International journal of computer integrated manufacturing , tập 23, số 11, 2010

International Journal of Computer Integrated Manufacturing
Vol. 23, No. 11, November 2010, 957–967

Knowledge value chain: an effective tool to measure knowledge value
Yang Xu* and Alain Bernard
IRCCyN, Ecole Centrale de Nantes, Nantes, France
(Received 21 September 2009; final version received 19 May 2010)
Knowledge value is a significant issue in knowledge management, but its related problems are still challenging. This
paper aims at discussing how knowledge value changes in the knowledge evolution process and develops a
knowledge value chain (KVC) to measure knowledge value. By applying the notions of knowledge state and
knowledge maturity, the knowledge finite state machine (KFSM) and knowledge maturity model (KMM) are
introduced to characterise the KVC. Based on these concepts, knowledge value is measured by calculating the
difference between two maturity states rather than by direct calculation. This point of view of knowledge value, the
construction of KVC and the association of knowledge value and knowledge maturity are insightful for both
researchers and practitioners.
Keywords: knowledge management; knowledge value; value chain

1.

Introduction

Nowadays, more and more enterprises and entrepreneurs realise that knowledge plays an important role in
business success and that knowledge management is
becoming a core activity. The capacity of knowledge
management becomes a crucial issue for companies,
and essential for enterprise competitiveness (Bernard
and Tichkiewitch 2008).
However, although ‘knowledge is power’ was
spelled out more than 400 years ago (Bacon 1597), it
is still easier said than done, and people can hardly

control and measure knowledge as they can electrical
or mechanical power. Therefore, there is a growing
need to represent this ‘power’ in an explicit way and
specify the process during which this ‘power’ works.
Before companies became aware of the importance
of knowledge management, knowledge activities were
usually ill-defined, and as a result, knowledge innovation, application and abandonment mostly happen
without rigid control. One of the main purposes of
knowledge management is to standardise and formalise knowledge changing and transmission processes,
so as to follow the rules concerning knowledge and to
control knowledge in order to improve production
activities (Leonard-Barton 1995). As tacit knowledge,
which exists in the form of mental models, beliefs,
experience or other forms of know-how of individuals,
it is not easy to convey in formalised patterns and is
usually represented and stored using storytelling
(Guerra-Zubiaga and Young 2008).

*Corresponding author. Email:
ISSN 0951-192X print/ISSN 1362-3052 online
Ó 2010 Taylor & Francis
DOI: 10.1080/0951192X.2010.500677


The backbone of knowledge management is to
ensure the availability of knowledge to all people or
cases that may require it. ‘Availability’ means not
only an extensive, living and sharable knowledge
base accessible to all users within an organisation,
but also the probability and ease to satisfy requirements, and the degree of satisfaction. In recent years,

many researchers have made important contributions
to measure such ‘satisfaction’. Ahn and Chang
(2004) have introduced a KP3 methodology to assess
the contribution of knowledge to business performance and they have established logical links
between knowledge and business performance
through product and process concepts. Chen et al.
(2009) have integrated analytical network processes
and balanced scorecards to measure knowledge
management performance, so as to compare an
organisation’s knowledge management performance
with its rivals and to improve its knowledge
management activities. Bernard and Xu (2009) have
developed an integrated knowledge reference system
to describe the knowledge evolution process in
product development and show the mutual valueadding process between knowledge and product.
Wen (2009) has constructed a model to measure
knowledge management effectiveness by using focus
groups, analytic hierarchy process and questionnaire
analyses. Liu et al. (2005) have made some empirical
surveys to compare the effectiveness of different
knowledge management systems.


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Y. Xu and A. Bernard

As knowledge is inherently difficult to measure,
former researchers mostly assess the outcomes associated with knowledge, such as the performance of a
knowledge management system or how knowledge

could contribute to business performance, instead of
measuring knowledge directly. There is, therefore, a
lack of direct focus concerning the problem of knowledge value, which is a key point and a bottleneck in
knowledge management. To achieve this goal, this
paper will discuss issues of knowledge value and how
knowledge could be evaluated and acted upon in
production activities.
2.

Knowledge value

When talking about ‘knowledge is power’, intuitively,
people are aware of the fact that knowledge can make
things better. Furthermore, when saying ‘better’, it can
be ‘a little better’ or ‘much better’, so people are eager
to know ‘to what extent are things better’. As a result,
a measurement should be introduced. Unfortunately,
people think that a measurement is one of the most
difficult parts of the knowledge management field
(Ruggles 1998) and Liebowitz and Wright (1999) even
stated that it was not clear whether knowledge could
be measured.
In order to challenge this issue, the primary
problem is, above all, ‘to measure what’. This paper
proposes the term ‘knowledge value’ as what is to be
measured in considering ‘how powerful knowledge is’.
Consequently, we come to the question: what exactly is
‘knowledge value’?
‘Value’ is a flexible term that is used in a variety of
domains with different meanings. For example, in

economics, it means the market worth or estimated
worth of commodities, services, assets or work; in
mathematics, the output of a function is called the
value; in psychology, value explains why people prefer
or choose some things over others, i.e. the explanation
of an individual’s preferences in life goals, principles
and behavioural priorities (Renner 2003). From these
different meanings of ‘value’, we may conclude that
‘value’ describes how people positively or negatively
evaluate things and concepts, and the reasons used in
making their decisions.
Based on the fundamental meanings of ‘value’, this
paper uses the term ‘knowledge value’ to characterise
‘the ability of knowledge that can make things better’.
From the point of view of knowledge lifecycle
(Birkinshaw and Sheehan 2002), at the beginning,
knowledge cannot always meet the requirement, which
is determined by a given context. Knowledge has to
experience an evolution process to augment its ability,
i.e. its value, so as to arrive at a state which can meet
the requirement and solve the problem. The notion of

‘maturity state’ is thus introduced to represent such a
state and it integrates the knowledge itself within the
context.
Before starting the survey on knowledge value and
knowledge maturity, some insightful arguments on
information value and information maturity should be
presented, as knowledge is usually linked to information and information is a concept that is similar to
knowledge in some points of view.

Sillince (1995) argued that information value
depends on the probability of information transfer
from one person to another within an information
system and is affected by whether such a transfer is
costless or not. By modeling three types of information
(related information, alternative information and
unrelated information), some useful formulations are
deduced, which make is possible to discover which
organisational or market forms are most able to deliver
high information value through empirical testing.
Zhao et al. (2008) clearly distinguished information
value from information quality and defined it as
‘(Benefits of having information) / (Resource spending
on storing and retrieving)’ which is equal to ‘(Quality
Relevance) * Saving’. Then they developed an
assessment system integrating information characteristics, the Bayesian Network theory, and conditional
probability statistical data to evaluate information
value. For the concept of information maturity, the
Meta Group proposed a five-level model of information maturity, which enables organisations to assess
their information management practices (MIKE 2010)
and Blanco et al. (2007) presented a tool called
PIQUANT to illustrate an information maturity
management model which can manage information
types within different workspaces during the design
process.
Knowledge is not simply information, so the
notions of knowledge value and knowledge maturity
differ from the concepts introduced above, and they
will be introduced in detail in the following sections
with explicit and formal definitions.

2.1. Features of knowledge value
Knowledge is different from traditional resources,
having many features which make it difficult to judge
its value (Stewart 1997). For example, knowledge can
be used without being consumed and some knowledge
can only be sold once. For traditional power such as
petrol, sales personnel can calculate how much is
consumed by clients and take this parameter as an
index in evaluating its value. But for knowledge power
that can only be sold once, such as an idea, people can
hardly evaluate its value by estimating how much it
is expected to be used. For example, the value of


International Journal of Computer Integrated Manufacturing
software depends on the number of times it is used
once bought and different clients may use it with
different frequencies. Sometimes some frequencies can
be obtained, such as the ‘impact factor’ of a scientific
paper or journal, but when such a ‘frequency’ or ‘the
number of times’ is not available, it will be more
practical to consider ‘knowledge value’ as the value it
provides each time it is used instead of its ‘whole’ value
that the knowledge could provide in its lifecycle.
Another feature is that the values of different kinds
of knowledge are, for the most part, not comparable,
for example, how can we compare the value of
‘specialised’ knowledge (e.g. the formula of diet pills)
and ‘common’ knowledge (e.g. doing exercises to keep
fit)? Both of them are useful for a similar purpose and

may have a same effect. Although people would have
to pay more to buy diet pills than jogging along a river,
we cannot say ‘common’ knowledge is less valuable
than ‘specialised’ knowledge. But in reality, why
should we always pay more for ‘specialised’ knowledge? It is not because ‘specialised’ knowledge is more
valuable than ‘common’ knowledge but because it
costs more, and people may think something that costs
more is more valuable, which might not be true. In
practice, admittedly, cost and time both have a great
impact on knowledge evaluation, as knowledge should
always be acquired within the financial budget and a
tolerable time delay, and conflicts between planned
cost/time and actual cost/time may result in some risks,
so a comprehensive investigation should be based on
value/cost/risk. This paper mainly focuses on the issues
of value, and further research will take cost and risk
into consideration.
The third feature is that knowledge value can be
estimated only when knowledge is used and can hardly
be judged in advance. For example, when we go to a
lecture or take a course, we are not able to answer the
questions ‘Is it valuable to you? How much value could
it bring to you?’ before we have heard the content.
Sometimes, statistical studies based on experience or
other methods might be used to expect the value of an
object, but it is not the case for knowledge, because
judgments from other people (or other cases) may vary
greatly from each other. As a result, ‘knowledge value’
can not be ‘predicted’, and it is defined on condition
that objectives are already known. In other words,

knowledge value should be simulated within a context
that is known.
From these features, which are still far from covering
all features of knowledge value, we may reach the
following conclusions when defining knowledge value:
. Knowledge value is the value of its one-time use.
. Knowledge value does not have a direct link with
its cost.

959

. Knowledge value is assessed on the condition
that its context is already known.
The statement that ‘knowledge value is the value of
its one-time use’ means that in the present proposition
of knowledge value, knowledge reuse is not considered.
However, it does not mean knowledge reuse has no
link with knowledge value; in fact, the number of times
specific knowledge can be reused (reused directly or
reused after being revised or created in the view of
knowledge lifecycle) is an important parameter. In
other words, ‘the value of one-time use’ means
knowledge reuse is not considered when evaluating
the knowledge in ‘the current’ case, but it may
influence the ‘future’ value of knowledge. When
considering this ‘future’ value, current/past values,
current/past contexts and future contexts should be
taken into account, and this will be a very interesting
topic in the coming research projects.
Moreover, knowledge could exist in both explicit

and tacit forms, or as Chilton and Bloodgood (2008)
pointed out that knowledge has ‘its degree of tacitness’.
As explicit knowledge, is codified and formally stored
in specific media, people may find some numerical
parameters to characterise a given aspect, for example,
people can use ‘bit’ to measure the quantity of a kind
of explicit knowledge such as electronic documents or
even audio-visual files. The Impact Factor (IF)
founded by the Institute for Scientific Information
(ISI) is also a well-known parameter to evaluate the
importance of a specific type of explicit knowledge –
published scientific papers. On the other hand, tacit
knowledge can hardly be represented objectively.
People are not often aware of the fact that they are
using the tacit knowledge they possess, so it is more
difficult to recognise how valuable it is. Tacit knowledge is quite personal, as it can only be gained through
personal experience and transferred through personal
contacts. People may have their own judgement of it:
whether they possess it, whether they are using it,
whether it is important in helping them to carry out
certain actions or tasks, and more confusingly, whether
it is useful and valuable for others.
As a result, the objective or absolute measurements
are actually very limited in ‘measuring’ knowledge
value. We may even say that knowledge does not have
a philosophically absolute value that is independent of
the context, such as a stone of 5 kg which is always
‘5 kg’ regardless of the time, the site or the actor.
Furthermore, it is also difficult to capture the ‘outcome’ of knowledge activities directly and objectively
as people could easily fail to extract real causal

relationship between a knowledge activity and ‘its’
outcomes, so we should sometimes rely on parameters
that are subjective, depending on individuals,


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organisations and cases. Thus, we may conclude that
knowledge value should always be studied and
measured in context.
2.2. Knowledge value in context
The proposition that knowledge has value is ancient,
and arguments about knowledge value emerged
thousands of years ago. Pritchard (2007) has
analysed several problems concerning knowledge
value, including the primary value problem for
knowledge (the Meno problem concerning why
knowledge is more valuable than mere true belief),
the secondary value problem (concerning the issues
of why knowledge is more valuable than any proper
subset of its parts) and the tertiary value problem
(why knowledge is of more value to us than
whatever falls short of knowledge). Despite philosophical analysis of knowledge value, nowadays,
knowledge value is also recognised in a business
context, for example, a pair of shoes is worth more
than leather plus rubber, and it is knowledge that
augments the value of products in this manufacturing process. This example indicates a clear fact that
knowledge value is revealed in the process of product

development, thus, this paper limits the studies of
knowledge value to within such a context.
Definition 1. The knowledge context is characterised by
a space of:
C ¼ O1 Â O2 Â Á Á Á Â On
where Oi ¼ foi1 ; oi2 ; . . . ; oij g. Oi is the ith
attribute of the context and oij is the jth value of
attribute Oi.
Intuitively, a knowledge context could be regarded
as a super-cube which characterises the environment of
knowledge activities, and it may have different axes in
different cases.
In order to explain how this knowledge context
space can be concretely applied, this paper has chosen
three main aspects: participants (PA), knowledge
status (KS) and product lifecycle (PL).
Thus, we have:
C ¼ PA Â KS Â PL; ci ¼ fpai ; ksi ; pli g
where ci 2 C is a specific context, and pai 2 PA, ksi 2
KS, pli 2 PL.
Each set has several values, i.e. elements, and they
are illustrated as follows:
(1) PA ¼ {director, manager, engineer, technician,
operator}

PA refers to the human factor. Knowledge is a
thing which is always linked to people, and different
people may regard the same piece of knowledge
differently. For example, given a piece of knowledge
that describes the selling strategy of a competitor, the

manager of an enterprise may regard it as crucial but
the operators working in the manufacturing unit may
think it useless. PA has five elements, i.e. values, which
are derived from the common hierarchical structure of
a company. All providers, users and workers of
knowledge are treated as ‘human factors’, and they
correspond to one of these five elements. When a
human factor is assigned one value, it does not merely
refer to the name of the job, but the different levels of
points of view and functions, from a strategic outline
to a concrete operation.
(2) KS ¼ {initial,
intelligent}

ordered,

organised,

usable,

KS refers to how knowledge is organised and the
five elements are illustrated as follows:
. Initial. The knowledge is scattered unsystematically, such as the original data of a market
investigation.
. Ordered. The knowledge has already been stored
in text files and in formal forms, but at a relatively
low integration degree and data redundancy
exists. For example, the numerical data of
collected questionnaires belong to this status.
. Organised. The knowledge is structured logically,

and can be maintained and managed by effective
mechanisms. Organised knowledge abstracts the
core information of knowledge. Results after
synthesis, classification and calculation belong to
this status, e.g., the average income per family in
area X is $60,000 per year, the average number of
cars owned per family in area Y is 1.2, etc.
. Usable. The description and organisation about
knowledge is user-oriented, in other words,
usable knowledge is rather descriptive and is
able to describe phenomena which have been
analysed and concluded according to the desires
of users. E.g., people of area X tend to use fuelefficient cars rather than pursuing high performance in speed, young people of area Y are more
interested in car design than car size, etc.
. Intelligent. Knowledge and production activities
are integrated comprehensively, and knowledge
acts as a motivating power with a certain degree
of intelligence. General conclusions and suggestions for decision making belong to this status.
Intuitively, when knowledge is abstract and descriptive it is close to the ‘Intelligent’ side and when it is


International Journal of Computer Integrated Manufacturing
more likely to be in code or digital forms it approaches
the ‘Initial’ side. KS may seem to be similar to the
data-information-knowledge-wisdom (DIKW) hierarchy (Rowley 2007), but the difference is that, when
presenting DIKW, people mainly aim to explain how
to understand those literally abstract concepts (they do
not exist physically) in a more comprehensive way,
using illustrations, examples or metaphors. People may
be interested in exploring how the chain is constructed

and in clarifying the fuzzy transitions between different
concepts. For example, Hey (2004) examined the
transitions between data, information and knowledge
which link them as a DIKW chain. Similarly, ‘initial’
knowledge seems like ‘data’ and ‘intelligent’ knowledge
has a sense of ‘wisdom’. However, this paper
emphasises how knowledge is organised, rather than
the relationship between different knowledge statuses.
There is another important difference between the
description of knowledge status and DIKW chain. The
DIKW chain implies an evolution process, in other
words, wisdom is better or more advanced than data.
In our proposition of knowledge status, the five
elements are ‘equal’, that is to say, for example,
‘intelligent’ knowledge status is not always superior to
‘ordered’ status.
(3) PL ¼ {information gathering, design, development
and testing, manufacturing, sales, service}

PL links the evolution of knowledge with product
development. In product development, knowledge is
inseparable from a product, so the product lifecycle
should be introduced. For example, a product design
does not have the same purpose in the realisation
phase as in the selling phase. Currently, the concept of
product lifecycle no longer emphasises just financial
matters in an enterprise applying for business planning
and management. It has been more broadly used as an
engineering term to describe a comprehensive approach in managing enterprise performance (Ma and
Fuh 2008). It is usually integrated with knowledge

management (KM) methods especially in dynamic and
collaborative environments (Thimm et al. 2006) and
different stages of a product lifecycle have different
knowledge requirements (Xu and Bernard 2009).
The axis of PL enables the integration of product
lifecycle management (PLM) within the context.
PLM is the process of managing the entire lifecycle
of a product and it integrates people, products and
processes to form an all-encompassing system that
can provide companies with an overview of product
development. Typically, PLM aims at improving
product development processes and involves activities
such as information gathering, conception, design,
manufacturing, sales and services. Figure 1 shows the
wheel of PLM.

Figure 1.

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The wheel of PLM.

. The phase of Information Gathering mainly
includes investigation, data collection and analysis, etc.
. The phase of Design mainly includes requirement
specification, product definition, general conception, detail design, embodiment, etc.
. The phase of Development and Testing mainly
includes prototype simulation, acquirement and
adjustment of technical parameters, product
validation, etc.

. The phase of Manufacturing mainly includes
product manufacturing, assembly, packing, etc.
. The phase of Sales mainly includes advertising,
selling, product/service delivery, etc.
. The phase of Service mainly includes maintenance, after sales support, product retirement
and recycling, etc.
PA, KS and PL are the three axes chosen in this
paper to characterise the knowledge context in the
following studies. These three are chosen to perform as
an example of how the knowledge context space can be
implemented and other different attributes can be
chosen to characterise specific contexts. Not all the
other attributes can be treated in exactly the same way
as PA, KS and PL are, because different attributes
have different possible values, but the idea of
constructing a knowledge context space is the same:
the knowledge context is characterised by Cartesian
coordinates consisting of several attributes and elements of each attribute correspond to given points on
the axis.
2.3. Knowledge state
The notion of state is usually applied to simplify
problem characterisation by dividing a continuous


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developing process into discrete stages. When former
researchers describe knowledge activities by knowledge

states, they usually build the knowledge value chain
model with a linear series of knowledge stages and
steps (Lee and Yang 2000, Wong 2004). However,
those models are limited in their linear structure and
when different stages may have ambiguous boundaries,
they will bring uncertainty as well. As a result, this
paper proposes a knowledge finite state machine
(KFSM) to represent the knowledge evolution process.
Definition 2. A knowledge finite state machine (KFSM)
is a hextuple hQ, S, K, d, s0, Fi, where:
. Q is a finite and non-empty set of knowledge
states si;
. S is a finite and non-empty set of manipulations
required to change the knowledge states;
. K is a finite and non-empty set of knowledge
required to change the knowledge states, including two subsets Ka (knowledge available, namely
the knowledge existing in the knowledge base)
and Ki (knowledge imported, namely the knowledge that needs to be acquired from the
outside); the knowledge fragments are noted by
k, and k 2 K.
. d is the state transition function: d: Q 6
S 6 K ! Q, and when a transition from si to
siþ1 happens with the ‘right’ manipulation and
knowledge, it is called an effective state
transition;
. s0 is an initial state, which is an element of Q;
. F is the set of final states, which is a subset of Q,
and there is at least one state sn 2 F.
The KFSM provides us with a method to describe
the knowledge evolution process in a more flexible and

general way. When a knowledge evolution process
starts with s0 and ends up with sn, the aim of KM
activities is to eliminate the difference between s0 and
sn, and this target can be accomplished by bridging the
gap between si and siþ1 step by step.
2.4. Knowledge maturity
When characterising an evolution process, the notion
of maturity is usually referred and used to describe the
varying states of a thing during its development.
Maturity has different meanings in different domains,
such as:
. Biology. The age/stage when an organism can
reproduce.
. Geology. A measurement of a rock’s state in
terms of hydrocarbon generation.

. Psychology. A person responds to their circumstances or environment in an appropriate manner, being aware of the correct time and place to
behave and knowing when to act in serious or
non-serious ways.
. Software engineering. To what extent is it
planned how to do things, mainly described by
the capability maturity model (CMM) (Paulk
et al. 1995).
Inspired by these interesting applications of the
term ‘maturity’, we will introduce the maturity of
knowledge based on the notion of knowledge state.
As knowledge is context sensitive, given a knowledge state, it may be less mature in one situation than
another. For example, a mathematical theory is mature
for a professor (as the knowledge is ready for reuse in
solving some problems), when it is not mature enough

for a pupil (as the knowledge must be illustrated by
some simple means so that the pupil may understand
and apply it). Consequentially, the knowledge maturity
described in this paper associates the knowledge with
the context, and is defined as follows:
Definition 3. Knowledge maturity describes the state of
knowledge within a specific context, noted as:
mi ¼ ðsi ; ci Þ
where si is a knowledge state in KFSM and ci is a
specific context.
3.

The knowledge value chain

The evolution of knowledge is a challenging topic that
has aroused much interest. Nonaka and Takeuchi
(1995) proposed a spiral process which is regarded as a
basic model to describe how knowledge develops in its
lifecycle.
3.1.

Knowledge maturity model

In fact the subject of a knowledge maturity model
(KMM) has already incited interest among researchers, and they have conducted insightful surveys on
KMM. For example, Markow (2004) proposed a
KMM consisting of four levels: the Process cycle,
Roles, KIM (Knowledge Insight Model), and Inner
mechanism; Robinson et al. (2006) constructed a
knowledge management maturity roadmap of five

stages: Start-up, Take-off, Expansion, Progressive,
and Sustainability.
The main idea of these existing KMM mainly
comes from CMM, which is mostly applied in the field
of software engineering. CMM was originally intended


International Journal of Computer Integrated Manufacturing
as an objective evaluation and served as a tool to
measure the performance of various software engineering contractors. It measures the organisation’s current
state by providing a set of goals, a checklist indicating
what the organisation should accomplish to reach a
higher level and is now one of the most recognised
models in industry. Through a number of applications
and experiences, it has been shown to be well-suited for
organisations when characterising their key processes.
The basic idea of CMM is a five-level ladder
consisting of the initial, repeatable, defined, managed
and optimised levels, and the existing KMM is also
based on this idea of ‘level-ladder’. Here are several
limitations of the structure of ‘level-ladder’ when
describing
knowledge
activities
in
product
development:
. The direction of maturity development is always
‘up’. According to CMM, we can say that when
the company is at level 3, it is more mature than

when it is at level 2, and the company’s aim is
always to progress from a low level to a higher
level, i.e. the direction is always ‘up’. However,
does knowledge always go ‘up’? For example, the
mathematical formula ‘x þ y ¼ y þ x’ is the
induction of ‘1 þ 2 ¼ 2 þ 1, 3 þ 5 ¼ 5 þ 3,
etc.’ and it can describe a general mathematical
law, thus, it is supposed to be at a ‘higher’ level
and more mature than ‘1 þ 2 ¼ 2 þ 1, 3 þ 5
5 þ 3, etc.’ But when this knowledge is expected
to be taught to pupils in their first year of
primary school, ‘x þ y ¼ y þ x’ should be
transferred to ‘1 þ 2 ¼ 2 þ 1, 3 þ 5 ¼ 5 þ 3,
etc.’ In this case, the direction of knowledge
evolution is ‘down’ and knowledge at a lower
level is more mature.
. All tasks of one level should be accomplished in
order to go to the next. In CMM, the companies
should climb the ‘ladder’ level by level; however,
as knowledge is an active thing, can it not
transfer its maturity status by ‘leaping’ or
‘making a detour’? For example, some people
may follow this sequence: ‘have an idea -4 write
it down -4 realise it’, but others can make a
direct leap from ‘idea’ to ‘action’.
. The higher level overlays the information of the
lower level. But for knowledge, is the knowledge
in a ‘less mature’ status no longer useful? In the
traditional understanding about maturity levels,
the information of a less mature status is overlaid

by a more mature status. For example, in the
constructing process of a manufacturing system,
from the traditional view point, the result (a
system that is working) is more mature than
design drafts. However, knowledge contained in

963

the design drafts could be also valuable, such as
knowledge about ‘why Part X is designed like
that’, but the manufacturing system itself does
not communicate this knowledge: in other
words, the knowledge is overlaid by the result
which is more mature. Given a same manufacturing system, different participants need different knowledge. For engineers whose duty is to
maintain the system, they need the knowledge
about ‘why Part X is designed like that’, but for
technicians who are focusing on operating the
system, what is useful for them is knowledge
about ‘how to use Part X’.
In order to overcome the limitations above, the
KMM introduced in this paper implies the idea of
multi-dimension rather than linear structure. As
defined in Section 3.2, knowledge maturity is not
only determined by the knowledge state itself, but also
by its context. Together with KFSM introduced in
Section 3.1, the KMM will serve as a base for the
knowledge value chain.
3.2.

Knowledge value chain

The concept of value chain was described and
popularised by Porter (1996) as a value-adding process
in which an organisation might engage. Based on this
understanding, more researchers have continued to
improve the knowledge value chain (KVC) with their
own emphasis. Holsapple and Singh (2001) introduced
a knowledge chain model comprising five primary
activities that an organisation’s knowledge processors
perform in manipulating knowledge resources, with
four secondary activities that support and guide their
performance. King and Ko (2001) proposed an
information/knowledge value chain which is based on
three important levels at which value enhancing
activities are conducted. Eustace (2003) developed it
as a model that integrates different perspectives from
various interest groups. Carlucci et al. (2004) modelled
KVC as a series of stages of KM. Wang and Ahmed
(2005) developed a KVC which incorporates eight
types of KM processes and five kinds of KM enablers.
Those outstanding researchers mainly organised
the knowledge value chain by arraying different levels,
describing the stages of an organisation’s activities,
from acquiring knowledge to using it. However,
existing knowledge value chains are mainly descriptive.
This paper differs from former perspectives as it
regards the knowledge value chain as a sequential
flow of knowledge in which knowledge value increases,
and thus aims at proposing a model based on the
knowledge value chain to measure knowledge value

and survey the mutual impact between knowledge and


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product. It is in an explicit form to describe the
knowledge evolution process. By applying KVC in
knowledge activities, critical nodes of the evolution
process could be revealed and different evolution
‘paths’ could be compared to achieve an optimised
solution.
This paper characterises KVC with KFSM and
KMM, and Figure 2 shows a KVC ‘s0 ! s1 ! s2
‘which characterises the knowledge evolution process
in a 3-dimension super-cube.
By instantiating the elements of the three axes with
values, an example is set up to illustrate the KVC.
Given
s0: Results of marketing investigation
s1: Production plan
s2: Selling plan
o10 ¼ technician, o20 ¼ ordered, o30 ¼ information gathering
. o11 ¼ engineer, o21 ¼ organised, o31 ¼ conception
. o12 ¼ manager, o22 ¼ usable, o32 ¼ sales

.
.
.

.

The KVC ‘s0 ! s1!s2 ‘ represents the following
process:
(1) In the information gathering stage, data
analysts (technician) process the data coming
from investigations by transformation, filtration, calculation, etc., then the knowledge
gathered in the market is transferred to the
expected selling quantity of cars.
(2) Based on the results of marketing investigation
s0, the production schedule s1 is proposed, e.g.,
in the next season, 10,000 cars with a 1.6L
engine and 20,000 cars with a 2.0L engine will
be produced.
(3) The selling plan s2 is established by the
manager according to previous results.

Figure 2.

A KVC based on KFSM and KMM.

In this KVC, the selling plan s2 is the final state, as
the aim of the investigation is to help optimise the
selling plan. It should be noted that the selling plan is
influenced by a production schedule as well, because
the actual production capability of an enterprise is
limited so the ideal selling plan can not be obtained.
Thus, s1 is a critical node of the KVC. It is also
possible that the KVC might have different critical
nodes which form different ‘paths’. Furthermore, KVC

could be split into several sub-KVC according to
practical needs.
In a KVC, a knowledge state si is considered to be
more mature than sj when its maturity states mi is
‘closer’ to the final maturity state mn, namely its
distance jmi – mnj is smaller. Especially, mn is regarded
as completely mature. This distance is related to the
knowledge value and will be discussed in the following
section.
4. Knowledge evaluation based on knowledge value
chain
Based on the KVC introduced above, we have a brief
idea about knowledge value: knowledge value increases during its evolution process and the value is
greater when the knowledge is ‘closer’ to the end
(target). The notion of knowledge value is defined as
follows:
Definition 4. Knowledge value represents the knowledge evolution degree in a KVC, noted as V(si).
Generally, V(s0) ¼ 0, V(sn) ¼ 1.
Unlike length with ‘meter’, information with ‘bit’,
or price with ‘dollar’, there is still not a specific unit
to assess knowledge value, because people have not
yet found a metric by which knowledge value can be
added physically. For this reason, this paper does
not propose a unit either, but uses the percentage to
measure the degree of knowledge evolution. Given
an initial state s0 and a final state sn to arrive, there
is a distance jsn7s0j, and knowledge value is the
power to ‘travel the path’ (we may take as a
metaphor petrol that is providing power to a car
when it is travelling on the road). As there is no

physical unit measuring knowledge value, the usage
of percentages is suitable. A simple hypothesis of
measuring knowledge value using percentage is that:
if knowledge k1 can ‘make the travel further’ than
k2, then k1 has a higher value than k2, e.g., if k1 can
achieve 70% of the path jsn7s0j and k2 can achieve
50%, then k1 has a higher value.
As proposed above, KVC is a framework to
characterise knowledge evolution in a given context,
and therefore it can serve as a base to describe and
measure knowledge value. By associating knowledge


International Journal of Computer Integrated Manufacturing
evolution with knowledge maturity, knowledge value
could be measured by knowledge maturity, in other
words, when knowledge maturity states change from
mi to miþ1, knowledge values change from V(Si) to
V(Siþ1).
The procedure of calculating the difference of two
maturity states, namely jmi7mjj, is as follows:
(1) Supposing n indicators are considered in
calculating jmi7mjj. As an example, let us
take n ¼ 3, and the three indicators chosen are
financial cost f, the consumed time t and the
risk r. To unify the three indicators, the
calculating formulae are as follows:
(a) The financial cost f ¼ [(money spent in this
step)/(the total cost of the lifecycle)]6 100%
(b) The consumed time t ¼ [(time spent in this

step)/(the total time of the lifecycle)]6100%
(c) The risk r ¼ (17abg) 6 100%, where a, b
and g are calculated as follows:
(i) Suppose x1, x2, x3 are the incremental
steps of the elements in PA, KS and
PL respectively, and the ‘incremental
step’ means the number of step-by-step
changes. E.g. the incremental step of
‘technician to manager’ is 2; the
incremental step of ‘usable to organised’ is 1; the incremental step of
‘design to service’ is 3.
(ii) Suppose the success rates of one
leaping degree of the elements in PA,
KS and PL are y1, y2, y3 respectively.
The success rates indicate that risks
happen in states changes. As we know,
when knowledge states are changing,
the unexpected may happen. Different
reasons may cause yi 5 1, such as:
(1) For PA: incomplete efficiency of
execution, different understandings, etc.
(2) For KS: loss of data, calculation
errors, semantic differences, etc.
(3) For PL: deviation during the
product lifecycle evolution, e.g.
incomplete realisation of the
design.
(iii) a ¼ yx1 1 ; b ¼ yx2 2 ; g ¼ yx3 3 :
(2) The difference between the two maturity states
is the weighted mean of f, t and r:

mi À mj
¼ of f þ ot t þ or r  100%
of þ ot þ or
where of, ot and or are the weights of f, t and r
respectively, and of þ ot þ or ¼ 1.

965

In the procedure introduced above, the success
rates and weights are assigned as follows. The success
rates are assigned by experts based on specific methods
or their experience. For example, when a knowledge
state changes from ‘organised’ to ‘usable’, we should
map ‘people under 35 years old’ to ‘young people’. In
this case, such mapping may bring some risks caused
by different semantic contexts: some people of 40 years
old may be also considered as ‘young people’ in some
contexts, while not in all. Experienced experts will
assign y2 an appropriate number according to the fact
that whether the mapping process will bring big or
small risks to the case. The weights are assigned
according to different strategies or purposes, for
example, in the production of an airplane, or will be
given a big value such as 0.8, whereas of and ot will be
assigned small values.

This paper has chosen f, t and r as three indicators
to calculate jmi7mjj, and they are chosen according to
Malmi and Ika¨heimo’s (2003) analysis about value
management (VM). It should be noted that other
indicators could be introduced according to specific
cases as well. People may choose n indicators and
assign weights o1, o2, . . . , on respectively, on condition that Sni¼1 oi ¼ 1. More generally, we obtain:
Pn




Á indicatori Þ

mi À mj
¼ i¼1 ðo
Pi n
 100%
i¼1 oi
When we take n ¼ 3 and instantiate indicator1 with
financial cost, indicator2 with consumed time and
indicator3 with risk, we arrive at the procedure
introduced above.
Given two maturity states mi and mj, if a knowledge
fragment k (or human resource, or knowledge activity,
etc.) can complete the states transition mi !mj, then
according to Definition 4, V(k) ¼ jmi7mjj. Thus, if the
newly introduced knowledge k can make the maturity
state evolve from mi to mj, its has a value of
V(k) ¼ jmi7mjj. As an example that we have tested,

given f ¼ 0.6, t ¼ 0.75, r ¼ 0.3, and the weights
assigned are of ¼ 0.4, ot ¼ 0.4, and or ¼ 0.2, then
V(k) ¼ 0.6. This 0.6 means ‘the ability of knowledge k
to evolve si to sj’. In a global view, ‘si to sj’ is just one
part of ‘the initial state s0 to the required state sn’, and
this part is worth ‘60%’ of the whole. If V(k) ¼ 1, it
means that k is sufficient to achieve the ‘whole path’
and if V(k) ¼ 0, it means that ‘k’ is completely useless.
By qualitative analysis, we find that knowledge has a
higher value when it can fill the gap between two
maturity states that have higher financial cost, longer
consuming time and higher risk.
For any knowledge involved in production activities, V(k) could serve as a benchmark to measure
knowledge value and judge the financial quotations


966

Y. Xu and A. Bernard

from different knowledge providers such as consulting
companies or outsourcing services. For example, two
knowledge providers may offer knowledge k and k0 ,
whose values could be measured by V(k) ¼ jmi7mjj
and Vðk0 Þ ¼ jm0i À m0j j respectively. With the KVC
developed in this paper, the value of these two different
knowledge solutions can be compared. Moreover,
when there are several ‘paths’ from s0 to sn with
different critical nodes (c.f. Figure 3), different solutions can be compared quantitatively, thus KVC could
play an important role in cost management.

5. Discussions
‘Knowledge is power’, but how can the ‘power
provider’ be characterised and how can the ‘power’
be represented? In order to answer these questions, this
paper focuses on the problem of knowledge value,
which is used to describe ‘the power of knowledge’.
‘Value’ is a flexible term that has abundant meanings
and is applied in a variety of domains. To avoid
terminology inflation, this term should be given its
proper meaning in the specific context of knowledge
management. For this basic purpose, several definitions are given so as to make the problem of
knowledge value a practical job rather than a
theoretical game.
Attempts at measuring knowledge value directly
usually cause irrational assumptions, as it is still an
unproved proposition as to whether knowledge is
something that can be measured directly. It is well
known that people are using the electron cloud, the
probability density of an electron, to describe the
movement of an electron around an atomic nucleus. So
we can infer that some indirect descriptions may also
be a good choice to characterise something that is of
great complexity and flexibility, for example, knowledge. As a result, instead of focusing enterprise

attention on measuring knowledge value itself, this
paper regards knowledge value from a KVC viewpoint.
The KVC proposed in this paper is based on an
integration of KFSM and KMM, which are introduced to characterise knowledge and knowledge
context in an explicit way respectively. To make the
study more accessible, this paper has chosen three

‘dimensions’ which are well recognised in daily
production activities: participant, knowledge status
and product lifecycle.
In fact, with rapid technological development,
companies need to rely on knowledge outsourcing to
remain competitive (Gavious and Rabinowitz 2003),
and knowledge value measured and compared by KVC
can serve as a benchmark in evaluating different
knowledge providers and thus act as a support to
enterprise decisions.
Interesting perspectives for the study of knowledge
value may include: further analysis of other features
and attributes of knowledge and knowledge context,
development of different indicators and weights of the
measurement procedure according to different types of
industries, etc.
6.

Conclusions

This paper begins by analysing some features of
knowledge and knowledge value and proposes several
basic notions about knowledge such as knowledge
state, knowledge context, knowledge maturity and
knowledge value. They are then formalised and
integrated into a structured and explicit approach to
characterise the knowledge value chain. Finally, based
on the knowledge value chain, a procedure to measure
and compare knowledge value is introduced. This
method of measuring knowledge value based on KVC

may not solve all the problems involved in the field of
knowledge management; however, it does provide a
template for a knowledge reference system and makes
the application of knowledge measurement more
effective.
References

Figure 3.

Different solutions in a KVC.

Ahn, J.H. and Chang, S.G., 2004. Assessing the contribution
of knowledge to business performance: the KP3 methodology. Decision Support Systems, 36 (4), 403–416.
Bacon, F., 1597. Meditationes Sacræ.
Bernard, A. and Tichkiewitch, S., 2008. Methods and tools
for effective knowledge life-cycle-management. Berlin:
Springer.
Bernard, A. and Xu, Y., 2009. An integrated knowledge
reference system for product development. CIRP Annals,
58 (1), 119–122.
Birkinshaw, J. and Sheehan, T., 2002. Managing the knowledge life cycle. MIT Sloan Management Review, 4 (1), 75–
83.


International Journal of Computer Integrated Manufacturing
Blanco, E., Grebici, K., and Rieu, D., 2007. A unified
framework to manage information maturity in design
process. International Journal of Product Development, 4
(3–4), 255–279.
Carlucci, D., Marr, B., and Schiuma, G., 2004. The knowledge value chain: how intellectual capital impact on

business performance. International Journal of Technology Management, 27 (6/7), 575–590.
Chen, M.Y., Huang, M.J., and Cheng, Y.C., 2009. Measuring knowledge management performance using a competitive perspective: An empirical study. Expert Systems
with Applications, 36 (4), 8449–8459.
Chilton, M.A. and Bloodgood, J.M., 2008. The dimensions
of tacit and explicit knowledge: a description and
measure. International Journal of Knowledge Management, 4 (2), 75–91.
Eustace, C., 2003. A new perspective on the knowledge value
chain. Journal of Intellectual Capital, 4 (4), 588–596.
Gavious, A. and Rabinowitz, G., 2003. Optimal knowledge
outsourcing model. OMEGA: The International Journal
of Management Science, 31 (6), 451–457.
Guerra-Zubiaga, D.A. and Young, R.I.M., 2008. Design of a
manufacturing knowledge model. International Journal
of Computer Integrated Manufacturing, 21 (5), 526–539.
Hey, J., 2004. The Data, Information, Knowledge, Wisdom
Chain: The Metaphorical link. Intergovernmental Oceanographic Commission. December 2004, Paris, France.
Holsapple, C.W. and Singh, M., 2001. The knowledge chain
model: activities for competitiveness. Expert Systems
with Applications, 20 (1), 77–98.
King, William R. and Ko, Dong-Gil, 2001. Evaluating
knowledge management and the learning organization:
an information/knowledge value chain approach. Communications of the Association for Information Systems, 5.
Article 14.
Lee, C.C. and Yang, J., 2000. Knowledge value chain.
Journal of Management Development, 19 (9), 783–794.
Leonard-Barton, D., 1995. Wellsprings of Knowledge. Boston: Harvard Business School Press.
Liebowitz, J. and Wright, K., 1999. Does measuring knowledge make ‘cents’? Expert Systems with Applications, 17
(2), 99–103.
Liu, S.C., Olfman, L., and Ryan, T., 2005. Knowledge
management system success: empirical assessment of a

theoretical model. International Journal of Knowledge
Management, 1 (2), 68–87.
Ma, Y. and Fuh, J.Y.H., 2008. Product lifecycle modelling, analysis and management. Computers in Industry, 59
(2–3), 107–109.
Malmi, T and Ika¨heimo, S., 2003. Value Based Management
practices—some evidence from the field. Management
Accounting Research, 14 (3), 235–254.

967

Markow, T.T., 2004. A knowledge maturity model: an
integration of problem framing, software design, and
cognitive engineering. Thesis (MSc), North Carolina State
University, USA.
MIKE, 2010. Retrieved from: nmethod
ology.org/wiki/Information_Maturity_Model
Nonaka, I. and Takeuchi, H., 1995. The knowledge creating
company. New York: Oxford University Press.
Paulk, M.C., Weber, C.V., Curtis, B., and Chrissis, M.B.,
1995. The capability maturity model: guidelines for
improving the software process. Boston: Addison–Wesley
Longman Inc.
Porter, M.E., 1996. What is strategy? Harvard Business
Review, 74 (6), 61–78.
Pritchard, D.H., 2007. Recent work on epistemic value.
American Philosophical Quarterly, 44, 85–110.
Renner, W., 2003. Human values: a lexical perspective.
Personality and Individual Differences, 34 (1), 127–141.
Robinson, H.S., Anumba, C.J., Carrillo, P.M., and AlGhassani, A.M., 2006. STEPS: a knowledge management
maturity roadmap for corporate sustainability. Business

Process Management Journal, 12 (6), 793–808.
Rowley, J., 2007. The wisdom hierarchy: representations of
the DIKW hierarchy. Journal of Information Science, 33
(2), 163–180.
Ruggles, R.L., 1998. The state of notion: knowledge
management in practice. California Management Review,
40 (3), 80–89.
Sillince, J.A.A., 1995. A stochastic model of information
value. Information Processing and Management, 31 (4),
543–554.
Stewart, T.A., 1997. Intellectual Capital: The New Wealth of
Organizations. New York: Doubleday/Currency.
Thimm, G., Lee, S.G., and Ma, Y.S., 2006. Towards unified
modelling of product life-cycles. Computers in Industry,
57 (4), 331–341.
Wang, C.L. and Ahmed, P.K., 2005. The knowledge value
chain: a pragmatic knowledge implementation network.
Handbook of Business Strategy, 6 (1), 321–326.
Wen, Y.F., 2009. An effectiveness measurement model for
knowledge management. Knowledge-Based Systems, 22
(5), 363–367.
Wong, H.K., 2004. Knowledge value chain: Implementation of
new Product Development System in a Winery. The
Electronic Journal of Knowledge Management, 2 (1), 77–90.
Xu, Y. and Bernard, A., 2009. Knowledge organization
through statistical computation: A new approach.
Knowledge Organization, 36 (4), 227–239.
Zhao, Y., Tang, L.C.M., Darlington, M.J., Austin, S.A., and
Culley, S.J., 2008. High value information in engineering
organisations. International Journal of Information Management, 28 (4), 246–258.

International Journal of Computer Integrated Manufacturing
Vol. 23, No. 11, November 2010, 968–978

Computer integrated reconfigurable experimental platform for ergonomic
study of vehicle body design
Z.M. Bi*
Department of Engineering, Indiana University Purdue University Fort Wayne, Fort Wayne, IN 46805, U.S.A
(Received 4 January 2010; final version received 18 May 2010)
A computer integrated reconfigurable experimental platform is introduced for the ergonomic studies of vehicle body
design. Its hardware system consists of lights and camera equipment, and most importantly, many movable parts
whose positions and orientations can be adjusted to meet various requirements of ergonomic study. The movements
of these parts are driven by actuators. Its software components are a control system, a visualisation system, and a
data acquisition system. The control system is operated by human beings to issue the motion commands for system
reconfiguration, the visualisation system monitors the reconfigurable platform in the reconfiguration process and
ergonomic studies, and the data acquisition system acquires all meaningful data in the integrated experiment
platform. Those data include the positions and all movable parts, the status of lighting and camera equipment, and
trajectories and postures when a test driver enters or exits the platform. These data can be utilised to optimise the
vehicle exterior components to meet the ergonomic needs of different drivers.
In this paper, the virtual control and the validation of system set-up are mainly discussed. System architecture is
presented, and the designs of hardware and software components are introduced. Two graphical user interface
(GUI) systems have been developed for the control and the visualisation of the reconfigurable experimental
platform. The visibility study is conducted to ensure a type of motion tracking equipment is appropriate to collect
the data of entering and exiting movements by a driver during experiments.
Keywords: virtual reality; systems design; system simulation; sensors; automated inspection reconfigurable platform;
ergonomics; avatar; data acquisition

1. Introduction
A survey has shown that every year there are around

15,000 slip/fall injuries to the drivers of medium or
large-size vehicles. About half of the falls happened on
tractors, mostly during entering or exiting processes.
Some main features relating to the causes of the
injuries include steps (57%), handholds (7%), and
ground (20%). These injuries have caused a significant
economic loss; one large US fleet reported that the
slips/falls around vehicles directly incur over $20
million (Parkinson et al. 2007, Reed 2009). From the
perspective of vehicle body design, it is very important
to study the relation between the human variability
and appropriate geometrics and positions of vehicle
exterior components; note that the ergonomic study is
an essential means to evaluate the performance of a
specific design solution.
The importance of ergonomics study for successful
product design has been well recognised. Numerous
researches have been conducted to address various
issues relevant to ergonomics. The relation between
automation and productivities and ergonomics was
investigated through a case study, and it was suggested

*Email:
ISSN 0951-192X print/ISSN 1362-3052 online
Ó 2010 Taylor & Francis
DOI: 10.1080/0951192X.2010.500678


that the integration of the consideration of ergonomics
into production system design can increase the productivity of a partially automated system significantly

(Neumann et al. 2002). A similar conclusion was drawn
by Yeow and Sen (2003). In their study, the ergonomic
design brought significant reduction of cost, reduction
in rejection rate, increased monthly revenue, and the
improvement of productivity and quality. The interventions implemented were simple and inexpensive but
resulted in many benefits. An early review (Scapin and
Bastien 1996) has discussed the progress of ergonomic
study for product design and summarised some
ergonomic criteria for evaluations of the ergonomic
quality of interactive systems. Lee (1999) argued to use
a high-touch process to incorporate innovative ideas
into the development of ergonomic products and
backed up his arguments with many case studies.
For ergonomics study of vehicle design, traditionally, designers need to build a stationary, threedimensional mock-up model from wood and metal
based on the engineering drawings for examination
and test. Each round of design changes would require
another physical model to be built, adding time and


International Journal of Computer Integrated Manufacturing
cost to the development program. Moreover, it takes a
long time to build a specific model. For example, a
typical seating buck might take more than six weeks to
build and most often is already out of date by the time
it is finished. The data collection about the exact
positions of vehicle components is another issue; the
set-up and the data collection are mainly accomplished
manually. Numerous required experiments take a long
time, a considerable amount of labour, and still are
very error-prone. It is desirable to develop a computer

integrated experimental platform (physical model or
simulator) to simulate entering/exiting conditions
under various design parameters for medium or heavy
duty vehicles.
One reconfigurable programmable platform is commercially available but its application is confined to the
design of passenger cars and interior design. It has been
named as the programmable vehicle model (PVM) for
Ford (Prefix Corp., 2009). A designer can change the
dimensions of the interiors and look at some options
for placing seats and controls. The PVM works similar
to 3D mock-up models except the PVM allows larger
surfaces, such as roof sections, door panels and instrument panels, to be moved in and out with the help of
hydraulics. Once the dimensions are established, data
can be transferred from the device to a computer (Kobe
1995). Note that only the adjustments of internal components are implemented and they are inapplicable to
the ergonomic study of medium or heavy duty vehicles
owing to instrumentation and variability needs. For the
design of medium to heavy duty vehicles, a few
manually-operated experimental platforms have been
used for ergonomic study (Reed 2009); while no
automated reconfigurable platform has been reported.
Computer-aided virtual or physical experimental
platforms for ergonomic study are not new. Many
relevant researches have been published in literature.
Rubio et al. (2005) have reviewed the state of the art of
virtual reality (VR) for the next-generation manufacturing; VR is stated as a low-cost, secure and fast
analysis tool and it is a very helpful and valuable work
tool for the simulation of manufacturing systems. A
VR-based system was developed to support the
analysis of information requirements for the diecasting industry (Bal et al. 2008). Pappas et al. (2006)

proposed a web-based platform for collaborative
process and product design evaluation; this platform
provides real-time collaboration of multiple users at
different sites on the same project. A virtual assembly
approach was proposed to model assembly processes
(Jun et al. 2005). Mavrikios et al. (2006) developed a
prototype system which deployed VR for immersive
and interactive simulation of welding processes.
Wiendahl and Fiebig (2003) developed a computeraided tool for factory design; it took into consideration

969

the different viewpoints of all specialists involved in the
planning. The application of VR technologies has been
extended in many other areas. In the life-cycle design
of shoe products, the VR technology was used as a
support tool to increase the design efficiency (Vigano
et al. 2004).
The author’s focus is on the ergonomic study of
medium or heavy duty vehicles; while a large number
of experiments are required to optimise the locations
and positions of the exterior components. A computer
integrated experimental platform will be developed. It
is expected to (i) reduce the potential possibility of the
investment waste due to inappropriate design, (ii)
justify the design suitability and feasibility vividly and
cost-efficiently, (iii) accelerate the detailed design, (iv)
evaluate the solution of using a motion tracking system
as one of the means for data acquisition, and (v)
facilitate the accuracy of estimating required resources

and components at the phases of the detailed design,
fabrication and ergonomic study.
The objectives of the presented works are to (i)
develop a computer integrated reconfigurable experimental platform for vehicle exterior design, (ii)
implement the motion control system, and (iii) validate
if it is feasible to apply a motion tracking equipment to
track drivers’ motion in ergonomic studies. Therefore,
the following three tasks are mainly involved: (i) to
design a conceptual platform and visualise it in a VR
environment, (ii) to develop graphical user interface
(GUI) for motion control and visualisation, and (iii) to
conduct the visibility study for the motion tracking
system. The remainder of the paper is organised as
follows. In Section 2, architecture of the reconfigurable
experimental platform has been introduced. System
components and their relations have been discussed. In
Section 3, the virtual model of reconfigurable platform
has been developed, and the design considerations for
the determination of system components are explained.
In Section 4, two GUI systems are introduced. The
first one is to control the physical model and the
second one is to visualise the reconfigurable process
and ergonomic behaviours in real-time. In Section 5, a
visibility study has been discussed. The process of
preparing CAD files with different avatar models
under three different poses has been introduced. A
motion tracker model has been embedded with the
virtual platform for visual validation. In Section 6, the
system reconfiguration process has been discussed. In
Section 7, the presented works have been summarised,

and the limitation and future works are pointed out.
2. System architecture
System architecture of the developed reconfigurable
platform is shown in Figure 1. The system consists of


970

Z.M. Bi

six modules: the physical model, the virtual model, the
data acquisition system, the Labview-based control
system, the Eclipse-based visualisation system, and the
embedded database.
The physical model consists of movable components and their supporting frames; a set of the
actuators are installed to drive these movable components. The virtual model is a mirrored computer
representation of the physical model. For hardware
development, it is recommended to use as many offthe-shelf components as possible, their computer
models are often available from suppliers, and those
models can be directly imported and assembled with
others in the virtual model. The data acquisition
system obtains the statuses of the movable components, and tracks the movements of a test driver during
the test. Encoders are used to locate the positions of
linear actuators; to track the movement of a test driver;
a driver has to wear a suit with the markers. A set of
the cameras are installed to capture the light reflections
from the markers and thus detect the motion trajectory
of a test driver when he/she enters or exits the vehicle.
The Labview-based control system is developed to
control the actuators of the physical model; in

comparison, the Eclipse-based visualisation system is
to monitor the working condition of the reconfigurable
experimental platform.

Figure 1.

Architecture of reconfigurable platform.

At the end, an embedded database is included in
the system. It keeps the data relevant to all system
components such as the virtual model, the profiles and
the statuses of test drivers, the set-up of movable
components, the set-up of lights and camera, and the
results of ergonomics studies. After the experiments,
designers can retrieve any types of data for the design
optimisation of a vehicle.
3.

Virtual model of hardware system

The hardware system consists of lights and camera
equipment, and most importantly, many movable
parts whose positions and orientations can be adjusted
to meet various requirements of ergonomic study. The
movements of these parts are driven by actuators. As
shown in Figure 2, the main components include three
steps, door and B-pillar handles, side door, driver’s
seat, pedals, and steering wheel column. The virtual
model has been developed to detect a potential
collision in the operation of movable components.

3.1.

Design considerations

A total of 34 motions have been identified from design
requirements; however, each movable component
requires a simple and de-coupled linear or rotational
motion. Numberless linear and rotational actuators
are commercially available. To design these movable
components cost-effectively, the number of the customerised components have to be minimised by adopting
as many commercially available products as possible.
The design is focused on the selection of available
products which can meet the motion ranges, accuracy,
and loads for the specified components optimally.
The strategy of ‘divide and conquer’’ is applied. The
components are selected or designed to meet their
motion ranges individually. After the adjustable

Figure 2.
platform.

Virtual model of the hardware reconfigurable


International Journal of Computer Integrated Manufacturing
components are determined and placed, the geometric
dimensions of the support frames can be finally
designed to hold all components firmly on the platform.
The designed components will be assembled as a whole
to verify whether or not (i) the structure has an enough

space to place all components, and (ii) there are some
collisions when a component is under its adjustment.
The design has been an iterative process to generate a
final solution, where all of the components are arranged
appropriately and there is no collision in operation.
Note that some components, in particular the steps,
have an overlapping motion range in their height
adjustments. The potential collisions are unavoidable
in the design. However, this type of collision does
not affect the application since they are predictable.
The avoidance of collision is achieved easily by the
controlling program. SoildWorks is used as the main
tool for the conceptual design of reconfigurable platform. The CAD models of commercial products have
been converted into the formats such as IGES, STEP,
and VRML, which are acceptable to Unigraphics,
Envision, and Mockup. In the following sections, the
designs of some movable components are provided as
examples.
3.2.

Examples of component design

The design of steps. As shown in Figure 3, various steps
can be selected in vehicle design, the appearances and
geometrics of the steps have an impact on the
ergonomic behaviours. The steps are required to adjust
their lateral (X) and vertical (Z) positions automatically. In the Z direction, the adjustment ranges are

Figure 3.

Various steps in application (Source: Reed 2009).

Figure 4.

Various handles in application (Source: Reed 2009).

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overlapping with each other. The design specifications
also imply that the number of the steps in use can be
changed from one to three based on the height of the
cab floor relative to the ground floor. An extra
requirement for the steps is that they have to be
hidden under the cab floor when they are not in use.
Three design options for automatic height adjustments
are tested, and a final solution in Figure 2 is selected
based on the comparison among the alternatives.
Design of Handles. Handles are the most diversified
components in vehicle design. Figure 4 has shown
some examples of the handle designs. It is very difficult
to define a general requirement of various handles to
cover all of the ergonomic experiments. The design
meets the requirements of three types of the handles,
i.e., B-pillar handle, upper handle, and inside inclined
handle. The B-pillar handle is mounted on the moving
part of the slide door. Its position is determined by the
slide door, and its vertical position (Z) can be changed
manually. A commercially available lock is available
from Bosch to fix the position when the adjustment is
completed. The inclined handle can be mounted at any

position and orientation on the right door with its two
ends screwed into pre-fabricated holes. The upper
handle is mounted horizontally, and its two ends are
mounted on the left and right door panels, respectively,
to increase its rigidity. All of the handles adopt
telescopic parts for the changes of their lengths.
Design of Wheel Column. The steering wheel column is
adjustable in three directions: the fore/aft (Y) direction, the vertical direction (Z), and the orientation of


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Z.M. Bi

the wheel. Two design challenges are its potential
interference with the pedals support and its strengths
to endure the large force and moments applied on the
wheel by a driver. To meet these challenges, parallel
structures are considered.
As shown in Figure 5, four vertical profile columns
are mounted rigidly on the platform cab floor as the
rails for a Z-plate to move along the Z-direction. The
motion is provided by a linear actuator whose motor is
grounded on the cab floor. The Z-plate consists of four
inside profile columns and connected the closed rails by
four slides, respectively. On the top side of the Z-plate,
there are two horizontal profile beams; each connects
two vertical columns together rigidly. These two beams
are used as the rails for the movement in the Ydirection. Another linear actuator is mounted on the
Z-plate as a provider of the motion in the Y-direction.

The adjustment of the wheel orientation is depicted
in Figure 6. A cylinder actuator is used to drive the
motion, its fixed end is mounted on the Y-plate by a
revolute joint, and its moving end is connected to the
wheel link by a revolute joint. The wheel link is also
connected to the Y-plate by a revolute joint. The wheel
is welded on the wheel link rigidly. The cylinder

Figure 5.

Design of wheel support.

Figure 6.
wheel.

Mechanism for orientation change of steering

actuator will drive the orientation change of the
steering wheel.
Design of the Pedals. The pedals can be adjusted
manually or automatically in all three directions. Three
positioning systems are for clutch, accelerator, and
brake. Once a pedal is adjusted, the scale and pointer
attached on the positioning slide tells us the position of
the pedal.
4. Graphic user interface
The objective of developing a graphic user interface
(GUI) is to control the physical model and visualise the
virtual model. The adjustments can be made based on
the control commends specified by an operator. The

GUI has been developed for both the physical model
and the virtual model.
4.1. Labview-based control system
Labview from National Instruments (NI) is one of the
most popular systems for motion control, and it has a
module called ‘Mechatronics Toolkit. (NI Corp. 2009),
which is designed to develop complex multi-axis
motion profiles for machines and validate them using
simulation. The toolkit is capable of designing motion
profiles, detecting collisions, simulating the mechanical
dynamics of a machine including mass and friction
effects, estimating machine cycle time performance,
validating component selections for motors, drives and
mechanical transmissions, and evaluating engineering
tradeoffs between the mechanical, electrical, control
and embedded system aspects of the design.
Labview Mechatronics Toolkit has its graphic
motion simulation in SolidWorks. Therefore, the
virtual model can in fact be connected to the
controlling system directly. The motion constraints of
the components in the virtual model are defined by
assembly mates. The Mechatronics Toolkit provides a
communication channel to allow a Labview GUI to
access (read and write) all of the parameters used in
these assembly mates. As a result, the Labview GUI is
capable of manipulating the positions of adjustable
components by changing the coordinates of the
components in the assembly. As shown in Figure 7,
the Labview GUI has been developed to control the
physical model.

It is ideal that the Labview Mechatronics Toolkit
can support the real-time visualisation of the reconfigurable platform; but unfortunately, the developed
Labview GUI has a considerable time delay in the
graphic simulation. Two key reasons are the complexity of the virtual model and discontinued communication between the Labview and Solidworks. Whenever a