit and enduring success of the organization [48]. “Motivating the
Employees “to share and use knowledge is essential for successful
implementation of KM. Reward and Incentives increases the confidence on
the part of the source and recipient. Incentives are critical to the knowledge
transfer process, acting as signals for employees to engage in knowledge
transfer [49]. Osterloh and Frey [50] conclude that intrinsic motivation
enables the transfer of tacit knowledge. Compensation is a primary vehicle
for indirect motivating employee in which money is a goal independent of
activity [51]. Communities of practices are about sharing experiences and
knowledge in creative ways and this can lead to new approaches to problem
solving and innovation [52]. Leadership is and has always been the principal
approach to convince and motivate employees to do what managers have
planned for them in advance [53]. Leadership, by its influence component,
facilitates the implementation of knowledge activities in an organization.
Communication competence is the ability to demonstrate knowledge of the
appropriate communication behavior to effectively achieve organizational
goals. Communication between individuals requires both the decoding and
encoding of messages. Communication decoding competence refers to a
recipient’s ability to listen, be attentive and respond quickly; communication
encoding competence refers to a source’s ability to express one’s ideas clearly,
have a good command of the language, and can be easily understood [51].
Learning is another important enabler which underpins the
creation of knowledge. Organizations have to learn constantly, in order to
respond flexibly to changes in the environment and to stay competitive [54].
Learning is viewed as a process that allows workers to advance from novices
to experts through three levels of knowledge acquisition, that are know –
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what, know – how, know – why [55]. Learning improves the behavior and
capability of individuals so that the organization can more effectively respond
to its environment which increases the performance of manufacturing
organization [56]. Learning processes define the quality of knowledge
distributed across the organization as well as the effectiveness with which
knowledge is put in use [57]. The intangible knowledge resource, intellectual
capital is a key driver of innovation and competitive advantage in today’s
knowledge based economy [58].
“Information technology infrastructure” supports each step in
KM process and is very essential and helpful in successfully performing these
processes through Information Technology (IT) tools like – database systems,
intranet, extranet, internet, web, group support systems, multimedia
technologies, etc [2]. IT is a vital enabler of knowledge management in an
organization, which helps the firm to know “what they actually know”.
Sufficient time should be made available for the employees’ learning and
interaction, for their training related to complex technological systems, which
help them to communicate their creativity and ideas helpful in enhancing the
innovations of organization for new product development [22]. IT
infrastructure accelerates data transmission and reduces cost of
communication. It enhances knowledge sharing process to flourish
innovation. It helps in distant learning and access to market information.
Many organizations becoming dependent on it in order to satisfy their
business aims and meet their needs. Networking enables communication
between distant persons and technical devices through internet, intranet, etc.
It enhances the interaction of individuals group, organization and inter –
organization. For KM, the role of IT infrastructure is to support knowledge
repositories, enhance knowledge access and transfer, and facilitate the
knowledge environment [58].
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Collaboration increase competitiveness by achieving flexibility,
innovation quality and speed [59]. Collaborators were people who “worked
with the enemy”, collaboration is a word of which, today, most people seem to
approve. “Collaboration seems to capture the spirit and represent one of the
underpinning tenets of knowledge management, that of working together to
achieve common goals and objectives” [60]. Commitment is another strong
enabler of the organization. Commitment improves the managerial
involvement which leads to collaboration. “Commitment by the Top
Management” is necessary for full and continual support for the KM
initiatives across the organization, also there should be a separate “budgetary
support” for bringing knowledge management into practice and it will
actually be a clear signal for the employees that KM is an integral part of the
organization and is not an extra-activity apart from the routine ones [61].
Innovation is a critical enabler in the success of industries and it
will mainly arise by collaboration of ideas and feedback from all phases of the
product life cycle. “Product Life Cycle Knowledge Management” will lead to
creation of innovative ideas after collecting them from people involved in
product development and associated processes, and will ensure that all the
useful knowledge is saved, easily reused, and stored into a structured
knowledge repository for the achievement of business benefits like – overall
improved business performance, improved working conditions, and employee
satisfaction, improved customer satisfaction, reduction of product
development cycle time and increased responsiveness of the firms [62]. In
small and medium – sized companies nine knowledge streams in knowledge
management are of chief importance, they are, determine the knowledge
necessary, determine the knowledge available, determine the knowledge gap,
knowledge development, knowledge acquisition, knowledge lock, knowledge
sharing, knowledge utilization and evolution of utilized knowledge [28].
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Organizations need to identify, access, share, store and use
knowledge related to customers, competitors, suppliers and employees in
order to analyze the market demands and its own capabilities for enhancing
responsiveness and customer satisfaction [63]. This will enable the
organization to make better planning, decision making, manage
unanticipated changes made by the supplier and will allow reducing the
changes late in the product development process where the changes are more
expensive. “Success measures of Knowledge Management” enables in
knowing how successful was the KM efforts in the organization and these
successful indicators can be in terms of financial returns, froth in resources
linked to project and growth in volume of the knowledge content usage [64].
2.4 LITERATURE REVIEW FOR MODELS / FRAMEWORKS / ARCHTIECTURES FOR KNOWLEDGE MANAGEMENT
Every time a new technology is developed, for its
implementation researchers and scholars propose a number of models,
frameworks or architectures for its implementation. These models,
frameworks and architectures assist in successful implementation of the
technology. These can be designed for a specific purpose and can also be in a
generalized form which may be common for all. Knowledge Management is
also a new technology which is up coming in the market at a great pace.
For the successful implementation of KM, the researchers and
scholars have proposed a number of models, frameworks and architectures.
These models, frameworks and architectures have been designed for some
specific purpose as well as some of them are generalized. These have been
proposed for specific purposes as performance management in supply chain,
in improvement of customer relationship management, in human resource
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management, in improving the business performance of the organizations
and many more.
The researchers and scholars are constantly working on
different aspects of KM and exploring the various possibilities of using it in
different fields. They have proposed a number of frameworks based on
different aspects related to KM. These frameworks can be broadly classified
into two categories: descriptive frameworks and prescriptive frameworks [65].
The descriptive frameworks are frameworks which try to describe the
phenomena of Knowledge Management and the prescriptive frameworks are
those frameworks which advise the methodologies to be followed in
conducting KM.
Descriptive frameworks can further be classified as: broad
descriptive and specific descriptive frameworks [65]. A broad framework is
that descriptive framework which endeavors to give the details of the whole
phenomenon of KM. A specific framework is that descriptive framework
whose center of attention is a particular aspect of this phenomenon.
There are a number of frameworks proposed by lots of
researchers and scholars. Some of the frameworks based on the above stated
classification form a part of the discussion. These frameworks are discussed
below.
Wiig gave one framework for KM known as framework of pillars
of knowledge management [66]. It is comprised of three functions which he
calls the KM pillars. These are the dominant functions which are required for
managing knowledge. Figure 2.3 shows that the pillars are set up on a broad
perception of knowledge creation, manifestation, use and transfer.
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Figure 2.3 Pillars of Knowledge Management [66]
I
Survey & Categorize Knowledge
Analyze Knowledge & Related Activities
Elicit, Codify & Organize Knowledge
III
Synthesize Knowledge Related
Activities
Handle Use & Control Knowledge
Leverage, Distribute & Automate Knowledge
II
Appraise & Evaluate Value of Knowledge
&
Knowledge Related Actions
KNOWLEDGE MANAGEMENT
KNOWLEDGE MANAGEMENT FOUNDATION
BROAD UNDERSTANDING OF KNOWLEDGE
– CREATIONS – MANIFESTATIONS – USE – TRANSFER
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1. Physical Systems. 2. Managerial Systems.
kills dge.
3. Employee Sand Knowle
4. Values and Norms.
Figure 2.4 Core Capabilities and Knowledge Building Activities [67]
Leonard – Barton presented a KM framework which included
four core capabilities and four knowledge building activities [67]. These are
decisive for Knowledge – based organizations. It is shown in figure 2.4 that
there are four knowledge building activities which encircle the core
capabilities. They are shared and creative problem solving (to produce
current products), experimenting and prototyping (to build capabilities for
the future) and importing and absorbing technologies from outside of the
firm’s knowledge. These activities are related to knowledge creation and
diffusion.
4
3
2
1
Problem Solving
Importing
Knowledge
Implementing
&
Integrating
Experimenting
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Figure 2.5 Organizational Knowledge Management (KM) Model [68]
Arthur Anderson and American Productivity & Quality Center
presented a model for KM known as ‘Organizational Knowledge Management
(KM) Model’ [68]. It embodies the seven KM processes. These processes can
operate on the knowledge of an organization. The operations they can
perform is shown in figure 2.5. These are create, identify, collect, adapt,
organize, apply and share.
Choo presented a model known as “Model of Knowing
Organization” [69]. It is shown in figure 2.6 and this model supports that an
organization uses the information strategically. They use it for sense making,
knowledge creation and decision making.
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Figure 2.6 Model of the Knowing Organization [69]
A framework is introduced by van der Spek and Spijkervet
known as Framework for Knowledge Management [70]. It characterizes a
cycle of four Knowledge Management stages. The stages identified are
Sense making
Information Interpretation
Knowledge Creation
Information Transformation
Decision Making
Informat cessing ion Pro
Organizational Action
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conceptualize, reflect, act and retrospect. These stages govern the basic
operations on knowledge, as shown in figure 2.7.
Figure 2.7 A Framework for Knowledge Management [70]
Svieby supported the assumption of organizational knowledge as
intangible assets [71]. He gave a framework for this known as Intangible
Assets framework. As shown in figure 2.8, it is comprised of three
External and Internal External and Internal
Developments Developments
External and External and Internal Internal Developments Developments
Conceptualize
• Draw up inventory • Analyze strong & weak points
Retrospect
• Evaluate results achieved
• Compare old and new situation
Reflect
• Establish required improvement
• Plan the improvement process
Act
• Developing knowledge • Distributing knowledge • Combining knowledge • Holding knowledge
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components. These components are external structure, internal structure and
employee competence.
Figure 2.8 Intangible Assets Framework [71]
Petrash introduced a model which involved three types of
organizational resources [72]. These resources are referred as intellectual
capital and the model is known as “Intellectual Capital Model”. These are as
shown in figure 2.9: human capital, organizational capital and customer
capital.
Alavi gave a framework known as Model of Knowledge
Management process [73]. It elaborates the KM process in a consulting firm,
KPMG Peat Marwick. It defines KM as creating, leveraging and sharing of
know – how and intellectual assets. These are done across the firm by all the
individuals in order to improve the services to the clients. KPMG has built –
up a KM process model which consisted of a sequence of six phases. These six
phases as shown in figure 2.10 are: acquisition, indexing, filtering, linking,
distribution and application.
INTANGIBLE ASSETS
EXTERNAL STRUCTURE
(brands, customer and supplier relationships)
INTERNAL STRUCTURES
(The organization: management, legal structure, manual systems, attitudes, R & D, software).
EMPLOYEE COMPETENCE
(Education, experience)
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Figure 2.9 Intellectual Capital Model [72]
Figure 2.10 KPMG Knowledge Management Process [73]
Nonaka gave a model known as “Spiral of Organizational
Knowledge Creation” [74]. It recognizes four kinds of “knowledge conversion”
Human
Capital
Customer
Capital
Organizational
Capital
Value
DepictsKnowledge
Flow
Acquisition Indexing Filtering
Linking Distribution Application
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that drives knowledge creation. These are as shown in figure 2.11:
socialization, externalization, internalization and combination.
Figure 2.11 Spiral of Organizational Knowledge Creation [74]
Beckham also introduced a perspective framework. In this
framework, an eight – sequence is recommended for administering knowledge
Combination
Socialization
Explici
Externalization
t
Tacit
Organization Individual Group Inter – Organization
Knowledge Level
Internalization
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management. These eight stages are: identify, capture, select, store, apply,
create and sell [75].
Thus, these models are helpful in the implementation and form
the base of various other models and frameworks proposed for the
implementation of KM. These models and frameworks have helped in the
formation of the architectural models proposed in this thesis.
2.5 LITERATURE REVIEW FOR ANALYTIC NETWORK PROCESS (ANP) FOR STRATEGIC DECISION MAKING
Analytical Network process (ANP) is a special case of AHP
Analytical Hierarchy Process was developed by Saaty in 1996. AHP
maintains a unidirectional hierarchical relationship among decision level.
The overall goal of the problem is apex of ANP model. In this hierarchy, one
group of entities influences another set of entities. ANP allows more complex
interrelationships between decision levels and attributes. The major
difference between the AHP and ANP approaches is that ANP assumes that
the system has interdependent relationships among the attributes with no
strict hierarchical relationship. ANP allows a feedback relationship among
the criteria at different levels and interdependence between the criteria and
attributes at same sublevel through development of a super matrix.
Graphically summarizing an ANP model describes a model consisting of the
“ultimate goal” or “overall objective” at the top most level, followed by
“criteria” or “determinants”, which are followed by “Dimensions” and
“Alternatives” which are a means to achieve the overall objective. Criteria
have dominance over dimensions, dimensions dominate the attributes and
the whole model is dominated by the overall objective. The analytic network
process (ANP) is capable of taking into consideration the multiple dimensions
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of information into the analysis, a powerful and necessary characteristic for
any strategic decision [18].
A numerous application of ANP have been published in the
literature, like- marketing, transportation projects, forecasting, supply chain
management, medical, military, manufacturing process selection, R&D
project selection and etc.. ANP allows qualitative values to be transformed
into quantitative values for comparative analysis. The ANP is a relatively
simple, intuitive approach that managers and other decision-makers can
accept [13]. ANP has been used for analyzing the alternatives for
improvements in supply chain performance thus presenting a framework for
deciding the priorities for the performance improvement of a supply chain
[76]. ANP may be differentiated into two kinds of models: the feedback
system model and the series system model. In the feedback system model,
evaluation clusters link one by one in turn as a network system. This kind of
model can capture effectively the complex effects of interplay in human
society, especially when risk and uncertainty are involved [30]. Meade and
Sarkis [77] suggested a decision methodology that applied ANP to evaluate
alternatives (e.g. projects) and to help organizations become more agile, with
a specific objective of improving the manufacturing business processes.
Cheng and Li [78] illustrate how to empirically prioritize a set of projects by
using a five-level project selection model. In order to utilize the high level
model of the relationships influencing the selection of a third party reverse
logistics provider an ANP methodology must be determined [77]. Analytic
network process (ANP) based decision model was developed for the
evaluation of various alternatives for end – of – life reverse logistics in the
computer hardware industries [10]. Meade suggested that ANP is an
approach utilizing quantitative, qualitative, tangible and intangible factors
pertaining to the decision of whether and which third party logistics provider
should be selected. The decision model, using the ANP, is capable of taking
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into consideration the analysis, a powerful and necessary characteristic for
any strategic decision [79]. Momoh and Zhu [80] proposed an application of
AHP and ANP to enhance the selection of generating power units for
appropriate price allocation in a competitive power industry.
Sarkis [81] presented a systemic ANP model to evaluate
environmental practices and programs in analyzing various projects,
technological or business decision alternatives. Chung et al. [82] applied ANP
in selection of product mix for efficient manufacturing in a semiconductor
fabricator considering the aspects of product, equipment efficiency and
finance. ANP models problems involving systems in which the relationships
between the levels are not distinct (i.e. easily represented as higher or lower,
controlling or subordinate). These systems are known as ‘‘systems with
feedback’’ and refer to systems where a level may both dominate and be
dominated, directly or indirectly, by other decision attributes and levels [15].
ANP – based framework is to identify the level of impact of different factors
on total quality management (TQM) implementation and to assess the
readiness of the Turkish manufacturing industry to adopt TQM practices
[83]. Raisinghani and Meade [18] illustrated “what dimension of KM is most
important in developing an agile e – supply?” using the ANP research model,
and serves as the framework for our research study.
ANP is a coupling of two parts. The first consists of a control
hierarchy (or network) of criteria and sub criteria that controls the feedback
networks. The second part consists of the networks of influence that contain
the factors of the problem and the logical groupings of these factors into
clusters; control criterion (or sub – criterion) has a feedback network [17, 83].
A super matrix of limiting influence that gives the priorities of the factors in
the network is computed for each network. Each decision network is
composed of clusters, their elements, and links between the elements. A link
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between an element (the ‘‘parent’’) and the elements it connects to in a given
cluster (its ‘‘children’’) makes up the usual AHP pair wise comparison set.
The interactions, feedback, influences, and dependencies in the system are
expressed through these links. Links between elements within the same
cluster are called inner dependencies, whereas links between a parent
element in one cluster and its children in another cluster are called outer
dependencies [84]. Inner and outer dependencies are the best way decision-
makers can capture and represent the concepts of influencing or being
influenced, between clusters and between elements with respect to a specific
element. Pair wise comparisons are made systematically for all combinations
using the fundamental comparison scale (1 – 9) of AHP that is used to
indicate how many times an element dominates another. Preference between
each pair of elements verbally as equally important, moderately more
important, strongly more important, very strongly more important, and
extremely more important. These descriptive preferences would then be
translated into numerical values 1, 3, 5, 7, 9, respectively with 2, 4, 6, and 8
as intermediate values for comparisons between two successive judgments.
Reciprocals of these values are used for the corresponding transpose
judgments. In making judgments, the decision maker can incorporate
experience, knowledge and hard data [85]. Tangibles can be included in the
model alongside intangibles.
Thus, we can say that ANP is a “multi – criteria, multi –
dimension and multi – attribute” decision making model which has been
successfully implemented for strategic decisions in varied fields.
2.6 PROBLEM DISCUSSION
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The literature gives an idea that there is a lot of research work
is being done in the field of knowledge management, analytical hierarchy
process (AHP) and the Analytic Network Process (ANP) methodology. The
literature review gives support to the fact that an architectural model is
required for the implementation of knowledge management in industries.
Also ANP is a multi-criteria decision making tool which is used for strategic
decision making giving an idea to the managers about the interdependencies
and feedback between various attributes and helps in deciding which
alternative is best suited for the problem. Still, there is a necessity to
construct a framework describing how to determine the architectural model
for the implementation of KM in industries through the use of analytic
network process.
The research work in this field is going on, but ANP has not
been used much in the field of KM. While going through the papers related to
survey regarding KM in industries, the thought of proposing architectures
and using ANP for selecting the best architecture for the implementation of
KM. The variables taken are those which are used for the implementation of
KM and they play a major role in it.
Thus, this thesis work is intended to propose an ANP model
construct for the implementation of KM by determining the appropriate
architectural model. The architectural models that are to be used as
alternatives are knowledge extensive, knowledge intensive and the existing
systems. These are designed keeping in mind the dimensions and
determinants that have been chosen for achieving the goal. In order to
implement KM in industries, it needs to achieve Cost, Enhancing
Competence and Responsiveness which in this model are taken as criteria or
determinants. Organizational structure, Process integration and Innovation
are taken as dimensions. For this work ANP is adapted because, it is a
decision making tool which can determine the best architecture for the
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implementation of KM by considering systematic characteristics of
determinants and dimensions.
CHAPTER – 3
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RESEARCH MODEL FRAMEWORK
A model framework is being developed in this work to determine
the architectural model for the implementation of knowledge management in
the industries. ANP based framework is adopted which contains
determinants, dimensions, attributes and alternatives in different sublevels.
It finds the interdependencies and also feedback between the different levels.
This framework helps to take decisions regarding the selection of the
architectural model for the KM implementation. The framework showing the
defined variables for determinants, dimensions and attributes is shown, see
Figure 3.1.
3.1 DETERMINANTS
The determinants are those variables in the framework which
helps us in achieving the goal defined. In this work, the determinants for
achieving the goal i.e. “selection of the architectural model for the
implementation of knowledge management in Indian industries” are Cost,
Competitiveness and Responsiveness. These three determinants are most
suitable for selecting the architectural model for the implementation of KM
because they cover each and every aspect of an industry in wider prospects.
The three determinants are illustrated below in the following sub–sections:
3.1.1 COST
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Today cost has become the most important determinant in the
implementation of knowledge management. Whenever a new thing is
implemented, first thing that is taken care of is the cost and it is also seen
that how the new implementation going to help the industry in terms of
profit and loss. The cost for an industry is a market winner. Since, after
implementation of KM in the industry lead time is reduced, response time
increases and thus decreasing the overall cost of the product without
affecting the quality of the product. This is because of the factors which have
improved due to the implementation of KM.
3.1.2 COMPETITIVENESS
Competitiveness is the degree to which business meet the high quality product with lower cost, with high customer service and utilizing resources efficiently. It increases the effectiveness and efficiency of the organizations. Grant [86] proposed that integration of individual’s specialized knowledge as a salient organizational capability to create and sustain competitive advantage. In a dynamic environment, competitive advantage is defined as “the ability to create unique advantage and to protect these advantages against imitation” [87]. To sustain in the global market organizations are finding means of differentiating its products in order to compete with off – shore manufacturers [88]. This can be achieved by improving customer service, productivity and quality of the product using minimum resources. The new mind set in organizations is competitive mind set. CEO across the country agrees: Increased competitiveness is the issue of the day. They have applied resources to improve it, but continue to degrade. To achieve competitiveness, business executives, advisors and allies must provide their organization with a methodology for changing from functional, performance – at – any – cost mindset to a new mindset driven by KM. Competitiveness acts as a fundamental driver and is the effectiveness measurement of the business process. Applying KM program in
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organizations, removes the barriers which impede performance. Diversity between nations typically reflects different environmental conditions, which in turn affects the strategies, directions, and challenges of a specific industry. Information and communication technology (ICT) effectively managing information and knowledge have become a key element to improve the organization’s performance. ICT helps an organization to capture, distribute, and manage information effectively [89]. ICT works as knowledge enabling tool for supporting and enhancing the performance of the organizations to sustain the competitive edge. R&D, teaching and learning, developing knowledge workers, providing community services, supporting culture and enhancing competitiveness. It increases the performance by providing channels for storing, acquiring, transferring, exchanging, distributing, and reusing both internally and externally [90].
3.1.3 RESPONSIVENESS
The most critical capability for a manufacturing company today is its ability to manage change quickly. To survive in the global market, responsiveness decreases the affect by internal and external disturbances on production operation and increases the response of existing infrastructure. Responsiveness is the real time performance. It is the degree to which a business is able to meet the customer’s final needs within customer lead time. The crux of responsiveness is that make/market cycle time (the time it takes a company to forecast, schedule, acquire material, receive material, manufacture and distribute) is usually much longer than the customer’s lead time (the time from the customer’s last change to his required delivery). Likewise a long design/development cycle time relative to market intervals (the time in between significant new product introduction in the market) causes non – responsiveness. In short, the root cause of non – responsiveness is long cycle time in critical business processes. Non – responsiveness is usually blamed on three generic symptoms: poor forecasts, changing market needs and poor execution. Barley [91] defines Responsiveness as the ability
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of a production system to respond to disturbances (originating inside or outside the manufacturing organization) which impact upon production goals. The following areas have been identified as being of importance in the development of more responsive manufacturing systems [92]:
Human organizational
Equipment processes
Decision, control and information systems
Responsiveness is a measure of how well it adapts to changes in consumer behavior, customer requirements and changing trade and competitive structure [93]. Externally responding to customer’s needs by rapidly designing and manufacturing products satisfying those needs and internally by reducing the lead times for all tasks in a company, resulting in improved quality, lower cost and of course quick response. By enhancing faster delivery of the mass customized product which helps in building goodwill in market thus results in increased market share, and enables accelerated time performance which leads to higher efficiency by economizing the production, lower overheads, reduced lead times, faster inventory and higher productivity. All this helps in improving the business performance, thus making responsiveness a key business objective which today every organization strives to achieve [93].
3.2 DIMENSIONS & ATTRIBUTES
In the present model framework, organizational structure, process integration and innovation have been considered as dimensions for selection of architectural model for the implementation of KM. These are actually the basic sub – criteria for the selection of the architectural model for implementation of KM but the attributes or enablers under these dimensions
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may vary according to companies and process adopted in it. The following dimensions and attributes are considered for the selection purposes.
3.2.1 ORGANIZATIONAL STRUCTURE
Organizational structure consists of some sub – strategies in
itself or some standard set of principles or methodologies and culture in its
operations environment. Under the framework the dimension organizational
structure is characterized by culture, top management commitment, strategic
planning and IT and web for knowledge management. No knowledge
management strategy can lead to a success until it is well supported by the
organizational “culture” which should be such that, “the right person at the
right job at the right time”. Employees should “work for the organization
rather working for themselves” and also organization should go for a culture
of “knowledge sharing rather than knowledge hoarding”, this indeed creates
an atmosphere of trust organization wide.
The organizational culture effects personal attitudes and
working patterns, also it is one of the most important enablers of KM and IT
infrastructure requires more funds because without IT infrastructure, the
knowledge accessing, knowledge creating and knowledge sharing is not
possible in the present environment and all this requires sufficient financial
support [94]. Culture of the organization also includes motivating the
employees to share and reuse the knowledge which can be achieved by
rewards and incentives, through speeches and individual attention,
promotions of employees based on the amount of knowledge sharing and
usage and knowledge contribution by them [95].
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Organizations top management must appreciate and encourage
cross-boundary learning and sharing [2]. In order to show their
“commitment” towards knowledge management organization’s top
management appoints a full time executive and his /her subordinates to
handle KM initiatives in the organization for e.g. CKO (Chief Knowledge
Officer), Knowledge Managers, etc. Top management should help in building
KM setups and also should define and develop the skills of learning from
other people, at the same time the top management should keep faith in
knowledge management initiatives and remain patient in expecting results
from KM [24].
Knowledge capture, knowledge development, knowledge
sharing, and knowledge utilization are the important knowledge based
activities performed generally in any organization for successfully
implementing KM so that the organization could derive real benefits for
business performance improvement [45]. Knowledge capture is a process by
which knowledge (both external and internal to the organization) is obtained
and stored in the information systems like- traditional database systems,
document management systems, etc. After knowledge capturing this
captured knowledge is required to be organized and analyzed for strategic or
tactical decision making through on – line analytical process, and intelligence
systems, etc. Once the knowledge is captured and developed the next stage is
to distribute this knowledge through group support systems, computer
assisted communication technologies like – internet, EDI, e – mails, voice –
mails, video – conferencing, etc.
3.2.2 PROCESS INTEGRATION
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Process integration consists of some sub – strategies in itself or
some standard set of principles or methodologies and culture in its operations
environment. Under the framework the dimension process integration is
characterized by product and process architecture, knowledge sharing in
supply chain and integration of core processes. Every enterprise is part of the
“connected economy” and as such one need to extend the enterprise all the
way to the suppliers (and in turn to the supplier’s supplier) and the business
partners like distributors, retailers and ultimately the end customer [96].
Formal training not only enables learning but also ensures that practices in
the alliances are consistent and that these lead to standard processes [97].
Product and process architectures are the platforms that provide the back-
end flexibility so as to enable the organization in maintaining the core areas
despite highly dynamic and competitive market force. The most frequent
problem occurs due to conflicts between or within the product and process
architectures [98]. Therefore it is essential to develop modularity in the
architectures for solving conflicts and integrating the processes.
“Collaboration seems to capture the spirit and represent one of
the underpinning tenets of knowledge management, that of working together
to achieve common goals and objectives.” In knowledge-focused organizations,
knowledge sharing is highly dependent on effective ongoing collaboration.
Collaboration can reduce the cost of communication while expanding its reach
(time and distance), increase the number and quality of alternatives while
decreasing the cost of transactions, enable tight integration between firms
while reducing the cost of coordination. It comprises an important knowledge
management process that offers significant opportunities for improving
economic performance and competitiveness of many companies.
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Figure 3.1: ANP Framework Model
1. CULTURE 2. TOP MANAGEMENT
COMMITMENT 3. STRATEGIC
PLANNING 4. IT & WEB
5. PRODUCT AND PROCESS ARCHITECTURE
6. KNOWLEDGE FLOW 7. SUPPLY CHAIN 8. COLLABORATION
9. KNOWLEDGE CREATION 10. KNOWLEDGE WORKER 11. KNOWLEDGE SHARING 12. TRAINING &
EDUCATION
KNOWLEDGE INTENSIVE
EXISTING SYSTEM
KNOWLEDGE EXTENSIVE
Determination of the architectural model for the implementation of Knowledge Management in Indian Industries
ARCHITECTURAL WEIGHTED INDEX (AWI)
ORGANIZATIONAL PROCESS STRUCTURE INTEGRATION INNOVATION
COST COMPETITIVENESS RESPONSIVENESS
1
4 3
2 9
12 11
10 5
8 7
6
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3.2.3 INNOVATION
Today innovation has become the main stay of business and is
important for improving the business performance of organizations. Only
those organizations are able to differentiate themselves from their
competitors who make “intelligent use” of knowledge assets of the
organization for development of new products which help in attracting
customer’s interest and will give a competitive edge to the organization over
its rivals. Innovation of new products and services not only makes the
organization “ pro – active” in meeting needs of the customer which they have
not yet faced but will surely be facing in future but also opens the door for a
new venture, thus allowing the organization to go for business expansion
plans. Traditionally the innovation process in organizations is centralized to
one department like the Research and Development (R&D) department but
today the organizations are going for decentralized innovation process which
are well connected through the knowledge networks for the transfer of ideas,
creativity, information, etc based on which innovation of new product
development has become more collaborative than ever before [99]. Leaders
recognize the skills of human resources and understand that high
motivational supports creativity which is essential for innovation, also today
the innovation and knowledge creation are required for competitive
initiatives such as improving customer satisfaction, developing new products
and markets providing faster response [94]. For giving considerable benefits
of innovation and new product development, an organization have to
consistently apply knowledge management practices in its business which
helps them to understand market trends and customer – requirements and
puts useful knowledge into action for its innovation processes which leads to
improvement of business performance [100]. The classification and
codification of organizational knowledge leads to organizational innovation
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through two models on knowledge management for innovation, one is
“cognitive” model, in which knowledge for innovation is referred to as
objectively defined concepts and facts and the other is “community” model in
which knowledge for innovation is socially constructed and is based on
experience [101]. Thus, the selection of the right architectural model for the
implementation of KM is vital for the success of new innovations.
3.3 ALTERNATIVES
Alternatives are actually a way to achieve the overall objective
of the ANP model. The proposed work is to choose the best proposed
alternative through decision-making to select the architectural model for the
implementation of KM. Evaluation is carried out using ANP to determine the
best alternative. The three proposed alternatives are “Knowledge Extensive”
architectural model, “Knowledge Intensive” architectural model and
“Existing System” architectural model.
3.3.1 ALTERNATIVE 1: KNOWLEDGE EXTENSIVE ARCHITECTURAL MODEL
This architecture aims to implement KM on a wider frame,
which encompasses systems both within the organization and for its network
interactions. Recognizing the importance of KM at the basic level of
individual – group interactions, there is a need to develop individual and
group focus at all levels and interfacing of these is required with the view to
develop a culture of sharing in form of ‘working for company’ rather than
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‘working for myself’. The knowledge intensive architecture for a firm is shown
in fig 3.2.
Communities of practice are established frequently to generate
problem’s solution. Group discussions are used to bring out the ambiguities
developed by the individuals of the group. Realizing the importance of idea
management and innovations at the core of the organization, the firm
encourages creativity by establishing enhanced access facilitation to the
knowledge sources. The tacit interactions or facilitation are in the form of
guidance by experts and the establishment of “virtual teams” [102, 103]
across the enterprise or the establishment of “virtual organization” [104] with
collaborators. On a need – based manner consultancies are hired by firms
with an aim to acquire content, which is both the tacit and explicit
knowledge. To facilitate access to experts, the managers carefully assess
them to put ‘right person at right spot’, IT enabled “expert pointers” [105] are
developed for quick tracing by individuals in their own right from their
desktop via the intranet. The documented knowledge content existing in the
form of details related to product, process and technology as well as the
explicit knowledge content of the collaborators is put to easy and personalized
access for the employees, right at the desktop via intranets and extranets.
The access to knowledge that lies outside the company’s knowledge bases and
the relevant information form the Internet play equally important role in the
industries.
A non – hierarchical managerial structure facilitates the
architecture to avoid idea decay or knowledge loss due to social and
organizational factors. Along with this, knowledge managers and technical
filters need to exist to assess the quality and value of knowledge. Knowledge
managers being intermediary to the sources of knowledge and the
repositories play a crucial role from in assigning the taxonomy to the broad
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organizational knowledge and punitively deciding and developing channels
for knowledge flow [106].
The product and operational data is gathered and organized in
repositories for knowledge leverage and distribution. The warehoused atomic
data in the repositories is organized by database systems and extracted and
packaged by various IT tools like data mining and expert systems so as to
retrieve the data as knowledge at the appropriate time. This summarized
knowledge is presented with an intuitive, contextual and channelized access.
The repositories also serve to preserve knowledge by enabling capture and
storage of knowledge assets as well as experience and learning. These are put
to personalized access to enhance reuse, guarded by security systems and
privileged access to disallow misuse of knowledge. The filtration, storage and
analysis of historical knowledge are also done.
The product and process related data are distributed in
decentralized repositories for the functional components of modular product
and process architectures. They need to be integrated not only to support
concurrent engineering but also to enable a central single knowledge base
(CSKB) abstraction for enabling consistent and non – redundant behavior
among the processes and work teams [107]. “Knowledge Architecture”
identification allows improvement in functional components independently
and enables effective induction of technological advances. The modular
architectures allow leverage for likely changes due to environmental issues
and thus provide opportunity for the firm to remain competitive in the event
of stringent environmental regulations.
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COLLABORATORPEERS
K
N
O
W
L
E
D
G
E
C
U
S
T
O
M
E
R
KNOWLEDGE OUTFLOW
EXTERNAL CONTENT
TACIT
VIRTUAL TEAMS, SUPPLY CHAIN
NETWORK, CONSULTANCY, AND
ACQUISITION.
EXPLICIT
CONTENT SHARING,
RESEARCH, NEWS
FEEDS.
INTERNAL CONTENT
TACIT
EXPERTS, VIRTUAL TEAMS &
COMMUNITIES OF PRACTICE,
KNOWLEDGE WORKER.
EXPLICIT
PRODUCT, PROCESS,
TECHNOLOGY,
EXPERTISE.
KNOWLEDGE MANAGER & TECHNICAL
FILTER
QUALITY ASSESSMENT,
VALUE ASSESSMENT,
TAXONOMY ASSIGNMENT,
CHANNEL ASSIGNMENT,
HISTORICAL KNOWLEDGE FILTRATION.
COMMUNICATION TOOLS
INTRANETS & VIRTUAL TEAMS,
EXTRANET & VIRTUAL ORGANIZATION,
INTERNET & VIRTUAL EXPERTISE.
INFORMATION PROCESSING
TOOLS
GROUPWARE,
EXPERT SYSTEMS,
DOCUMENT & FILE MANAGEMENT,
DATABASE MANAGEMENT,
DATA MINING.
ORGANIZATIONAL PROCESSES &
REPOSITORIES
CONCERN FOR ENVIRONMENT
AGGREGATE, INDEX,
CLASSIFY,
CATEGORIZE, SORT,
SEARCH, PACKAGE,
SUMMARIZE.
PRODUCT DATA
MANAGEMENT,
INTEGRATION OF
INTRA & INTER
DEPARTMENTAL
DATABASES, DATA
WAREHOUSE.
EASY & CONTEXTUAL
ACCESS, KNOWLEDGE INPUT
ALONG AND UPDATING AFTER
THE JOB, SECURITY, PRIVILEGE OF
ACCESS.
CHANNELS FOR KNOWLEDGE FLOW
INFORMATION & LEARNING
CONTEXTUAL
INSIGHTS,
INTERPRETATIONS &
ANALYSIS,
PERSONALIZED
KNOWLEDGE, Ad –
HOC & SUMMARIZED
ACCESS.
FOCUS & DIRECT
MARTS, INTEGRATED
SUPPORT FOR
COCURRENT
ENGINEERING,
ABSTRACTION.
INTERNAL & EXTERNAL COMMUNITY,
DAILY BRIEFS,
WEBNAIRS,
INTERACTIVE TOOLS.
Figure 3.2: Architecture 1- The Knowledge Extensive Architectural Model
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Knowledge flow is channelized in the architecture for both
internal and external knowledge flow in the organization. Firm provides
personalized knowledge to employees. Experts’ insights and the summarized
analysis of information are available on a real time basis. The core processes
are facilitated through the access of knowledge both in the context they
require and also in an integrated manner with a view to provide consistent
information to work processes. Moreover, CSKB maintains consistent
knowledge of each customer, which enables personalized service. Knowledge
teams are set up to work as interface for potential bad customer experience.
Market knowledge is captured and integrated into the product data, which
can help in new product development. The learnings from the market test are
fed back to the tacit assets of the organization, which is useful in developing
multiple skills at all levels and improving intelligence of the organization.
These learnings are fed to the explicit assets for appropriate storage and
reuse. Off – site and on – site workers feed knowledge into the repositories
during their working. On completion of job they are encouraged to ‘rectify and
furnish’ the contents related to the specialty of job and process. Many
internal commentaries such as chairman’s speech, classroom sessions etc. are
continuously retained and disseminated. External commentary is in the form
of benchmarking data, and consultants’ involvement etc. Knowledge is shared
with the collaborators through extranets. Knowledge outflows for ‘non –
customers’ are mediated through whit papers, web portals and case studies.
3.3.2 ALTERNATIVE 2: KNOWLEDGE INTENSIVE ARCHITECTURAL MODEL
This architecture has inducted knowledge management
intensively within the organization, focuses market capture but
organizational interactions with collaborators are not perceived as a potential
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area for knowledge management endeavor. The knowledge intensive
architecture for a firm is shown in fig 3.3.
Like architecture 1, this architecture also terminates at meeting
the customer requirements and organizational needs of knowledge. It
contains the technological and non – technological infrastructure to support
knowledge management implementation within the organization. The firm
invests into developing very agile processes to support diverse market
strategies and a strong market interface rather than investing in costly, IT
enabled, knowledge infrastructure with its collaborators. Some relevant
strategies are on – demand of the customer; self – initiative in bringing a
wider range of products to market, ‘future - forecasting’ of the market
conditions and ‘event – based’ occasions to create an opportunity for group
meeting interaction and knowledge sharing with the marketing channels [108, 109].
It has established repositories to support these strategies on the
basis of lifecycle of the information they store. The IT and web uses are not so
extensive due to which the architecture is unable to sustain sharing of
knowledge within the supply chain partners. Such architectures may
occasionally lose few opportunities by not exploiting the knowledge
architectural aspect of suppliers’ technical know – how and their ability to fill
gaps in areas of mutual concern. However, knowledge architectures that are
strong in identifying opportunities within the organization are capable in
determining and solving the know – how scarcity in the organization [110].
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Knowledge Capture
Regular capture of tacit assets,
Capture of special events,
Capture from business environment.
Knowledge Storage
Integration through Data
Warehousing
Stable Storage
Dynamic Storage
Application Storage
Future Fore casting
Self – initiative
On – demand
Knowledge for Promotional
Purposes
Event – based Knowledge
Dissemination
C U S T O M E R
Information & Learning
Knowledge Refinement
Filtration Aggregation Sorting
De – redundancy Packaging
Dynamic Reprocessing
Periodic Review, Trend Analysis,
Value Added Knowledge Creation, Data Mining.
Figure 3.3: Alternative 2: The Knowledge Intensive Architectural Model
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3.3.3 ALTERNATIVE 3: EXISTING SYSTEM ARCHITECTURAL MODEL
This architecture corresponds to the existing system in the
organization. Typically, it lacks knowledge management as a separate
system. The architecture for the existing system of a firm is shown in fig 3.4.
Knowledge traverses in the organization but is generally
unexplored and uncaptured causing unnoticed loss and decay of knowledge. It
lacks knowledge – oriented architecture and infrastructure needed for
managerial and technological needs of the firm. Knowledge sharing is
requirement – triggered event and the transfer of knowledge occurs through
beauracratic procedures. A hierarchical management structure exists causing
decay of ideas and leading to lesser initiatives for innovation. Use of IT is not
knowledge oriented. Knowledge losses are sometimes uncontrolled.
The distinction of knowledge as tacit and explicit is absent due
to established knowledge management practices. There is only accidental
reuse of knowledge because knowledge sources, tacit (experts) and explicit
(product or customer information), are not easily available and hence avoided
by the employees. Individuals and group are seen as the basic knowledge
sources only in departments of research i.e., innovations are restricted to the
R&D department and hence no ‘innovation – culture’ exists. The inter –
departmental innovations and sharing are scanty. Discussions and meetings
form the major source of interactions for the integration of individual ideas.
Human Resource Department (HRD) focuses on improving the performances
and efficiency of individuals rather than creating a knowledge culture.
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S U P P L I E R S
C O M P E T I T O R S S T A K E H O L D E R S
HRD, R&D, SOCIETY, GOVERNMENT & TOP
MANAGEMENTCULTURE
C U S T O M E R S
Organizational Processes
Market Engagement Teams,
Equipments,
Flexibility and Integrity of
Processes,
Concurrent Engineering
Product Data Management
Processes
Collate & organize,
Integration of market
information into
product structure,
Integration of
databases in-and-
across Departments,
Easy retrieval of
product data, Security,
privilege of access,
and audit trials.
Tools
Data index,
Product structure
management,
File management,
Data translation and transfer,
Change and workflow control
and messages,
Application interfaces,
Reports and technology
Results
Data integrity,
Consistent and complete
information,
Reutilization of data,
Smooth information flow,
Non – redundancy of data,
Single source of data,
Integrated environment
support for concurrent
engineering.
INFORMATION & LEARNING
Figure 3.4: Alternative 3 – The Existing System Architectural Model
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Sharing knowledge with the supply chain partners is in form of
the assistance in technology and information emanating from customers.
Suppliers use their experience of supplying to several manufacturers under
varied market conditions. Knowledge pertaining to business environment is
collated via in – person reporting or through face – to – face interactions that
are generally time consuming and struggle in preserving knowledge for
reuse. The firms also hire consultancy firms and share their operational
knowledge.
The person seeking experts’ assistance has to put some efforts
on his part to get himself guided to the appropriate maturity that is needed
for getting communicable with experts. He may also reach a wrong person or
may be guided ineffectively resulting into a bad experience. So, next time he
would avoid this effort, leading to loss of ideas. Major reason for this is due to
less initiative taken on part of managers to put the ‘right person on the right
spot’ or lack of checking the relevancy and completeness of the knowledge
transferred. These would culminate into poor knowledge sharing culture and
inefficient knowledge management endeavors.
The firm loses huge knowledge in form of defections of
experienced employees for joining the competitor or the retirement of a top
manager. In the present system (architecture 3) there is no concept of storing
the knowledge of these experts for preservation. The historical knowledge in
papers or in computers is not filtered or analyzed exhaustively.
Flexible ‘product structure management’ and processes exist allowing
the firm to adapt to the changing market conditions. Data is collated in ‘separate
and independent’ product databases according to the product and process structures.
Production process integration is enabled by applications that facilitate interactions
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among product and process segments and data translation and transfer tools for
supporting concurrent engineering by facilitating an integrated and transparent
view [107]. The homogeneous departmental databases are integrated with the
heterogeneous interdepartmental databases. However, less integration among
databases across various functional segments of the firm such as production,
marketing, sales act as a bottleneck in developing a single central source of
knowledge.
The firm has plans to inform the reengineered changes to the
employees. No structured channels exist for the flow of knowledge in the
organization. There is no concept of providing knowledge access on personal basis.
Security system is established by a password – based system for access to the
information. Audit trials are set up to avoid loss due to breakdown of databases.
Knowledge is exchanged with other organizations by face – to – face interactions
among the experts or by sharing strategies and technical know – how. As part of its’
marketing interface, sales and marketing teams are set up but some customer loss
does occur due to inconsistent employee behavior towards the customer. Major
reason behind this is either less interactions among multiple knowledge teams
handling an employee or non – availability of a CSKB providing consistent
information about the customer. Customer loss is also due to the sluggish policies
like identification of product design improvement captured only through complaints
and service teams, which is seldom through customer demands.
Sharing knowledge in the enterprise is restricted to expert visits and
video conferencing. Language barriers exist among the globally distributed branches
due to cultural differences. Application of strategies shared is not very successful
due to cost factors hindering the communication.
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3.4 COMPARISON OF THE ALTERNATIVES
The comparison of the three architectural models is based on the
knowledge processes. KM process consists of three basic activities of creation,
organization and dissemination for application. The knowledge life cycle,
developed by APQC [111] describes the phases of organizational knowledge
from its creation to its application. At the core of Knowledge Management lie
the four processes: generating, organizing, developing and distribution [105].
Therefore, the three models are compared (Table 3.1) according to the type of
practices they establish corresponding to the KM activities at the identified
levels of individual or group, organization, market and enterprise level. The
categorical comparison is done according to [10]:
(i) creation of new knowledge at individual or group and organizational
level,
(ii) organization of knowledge at the individual or group and
organization level,
(iii) dissemination and application of knowledge at the individual or
group, organization and market level, and
(iv) creation, organization and distribution at enterprise level.
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Table 3.1
Comparison of the three architectural models
S.No ISSUES KE KI ES
CREATION: Individual / Group & Organization Level
1 Group Interaction
Individual and group focus at all levels, Communities of practice is established
frequently to generate problem's solution. Through discussions
2 Depth of Innovations
Enhanced knowledge source access facilitation for enhanced innovations by all individuals at organization and enterprise
level. An 'Innovation Culture' exists.
Innovations localized to R & D, cross departmental
innovations are scanty.
3 Role of HRD HRD with focus on establishing the
knowledge - culture of sharing and reuse of knowledge.
HRD with focus on performance of
individual.
4 Supply Chain Aspect
Virtual teams, knowledge sharing,
supply chain network, virtual
organization, and interface plug filling
in modular architecture.
Knowledge management
unexplored along the supply chain.
Supply chain assistance in
technology and expertise.
5 Environmental Awareness
Knowledge oriented IT tools for scanning competitor specific knowledge.
Face to face interactions and in person reporting for
environmental scanning.
6 Interactions
with Peers and Collaborators
Consultancy acquisitions, and mergers not just aim of wider tacit
knowledge and greater customer breadth but also
able to share explicit knowledge
content.
Explicit knowledge not shared with the
collaborators.
Mergers, consultancy
acquisitions for better performance.
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ORGANIZATION: Individual / Group & Organization Level
1 Expertise reach ability
Right person at right spot, Expert pointers for quick and effective tacit sharing.
Expertise source vagueness and
ambiguity.
2 Knowledge Capture
Capture and storage of knowledge assets fro preservation, easy access and availability of
explicit knowledge for reuse.
No defined concept of Knowledge assets
capture, hence its preservation and
reuse accidental and scanty.
3
Pointers to be captured
and organized
information
Pointers to enterprise wide data / information / knowledge and any
modifications available right at the desktop with the abstraction of a single source of
access.
Data index as pointers to data,
Data translation and transfer allowing transparency and Change control to identify range of
reengineering affected fields and
Messaging to broadcast resulting
changes in architectures.
4 Managerial Assessment
of Knowledge
Non - hierarchical managerial structure for no idea decay or knowledge loss, knowledge managers and technical filters for quality
and value assessment, taxonomy and channel assignment by the knowledge
managers.
No filter to access the value of
knowledge or to filter out the
relevant contents.
5 Historical Knowledge
Filtration, storage and analysis of historical
knowledge for reuse, Contextual access.
Issue history for historical data and
information.
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6 Operational
Integration & Support
Knowledge architecture identification
allowing independent component
improvement, Integration of decentralized
databases ( for modularity and
concurrent engineering
support) to form a central single
knowledge base (CSKB) for enabling consistent and non - redundant behavior
among processes and work teams.
Knowledge architecture
determining the know - how scarcity in the organization but not exploring
the suppliers technical know -
how storage repositories established
according to the diverse customer
oriented strategies and hence lifetime of the information
Product structure management and
processes management exist
for flexibility but no concept of modular
knowledge architecture, Integrated
homogeneous and heterogeneous
databases across departments but no
central single source of access exists ensuring consistency of
processes and work teams.
7 Information Analysis & Processing
Data warehousing, Data mining tools and Expert systems.
Product structure management, MRP
based product planning.
8 Environmental Concern
Induction of projected concern
for environment in the interfaces.
Processes inflexible to the changes
required for environmental
concern.
Modification required in
processes to build in the concern for
environment, driven by legislation and external checks.
DISSEMINATION & APPLICATION: Individual / Group, Organization & Market Level
1 Channels for Knowledge flow in the
organization
Channels for knowledge
dissemination to enable context based leverage,
personalized knowledge.
Isolated intranets for knowledge dissemination.
No structured channel of
knowledge inflows or outflows.
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2 Security Firewalls for selective inflow of
information, privilege of access to allow sharing and distribution of knowledge.
Security by password based
level access, audit trials and file management.
3 Organizational
knowledge exchange
Extranets set up for the collaborators
and peers knowledge sharing, knowledge outflow through company website and white
papers.
No extranets, just the intranets.
Face to face or report form
knowledge sharing with collaborators
and peers.
4 Customer
facing interface
Knowledge team as interface to avoid customer loss, and capturing greater
market share.
Strategically diverse interface for
greater customer capture.
Sales and marketing teams,
customer loss due to inconsistent
behavior on part of customer facing
teams.
5 Market awareness
Quick market knowledge capture and integration, expert tips available on a continual mode and integrated into
business processes.
Identification and integration into
system for product design modifications
captured through complaints and
service teams and sometimes through customer demands.
6 Intelligence loop
Complete diffusion and integration of learning into the organization for skill
development at all levels and knowledge preservation, knowledge input along the work and rectification and furnishing in
completion.
Learning localized to the customer
facing teams and on - site workers,
knowledge input along the job.
ISSUES AT ENTERPRISE LEVEL
1
Creation of new strategies
by sharing diverse market
knowledge
"Virtual teams" to share the strategies successful in their markets.
Sharing according to requirement, and restricted to expert
visits and video conferencing.
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2
Centrally organizing
the enterprise knowledge
Knowledge hub and centers for centralized
innovations, and integrating and
multiplexing knowledge of enterprise's branches.
Sharing only on requirement basis.
3 Knowledge dissemination
Knowledge de -multiplexers to bring down the
language barriers, view of a common
culture establishment
across the enterprise.
IT to bring down the language barriers
and facilitating access to global
enterprise knowledge on
desktop.
Language barriers due to cultural
differences among the globally distributed branches.
4 Application of
Shared strategies
IT advances allowing the enhanced communication for effective strategy
application.
Less successful due to cost factors hindering the
communication.
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CHAPTER 4 CHAPTER 4
DECISION ENVIRONMENT DECISION ENVIRONMENT
4.1 Analytic Network Process (ANP)4.1 Analytic Network Process (ANP)
The Analytic Hierarchy Process (AHP) was developed and
documented primarily by Thomas Saaty. The strength of the AHP method
lies in its ability to structure a complex, multi – person, multi – attribute,
and multi – period problem hierarchically. Pair wise comparisons of the
elements (usually, alternatives and attributes) can be established using scale
indicating the strength with which one element dominates another with
respect to a higher element. This scaling process can be translated into
priority weights (scores) for comparisons of alternatives. Analytic Network
Process (ANP) [112] is a comprehensive decision – making technique that
captures the outcome of the dependence and feedback within and between the
clusters of elements. Analytical Hierarchy Process (AHP) serves as a starting
point for ANP. Analytical Network Process (ANP) is a more general form of
AHP, incorporating feedback and interdependent relationships among
decision attributes and alternatives.
ANP is a coupling of two parts, where the first consists of a control
hierarchy or network of criteria and sub – criteria that controls the
interactions, while the second part is a network of influences among the
elements and clusters [12].
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ANP based approach is used for selecting architectural model for the
implementation of Knowledge Management in Indian Industries. The reasons
for selecting ANP are: (i) selecting architectural model for the
implementation of KM is a multi – criteria decision – making problem,
(ii) many factors, enablers and criteria in decision environment are
interdependent on one another and (iii) most importantly some of the criteria,
enablers and dimensions are subjective due to which synthetic score through
simple weightage method is difficult to arrive at. Analytical Hierarchical
Process (AHP) is similar to ANP but it cannot capture interdependencies [12,
113].
Hierarchical representation is an important component of ANP,
however strict hierarchical structure is not recommended, as is the case with
AHP. The ANP technique allows for more complex relationships among the
decision levels and attributes. The ANP consists of coupling of two phases.
The first phase consists of a control hierarchy of network of criteria and sub-
criteria that control the interactions. The second phase is a network of
influences among the elements and clusters. The network varies from criteria
to criteria and thus different super matrices of limiting influence are
computed for each control criteria. Finally each one of these super matrices is
weighted by its priority of its control criteria and results are synthesized
through addition for the entire control criterion. [113].
Some of the fundamental ideas in support of ANP are [84]:
ANP is built on the widely used AHP technique.
ANP allows for interdependency, therefore ANP goes beyond AHP.
The ANP technique deals with dependence within a set of elements
(inner dependence) and among different sets of elements (outer
dependence).
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The network structure of the ANP makes possible the representation
of any decision problem without concern for what criteria comes first
and what comes next as in a hierarchy.
The ANP is a non – linear structure that deals with sources, cycles and
sinks having a hierarchy of linear form with the goals in the top level
and the alternatives in the bottom level.
ANP portrays a real world representation of the problem under
consideration by prioritizing not only just the elements but also the
groups or clusters of elements as is often necessary.
The ANP utilizes the idea of a control hierarchy or a control network in
dealing with different criteria, eventually leading to the analysis of
benefits, opportunities, costs and risks.
4.2 Advantages of ANP
ANP is a comprehensive technique that allows for the inclusion of all
the relevant criteria; tangible as well as intangible, which have some
bearing on decision – making process [112].
AHP models a decision – making framework that assumes uni –
directional hierarchical relationship among decision levels, whereas
ANP allows for more complex relationship among the decision levels
and attributes as it does not require a strict hierarchical structure.
In decision – making problems, it is very important to consider the
interdependent relationship among criteria because of the
characteristics of interdependence that exists in real life problems.
The ANP methodology allows for the considerations of
interdependencies among and between levels of criteria and thus is an
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attractive multi – criteria decision – making tool. This feature makes it
superior from AHP which fails to capture interdependencies among
different enablers, criteria and sub – criteria [113].
ANP methodology is beneficial in considering both qualitative as well
as quantitative characteristics which need to be considered, as well as
taking non – linear interdependent relationship among the attributes
into consideration [77].
ANP is unique in the sense that it provides synthetic scores, which is
an indicator of the relative ranking of different alternatives available
to the decision maker.
4.3 Disadvantages of ANP
Identifying the relative attributes of the problem and determining
their relative importance in decision – making process requires
extensive discussion and brainstorming sessions. Also, data acquisition
is a very time intensive process for ANP methodology.
ANP requires more calculations and formation of additional pair – wise
comparison matrices as compared to the AHP process. Thus, a careful
track of matrices and pair – wise comparisons of attributes is
necessary.
The pair – wise comparison of attributes under consideration can only
be subjectively performed, and hence their accuracy of the results
depends on the user’s expertise knowledge in the area concerned.
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4.3 Outline of the steps of the ANP useful for calculation:
Determine the control hierarchies including their criteria for
comparing the components of the system and their sub criteria for
comparing the elements of the system.
For each control criterion or sub – criterion, determine the clusters of
the system with their elements.
Arrange the clusters and their elements in a convenient way (perhaps
in a column). Use the identical label to represent the same cluster and
the same elements for all the control criteria.
Perform paired comparisons on the clusters as they influence each
cluster and on those that it influences, with respect to that criterion.
Perform paired comparisons on the elements within the clusters
themselves according to their influence on each element in another
cluster they are connected to (or elements in their own cluster). The
comparisons are made with respect to a criterion or sub – criterion of
the control hierarchy.
Sum the values in each column of the pair – wise comparison matrix.
Divide each element in a column by the sum of its respective column.
The resultant matrix is referred to as the normalized pair wise
comparison matrix.
Sum the elements in each row of the normalized pair wise comparison
matrix, and divide the sum by the n elements in the row. These final
numbers provide an estimate of the relative priorities for the elements
being compared with respect to its upper level criterion.
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Find the consistency ratio of each pair wise matrix. It should not be
above 0.05 for n = 3 and 0.08 for n = 4.
For each control criterion, construct the super matrix (M) by laying out
the clusters in the order they are numbered and all the elements in
each cluster both vertically in the left and horizontally at the top.
Assign value zero for no influence.
Compute the limiting priorities of each super matrix according to
whether it is irreducible (primitive or imprimitive [cyclic] or it is
reducible with one being a simple or a multiple root and whether the
system is cyclic or not.
Raise MW to stabilize the values in the super matrix.
Synthesize the limiting priorities by weighting each limiting super
matrix by the weight of its control criterion and adding the resulting
super matrices.
Repeat the synthesis for each of the four control hierarchies: one for
benefits, one for costs, one for opportunities and a fourth for risks.
Synthesize the results from the four control hierarchies by multiplying
the benefits by the opportunities and dividing by the costs multiplied
by the risks. Then, read off the highest priority alternative or the
desired mix of alternatives.
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CHAPTER – 5
APPLICATION OF ANP FRAMEWORK MODEL
ANP methodology is applied in this thesis for the determination
of the architectural model for the implementation of KM in industries. The
analysis and implementation of the ANP model with the determinant
Responsiveness is illustrated as follows:
5.1 STEP 1: MODEL DEVELOPMENT & PROBLEM FORMULATION
In this step, the decision problem is structured into its
important components. The relevant criteria and alternatives are chosen on
the basis of the review of literature and discussions with the experts from the
academia and industry. The development of the model involves formulating
the problem and carefully selecting the attributes at different levels and
structuring them in the form of a control hierarchy where the criteria at the
top level in the model have the highest strategic value. The top – level
criteria in this model are Cost, Competitiveness (COMP) and Responsiveness
(RESP). These three criteria are termed as the determinants. In the second
level of hierarchy, three sub – criteria termed as dimensions of the model is
placed which supports all the three determinants at the top level of
hierarchy. These are Organizational Structure (OS), Process Integration (PI)
and Innovation (INN). In this ANP model, each of the four dimensions has
some enablers, which help to achieve that particular dimension. For example,
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the dimension OS is supported by the enablers Culture (CU), Top
Management Commitment (TMC), Strategic Planning (SP) and Information
Technology & Web (ITW). These enablers also have some interdependency on
one another. The degree of interdependency may vary from case to case and
would be captured in later steps. The strength of the ANP model is the
feedback and the network structure of the ANP makes the representation of
the decision problem possible without much concern for what comes first and
what comes next in a hierarchy. The objective of this hierarchy is to select the
best possible alternative that will best meet the goal of implementing
knowledge management in industry. The ANP model so developed is
presented in Fig. 3.1. The alternatives that the decision maker wishes to
evaluate are shown at the bottom of the model. The opinion of the knowledge
worker of the company was sought in the comparisons of the relative
importance of the criteria and the formation of pair – wise comparison
matrices to be used in the ANP model. The results of all the three
determinants would be used in the calculation of Architectural Weighted
Index (AWI), which indicates the score assigned to an architectural model for
the implementation of KM in the industries. One of the determinants can
have more important criteria for a particular alternative, but here the
analysis was made by the combined effect of all the determinants.
5.2 STEP 2: DECISION MAKING
ANP requires decision making for the pair wise comparisons in
between different sublevel components and interdependency relationships
among attributes and dependency relationships across two different sublevel
hierarchies. Decision making is performed by critical review of the literature
and also by considering expert opinion from the academia and industry. On a
scale of one to nine (Saaty scale) [112], for a series of pair – wise comparisons
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with respect to upper level criteria, decisions were given and in case of
interdependencies, components within the same level are viewed as control
components for each other.
5.2.1 PAIR – WISE COMPARISON OF DETERMINANTS
In this step, a pair wise comparison is made between the
determinants for obtaining the relative weights in between them. The
weighted priority (e–vector) is calculated after obtaining the relative weights
in between the determinants.
For obtaining the relative weights in between the determinants,
a question should be asked to make a decision. The question is like “what is the relative impact on selection of architectural model, when cost is compared to competitiveness?” The answer on a scale of 1 – 9 was 2 and this is placed
as a second entry of cost row. Similarly, for the remaining, the comparisons
are made and the weighted priority (e–vector) is calculated, see Table 5.1.
Table 5.1
Pair wise Comparison Matrix for relative importance of Determinants
COST COMP RESP e–Vector
COST 1 2 4 0.5714
COMP 0.5 1 2 0.2857
RESP 0.25 0.5 1 0.1429
Total 1.75 3.5 7 1
Consistency Ratio: 0 (See Appendix I).
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The e–vectors (also referred to as local priority vector) are the
weighted priorities of the determinants and shown in the last column of the
matrix. A two–stage algorithm is used for computing e–vector. For the
computation of the e–vector, we first add the values in each column of the
matrix. Then, dividing each entry in each column by the total of that column,
the normalized matrix is obtained which permits the meaningful comparison
among elements. Finally, averaging over the rows is performed to obtain the
e–vectors. These e–vectors would be used in Table 6.1 for the calculation of
architectural weighted index of alternatives.
5.2.2 PAIR – WISE COMPARISON OF DIMENSIONS
In this step, a pair wise comparison matrix is prepared for
determining the relative importance of each of these dimensions in the
implementation of the architectural model clusters on the determinant. One
such matrix for the determinant responsiveness is shown in Table 5.2. There
will be two more matrices, one for each of the determinants cost and
competitiveness. One can also put up a question here like “how important is the organizational structure in comparison to process integration for selection of architectural model when considering control hierarchy for responsiveness?” From this table, the results of the comparison (e-vectors) of
the dimensions for the determinant responsiveness are carried as Pja in Table
5.8.
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Table 5.2
Pair wise Comparison Matrix for relative importance of Dimensions on Determinant / Responsiveness:
OS PI INN e – vector
OS 1 4 6 0.6584
PI 0.2500 1 5 0.2618
INN 0.1667 0.2000 1 0.0798
Total 1.4167 5.2 12 1
5.2.3 PAIR – WISE COMAPRISON MATRICES BETWEEN COMPONENT / ATTRIBUTE LEVEL
In this step, for a determinant and within a given dimension cluster
pair – wise comparison is done between the applicable attributes. The pair –
wise comparison matrix for the dimension Organizational Structure under
the determinant Responsiveness is shown in Table 5.3. One can also put up a
question here like “what is the relative importance of CU in comparison to TMC in achieving responsiveness?” Under each determinant for all of the
dimension clusters, the dependency relationships between attribute enablers
are made.
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Table 5.3
Pair wise Comparison Matrix for attribute enablers under determinant / Responsiveness and the dimension / Organizational Structure:
CU TMC SP ITW e – vector
CU 1 0.2500 4 0.3333 0.2121
TMC 4 1 0.5000 0.3333 0.2163
SP 0.2500 2 1 0.5000 0.1786
ITW 3 3 2 1 0.3930
Total 8.25 6.25 7.5 2.1666 1
In Table 5.3, the relative importance of CU when compared to
SP with respect to OS, in achieving the Responsiveness, is 4. From Table 5.3,
it is also observed that the enabler ITW has the maximum influence (0.3930)
on OS in improving the Responsiveness. Similarly, SP has the minimum
influence (0.1786) on OS in improving the Responsiveness. The number of
such pair – wise comparison matrices depends on the number of
determinants and the dimensions in the ANP model. In this model, 9 such
pair – wise comparison matrices are formed. The e – vectors obtained from
these matrices are imported as Adkja in Table 5.8.
5.2.4 PAIR – WISE COMAPRISON MATRICES FOR INTERDEPENDENCIES
In this step, pair wise comparisons are done to consider the
interdependency relationship in between the attribute enablers for a
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particular dimension cluster. One such comparison under determinant
responsiveness is illustrated in Table 5.4.
It represents the result of OS / Responsiveness cluster with CU
as the control attribute over other enablers. The question asked to the
decision maker for evaluating the interdependencies is ‘when considering CU
with regards to increasing Responsiveness, “what is the relative impact of
enabler a when compared to enabler b?”. For example, ‘when considering CU,
with regards to increasing Responsiveness, what is the relative impact of
TMC when compared to SP?’
Table 5.4
Pair-wise comparison matrix for attribute enablers under determinant – Responsiveness and the Dimension, OS / Responsiveness / CU:
CU TMC SP ITW e – vector
TMC 1 0.1667 4 0.2200
SP 6 1 5 0.6864
ITW 0.2500 0.2000 1 0.0936
Total 7.25 1.3667 10 1
From Table 5.4, it is observed that SP (0.6864) has the
maximum impact on OS/Responsiveness cluster with CU as the control
enabler over others. For each determinant, there will be 12 such matrices at
this level of relationship. The e – vectors from these matrices are used in the
formation of super matrices. As there are three determinants, 36 such
matrices will be formed. The e – vectors from matrix in Table 5.4 have been
used in 2nd row of the super matrix in Table 5.5.
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5.3 STEP 3: SUPERMATRIX FORMATION AND ANALYSIS
The super matrix allows for a resolution of the
interdependencies that exist among the elements of a system. It is a
partitioned matrix where each sub–matrix is composed of a set of
relationships between and within the levels as represented by the decision
maker’s model. In this model, there are three super matrices for each of the
three determinants of knowledge management Analytical network, which
need to be evaluated. One such super matrix M, shown in Table 5.5, presents
the results of the relative importance measures for each of the enablers for
the determinant Responsiveness. The values of the elements of the super
matrix M have been imported from the pair–wise comparison matrices of
interdependencies (for example, Table 5.4). As there are 12 such pair-wise
comparison matrices, one for each of the interdependent enablers in the
competitiveness, there will be 12 non–zero columns in this super matrix.
Each of the non–zero values in the column is the relative importance weight
associated with the interdependent pair–wise comparison matrices. In the
next stage, the super matrix M is made to converge to obtain a long–term
stable set of weights. For convergence to occur, super matrix needs to be
‘column stochastic’, i.e. the sum total of each of the columns of the super
matrix needs to be one. Raising the super matrix M to the power Mk, where k
is an arbitrarily large number, allows for the convergence of the
interdependent relationships [77]. In this example, convergence is reached at
M69. The converged super matrix is shown in Table 5.6.
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Table 5.5 Super matrix for Responsiveness before Convergence RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0 0.3699 0.1018 0.3137 0 0 0 0 0 0 0 0
TMC 0.2200 0 0.5321 0.3331 0 0 0 0 0 0 0 0
SP 0.6864 0.2979 0 0.3532 0 0 0 0 0 0 0 0
ITW 0.0936 0.3323 0.3661 0 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0 0.1199 0.1038 0.1560 0 0 0 0
KF 0 0 0 0 0.1279 0 0.6651 0.6196 0 0 0 0
SC 0 0 0 0 0.3601 0.2721 0 0.2243 0 0 0 0
COL 0 0 0 0 0.5120 0.6080 0.2311 0 0 0 0 0
KC 0 0 0 0 0 0 0 0 0 0.0952 0.1181 0.1199
KW 0 0 0 0 0 0 0 0 0.2635 0 0.2431 0.6080
KS 0 0 0 0 0 0 0 0 0.1275 0.2543 0 0.2721
TE 0 0 0 0 0 0 0 0 0.6091 0.6505 0.6389 0
Table 5.6 Super matrix for Responsiveness after Convergence (M69)
RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0.2027 0.2027 0.2027 0.2027 0 0 0 0 0 0 0 0
TMC 0.2779 0.2779 0.2779 0.2779 0 0 0 0 0 0 0 0
SP 0.3001 0.3001 0.3001 0.3001 0 0 0 0 0 0 0 0
ITW 0.2212 0.22123 0.2212 0.2212 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0.1142 0.1142 0.1142 0.1142 0 0 0 0
KF 0 0 0 0 0.3259 0.3259 0.3259 0.3259 0 0 0 0
SC 0 0 0 0 0.2093 0.2093 0.2093 0.2093 0 0 0 0
COL 0 0 0 0 0.3214 0.3214 0.3214 0.3214 0 0 0 0
KC 0 0 0 0 0 0 0 0 0.1001 0.1001 0.1001 0.1001
KW 0 0 0 0 0 0 0 0 0.3124 0.3124 0.3124 0.3124
KS 0 0 0 0 0 0 0 0 0.1986 0.1986 0.1986 0.1986
TE 0 0 0 0 0 0 0 0 0.3910 0.3910 0.3910 0.3910
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5.4 STEP 4: EVALUATION OF ALTERNATIVES
The final set of pair–wise comparisons is made for the relative
impact of each of the alternatives Knowledge Extensive, Knowledge Intensive
and Existing System on the enablers in influencing the determinants. The
number of such pair–wise comparison matrices is dependent on the number
of enablers that are included in each of the determinants. In our present case,
there are 12 enablers for each of the determinants, which lead to 36 such
pair–wise matrices. One such pair–wise comparison matrix is shown in Table
5.7, where the impacts of three alternatives are evaluated on the enabler CU
in influencing the determinant Responsiveness. The e–vectors from this
matrix are used in columns 6 – 8 of compatibility desirability indices matrix
in Table 5.8. The columns 6 – 8 in Table 5.8 correspond to Knowledge
Extensive, Knowledge Intensive and Existing System architectural models
respectively.
Table 5.7
Pair wise comparison matrix for the relative importance of alternatives on enablers for RESP / OS / CU
KE KI ES e – vector
KE 1 3 4 0.5940
KI 0.3333 1 4 0.2967
ES 0.2500 0.2500 1 0.1093
Total 1.5833 4.25 9 1
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5.5 STEP 5: DESIRABILITY INDEX
The equation of desirability index, Dia for alternative i and
determinant a is defined as [113]:
(1) D I
ikjakja kjaja1 1
SA APjaj K
iai k
D= =
≡∑∑
Where
Pja is the relative importance weight of dimension j on the determinant of a,
ADkja is the relative importance weight attribute enabler k of dimension j in
the determinant of (D) relationships between component levels,
AIkja is the stabilized relative importance weight (determined by the super
matrix) of attribute enabler k of dimension j in the determinant of network
for interdependency (I) relationships within the knowledge management
attribute enablers’ component level,
Sikja is the relative impact of knowledge management implementation
alternative i on knowledge management enabler k of dimension j of
knowledge management control network a,
Kja is the index set of attribute enablers for dimension j in for determinant
control a, and J is the index set for the dimensions (same for all control
hierarchies).
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Table 5.8 shows the desirability indices calculated for the
determinant (Di responsiveness). It is based on using the relative weights obtained
from the pair – wise comparison of alternatives, dimensions and weights of
enablers from the converged super matrix. These weights are used to
calculate a score for the determinants of architectural weighted index (AWI)
for each of the alternatives. In Table 5.8, the values of third column are
copied from Table 5.2 which is obtained by comparing the relative impact of
the dimensions on the determinant Responsiveness. For example, in
improving the responsiveness, the role of organizational structure is found to
be the most important (0.6584), which is followed by process integration
(0.2618) and innovation (0.0798). The values of the fourth column are copied
from Table 5.3 which is obtained by comparing the relative impact of the
enablers on the dimension Organizational Structure for the determinant
Responsiveness and values are also obtained from two more tables made for
the other two dimensions Process Integration and Innovation. The values in
the fifth column of Table 5.8 are the stable independent weights of the
enablers obtained from the converged super matrix (Table 5.6).
The next three columns are from the pair – wise comparison
matrices giving the relative impact of each of the alternatives on the
enablers. The final three columns represent the weighted values of the
alternatives (Pja× ADkja ×AIkja× Sikja) for each of the enablers.
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Table 5.8
Architectural Weighted Desirability Index for Responsiveness
Dimension Enablers Pja ADkja AIkja S1 S2 S3 KE KI ES
CU 0.6584 0.2121 0.2027 0.5940 0.2967 0.1093 0.0168 0.0084 0.0031
OS TMC 0.6584 0.2163 0.2779 0.5516 0.2768 0.1716 0.0218 0.0110 0.0068
SP 0.6584 0.1786 0.3001 0.6777 0.2418 0.1281 0.0239 0.0085 0.0045
ITW 0.6584 0.3930 0.2212 0.6505 0.2543 0.0952 0.0372 0.0146 0.0054
PPA 0.2618 0.0735 0.1142 0.7093 0.2141 0.0958 0.0016 0.0005 0.0002
PI KF 0.2618 0.4890 0.3529 0.6194 0.2842 0.0964 0.0280 0.0128 0.0044
SC 0.2618 0.3049 0.2093 0.5963 0.3191 0.0846 0.0100 0.0053 0.0014
COL 0.2618 0.1326 0.3214 0.6807 0.2014 0.1179 0.0076 0.0022 0.0013
KC 0.0798 0.0816 0.1001 0.6505 0.2543 0.0952 0.0004 0.0002 0.0001
INN KW 0.0798 0.2344 0.3124 0.7071 0.2014 0.0915 0.0041 0.0012 0.0005
KS 0.0798 0.1744 0.1986 0.6853 0.2213 0.0934 0.0019 0.0006 0.0003
TE 0.0798 0.5097 0.3910 0.6080 0.2721 0.1199 0.0097 0.0043 0.0019
Total 0.1630 0.0696 0.0299
For the purpose of illustration, the value corresponding to the above formula is shown below:
(0.6584 × 0.2121 × 0.2027 × 0.5940 = 0.0168) Knowledge Extensive
(0.6584 × 0.2121 × 0.2027 × 0.2967 = 0.0084) Knowledge Intensive
(0.6584 × 0.2121 × 0.2027 × 0.1093 = 0.0031) Existing System
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The summations of these results, for Responsiveness of each of
these alternatives, are presented in the final row of Table 5.8. These results
indicate that the Knowledge Extensive architectural model with a value of
0.1630 has maximum influence on the responsiveness. It is followed by
Knowledge Intensive architectural model (0.0696) and Existing System
architectural model (0.0299). Till this step, the analysis has been conducted
only for the determinant Responsiveness. Similar analysis is carried out for
other two determinants. In the next step, an index would be calculated to
capture the achievement of overall goal of selecting an alternative.
5.6 STEP 6: CALCULATION OF ARCHITECTURAL WEIGHTED INDEX (AWI)
The architectural weighted index (AWI) for an alternative i is
the summation of the products of the desirability indices (Dia) and the
relative importance weights of the determinants (Ca) for the selection of the
architectural model for the implementation of KM. The pair – wise
comparison relationships among different determinants from Table 5.1 shows
that the determinant Cost (0.5714) has got maximum importance in the
selection of the architectural model for the implementation of KM followed by
determinant Competitiveness (0.2857) and determinant Responsiveness
(0.1429). So the organization should focus on Cost by having good
infrastructure, good networking, good organizational structure, and also
should enhance their innovation for attracting customer’s interest and being
pro – active in meeting their demands.
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CHAPTER – 6
RESULTS AND ANALYSIS
6.1 RESULTS
The Knowledge Extensive architectural model intends to
implement Knowledge Management (KM) in a wider prospect. It is comprised
of the systems both within the organization and for its network interactions.
It believes in developing a culture of sharing in the form of “working for the
industry” rather than “working for myself”. It supports non – hierarchical
managerial structure to avoid idea of decay of knowledge. It advocates
development of multiple skills at all levels and improving intelligence of the
organization. Thus, it works for the overall improvement of the individual
and the organization. The Knowledge Intensive architectural model
implements KM intensively within an organization. It focuses on the market
capture and developing very agile processes to support diverse market
strategies. It does not perceive organizational interaction with the
collaborators as a potential area for KM strives. It is capable of determining
and solving the know – how scarcity in the organization. The Existing System
architectural model represents the existing structure of an organization when
KM has not been implemented. In this model, everything is requirement
initiated meaning that whenever requirement arises for a particular thing
then it is done. It has a hierarchical management structure then causing
decay of ideas and leading to lesser initiatives for innovation. In this type of
system, innovation is restricted to the R & D department. It does not have a
concept of storing knowledge of the experts who are retiring for preservation,
so that it can be used in future.
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The ANP model proposed is an aid to the people working for the
implementation of KM in arriving at prudent decisions when the complexity
of decision variables and multi – criteria decision environment make their
decision task quite complicated. This ANP model is used for the selection of
the architectural model best suited for the implementation of KM in
industries. This could serve as one of the important tools for taking a
strategic decision of this type. The criteria and attributes that are used in
this model focus on KM and the requirements for the selection of the
architectural model. Here, it is essential to discuss the priority values for the
determinants, which influence the decision of selecting the architectural
model for the implementation of KM.
It has been observed (from Table 5.1) that the cost (0.6232) is
the most important criteria in the selection of the architectural model for the
implementation of KM. This is followed by competitiveness (0.2305) and
responsiveness (0.1373). In the selection of the architectural model, it is seen
that the architectural model should take care of the Cost and should also
increase the Competitiveness. It means that the architectural model
supporting cost and competitiveness is considered as the favorable one.
Responsiveness is less supported because if an organization is having
responsiveness then only it can think of achieving competitiveness and also
take care of the cost.
The ANP model is capable of handling interdependencies and
present decision model provides values in the form of weighted index for the
three different architectural models in order to select the model for the
implementation of KM. The final normalized values for Architectural
Weighted Index (AWI) relationship (see Table 6.1) for Knowledge Extensive,
for Knowledge Intensive and for Existing system architectural model.
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Table 6.1
Architectural Weighted Index (AWI) for various alternative KM architectural models under selection of the architectural model
Alternatives
(Models)
Criteria Calculated weights
for alternatives
Cost Competitiveness ResponsivenessAWI NORM
0.5714 0.2857 0.1429
Knowledge
Extensive 0.1661 0.1606 0.1630 0.1641 0.5758
Knowledge
Intensive 0.0938 0.0692 0.0696 0.0833 0.2924
Existing
System 0.0421 0.0323 0.0299 0.0376 0.1318
Total 0.285 1
For the successful implementation of KM, the ANP framework
suggests that with the existing priority levels of architectural weighted
determinants, normalized value for Knowledge Extensive architectural model
is higher than that of Knowledge Intensive or Existing System architectural
model.
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6.2 SENSITIVITY ANALYSIS Sensitivity analysis is as important concept for the effective use
of any quantitative decision making model. Sensitivity analysis is carried out
in this present thesis work to analyze the changes in the Architectural
Weighted Index (AWI) for the Knowledge Extensive, Knowledge Intensive
and the Existing System architectural models with the variations in the
expert’s opinion towards Cost with respect to Competitiveness, Cost with
respect to Responsiveness and Competitiveness with respect to
Responsiveness. Overall objective of the sensitivity analysis is to see the
robustness of the proposed framework due to variation in the expert opinion
from academia in assigning the weights during comparison. Table 6.1
indicates how the architectural weighted index (AWI) for the proposed
framework of the three architectural models varies with changing priority of
cost, competitiveness and responsiveness. When the overall objective is to
reduce the cost, desirability indices are higher for Knowledge Extensive
architectural model than the Knowledge Intensive and Existing system
architectural model. In relation to enhancing the competitiveness, knowledge
extensive architectural model is having a higher desirability index than
Knowledge intensive and existing system architectural model. In an effort to
increase responsiveness, again the knowledge extensive architecture is
having a higher desirability index than knowledge intensive and existing
system architectural model. In Fig 6.1, X – axis represents the relative weight of cost
compared to competitiveness. The relative weights are in the scale of 1/9 – 9
(Saaty scale). Y – axis represents the normalized value of architectural
weighted index (AWI). These weights are obtained using the ANP framework,
which captures the interdependence among variables for selection of the
architectural model. In the present ANP framework, the experts have
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assigned relative weight of 2 when cost is compared to competitiveness on
selection of architectural model. The graph shown in fig 6.1 is drawn using three sets of
normalized values for Knowledge Extensive, Knowledge Intensive and the
Existing System architectural models.
Figure 6.1 Variation in priority of KM Architectural Models with the Changes in the Weights assigned to Cost with respect to Competitiveness.
These values are plotted against the relative weights assigned
when cost is compared to competitiveness. When the graph is considered for
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Knowledge Extensive architectural model, it shows that when the
competitiveness is increased, the cost remains constant initially but after
sometime it starts decreasing. When considered for Knowledge Intensive
architectural model, it shows that when competitiveness is increased, the cost
remains constant initially but after sometime it starts increasing. Similarly
for Existing System architectural model, when competitiveness is increased,
the cost remains stable. Thus, it can be concluded from the graph that
Knowledge Extensive architectural model is the model which increases the
competitiveness without increasing the cost and sometimes in long run, it
also reduces the cost.
In Fig 6.2, X – axis represents the relative weight of cost
compared to responsiveness. The relative weights are in the scale of 1/9 – 9
(Saaty scale). Y – axis represents the normalized value of architectural
weighted index (AWI). These weights are obtained using the ANP framework,
which captures the interdependence among variables for selection of the
architectural model. In the present ANP framework, the experts have
assigned relative weight of 4 when cost is compared to responsiveness on
selection of architectural model.
The graph shown in fig 6.2 is drawn using three sets of
normalized values for Knowledge Extensive, Knowledge Intensive and the
Existing System architectural models. These values are plotted against the
relative weights assigned when cost is compared to responsiveness.
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Normalized Weighted Index
Variation in the weights assigned when Cost is compared to Responsiveness
Knowledge ExtensiveKnowledge IntensiveExisting System
Figure 6.2 Variation in priority of KM Architectural Models with the Changes in the Weight assigned to Cost with respect to Responsiveness.
When the graph is considered for Knowledge Extensive
architectural model, it shows that when the responsiveness is increased, the
cost remains constant initially but after sometime it starts decreasing. When
considered for Knowledge Intensive architectural model, it shows that when
competitiveness is increased, the cost is increasing gradually. Similarly for
Existing System architectural model, when competitiveness is increased, the
cost remains stable. Thus, it can be concluded from the graph that Knowledge
Extensive architectural model is the model which increases the
responsiveness without increasing the cost and sometimes in long run, it also
reduces the cost.
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In Fig 6.3, X – axis represents the relative weight of
competitiveness compared to responsiveness. The relative weights are in the
scale of 1/9 – 9 (Saaty scale). Y – axis represents the normalized value of
architectural weighted index (AWI). These weights are obtained using the
ANP framework, which captures the interdependence among variables for
selection of the architectural model. In the present ANP framework, the
experts have assigned relative weight of 2 when competitiveness is compared
to responsiveness on selection of architectural model. The graph shown in fig 6.3 is drawn using three sets of
normalized values for Knowledge Extensive, Knowledge Intensive and the
Existing System architectural models. These values are plotted against the
relative weights assigned when competitiveness is compared to
responsiveness.
When the graph is considered for Knowledge Extensive
architectural model, it shows that the competitiveness and responsiveness
are stable and that they do not affect each other much. Therefore, the graph
is a straight line with not much variation. When considered for Knowledge
Intensive architectural model, it shows the same thing as for Knowledge
Extensive architectural model. Similarly for Existing System architectural
model, it gives the same result as for the other two architectural models.
Thus, it can be concluded from the graph that competitiveness and
responsiveness does not affect each other much but they go hand in hand. It
means that for increasing competitiveness you are required to increase
responsiveness.
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0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Normalized Weighted Index
Variation in the weights assigned when Competitiveness is compared to Responsiveness
Knowledege ExtensiveKnowledge Intensive
Existing System
Figure 6.3 Variation in priority of KM Architectural Models with the Changes in Weight assigned to Competitiveness with respect to Responsiveness.
The final conclusion that can be drawn from the three graphs
shown above is that competitiveness and responsiveness affect the cost but
competitiveness and responsiveness go together. Therefore, the best
architectural model will be that model which increases competitiveness and
responsiveness without affecting the cost much. Then the best architectural
model for the successful implementation of KM is Knowledge Extensive
architectural model.
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6.3 FUTURE SCOPE OF WORK
The analysis using ANP is relatively cumbersome as in the
present work nearly 90 pair – wise comparison matrices are required. It
requires long and exhaustive discussion with experts from the case
knowledge management to arrive at the relationship among enablers.
Therefore, the advantages of ANP technique could be derived for making
strategic decisions that are vital for the growth and survival of knowledge
management. The values for pair-wise comparisons depend on the knowledge
of the decision – makers. Therefore group of decision-makers should include
those experts who understand the implications of enablers for the selection of
the architectural model for the successful implementation of KM. The
proposed framework has been developed by considering a general scenario of
industries; it can be made specific by applying to specific industries. The
framework can also be improved by considering more variables for the
selection of the architectural models for the implementation of KM. The three
architectural models proposed can be improved by considering more aspects
of the industries that might have been missed out in the work. Thus, there is
a scope of improvement of the architectural models.
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CHAPTER – 7
CONCLUSIONS
The effective implementation of KM largely depends on the
decision making framework and a proper architectural model. Hence, in order
to implement KM successfully and, in order to reduce cost and improve the
competitiveness and responsiveness of an organization we have to evaluate
and select a suitable architectural model. The selection of the architectural
model usually involves both subjective and quantitative judgment. It also
requires handling of several complex factors in a better objective and logical
manner. Thus, the selection of KM architectural model is a kind of multiple
criteria decision – making problem, and requires multiple criteria decision –
making methods to solve it appropriately. Unlike traditional methods which
are based on independent assumptions, the ANP is a relatively new multiple
criteria decision – making method which can deal with all kinds of
dependencies systematically.
In the context of industries, ANP method helps to evaluate and
select the architectural model successfully. The results of this analytic study
show that the most required purpose is to reduce the cost of implementation
of KM and therefore, the “Knowledge Extensive” architectural model is
preferred. Because the proposed ANP framework can handle the effects of
dependencies, it is relatively useful and makes the evaluation result to be
more reasonable. Additionally, this study has contributed to extend practical
applications of ANP in KM field. The ANP methodology adopted here arrives
at a synthetic score, which may be quite useful for the decision – makers.
Further, using the suggested analytical procedure, it can effectively handle
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any problem of selection with multi – faceted criteria. It also analyzes the
relative impact of different enablers on the three KM architectural models
considered for an industry.
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APPENDIX 1
CONSISTENCY RATIO FOR THE RELATIVE COMPARISON MATRIX OF THE DETERMINANT
Comparison Matrix (A):
COST COMPETITIVENESS RESPONSIVENESS
COST 1 2 4
COMPETITIVENESS 0.5 1 2
RESPONSIVENESS 0.25 0.5 1
Total 1.75 3.5 7
Normalized Matrix (B):
COST COMPETITIVENESS RESPONSIVENESS WEIGHT(w)
COST 0.5714 0.5714 0.5714 0.5714
COMPETITIVENESS 0. 2857 0.2857 0.2857 0.2857
RESPONSIVENESS 0.1429 0.1429 0.1429 0.1429
Method of calculating the normalized matrix:
STEP 1:
Compute A*C (Comparison Matrix multiplied by Normalized Matrix)
and calculate EV as A*C is divided by individual priority weight of the
determinant.
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A*C EV 1.7143 3 0.8571 3 0.4286 3
A*C = d1 = (1*0.5714) + (2*0.2857) + (4*0.1429) = 1.7143
d2 = (0.5*0.5714) + (1*0.2857) + (2*0.1429) = 0.8571
d3 = (0.25*0.5714) + (0.5*0.2857) + (1*0.1429) = 0.4286 EV = A*C / w
e1 = 1.7143 / 0.5714 = 3
e2 = 0.8571 / 0.2857 = 3
e3 = 0.4286 / 0.1429 = 3 STEP 2: Calculate δ δ = Σ EV / m Where, m is the number of variables in the criteria.
δ = (e1 + e2 + e3) / m (since m = 3)
= (3 + 3 + 3) / 3
= 3 STEP 3: Compute Consistency Index (CI) CI = (δ – m) / (m – 1)
= (3 – 3) / (3 – 1)
= 0 / 2
= 0.
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STEP 4:
Comparing Consistency Index (CI) with Random Index (RI) for the
appropriate value of ‘m’ to determine the degree of consistency is satisfactory
or not.
CI / RI = [(δ – 3) / 2] / 0.58 (RI for m=3 is 0.58)
= 0 / 0.58
= 0
Table D: Consistency Ratio Random Index (RI) m 2 3 4 5 6 7 8 9 10 RI 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.51
If CI / RI is less than 0.1 then the degree of consistency is considered to
be satisfactory.
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APPENDIX – II (Tables and Calculations)
Determinant – COST (a) Pair – wise comparison matrices for the relative importance of
dimensions on determinant / COST: (Pja)
OS PI INN e – vector OS 1 5 8 0.7020 PI 0.2000 1 6 0.2382
INN 0.1250 0.1667 1 0.0630 Total 1.3250 6.1667 15 1
(b) Pair – wise comparison matrices for dependencies in between
enablers: (ADkja)
i) Pair – wise comparison matrix for attribute enablers under determinant /
COST and the dimension / ORGANIZATIONAL STRUCTURE:
CU TMC SP ITW e – vector
CU 1 0.1250 0.5000 0.5000 0.0832 TMC 8 1 4 2 0.5353 SP 2 0.2500 1 0.3333 0.1229
ITW 2 0.5000 3 1 0.2586 Total 13 1.8750 8.5 3.8333 1
ii) Pair – wise comparison matrix for attribute enablers under determinant /
COST and the dimension / PROCESS INTEGRATION:
PPA KF SC COL e – vector
PPA 1 0.2500 0.1429 0.2000 0.0629 KF 4 1 3 4 0.4847 SC 7 0.3333 1 3 0.2957
COL 5 0.2500 0.3333 1 0.1568 Total 17 1.8333 4.4762 8.2 1
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iii) Pair – wise comparison matrix for attribute enablers under determinant /
COST and the dimension / INNOVATION:
KC KW KS TE e – vector
KC 1 0.2000 0.2500 0.1667 0.0580 KW 5 1 0.3333 0.1429 0.1383 KS 4 3 1 0.2500 0.2143 TE 6 7 4 1 0.5894
Total 16 11.2 5.5833 1.5596 1 (c) Pair–wise comparison matrices for Interdependencies among
Enablers: (AIkja)
i) OS / COST / CU
CU TMC SP ITW e – vector TMC 1 3 0.5000 0.3656 SP 0.3333 1 4 0.3542
ITW 2 0.2500 1 0.2802 Total 3.3333 4.25 5.5 1
ii) OS / COST / TMC
TMC CU SP ITW e – vector CU 1 0.3333 0.2500 0.1275 SP 3 1 5 0.6091
ITW 4 0.2000 1 0.2635 Total 8 1.5333 6.25 1
iii) OS / COST / SP
SP CU TMC ITW e – vector CU 1 0.2000 0.3333 0.1038
TMC 5 1 4 0.6651 ITW 3 0.2500 1 0.2311 Total 9 1.45 5.3333 1
iv) OS / COST / ITW
ITW CU TMC SP e – vector CU 1 0.2000 3 0.2585
TMC 5 1 2 0.5703 SP 0.3333 0.5000 1 0.1711
Total 6.3333 1.7000 6 1
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v) PI / COST / PPA
PPA KF SC COL e – vector KF 1 5 4 0.6597 SC 0.2000 1 3 0.2236
COL 0.2500 0.3333 1 0.1167 Total 1.45 6.3333 8 1
vi) PI / COST / KF
KF PPA SC COL e – vector PPA 1 0.3333 0.2500 0.1199 SC 3 1 0.3333 0.2721
COL 4 3 1 0.6080 Total 8 4.3333 1.5833 1
vii) PI / COST / SC
SC PPA KF COL e – vector PPA 1 0.2000 0.2500 0.0962 KF 5 1 4 0.6505
COL 4 0.2500 1 0.2543 Total 10 1.45 5.25 1
viii) PI / COST / COL
COL PPA KF SC e – vector PPA 1 0.2500 0.3333 0.1199 KF 4 1 3 0.6080 SC 3 0.3333 1 0.2721
Total 8 1.5833 4.3333 1 ix) INN / COST / KC
KC KW KS TE e – vector KW 1 4 0.2000 0.2635 KS 0.2500 1 0.3333 0.1275 TE 5 3 1 0.6091
Total 6.25 8 1.5333 1
x) INN / COST / KW
KW KC KS TE e – vector KC 1 0.3333 0.25 0.1181 KS 3 1 0.25 0.2431 TE 4 4 1 0.6389
Total 8 5.3333 1.5 1
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xi) INN / COST / KS
KS KC KW TE e – vector KC 1 0.2500 0.2000 0.0936 KW 4 1 0.1667 0.2200 TE 5 6 1 0.6864
Total 10 7.25 1.3667 1 xii) INN / COST / TE
TE KC KW KS e – vector KC 1 0.2000 0.1667 0.0768 KW 5 1 0.2500 0.2618 KS 6 4 1 0.6584
Total 12 5.2000 1.4167 1 (d) i) Super Matrix for COST before Convergence: RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0 0.1275 0.1038 0.2585 0 0 0 0 0 0 0 0
TMC 0.3656 0 0.6651 0.5703 0 0 0 0 0 0 0 0
SP 0.3542 0.6091 0 0.1711 0 0 0 0 0 0 0 0
ITW 0.2802 0.2635 0.2311 0 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0 0.1199 0.0952 0.1373 0 0 0 0
KF 0 0 0 0 0.6597 0 0.6505 0.6232 0 0 0 0
SC 0 0 0 0 0.2236 0.2721 0 0.2335 0 0 0 0
COL 0 0 0 0 0.1167 0.6080 0.2543 0 0 0 0 0
KC 0 0 0 0 0 0 0 0 0 0.1181 0.0936 0.0798
KW 0 0 0 0 0 0 0 0 0.2635 0 0.2200 0.2618
KS 0 0 0 0 0 0 0 0 0.1275 0.2431 0 0.6584
TE 0 0 0 0 0 0 0 0 0.6091 0.6389 0.6864 0
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ii) Super Matrix for COST after Convergence:
RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0.1304 0.1304 0.1304 0.1304 0 0 0 0 0 0 0 0
TMC 0.3649 0.3649 0.3649 0.3649 0 0 0 0 0 0 0 0
SP 0.3031 0.3031 0.3031 0.3031 0 0 0 0 0 0 0 0
ITW 0.2027 0.2027 0.2027 0.2027 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0.1204 0.1204 0.1204 0.1204 0 0 0 0
KF 0 0 0 0 0.3870 0.3870 0.3870 0.3870 0 0 0 0
SC 0 0 0 0 0.2100 0.2100 0.2100 0.2100 0 0 0 0
COL 0 0 0 0 0.3006 0.3006 0.3006 0.3006 0 0 0 0
KC 0 0 0 0 0 0 0 0 0.0852 0.0852 0.0852 0.0852
KW 0 0 0 0 0 0 0 0 0.1974 0.1974 0.1974 0.1974
KS 0 0 0 0 0 0 0 0 0.3211 0.3211 0.3211 0.3211
TE 0 0 0 0 0 0 0 0 0.3984 0.3984 0.3984 0.3984
(e) Pair – wise comparison matrices for the relative importance of
alternatives on enablers for the determinant / COST:
i) COST / OS / CU
KE KI ES e – vector KE 1 2 3 0.5247 KI 0.5000 1 3 0.3338 ES 0.3333 0.3333 1 0.1416
Total 1.8333 3.3333 7 1
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ii) COST / OS / TMC
KE KI ES e – vector KE 1 2 2 0.4778 KI 0.5000 1 3 0.3500 ES 0.5000 0.3333 1 0.1722
-Total 2 3.3333 6 1 iii) COST / OS / SP
KE KI ES e – vector KE 1 4 5 0.6651 KI 0.2500 1 3 0.2311 ES 0.2000 0.3333 1 0.1038
Total 1.45 5.3333 9 1 iv) COST / OS / ITW
KE KI ES e – vector KE 1 2 4 0.5437 KI 0.5000 1 4 0.3459 ES 0.2500 0.2500 1 0.1103
Total 1.75 3.25 9 1
v) COST / PI / PPA
KE KI ES e – vector KE 1 3 4 0.6232 KI 0.3333 1 2 0.2395 ES 0.2500 0.5000 1 0.1373
Total 1.5833 4.5 7 1 vi) COST / PI / KF
KE KI ES e – vector KE 1 3 5 0.6334 KI 0.3333 1 3 0.2605 ES 0.2000 0.3333 1 0.1061
Total 1.5333 4.3333 9 1 vii) COST / PI / SC
KE KI ES e – vector KE 1 3 4 0.6772 KI 0.3333 1 3 0.2952 ES 0.2500 0.3333 1 0.1276
Total 1.5833 4.3333 8 1
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viii) COST / PI / COL
KE KI ES e – vector KE 1 3 6 0.6394 KI 0.3333 1 4 0.2737 ES 0.1667 0.2500 1 0.0869
Total 1.5 4.25 11 1 ix) COST / INN / KC
KE KI ES e – vector KE 1 2 4 0.5571 KI 0.5 1 3 0.3202 ES 0.25 0.3333 1 0.1226
Total 1.75 3.3333 8 1
x) COST / INN / KW
KE KI ES e – vector KE 1 2 2 0.4778 KI 0.5000 1 3 0.3500 ES 0.5000 0.3333 1 0.1722
Total 2 3.3333 6 1 xi) COST / INN / KS
KE KI ES e – vector KE 1 4 6 0.6584 KI 0.2500 1 5 0.2618 ES 0.1667 0.2000 1 0.0798
Total 1.4167 5.2 12 1 xii) COST / INN / TE
KE KI ES e – vector KE 1 3 4 0.6232 KI 0.3333 1 2 0.2395 ES 0.2500 0.5000 1 0.1373
Total 1.5833 4.5 7 1
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(f) Architectural Weighted Desirability Index for COST:
Dimension Enablers Pja ADkja AIkja S1 S2 S3 KE KI ES
CU 0.6996 0.0832 0.1304 0.5247 0.3338 0.1416 0.0040 0.0025 0.0011
OS TMC 0.6996 0.5353 0.3649 0.4778 0.3500 0.1722 0.0653 0.0478 0.0235
SP 0.6996 0.1229 0.3031 0.6651 0.2311 0.1038 0.0173 0.0060 0.0027
ITW 0.6996 0.2586 0.2027 0.5437 0.3459 0.1103 0.0199 0.0127 0.0040
PPA 0.2375 0.0629 0.1204 0.6232 0.2395 0.1373 0.0011 0.0004 0.0002
PI KF 0.2375 0.4847 0.3870 0.6334 0.2605 0.1061 0.0282 0.0116 0.0047
SC 0.2375 0.2957 0.2100 0.6772 0.2952 0.1276 0.0100 0.0044 0.0019
COL 0.2375 0.1568 0.3006 0.6394 0.2737 0.0869 0.0072 0.0031 0.0010
KC 0.0627 0.0580 0.0852 0.5571 0.3202 0.1226 0.0002 0.0001 0.0004
INN KW 0.0627 0.1383 0.1974 0.4778 0.3500 0.1722 0.0008 0.0006 0.0003
KS 0.0627 0.2143 0.3240 0.6584 0.2618 0.0798 0.0029 0.0011 0.0003
TE 0.0627 0.5894 0.3984 0.6232 0.2395 0.1373 0.0092 0.0035 0.0020
Total 0.1661 0.0938 0.0421
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Determinant – COMPETITIVENESS (a) Pair – wise comparison matrices for the relative importance of
dimensions on determinant / COMPETITIVENESS: (Pja)
OS PI INN e – vector OS 1 4 3 0.6080 PI 0.2500 1 0.3333 0.1199
INN 0.3333 3 1 0.2721 Total 1.5833 8 4.3333 1
(b) Pair – wise comparison matrices for dependencies in between
enablers: (ADkja)
i) Pair – wise comparison matrix for attribute enablers under determinant / COMPETITIVENESS and the dimension / ORGANIZATIONAL STRUCTURE:
CU TMC SP ITW e – vector
CU 1 4 3 0.2500 0.2710 TMC 0.2500 1 4 0.3333 0.1815 SP 0.3333 0.2500 1 0.5000 0.1075
ITW 4 3 2 1 0.4400 Total 5.5833 8.25 1 2.0833 1
ii) Pair – wise comparison matrix for attribute enablers under determinant /
COMPETITIVENESS and the dimension / PROCESS INTEGRATION:
PPA KF SC COL e – vector PPA 1 0.2500 0.3333 0.3333 0.0829 KF 4 1 4 3 0.5060 SC 3 0.2500 1 3 0.2487
COL 3 0.3333 0.3333 1 0.1624 Total 11 1.8333 5.6666 7.3333 1
iii) Pair – wise comparison matrix for attribute enablers under determinant /
COMPETITIVENESS and the dimension / INNOVATION:
KC KW KS TE e – vector KC 1 0.2000 0.3333 0.1667 0.0583 KW 5 1 4 0.1429 0.2430 KS 3 0.2500 1 0.2500 0.1243 TE 6 7 4 1 0.5746
Total 15 8.45 9.3333 1.5596 1
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(c) Pair–wise comparison matrices for Interdependencies among Enablers: (AIkja)
i) OS / COMP / CU
CU TMC SP ITW e – vector TMC 1 0.3333 0.2000 0.1200 SP 3 1 4 0.5780
ITW 5 0.25 1 0.3019 Total 9 1.5833 5.2 1
ii) OS / COMP / TMC
TMC CU SP ITW e – vector CU 1 4 3 0.6196 SP 0.2500 1 2 0.2243
ITW 0.3333 0.5000 1 0.1560 Total 1.5833 5.5 6 1
iii) OS / COMP / SP
SP CU TMC ITW e – vector CU 1 0.2500 3 0.2311
TMC 4 1 5 0.6651 ITW 0.3333 0.2000 1 0.1038 Total 5.3333 1.4500 9 1
iv) OS / COMP / ITW
ITW CU TMC SP e – vector CU 1 4 0.3333 0.2842
TMC 0.2500 1 0.2000 0.0964 SP 3 5 1 0.6194
Total 4.25 10 1.5333 1
v) PI / COMP / PPA
PPA KF SC COL e – vector KF 1 5 4 0.6768 SC 0.2000 1 2 0.1925
COL 0.2500 0.5000 1 0.1307 Total 1.45 6.5 7 1
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vi) PI / COMP / KF
KF PPA SC COL e – vector PPA 1 0.2500 0.3333 0.1181 SC 4 1 4 0.6389
COL 3 0.2500 1 0.2431 Total 8 1.5 5.3333 1
vii) PI / COMP / SC
SC PPA KF COL e – vector PPA 1 0.2000 0.3333 0.1200 KF 5 1 0.2500 0.3019
COL 3 4 1 0.5780 Total 9 5.2 1.5833 1
viii) PI / COMP / COL
COL PPA KF SC e – vector PPA 1 0.2500 0.3333 0.1199 KF 4 1 3 0.6080 SC 3 0.3333 1 0.2721
Total 8 1.5833 4.3333 1 ix) INN / COMP / KC
KC KW KS TE e – vector KW 1 4 0.2000 0.2474 KS 0.25 1 0.2500 0.1078 TE 5 4 1 0.6447
Total 6.25 9 1.45 1
x) INN / COMP / KW
KW KC KS TE e – vector KC 1 0.2500 0.2000 0.0936 KS 4 1 0.1667 0.2200 TE 5 6 1 0.6864
Total 10 7.25 1.3667 1 xi) INN / COMP / KS
KS KC KW TE e – vector KC 1 0.1667 0.3333 0.1148 KW 6 1 0.2000 0.2975 TE 3 5 1 0.5877
Total 10 6.1667 1.5333 1
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xii) INN / COMP / TE
TE KC KW KS e – vector KC 1 0.2000 0.3333 0.1038 KW 5 1 4 0.6651 KS 3 0.2500 1 0.2311
Total 9 1.45 5.3333 1 (d)
i) Supermatrix for COMPETITIVENESS before Convergence:
RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0 0.6196 0.2311 0.2842 0 0 0 0 0 0 0 0
TMC 0.1200 0 0.6651 0.0964 0 0 0 0 0 0 0 0
SP 0.5780 0.2243 0 0.6194 0 0 0 0 0 0 0 0
ITW 0.3019 0.1560 0.1038 0 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0 0.1181 0.1200 0.1199 0 0 0 0
KF 0 0 0 0 0.6768 0 0.3019 0.6080 0 0 0 0
SC 0 0 0 0 0.1925 0.6389 0 0.2721 0 0 0 0
COL 0 0 0 0 0.1307 0.2431 0.5780 0 0 0 0 0
KC 0 0 0 0 0 0 0 0 0 0.0936 0.1148 0.1038
KW 0 0 0 0 0 0 0 0 0.2474 0 0.2975 0.6651
KS 0 0 0 0 0 0 0 0 0.1078 0.2200 0 0.2311
TE 0 0 0 0 0 0 0 0 0.6447 0.6864 0.6877 0
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ii) Supermatrix for COMPETITIVENESS after Convergence:
RES CU TMC SP ITW PPA KF SC COL KC KW KS TE
CU 0.2746 0.2746 0.2746 0.2746 0 0 0 0 0 0 0 0
TMC 0.2555 0.2555 0.2555 0.2555 0 0 0 0 0 0 0 0
SP 0.3122 0.3122 0.3122 0.3122 0 0 0 0 0 0 0 0
ITW 0.1552 0.1552 0.1552 0.1552 0 0 0 0 0 0 0 0
PPA 0 0 0 0 0.1066 0.1066 0.1066 0.1066 0 0 0 0
KF 0 0 0 0 0.3254 0.3254 0.3254 0.3254 0 0 0 0
SC 0 0 0 0 0.3011 0.3011 0.3011 0.3011 0 0 0 0
COL 0 0 0 0 0.2671 0.2671 0.2671 0.2671 0 0 0 0
KC 0 0 0 0 0 0 0 0 0.0906 0.0906 0.0906 0.0906
KW 0 0 0 0 0 0 0 0 0.3366 0.3366 0.3366 0.3366
KS 0 0 0 0 0 0 0 0 0.1755 0.1755 0.1755 0.1755
TE 0 0 0 0 0 0 0 0 0.3969 0.3969 0.3969 0.3969
(e) Pair–wise comparison matrices for the relative importance of alternatives on enablers for the determinant / COMPETITIVENESS:
i) COMP / OS / CU
KE KI ES e – vector KE 1 3 4 0.6232 KI 0.3333 1 2 0.2395 ES 0.2500 0.5000 1 0.1373
Total 1.5833 4.5 7 1
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ii) COMP / OS / TMC
KE KI ES e – vector
KE 1 2 3 0.5390 KI 0.5000 1 2 0.2972 ES 0.3333 0.5000 1 0.1638
Total 1.8333 3.5 6 1 iii) COMP / OS / SP
KE KI ES e – vector KE 1 4 5 0.6807 KI 0.2500 1 2 0.2014 ES 0.2000 0.5000 1 0.1179
Total 1.45 5.5 8 1 iv) COMP / OS / ITW
KE KI ES e – vector KE 1 4 6 0.6584 KI 0.2500 1 5 0.2618 ES 0.1667 0.2000 1 0.0798
Total 1.4167 5.2 12 1
v) COMP / PI / PPA
KE KI ES e – vector KE 1 4 3 0.6034 KI 0.2500 1 3 0.2580 ES 0.3333 0.3333 1 0.1386
Total 1.5833 5.3333 7 1 vi) COMP / PI / KF
KE KI ES e – vector KE 1 3 5 0.6194 KI 0.3333 1 4 0.2842 ES 0.2000 0.2500 1 0.0964
Total 1.5333 4.25 10 1
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vii) COMP / PI / SC
KE KI ES e – vector KE 1 4 6 0.6584 KI 0.2500 1 5 0.2618 ES 0.1667 0.2000 1 0.0798
Total 1.4167 5.2 12 1
viii) COMP / PI / COL
KE KI ES e – vector KE 1 3 4 0.6080 KI 0.3333 1 3 0.2721 ES 0.2500 0.3333 1 0.1233
Total 1.5833 4.3333 8 1 ix) COMP / INN / KC
KE KI ES e – vector KE 1 3 5 0.6334 KI 0.3333 1 3 0.2605 ES 0.2000 0.3333 1 0.1061
Total 1..5333 4.3333 9 1
x) COMP / INN / KW
KE KI ES e – vector KE 1 4 4 0.6389 KI 0.2500 1 3 0.2431 ES 0.2500 0.3333 1 0.1181
Total 1.5 5.3333 8 1 xi) COMP / INN / KS
KE KI ES e – vector KE 1 2 5 0.5813 KI 0.5000 1 3 0.3091 ES 0.2000 0.3333 1 0.1096
Total 1.7 3.3333 9 1 xii) COM / INN / TE
KE KI ES e – vector KE 1 2 4 0.5714 KI 0.5000 1 2 0.2857 ES 0.2500 0.5000 1 0.1429
Total 1.75 3.5 7 1
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(f) Architectural Weighted Desirability Index for COMPETITIVENESS:
Dimension Enablers Pja ADkja AIkja S1 S2 S3 KE KI ES
CU 0.6080 0.2710 0.2746 0.6232 0.2395 0.1373 0.0282 0.0108 0.0062
OS TMC 0.6080 0.1815 0.2555 0.5390 0.2972 0.1638 0.0152 0.0084 0.0046
SP 0.6080 0.1075 0.3122 0.6807 0.2014 0.1179 0.0139 0.0041 0.0024
ITW 0.6080 0.4400 0.1552 0.6584 0.2618 0.0798 0.0273 0.0109 0.0033
PPA 0.1199 0.0829 0.1066 0.6034 0.2580 0.1386 0.0006 0.0003 0.0001
PI KF 0.1199 0.5060 0.3254 0.6194 0.2842 0.0964 0.0122 0.0056 0.0019
SC 0.1199 0.2487 0.3011 0.6584 0.2618 0.0798 0.0059 0.0024 0.0007
COL 0.1199 0.1624 0.2671 0.6080 0.2721 0.1233 0.0032 0.0014 0.0006
KC 0.2721 0.0583 0.0905 0.6334 0.2605 0.1061 0.0009 0.0004 0.0002
INN KW 0.2721 0.2430 0.3366 0.6389 0.2431 0.1181 0.0142 0.0054 0.0026
KS 0.2721 0.1243 0.1755 0.5813 0.3091 0.1096 0.0035 0.0018 0.0007
TE 0.2721 0.5746 0.3969 0.5714 0.2857 0.1429 0.0355 0.0177 0.0089
Total 0.1606 0.0692 0.0323
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Determinant – RESPONSIVENESS (a) Pair – wise comparison matrices for dependencies in between
enablers: (ADkja) ii) Pair – wise comparison matrix for attribute enablers under determinant /
RESPONSIVENESS and the dimension / PROCESS INTEGRATION:
PPA KF SC COL e – vector
PPA 1 0.2500 0.2000 0.3333 0.0735 KF 4 1 3 4 0.4890 SC 5 0.3333 1 4 0.3049
COL 3 0.2500 0.2500 1 0.1326 Total 13 1.8333 4.45 9.3333 1
iii) Pair – wise comparison matrix for attribute enablers under determinant /
RESPONSIVENESS and the dimension / INNOVATION:
KC KW KS TE e – vector
KC 1 0.2500 0.2000 0.3333 0.0816 KW 4 1 3 0.2000 0.2344 KS 5 0.3333 1 0.2500 0.1744 TE 3 5 4 1 0.5097
Total 13 6.5833 8.2 1.7833 1
(b) Pair – wise comparison matrices for Interdependencies among
Enablers: (AIkja) ii) OS / RESP / TMC
TMC CU SP ITW e – vector CU 1 3 0.5000 0.3699 SP 0.3333 1 2 0.2979
ITW 2 0.5000 1 0.3323 Total 3.3333 4.5 3.5 1
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iii) OS / RESP / SP
SP CU TMC ITW e – vector CU 1 0.2500 0.2000 0.1018
TMC 4 1 2 0.5321 ITW 5 0.5000 1 0.3661 Total 10 1.75 3.2 1
iv) OS / RESP / ITW
ITW CU TMC SP e – vector CU 1 0.2500 3 0.3137
TMC 4 1 0.2500 0.3331 SP 0.3333 4 1 0.3532
Total 5.3333 5.25 4.25 1
v) PI / RESP / PPA
PPA KF SC COL e – vector KF 1 0.2500 0.3333 0.1279 SC 4 1 0.5000 0.3601
COL 3 2 1 0.5120 Total 8 3.25 1.8333 1
vi) PI / RESP / KF
KF PPA SC COL e – vector
PPA 1 0.3333 0.2500 0.1199 SC 3 1 0.3333 0.2721
COL 4 3 1 0.6080 Total 8 4.3333 1.5833 1
vii) PI / RESP / SC
SC PPA KF COL e – vector PPA 1 0.2000 0.3333 0.1038 KF 5 1 4 0.6651
COL 3 0.2500 1 0.2311 Total 9 1.45 5.3333 1
viii) PI / RESP / COL
COL PPA KF SC e – vector PPA 1 0.3333 0.5000 0.1560 KF 3 1 4 0.6196 SC 2 0.2500 1 0.2243
Total 6 1.5833 5.5 1
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ix) INN / RESP / KC
KC KW KS TE e – vector KW 1 4 0.2000 0.2635 KS 0.2500 1 0.3333 0.1275 TE 5 3 1 0.6091
Total 6.25 8 1.5333 1
x) INN / RESP / KW
KW KC KS TE e – vector KC 1 0.2500 0.2000 0.0952 KS 4 1 0.2500 0.2543 TE 5 4 1 0.6505
Total 10 5.25 1.45 1 xi) INN / RESP / KS
KS KC KW TE e – vector KC 1 0.3333 0.2500 0.1181 KW 3 1 0.2500 0.2431 TE 4 4 1 0.6389
Total 8 5.3333 1.5 1 xii) INN / RESP / TE
TE KC KW KS e – vector KC 1 0.2500 0.3333 0.1199 KW 4 1 3 0.6080 KS 3 0.3333 1 0.2721
Total 8 1.5833 4.3333 1 (e) Pair – wise comparison matrices for the relative importance of
alternatives on enablers for the determinant / RESPONSIVENESS: ii) RESP / OS / TMC
KE KI ES e – vector
KE 1 4 2 0.5516 KI 0.2500 1 3 0.2768 ES 0.5000 0.3333 1 0.1716
Total 1.75 5.3333 6 1
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iii) RESP / OS / SP
KE KI ES e – vector KE 1 3 5 0.6777 KI 0.3333 1 2 0.2418 ES 0.2000 0.5000 1 0.1281
Total 1.5333 4.5 8 1 iv) RESP / OS / ITW
KE KI ES e – vector KE 1 4 5 0.6505 KI 0.2500 1 4 0.2543 ES 0.2000 0.2500 1 0.0952
Total 1.45 5.25 10 1
v) RESP / PI / PPA
KE KI ES e – vector KE 1 5 7 0.7093 KI 0.2000 1 4 0.2141 ES 0.1429 0.2500 1 0.0958
Total 1.3429 6.25 12 1 vi) RESP / PI / KF
KE KI ES e – vector KE 1 3 5 0.6194 KI 0.3333 1 4 0.2842 ES 0.2000 0.2500 1 0.0964
Total 1.5333 4.25 10 1
vii) RESP / PI / SC
KE KI ES e – vector KE 1 3 5 0.5963 KI 0.3333 1 6 0.3191 ES 0.2000 0.1667 1 0.0846
Total 1.5333 4.1667 12 1
viii) RESP / PI / COL
KE KI ES e – vector KE 1 4 5 0.6817 KI 0.2500 1 2 0.2014 ES 0.2000 0.5000 1 0.1179
Total 1.45 5.5 8 1
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ix) RESP / INN / KC
KE KI ES e – vector KE 1 4 5 0.6505 KI 0.2500 1 4 0.2543 ES 0.2000 0.2500 1 0.0952
Total 1.45 5.25 10 1
x) RESP / INN / KW
KE KI ES e – vector KE 1 5 6 0.7071 KI 0.2000 1 3 0.2014 ES 0.1667 0.3333 1 0.0915
Total 1.3667 6.3333 10 1 xi) RESP / INN / KS
KE KI ES e – vector KE 1 4 6 0.6853 KI 0.2500 1 3 0.2213 ES 0.1667 0.3333 1 0.0934
Total 1.4167 5.3333 10 1 xii) RESP / INN / TE
KE KI ES e – vector
KE 1 3 4 0.6080 KI 0.3333 1 3 0.2721 ES 0.2500 0.3333 1 0.1199
Total 1.5833 4.3333 8 1