A methodology for knowledge management implementation

A methodology forknowledge managementimplementation

Gavin P. Levett and

Marin D. Guenov

Introduction

The past several decades have witnessed

fundamental changes in the structure of

organisations which have led to massive

increases in productivity. The driving force

for many of these changes have come through

total quality management (TQM) and

business process re-engineering (BPR)

concepts. These philosophies precede

knowledge management (KM) as a concept

for gaining improvements in the company

knowledge base and are presently considered

as complementary to KM.

TQM fosters a stable, efficient and creative

working environment whereas BPR is aimed

at producing `̀ bottom line'' improvements,

thus creating a conflict between long-term

and short-term gains.

This research recognises that, in essence,

KM is the examination of mechanisms that

facilitate critical organisational processes, the

measurement of their performance and the

development of practical solutions that deliver

one or more KM objectives. The business

community has articulated the following core

KM objectives, through an analysis described

in KPMG (1999), as:. supporting innovation, the generation of

new ideas and the exploitation of the

organisation's thinking power;. capturing insight and experience to make

them available and usable when, where

and by whom required;. making it easy to find and reuse sources

of know-how and expertise, whether they

are recorded in a physical form or held in

someone's mind;. fostering collaboration, knowledge

sharing, continual learning and

improvement;. improving the quality of decision making

and other intelligent tasks;. understanding the value and contribution

of intellectual assets and increasing their

worth, effectiveness and exploitation.

This research work was directed towards

understanding how the automotive industry

could implement KM in order to realise some

or all of its benefits. The key facets of

competitive advantage in the automotive

The authors

Gavin P. Levett is an Engineering Doctorate Research

Engineer and Marin D. Guenov is a Senior Lecturer,

Computer Integrated Design Technology Group, both at

Cranfield College of Aeronautics, Cranfield University,

Cranfield, UK.

Keywords

Knowledge management, Implementation, Engineering,

Measurement, Motor industry

Abstract

This article describes research work which was directed

towards providing the automotive industry with a

practical methodology that translates the conceptual

ideas of knowledge management (KM) into a working

programme with defined objectives, using industry

terminology. The research also developed a supporting

analysis methodology that enables an effective analysis of

the influences on employee activities when creating and

sharing valuable corporate knowledge, that spans

technical and cultural boundaries. This happens through

identifying the factors that impact on defined KM metrics.

The analysis identifies the key influencing factors within a

working environment. The research benefits are felt when

the ground-level drivers of KM behaviour are improved

through links to an appropriate KM strategy. KM strategy

may emphasise organisational cultural changes or IT

changes or both in an endeavour to improve innovation,

reduce business costs and reduce time to market of new

products. An industrial case study was undertaken to

validate the research.

Electronic access

The current issue and full text archive of this journal is

available at

http://www.emerald-library.com

The authors are very grateful to the Cranfield

Impact Centre Ltd for their sponsorship of the

research and to the automotive manufacturer who

was involved in the case-study validation.

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Journal of Knowledge Management

Volume 4 . Number 3 . 2000 . pp. 258±269

# MCB University Press . ISSN 1367-3270

industry lie in the continual improvement in

product innovation and the ability to bring

new products to the market quickly and at low

cost to the consumer.

Experience gained from studying

automotive product development

environments suggested that the industry

would benefit from KM; however, it was

found that few manufacturers had fully

embraced KM. The clues that lay behind the

apparent poor adoption rate of KM were that

the academic development of KM has not

stabilised and filtered into industry as

organisations usually implement well-

established practices. In addition, there was a

lack of examples describing practical KM

applications within the manufacturing

industry. Many companies simply did not

know how to apply KM because the theory

covers a broad spectrum of concepts that

describe how companies should create, share

and store valuable knowledge in its numerous

formats. This research uncovered a gulf in

academic research between the conceptual

frameworks of processes that must be

undertaken for KM and their practical

implementation within an organisation.

The objectives of this research work were

focused on providing the automotive industry

with a practical methodology which could be

used to translate the conceptual ideas of KM

into a working programme with defined

objectives, or deliverables, using terminology

that the industry could readily understand.

The research highlighted the requirement to

develop a supporting analysis methodology to

examine employee actions and behaviour in

regard to how they shared and created

knowledge. This would have the benefit of

identifying the main influences on existing

KM which could be improved through the

application of an appropriate KM strategy,

that may emphasise organisational cultural

changes or IT changes or both in an

endeavour to improve innovation, reduce cost

and reduce the product development

timescale.

KM is a relatively recent management

philosophy and because of the diversity within

organisational working environments and

KM requirements, there is no detailed model

for developing a particular KM strategy. The

work by Wiig (1998) offers a generic

conceptual model which provided a good

starting point for the development of a

practical KM programme.

Wiig (1998) offers a six-step procedure for

an initial KM introduction programme for an

organisation with limited experience in KM:

(1) Build management understanding and

commitment to pursue KM.

(2) Map perspectives of the knowledge

landscape.

(3) Plan the organisation KM priorities,

focus and strategy.

(4) Identify sought KM benefits.

(5) Adjust KM priorities.

(6) Create KM-related incentive

programmes.

Although the work by Wiig (1998) provides

useful starting points for implementing KM,

it is highly generic and cannot be followed to

the letter without putting the ideas into

context. The next section of this paper

describes how a practical implementation

programme was developed in order to supply

industry with the means to realise the benefits

of KM.

Proposed KM introduction programme

The KM pilot programme indicates the steps

that an automotive organisation must take in

order to achieve a practical and feasible KM

programme and is depicted in Figure 1.

The development of the pilot programme

was approached in the manner of assuming

that the company would have limited

experience of undertaking KM within an

organisation. This was the first major

assumption to be made as it provokes the

requirement for the pilot programme to cover

all aspects of undertaking a KM initiative

from preparation to implementation.

The second major assumption to be

established was the notion that an

organisation would require the pilot

programme to be initially implemented as an

experiment. Organisations, especially

automotive manufacturers, are inherently

cautious when approaching any new process

because of the large financial investment that

they usually require. Once the success of the

pilot can be demonstrated then organisations

may wish to expand the scope of application.

The third assumption to be established was

that the target organisation would be a

medium to large-sized automotive

manufacturing company. The premises at this

stage was that the user must have a multi-

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Volume 4 . Number 3 . 2000 . 258±269

disciplinary design environment which would

benefit greatly from KM and it was felt that

contacts made with automotive companies

during the early stages of the research would

offer an avenue for advice and support.

The final assumption was the idea that the

design of the pilot programme should be

procedurally logical and influenced by

existing process design programmes. It would

assist the end user in learning and

understanding the requirements of the pilot

KM programme if familiar signposts were

present, e.g. those found in British Aerospace

PLC (1997). The following sections of this

paper describe the key components of the KM

pilot programme.

Phase 1 ± Case study definition

This phase represents the activities of

undertaking KM feasibility studies.

Preliminary research provides information on

past problems in creating and sharing

knowledge within the organisation. The

research findings are also used to define a

number of critical KM metrics which will help

to measure the effectiveness of employee

activities in regard to KM in the organisation.

These metrics form the inception of the

working environment assessment and analysis

methodology. Once the preliminary research

is complete the next step is to focus the pilot

programme around a particular department

or process that is in most need of KM.

Finally, the resources that are required to

undertake the KM programme are defined.

Preliminary research

Research is undertaken into company

operations in order to identify and quantify

the main problems in the product

development processes. Previous re-

engineering programmes are reviewed in

order to reveal any lessons learned in

identifying problems and/or potential time

and cost savings in the product development

system. In addition, this review may provide

invaluable feedback from the development

and implementation of process solutions, as

the reactions from employees can uncover

cultural aspects of the organisation which may

assist or hinder any KM strategy.

Later phases of the KM pilot programme

undertake an extensive analysis of the critical

operations of a selected department or

process. To perform these tasks, internal

consultants are usually engaged to ensure the

security of all gathered data. The act of

selecting internal consultants helps to secure

ownership of the KM process which must be

defined to ensure management support.

The introduction to this phase highlights

the critical output of the preliminary research

which is to identify appropriate KM metrics

which will be used to measure KM practice

that will lead to an analysis of the working

environment. This is step one of the analysis

methodology, which is introduced in stages

throughout this paper.

The most suitable guides to devising KM

metrics can be found in Radding (1998),

Malone (1997) and Chiesa et al. (1996). The

metrics are used to focus the recording of KM

Figure 1 The conceptual perspective of the pilot KM programme

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practice in key areas, and are used in the

analysis which is undertaken in the following

phase. This research has developed a template

which can be used as a guide so that relevant

measures appropriate to the organisation are

defined. In addition, accurate intelligence

data on competitors can reveal industry KM

initiatives and benchmarks which may

provoke metric development if the

organisation is striving to become an industry

leader in KM.

This research filtered the numerous

possible metrics down to the following list of

eight that are considered appropriate to the

automotive product development

environment (they are also generic enough to

cover a range of industries and companies):

Eight metrics for KM analysis

(1) Motivation (how well the employees are

motivated to work productively).

(2) Knowledge capture (the ability to capture

important knowledge).

(3) Stored knowledge (the usefulness of

captured knowledge in solving new

problems).

(4) Personnel training (the effectiveness of

employee learning mechanisms).

(5) Knowledge transfer (the effectiveness of

sharing important knowledge).

(6) Creative thinking (the ability of

employees to create new solutions).

(7) Knowledge identification (the

effectiveness of identifying knowledge).

(8) Knowledge access (the effectiveness of

accessing important knowledge).

These eight metrics cover a wide scope of KM

practice as the emphasis of the analysis

methodology is to show how it is possible to

measure many aspects of KM. These metrics

are not assumed to be totally independent. In

reality it may not be feasible to simultaneously

measure all of the above because of the time

overhead that would be required to monitor

every aspect of employee practice. It is

proposed that a practical number of metrics

would lie in the range of five to eight.

Following the definition of the KM metrics,

the next step is to design a means of collecting

the data. The analysis methodology requires

that each metric, which represents how well

the organisation is performing in a KM aspect,

is quantifiable for it to be a true measurement

technique. To collect the KM practice data ±

an activity that will be described in more detail

in phase 2 ± work tasks are observed and the

employees are questioned in order to obtain

the reasons behind their actions and practice.

For example, an employee may not be able to

gain access to particular expertise because the

colleague may work in another country with a

time-zone difference, thus making

communication difficult. This process reveals

organisational KM practice. The analysis

methodology not only identifies the

underlying causes of KM practice but must

also provide an indication of how the

environment could be changed to improve

KM practice.

To facilitate this process a questionnaire is

developed that the consultant uses to translate

employee KM practice into quantifiable

metric data. This necessitates each question

to capture important KM practice and to have

a range of possible answers. The answers are

then translated into numerical scores in the

range of 0 (irrelevant) and 1 (satisfactory/best

practice). In addition, the questionnaire must

capture the influencing factors which gave a

score, e.g. office location could be a factor

that affects knowledge sharing/transfer, and a

low score attributed to this factor could be the

result of inappropriate office location.

The questionnaire must enable the

employees to suggest how the influencing

factors could be improved (including the

addition of new factors) in order to increase

the metric score. For example, an employee

may wish to suggest how knowledge

identification, that is required during a

particular work task, could be improved.

There may also be potential task time and cost

changes that would result from improving a

factor which must also be recorded. The

questionnaire must also capture any instances

of changing a influencing factor having a

negative effect on metric performance.

Scope of the case study

In this sub-phase a case study is proposed that

focuses the KM pilot programme on a

particular department or process that is

considered to exhibit poor KM practice. The

preliminary research is reviewed to identify a

particular process, function or department

that has a critical influence in the

development programme.

Phase 2 - Capture KM practice

This phase represents the activities of

preparing the personnel who will be involved

in the KM programme and collecting the raw

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data and information concerning current KM

practice.

Collect current KM information

In this sub-phase the consultants interview

and observe the personnel involved in the case

study in order to collect information on how

they perform their respective roles within the

product development process. This procedure

gathers subjective data as the answers are

based on personal opinions and motivation.

KM practice is recorded by asking the

employees to review their work tasks and to

rate each appropriate KM metric, and to state

the factors that influenced all ratings. The

questionnaire is used to ask appropriate

questions to elicit this information. KM

practice is influenced by organisational

culture that affects personnel attitudes and

working patterns. The resulting KM data

depict a holistic view of the working

environment so that the main influences and

pressures on the employees can be linked to

how they create and share knowledge.

Data gathering example

The case study would have identified a critical

activity in the product development schedule.

The activity is a design process which

includes the following work tasks:. Retrieve previous design drawings of a

mechanical system.. Talk to expert stylist colleague in US

office.. Search for best design practice on the

intranet.. Spend time creating a solution.. Modify design in CAE tool and run an

engineering analysis.

The analytical data are collected in the

following manner:

Key:

M represents a metric, F represents an

influencing factor, ±> represents the link

between a rating and the factor (cf. `̀ because

of'').

Rating system with weight values:. not applicable (N/A) = 0;. urgently requiring attention (URA) =

0.33;. further improvement possible (FIP) =

0.67;. satisfactory/best practice (BP) = 1.

Metric definitions:

(1) motivation;

(2) knowledge capture;

(3) stored knowledge;

(4) personnel training;

(5) knowledge transfer;

(6) creative thinking;

(7) knowledge identification;

(8) knowledge access.

The data can be gathered by reviewing the

work tasks and rating the appropriate metrics

as follows:

Task 2 ± Talk to expert stylist colleague in USA

office

M1 = FIP ±> F1 (F1 = location of stylist;

colleague is distant and has expertise in other

markets. Therefore, the stylist does not

properly understand European market

requirements and the engineer feels frustrated

to ask for his advice). There is not much that

can be done to F1 to improve the situation.

However, if F5 was introduced (F5 = an

expert stylist who understands European

market style and is based locally) the

employee would consider that the rating for

M1 would improve to best practice and the

task time would be reduced by approximately

23 per cent and the task cost by

approximately 10 per cent.

M5 = FIP ±> F2 (F2 = mentoring by

colleagues who taught the employee how to

incorporate styling changes into the

mechanical system). If F2 was improved

through a more formal company mentoring

programme then the employee would

consider that the rating for M5 would

improve to best practice, for example.

Once all the tasks have been observed and

the employees interviewed, the final database

of information should appear similar to the

example depicted in Table I.

Phase 3 - Building a KM strategy

In this phase, the collected data on current

KM practice are analysed to calculate overall

KM metric performance and to identify the

main influences on the metric scores. An

additional contribution of the analysis

methodology is the identification of the most

significant improvements in metric

performance that are possible, showing the

main factors that will improve KM practice.

The proposed method provides a facility to

calculate the overall scores for each KM

metric, with the influencing factors ranked

according to how their improvement can

contribute to an increase in the overall metric

score. It is also possible to display a rank of

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the factors which have had the greatest

contribution to current performance, which is

essential in any analysis methodology. If a

factor is linked to more than one metric this

information is incorporated into the

calculations, as there may be knock-on effects

by changing a factor (as several metric scores

might be affected). Changes in task time and

task costs are also incorporated into the rank

of factors that will improve current metric

performance.

The relationship between the KM metrics

and influencing factors is plotted graphically

in Figure 2. This Figure shows how the metric

scores can be displayed, and shows the

conceptual links to the influencing factors.

Metric score calculation

The first set of calculations for the KM

analysis methodology focus on producing an

overall score for each metric. For example, if

the KM metric `̀ M3'' was to have its score

calculated, all the citations of M3 from the

raw database would be collated and the

numbers then fed into the metric score

equation. The collation process would result

in a table such as the one offered in Table II.

The sample data show that the employees

cited a total number of three influencing

factors on the KM metric M3. F1 had 200

best practice (BP) citations (each with a

weighting value of 1), 100 further

improvement possible (FIP) citations (each

with a weighting value of 0.67), and 15

urgently requiring attention (URA) citations

(each with a weighting value of 0.33). F2 and

F3 also display their respective number of

citations in each rating category. There are no

other factors which are linked to the score for

M3.

The calculation of the overall metric score

is made by equation (1):

Key:

KM metrics: MI, I = 1, 2, 3, 4, 5, 6, 7, 8

Influencing FiBP,FIP,URA, i = 1. . .n (there

factors: are between 1 and n number of

factors)

Table I Example raw database of collected KM practice information

Task No. Metric Factor Add factor Rating

New rating if

factor

improves (%)

Task time

change

(%)

Task cost

change

(%)

1 4 2 0.67 1 ±5 ±3

1 4 6 1 ±10 ±15

2 1 1 0.67 0.67

2 1 5 1 ±23 ±10

2 5 2 0.67 1 ±1 ±3

3 1 2 0.33 0.67 ±20 ±1

3 5 2 0.67 0.67

3 6 1 0.67 1 ±3 ±2

4 1 1 1 1

5 3 2 1 0.67 2 3

Figure 2 Metric scores showing imaginary links to the influencing factors

Table II Example of collated information on KM metric `̀ M3''

M3

Raw data

F1

Raw data

F2

Raw data

F3

Best practice 200 100 29

Further improvement possible 100 80 20

Urgently requiring attention 15 29 22

Overall score 0.816

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Fi (BPi, FIPi, URAi) = each

factor has three rating values

BPi = best practice rating for

factor i

FIPi = further improvement

possible rating for factor i

URAi = urgently requiring

attention rating for factor i

FiBP = citation of best practice

for factor i

FiFIP = citation of further

improvement possible for

factor i

FiURA = citation of urgently

requiring attention for factor i

SMI= score of the Ith KM

metric

SMI�X

MI

FBPi � 0:67

XMI

FFIPi � 0:33

XMI

FURAiX

MI

Fi

�1�

The overall metric score (using equation (1)

with data from Table II) is 0.816. If this figure

is compared against the weightings (BP = 1,

FIP = 0.67 and URA = 0.33), the score for

M3 lies between BP and FIP.

The overall metric scores can be depicted as

demonstrated in Figure 3. The outer value of

the radar diagram equals best practice, i.e.

value 1, and the inner ring value equals

urgently requiring attention, i.e. value 0.33,

with the remaining further improvement

possible lying at the mid-point.

Factor ranking and analysis simplification

The second set of calculations for the KM

analysis methodology focus on producing a

rank of the factors that influence the KM

behaviour of the organisation. There are two

sets of results that are possible:

(1) A rank of the factors that affect individual

metric performance ± using the data

presented in Table II, the rank of factors

contributing towards the performance of

M3 are: F1(315), F2(209), F3(71). This

rank shows factor importance through the

quantity of citations given and offers an

adequate starting point for analysing a

particular aspect of KM behaviour.

(2) A rank of the factors that contribute to

overall KM behaviour ± the rank of

factors that contribute towards overall

KM behaviour is based on the summation

of all citations for each factor and then

ranking in order of totals, e.g. F1(400),

F3(300), F5(200), F2(150). This rank

shows factor importance through the

global quantity of citations given and

offers an adequate guide for identifying

critical influences on KM behaviour.

Please note that all ranking of factors is

based purely on the number of citations.

Factor improvements ranking

The third set of calculations of the KM

analysis methodology focus on examining the

changes in metric scores that would result

from improving the influencing factors and

from introducing new factors into the working

environment.

The raw database contains the KM metric

ratings that the employees have stated they

would give following an improvement in the

appropriate influencing factors. The

employees may have stated an increase in

metric rating (e.g. from URA to FIP), a drop

in performance (e.g. from BP to FIP) or the

status quo. The database will also contain an

estimate of the likely changes in task time and

cost that would result from an improvement

in the factors. The descriptions of how the

employees would improve the influencing

factors are also recorded in a textual database

which is not depicted here. These results can

be collated and displayed in a table similar to

the one depicted in Table III. It is important

to emphasise that the collected data are

subjective as they are based on employees'

suggestions and estimates that may not have a

mathematical basis.

The example values given in Table III are an

extension of those found in Table II, related to

a particular metrics M3. The data gathering

process has recorded the new metric ratings

for each work task that the employees would

give following an improvement in the

Figure 3 Example overall KM metric scores

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influencing factors and/or the addition of new

factors that are not currently present. The

data changes column for factor F1 shows the

original number of citations in each rating

category, i.e. BP (200), FIP (100) and URA

(15), and against each citation index are three

other numbers, e.g. for BP there are 0-10 190.

The first figure indicates that out of 200

citations of BP none would be given a higher

rating following an improvement in factor F1

(since BP is the highest rating category this

figure will always be zero; however, the

method is the same for the other rating

categories). The second figure indicates that

ten citations of F1 would fall in rating to the

next category down (e.g. ten persons have

expressed that any improvement in F1 would

be detrimental) which is FIP. This analysis

assumes that any changes in rating values will

not exceed one category.

The final figure indicates the number of

citations of F1 that are unchanged following

an improvement of factor F1. The remaining

categories and factors have the changes

displayed in a similar manner.

The next set of figures, shown in the data

changes column for factor F1, are the final

results of re-calculating the citation numbers

for each rating category based on the changes

data described above. For example the figure

`̀ 220'' in the BP rating category is calculated

from a drop of ten citations to FIP and an

increase of 30 citations coming from the FIP

category. The figure `̀ 88'' in the FIP rating

category is calculated from a drop of two

citations to URA, an increase of ten from

URA and a fall of ten from BP. The figure `̀ 7''

in the URA rating category is calculated from

ten citations moving up to FIP and two

citations falling from FIP to URA. The new

citation indexes for the remaining influencing

factors are calculated in the same manner. It

is also assumed that any movements of

citations are limited to one value, e.g. a

citation from BP cannot drop to URA and

vice versa.

Once the new citation indexes are complete

the change to the overall metric scores can be

calculated. The important point to make clear

is that each factor is examined in isolation so

that the impact on the metric score can be

attributed to the improvement in one factor.

To perform this calculation, equation (1) is

invoked.

The calculation provides a new metric score

of 0.83. If this figure is compared against the

original score for M3 (shown in Table II as

0.816) it can be seen that there has been an

increase of 0.014 by improving factor F1

alone (all other factors' data remain

unchanged). The remaining factors have their

individual impact on the metric score

calculated in the same manner.

The final set of figures shown in the data

changes column for factor F1 are the results

from averaging the changes in work task time

and costs that were estimated if factor F1

were to be improved. These results are

calculated from all the data relating to factor

F1 and, therefore, span all the metrics. It was

not considered useful to calculate average task

time and cost changes relating to each

individual metric as there may be several

metrics that were used to measure each task.

A further sub-division of results would

complicate the analysis as the important issue

Table III Example results for ranking KM metric influencing factors

Data changes

M3 Improve F1 Improve F2 Improve F3 Add F4

BP 200 (0-10 190) 100 (0-3 97) 29 (0-1 28) 40

FIP 100 (+30 ±2 68) 80 (+30 ±5 45) 20 (+15 0 5) 10

URA 15 (+10 0 5) 29 (+9 0 20) 22 (+11 0 11) 2

New citation index

BP 220 127 43 40

FIP 88 57 17 10

URA 7 25 11 2

New metric score (%) 0.83 0.829 0.829 0.823

Score changes (%) 0.014 0.013 0.013 0.007

Overall task changes

Average time (%) ±3 ±6 ±2 ±15

Average cost (%) ±2 ±10 ±1 12

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for clarification is to produce an overall

ranking of the influencing factors on the

working environment and a ranking of the

most effective improvements.

Once all the factor improvement

calculations have been made then they can be

ranked. Using the figures provided in Table

III it can be seen that for the KM metric M3

(original score 0.816) the following rank of

factors can be made:

M3 improve = F1(0.015), F2(0.014),

F3(0.014), F4(0.008)

This rank is based on the amount of increase

in overall metric score and the figures in

brackets represent the individual contribution

of each factor to improving the metric score.

It is possible that the consultants may wish to

improve several factors in order to have a

significant increase in overall metric score and

this is examined in later phases.

Global factor ranking

This analysis has so far described the

methodology for analysing each metric score

in isolation and to produce a rank of the

factors that will have the greatest positive

increase in overall metric score. This

information is useful in its own right if the

consultant were concentrating on one

particular aspect of KM behaviour. However,

the conceptual diagram shown in Figure 2

indicates that some factors may be linked to

the performance of several metrics. If the

consultant were to sanction the improvement

of one factor it may have a knock-on effect on

the overall score of another metric, and may

be positive or negative. For example the

ranking for M3 may appear as follows:

M3 improve = F1(0.015), F2(0.014),

F3(0.014), F4(0.008), but for the KM

metric M4, its ranking may appear as: M4

improve = F2(0.01), F5(0.009), F1(-0.02)

This information shows that the employees

stated that if F1 were to be improved then the

performance of metric M4 would fall and was

calculated to get worse by 0.02. This

information is best displayed graphically as

shown in Figure 4.

The information depicted in Figure 4 shows

that an improvement in factor F1 has a

generally positive effect on KM behaviour

(the effect is exaggerated for illustrative

purposes). It has a negative effect on M4 and

M6 and a positive effect on the others.

One of the main aims of the analysis

methodology is to show how to produce a

global ranking of factors for improving KM

behaviour in the working environment. To

achieve this the average is calculated in the

changes in metric scores resulting from the

improvements in each factor, and then ranks

the results in order of significance. For

example, the factor rankings for each metric

may show the following data:

M1 improve = F3(0.2), F1(0.1), F4(± 0.1)

M2 improve = F1(0.02), F3(0.05),

F2 (± 0.002)

M3 improve = F1(0.015), F2(0.014),

F3(0.014), F4(0.008)

M4 improve = F2(0.01), F5(0.009),

F1(± 0.02)

M5 improve = F5(0.02), F1(0.01),

F3(± 0.01)

M6 improve = F2(0.1), F1(0.05), F3(± 0.2)

M7 improve = F1(0.02), F3(0.05),

F2 (± 0.002)

M8 improve = F3(0.2), F1(0.1), F4(± 0.1)

The results of the factor impacts are averaged,

e.g. for F1 this would be calculated as:

F1AV �0:1� 0:02� 0:015� �ÿ0:02� � 0:01� 0:05� 0:02� 0:1

8

� 0:037

The other factors' contributions would be

averaged in the same manner and produce a

global rank as follows:

KM behaviour improvements = F3(0.043),

F1(0.037), F2(0.024), F5(0.015),

F4(± 0.064)

This rank is based on global changes in metric

performance that would result from

improving the influencing factors or

introducing new factors into the working

environment. This ranking shows that

improving factor F3 would provide the

Figure 4 Example showing the global effect of improving factor F1

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greatest individual boost to metric

performance. The consultant may wish to

improve several factors to obtain large

improvements in global KM behaviour. It is

also possible to rank the factors based on

other criteria such as average task time and/or

cost improvements. For example, the data

provided in Table III show that the factors

could be ranked as follows:

Based on average task time changes:

F4(± 15 per cent), F2(± 6 per cent),

F1(± 3 per cent), F3(±2 per cent)

Based on average task cost changes:

F2(± 10 per cent), F1(± 2 per cent),

F3(± 1 per cent), F4(12 per cent)

This rank shows that factor F4 introduces a

conflict between task time and cost

improvements. It is the consultant's job to

decide the importance.

The remainder of this phase uses the KM

analysis as an input for the formation of

appropriate improvement strategies. The

design of any new KM strategy is dependent

on an accurate analysis of current KM

practice which reveals problems and the root

causes of those problems. The analysis

methodology is employed to provide this

information. Once new strategies have been

formulated, a cost-benefit analysis must be

undertaken with a return on investment

calculation being made (if possible). Once the

strategies have been approved by

management then a detailed implementation

plan can be devised and approved.

Phase 4 ± Implement and evaluate

This phase represents the comparison of pre-

pilot and post-pilot KM practice in order to

determine if the KM strategies have been

successful. If the pilot programme has been

successful then management may wish to

consider expanding the scope of the pilot into

other areas of the organisation which could

benefit from the implementation of KM.

Case study ± validating the pilot KM programme

and analysis methodology

The collaboration with an automotive

manufacturer, hereafter referred to as

`̀ Company A'', enabled the pilot programme

methodology and analysis theory to be tried

and tested in an environment for which they

were intended. The validation case study was

focused around a process which is used in the

preparation for a new assembly or system

design.

The constraints that were imposed meant

that during the data collection phase it was

not possible to interview the engineers;

therefore the project relied on data collected

solely from the questionnaire. It is proposed

that there should be interviews to back up the

questionnaire when the data are collected.

This would help to minimize data absence,

interpretation errors and would maximize

commitment to the project by individuals and

would provide more detailed responses.

Another constraint on the validation meant

that the latter aspects of phase 3 and the

whole of phase 4 could not be implemented.

These phases could be validated if a larger-

scale case study was undertaken. For

example, an entire department could be

analysed with an increased number of

metrics. A larger perspective would provide

more valuable information on which to base a

complete KM strategy.

The data collection questionnaire was

submitted to all engineers who had used the

case-study process. The total number of

returned questionnaires represented a 72 per

cent response rate. The large proportion of

returned questionnaires meant that the results

presented a realistic assessment of the

Company A process and how it facilitates KM

for the engineers that use it.

The data were collected and entered into

the analysis methodology which was followed

carefully as outlined in this paper. The results

from the analysis of the current environment

are displayed in Figure 5. The experience of

manually collecting and analysing the data led

to the proposal that a computer program

should be written that would improve data

collation and analysis for future projects.

The results were analysed and the following

recommendations were made:. Establish a database of past component

and process examples with problems

traced to the root causes.. Encourage on-the-job training as the best

method for learning the process.. Implement a series of presentations of

specific system requirements by managers

at the beginning of the process, especially

for unfamiliar engineers so that guidance

notes may be taken.. Improve the flow of information between

relevant parties, e.g. have earlier supplier

input into concept stage and identify the

responsibilities of the relevant parties.

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. Increase interaction with headquarters so

that concerns and requirements would

have better consideration.. Have a long-term understanding of

product planning in order to plan new

concepts sooner.

Conclusions and future work

The pilot programme follows a logical

sequence of activities that delivers a practical

and effective strategy for improving the

working environment through relevant KM

objectives. The activities are generic in the

sense that any automotive manufacturer, or in

fact any manufacturing organisation, e.g. an

aerospace company, can apply the

programme. The development of the pilot

went through many iterations in order to

remove superfluous activities. The emphasis

lay on the programme design being as simple

and practical as possible, with clear

deliverables and milestones within each phase

that were analogous to the project

management environment in which it would

be embraced.

The working environment analysis

methodology enables the identification of the

root causes, or influencing factors, of KM

practice that can be linked to the development

of a KM strategy, that the organisation would

respond well to, by the following means:

. The individual KM metric scores show

the areas of current KM practice that are

strong and weak.. The ranking of metrics shows the critical

KM mechanisms that are prevalent in the

working environment.. The ranking of factors that have

contributed to individual metric scores

shows how the prevalent KM

mechanisms are facilitated.. The ranking of factors that have

contributed to global KM practice shows

the critical influences on the working

environment as a whole.. The ranking of factors that shows how the

organisation would improve individual

metric scores can be used to link new

initiatives with the reality of the working

environment.. The ranking of factors that shows how the

organisation would improve global KM

practice can be used to link a KM strategy

with the reality of the working

environment.. The ranking of factors that shows

improvements in global work task time

and cost can be used in a cost-benefit

assessment if the organisation insists on

implementing a version of ROI theory.

To the best knowledge of the authors there is

no other current research that enables such an

accurate and effective analysis of the root

Figure 5 Results from an analysis of the current operating environment of the Company A process

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causes of KM behaviour. KM behaviour

spans across technical and cultural

boundaries and the analysis theory enables

such a crossing of boundaries to be made,

through the employees articulating what

factors influence the defined KM metrics.

These factors can have many representations,

e.g. a software system or the personnel reward

policy of the company. The analysis delivers a

list of the key factors within a working

environment, whatever they may be.

The future efforts of the research should

provide a facility for identifying any trade-offs

that may be required when introducing or

modifying factors. This would secure more

information on possible implementation

problems for a new KM strategy. This

absence of theory made the estimation of task

time and cost savings, from the introduction/

modification of influencing factors, difficult.

References

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Further reading

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Chapman, C.B. and Pinfold, M. (1999), `̀ Designengineering ± a need to rethink the solution usingknowledge-based engineering'', Knowledge-basedSystems, Vol. 12, pp. 257-67.

Davenport, T.H. and Prusak, L. (1998), WorkingKnowledge: How Organisations Manage What TheyKnow, Harvard Business School Press, Boston, MA.

Delphi (1997), Delphi on Knowledge Management, TheDelphi Group, Inc., Boston, MA.

Hansen, M.T., Nohria, N. and Tierney, T. (1999), `̀ What'syour strategy for managing knowledge?'', HarvardBusiness Review, March-April, pp. 106-16.

Harvard (1999), `̀ Knowledge management ± return oninvestment'', Harvard Computing Group Report,Vol. 1 No. 2.

Levett, G.P. and Guenov, M.D. (1999), `̀ Genericmethodology for capturing crashworthinessexpertise'', in Automotive Mechatronics Design &Engineering, Proceedings of the 32nd InternationalSymposium on Automotive Technology andAutomation (32nd ISATA), Vienna, Austria,14-18 June.

Levett, G.P. and Vignjevic, R. (1997), `̀ Knowledge-basedsystem for crashworthiness design'', in: Road andVehicle Safety, Proceedings of the 30thInternational Symposium on Automotive Technologyand Automation (30th ISATA), Florence, Italy,16-19 June.

Malhotra, Y. (1998), `̀ Business process redesign: anoverview'', IEEE Engineering Management Review,Vol. 26 No. 3.

Nonaka, I. and Takeuchi, H. (1995), The Knowledge-creating Company, Oxford University Press, NewYork, NY.

Robbins, S.P. (1996), Organizational Behavior: Concepts,Controversies, Applications, 7th ed., Prentice-HallInternational, Inc., NJ.

Roepnack, A., Schindler, M. and Schwan, T. (1998),`̀ Concepts of the enterprise knowledge medium'', inProceedings of the 2nd International Conference onPractical Aspects of Knowledge Management(PAKM98), Basel, Switzerland, 29-30 October.

Scarbrough, H. (1996), Business Process Re-design: TheKnowledge Dimension (WWW document), http://bprc.warwick.ac.uk/rc-rep-8.html (accessed1 November 1999).

Scarbrough, H. and Swan, J. (1999), KnowledgeManagement and the Management FashionPerspective, a paper for the British Academy ofManagement Conference 1999 (WWW document),http://bprc.warwick.ac.uk/wp2.html (accessed1 November 1999).

Schein, E.H. (1999), Three Cultures of Management: TheKey to Organizational Learning in the 21st Century(WWW document), http://learning.mit.edu/res/wp/10011.html (accessed 26 July 1999).

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van der Spek, R. and Spijkervet, A. (1997), KnowledgeManagement: Dealing Intelligently with Knowledge,IBIT nr.1, Kenniscentrum CIBIT, The Netherlands.

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Documents

A methodology for knowledge management implementation