Accelerated Knowledge Management (AKM):
Developing Rapid KM Methodology for an
Adaptive Service-Orientated Business Model
Peter Balafas1,2, Dr Thomas Jackson2, Ray Dawson2, Darren Wilson1
1
Danwood, Lincoln, United Kingdom, 2 Department of Computer Science,
Loughborough University, Leicestershire, United Kingdom.
(p.j.balafas@lboro.ac.uk, t.w.jackson@lboro.ac.uk, r.j.dawson@lboro.ac.uk, darren.wilson@danwood.co.uk)
Abstract
This paper describes the findings of research in the area of Knowledge
Management (KM) that is being carried out primarily at the headquarters of
The Danwood Group in Lincoln and in collaboration with the Department
of Computer Science, Loughborough University. The majority of modern
KM strategies seem to suggest a long-term company-wide programme that
requires large amounts of time, effort and investment for results that take a
long time to come to fruition (e.g. McElroy, 2001; Ndlela and Toit, 2001).
The methodology that is recommended in this paper aims to provide a new
approach, whereby KM is focused on improving business processes and
achieving faster tangible results as well as longer-term benefits. The
methodology that has been developed by the authors has been named
Accelerated Knowledge Management (AKM) and has resulted from the
combination of third-generation KM methodologies (e.g. Morey, 2001;
Heisig, 2001; Magnani, 2001) with Goldratt’s Theory of Constraints
(Goldratt and Cox, 1993; Goldratt, 1994). Although AKM has been
developed based on a service-orientated business model, it is believed that
the methodology could be adapted and applied to other business models
with similar results expected.
Developing a KM Strategy for Danwood
The Danwood Group’s core business resides in the provision of service and support
for reprographic machinery on a UK nationwide scale. It is thus obvious that
maintaining as well as improving the service administration system becomes a high
priority for the organisation. As strategies for providing service change in
accordance with market needs and trends, so must the systems that support them be
able to evolve and manage accordingly. It therefore becomes apparent that managing
the knowledge on which these systems depend becomes increasingly important,
especially in terms of having a methodology. This paper demonstrates the first phase
in the development of Accelerated Knowledge Management (AKM) methodology,
and is expected to bring business-term results as well as longer-term benefits to the
organisation.
After a review of the current service administration system at Danwood it was
decided by top-level management that an investigation had to take place into
discovering methods for further improvement in efficiency and cost-effectiveness.
The responsibility of finding new methods for improvement was assigned to one of
the authors as part of a continuous research initiative between Danwood and
Loughborough University.
After an extensive literature review of business improvement strategies it was
realised that developing a knowledge management programme would bring renewed
opportunity for improvement to Danwood. Analysis of the current service
administration system lead the authors to believe that action had to be taken to
increase the capacity for managing, sharing and utilising both tacit and explicit
knowledge. The authors also believe that this knowledge is embedded in the relevant
business processes and could be utilised to dramatically improve the performance
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and efficiency of service administration. The nature and complexity of these
processes called upon human expertise, innovation and experience in order to
capture the very knowledge that is so important for future improvement. In
implementing the (KM) strategies it is important to prioritise activities and ensure
integration with other business processes. Effective implementation of knowledge
management strategies is about defining what needs to be achieved and about
motivating capable people to want to achieve it (Campbell and Luchs, 1997; Ndlela
and Toit, 2001). Therefore the authors decided that an investigation into KM
strategies had to take place in order to identify an approach that would be
compatible with the Danwood business model and ethos, and more importantly
would aid in meeting the business objectives.
Evaluation of KM Strategies
The analysis and evaluation of the documented KM strategies identified three
generations of KM. The first generation of KM strategies that appeared in earlier
works highlighted the importance of using technology to share knowledge (e.g.
Carayannis, E.G. (1998); Hitt et al., 2000). However, there were many important
flaws in these techniques and not much proof of success. The most typical mistake
being that many failed to acknowledge the human factor of knowledge management.
The second generation of KM strategies that was identified generally suggested a
company-wide knowledge-sharing programme where communities of practise and
the human factors are the main focus (e.g. McElroy, 2001; Ndlela and Toit, 2001).
Technology in this generation of KM strategies is also utilised but mostly used as a
support tool rather than a driver for KM. Without doubt, this approach has indeed
created some success stories for large organisations. An example is that of Xerox’s
Year 2005 plan, where managing knowledge in a sharing community played a very
significant role in the success of their 15-year strategic plan (Ahmed et al., 2002).
However, the approach of the second generation of KM also failed on many
occasions because top-level management would abandon the scheme when they
experienced very slow return on their investments or simply when the benefits were
so intangible that they could not be identified. The third generation of KM strategies
that has been identified is business process orientated (e.g. Morey, 2001; Heisig,
2001; Magnani, 2001). This new generation of KM is still considered to be in its
infancy by many supporting authors (e.g. Morey, 2001; Magnani, 2001) and
therefore provides the grounds for new theory development.
The authors identified third-generation KM as the most appropriate provider of
guidelines for developing an adapted KM methodology. A recent study carried out
by Sopheon, a leading knowledge management consultancy, showed that out of 100
companies that were studied over a 5-year period, those that took the business
process direction for knowledge management achieved the highest impact on their
goals (Magnani, 2001). Figure 1 illustrates the results of the Sopheon study.
K M Foc us of P a r t i c i pa t i ng C om pa ni e s i n S ophe on S t udy
45%
40%
40%
40%
35%
30%
30%
30%
25%
21%
20%
15%
10%
5%
0%
0%
Foc us e d On B us i ne s s P r oc e s s
A ppl y Ge ne r a l K M P r i nc i pl e s
Companies Studied (%)
Ge ne r a l B us i ne s s I mpr ov e me nt
High Impact (%)
Figure 1: Results of the Sopheon study
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Integrating Third-Generation KM with Operations Theory
As mentioned earlier, third-generation KM focuses specifically on improving those
business processes that can potentially aid the organisation in achieving its stated
goals (e.g. maximising profits). Nearly all approaches to knowledge management
aim to improve the results of the organisation. These results are achieved by
delivering a product or/and service to a client by fulfilling certain tasks, which are
linked to each other, thereby forming business processes. Often, knowledge is
understood as a resource used in these processes, but only very few approaches to
knowledge management have explicitly acknowledged this relation and even fewer
approaches have tried to develop a systematic method to integrate knowledge
management activities into the business processes (Heisig, 2001). Accelerated
Knowledge Management not only identifies knowledge as a resource to business
processes, but also sets its focus on integrating its methodological activities into the
lifecycle of business processes. The authors believe that this approach has the
potential to generate faster bottom-line results as well as provide the grounds for
long-term benefits to the structure of the business processes and the organisation as
a whole. The following sections provide further explanation of how the AKM
methodology has been constructed.
Goldratt’s Theory of Constraints (TOC)
Many will be familiar with this leading theory for operations management that was
developed back in the early 90’s. Goldratt identified that within an organisation’s
operational framework there are business processes that act as the "weaker links" in
the system and provide a "constraint" that stops the organisation from achieving
higher performance. Rather than focusing on improving the entirety of the business
processes, as suggested by other theories in the same field e.g. Total Quality
Management (TQM), Goldratt believes that management should invest their limited
resources into the processes that will actually have an impact on the bottom-line.
According to TOC theory, business processes are interlinked in a way that is
analogous to a steel chain. In order to strengthen the chain, one must strengthen the
weakest link. If a link other than the weakest is strengthened, the strength of the
whole chain is not increased (Umble and Spoede, 1991; Motwani et al., 1996a). In
comparing this analogy to that of a modern real-world organisation like Danwood, it
would perhaps be better described as a matrix of interlaced business process chains.
However, optimising any of the business processes of the chains will not necessarily
have an impact on overall performance. It is upon this point where the Accelerated
Knowledge Management methodology starts to formulate.
Accelerated Knowledge Management
Accelerated Knowledge Management (AKM) is based on the combination of thirdgeneration KM to TOC theory. Through combining these two, it is possible to create
a methodology where KM efforts are focused on the business processes that need
the most attention. By identifying the weakest business processes and delivering
knowledge that will aid in strengthening them businesses can achieve faster results
that will also attract further investment from top-level management. This will also
mean that longer-term benefits can also be expected as the KM programme grows
within the organisation. Through studying, developing and evaluating a customised
KM methodology the authors propose the approach shown in Figure 2.
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Identify Restrictive
Business Processes (RBPs)
Refine RBPs by Distributing
Applied-Knowledge
Reorganise Non-RBPs for
Adaptation to Refined RBPs
Reengineer RBPs through New
Knowledge Development
Figure 2: The Accelerated Knowledge Management (AKM) Methodology
Identify the Restrictive Business Processes (RBPs)
The first step in the AKM methodology is to analyse the business process matrix in
order to identify those processes that form a restriction to higher performance and
achievement of organisational objectives. These processes are referred to as
Restrictive Business Processes (RBPs) as they create a "restriction" on performance.
It is also important to identify for each RBP the stage in which the restriction occurs,
whether it is at the input or output stage and in some cases the marketing stage.
Some examples for the input stage are, pending customer information for the
processing of a new service contract; and for the output stage, insufficient
availability of service engineers to deal with a customer’s machine problem; and for
the marketing stage, insufficient awareness of the availability of a new service
contract.
Refine RBPs by Distributing Applied-Knowledge
The following action to take place is to attempt to improve the RBPs by acquiring
and applying knowledge that has proven to be useful in other areas. This also links
to the effectiveness of the organisational learning of the company that is
participating in the study. For example, a monthly report that is generated for
Danwood lists all the service engineers in a performance league. Recent analysis has
shown that specific profiles of engineers in accordance with factors such as location,
age, years of service to the company, demonstrate substantial differences in their
performance levels. The highest performers are more likely to have developed their
own expert knowledge over a substantial period of time and will be able to select the
best knowledge that has been successfully applied and empirically collected.
Therefore a good example of distributing applied-knowledge that could refine many
RBPs would be to arrange sessions where the top performers would share their
experiences with the lower performers, in order to help them increase their
performance and the productivity of the RBPs that they affect.
Reorganise Non-RBPs for Adaptation to Refined RBPs
As described earlier, this approach is adapted to the TOC representation of business
processes as a chain or a matrix of chains. As a consequence, any modifications
made to a RBP will somewhat affect the way in which it interacts with other
processes that are closely connected to it in the chain. The processes that are most
likely to be negatively affected are non-RBPs. This is because even if an RBP is
affected by the refinement of another RBP, eventually it will be processed for
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optimisation. In contrast, a non-RBP will only be reviewed when a connected RBP
has been refined. It is therefore essential to perform this stage of reorganising NonRBPs. If "fine-tuning" is not performed there is a higher risk of trying to refine a
non-RBP, because a connected RBP was also refined. This can potentially lead to
unnecessary work being generated for the non-RBP. This stage therefore aims to
only undertake optimisation when necessary, in order to improving the bottom-line.
Reengineer RBPs through New Knowledge Development
By executing the three previous steps each RBP can be refined and further optimised
to give better results. However, TOC works on the assumption that improvement can
never end. This seems quite logical because if it was possible to find the maximum
optimisation of a restrictive business process, it would no longer be restrictive and
there would be no limit on its productivity. Therefore, it can be conclude that
sharing existing knowledge will not always provide enough edge to achieve a
competitive advantage. Further knowledge development must take place in order to
exploit new ideas and for innovation to flourish. However, developing new
knowledge is a long-term process that requires the most effort and resources to be
achieved. Many theories exist into how knowledge should follow a development
cycle, but most would seem suitable for the first and second generation of KM
where there is less restriction on timescale. For this reason it has also been necessary
to develop the Accelerated Knowledge Development Cycle (AKDC) which has been
customised for applications with the AKM methodology. This is discussed further in
the following sections.
Knowledge Development vs. Knowledge Sharing
Older generations of KM, especially those that belong to the first generation, focus
on knowledge sharing as the primary driver for success. However, there is a
fundamental flaw to this approach as it assumes that the existing knowledge within
the organisation is adequate for achieving the company goals. So sharing this
"unutilised" knowledge becomes the primary objective and in most cases this is
simply not enough. Helping organisations to create knowledge faster (i.e., to
accelerate their rate of innovation) is seen as a powerful way of increasing a firm’s
competitive stance in the marketplace (McElroy, 2001). This could be identified as
knowledge development where new knowledge is generated to revitalise competitive
advantage. This new knowledge may have been created in its original form in a
research department or institution. However, in a competitive industrial
environment, it is perhaps more realistic to assume that knowledge is acquired from
an external or internal source and then adapted, in order to be transformed into new
knowledge. Creating new knowledge in a completely original form does have the
potential of creating market-leading innovation. However, there are some serious
drawbacks, the most serious of which could be identified as the high risk of failure
due to the complexities inherit in generating original ideas, as well as slow return on
investment due to the long timescales required and the subsequent delays in the
surfacing of tangibles results.
As an alternative, the authors recommend that creating "new knowledge" should
mainly be the result of acquiring knowledge that has already been proved successful
elsewhere and adapting it in order to manipulate the advantages. Knowledge
creation is an extremely difficult activity and many firms choose a simpler route
through acquiring knowledge from other sources and applying it to their specific
environment (Bhatt, 2000). This approach is much more in line with the AKM
methodology as it is expected to bring acceleration to the entire knowledge
development phase. This is therefore reflected in the design of the Accelerated
Knowledge Development Cycle (AKDC).
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The Accelerated Knowledge Development Cycle (AKDC)
As discussed in the previous section knowledge development in AKM takes the
form of re-using and modifying existing successful knowledge and transforming it
into new knowledge. This choice has been made because it is more likely to achieve
faster business-term results, which is in line with the focus of AKM. The literature
review that took place as part of this project demonstrated a number of approaches
to knowledge development. In many cases the knowledge flow within the proposed
approaches was omni-directional (Bhatt, 2000; Morey, 2001). In our proposition
knowledge flows are one direction throughout the development cycle, which has
been adapted for acceleration. The Accelerated Knowledge Development Cycle
(AKDC) is shown in Figure 3.
Knowledge
Acquisition
(1)
Knowledge
Review
(6)
Knowledge
Filtering
(2)
AKDC
Knowledge
Embedding
(5)
Knowledge
Adaptation
(3)
Knowledge
Distribution
(4)
Figure 3: The Accelerated Knowledge Development Cycle (AKDC)
Knowledge Acquisition
Acquiring knowledge may come from a variety of sources, whether internal or
external. However this is also particularly relevant to the organisation’s size and
structure of interdependent units. For example, when a company like the Danwood
Group is essentially comprised of many smaller companies, each sister company
may run procedures and strategies in a slightly different way that is particular to
factors such as geographical location and local culture, even within the boundaries
of the same country. Acquiring knowledge from the higher performing members and
transmitting and applying it to the lower performers will most likely have a positive
effect on Danwood’s overall performance ratings. Other forms of internal
knowledge acquisition are learning from case studies of past projects that were
performed at the company, utilising internal search engines and content-specific
alert management tools, as well as internal expert-finding tools. The last of these
examples is particularly interesting and relevant to AKM and is further analysed in
the Knowledge Distribution section. If internal knowledge is still not successful in
addressing RBPs then the next logical step is to seek knowledge externally.
There are many methods of acquiring external knowledge. Some examples of
knowledge acquisition include, conducting an external survey, acquiring a
knowledge-rich company, subjecting employees to external training, hiring an
employee (thereby bringing that person’s knowledge into the organisation),
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purchasing data sets, monitoring the technological advances, purchasing a patented
process, and gathering knowledge via competitive intelligence (Holsapple and
Singh, 2001). Some of these methods are commonly used amongst organisations.
For example external training is often chosen because it can aid both individual as
well as organisational learning. After the training is over, employees may transfer
their acquired knowledge to the organisation (e.g. by conducting presentations to
their colleagues). Or, they use their acquired knowledge to generate other
knowledge (e.g. make decisions) (Holsapple and Singh, 2001). On the other hand
some of the methods mentioned are more suitable to specific organisational business
models. An example of a knowledge acquisition method that is particularly suitable
to Danwood is acquiring a knowledge-rich company. This is because a large
proportion of its growth and organisational learning is based on the continuous
process of acquiring successful service companies. This method also provides
acceleration and is therefore compatible with AKM.
Knowledge Filtering
The second stage of the AKDC involves selecting and filtering knowledge according
to its particular relevance and applicability to the identified RBPs that are being
addressed. The reason this stage is needed is because of the overload of knowledge
that can occur from multiple internal and external sources. Thus, selecting and
filtering the acquired knowledge becomes a necessity. To foster intelligent and
customised knowledge selection, it is crucial to internalise knowledge about
knowledge (i.e. meta-knowledge). Meta-knowledge allows knowledge selection
based on context as well as content (Joshi, 1998; Holsapple and Singh, 2001). By
building knowledge about knowledge, the process of locating and linking newly
developed knowledge to the corresponding RBPs is accelerated.
Knowledge Adaptation
In this stage of the AKDC knowledge that has been acquired and filtered has to be
adapted to the organisational environment and modified in order to potentially create
new knowledge that will address RBPs. Modelling tools, such as systems thinking
tools, are powerful tools in speeding up the knowledge-creation and modification
process, especially for cross-domain groups which are sharing knowledge. A model
creates a syntax and a visual representation of understanding that unleashes the
merging of inference rules and spurs innovation (Morey, 2001). Therefore
knowledge modelling that ties in with business process modelling is the support tool
used for knowledge adaptation in the AKDC. This accounts for accelerated
identification of improvements to targeted RBPs based on the newly adapted
knowledge that has been expressed in a clear diagrammatic form.
Knowledge Distribution
Knowledge needs to be distributed and shared throughout the organisation, before it
can be exploited at the organisational level (Nonaka and Takeuchi, 1995; Bhatt,
2000). In many KM frameworks knowledge distribution takes the form of
enterprise-wide knowledge sharing programmes, developing communities of
practise, attempting to adapt to the organisational culture and converting tacit into
explicit knowledge. This is not the case with AKM as the focus of AKM is to
deliver knowledge specifically to address restrictive business processes. Therefore
knowledge distribution takes the form of using new knowledge during process
review and improvement, in order to achieve faster bottom-line results. However, it
might be argued that it is not only the design of the business process that is
important, but it is also essential to supply new knowledge to members of staff that
participate in the business processes. Some classic methods already exist for
delivering new knowledge to staff such as organising frequent re-training and
keeping procedural documents up-to-date. However these only cover the distribution
of explicit knowledge, as tacit knowledge is harder to communicate via training or
by converting to an explicit form. So how can we actually deliver relevant
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knowledge to people without going through the lengthy and often error-prone
process of tacit-to-explicit knowledge conversion? The answer perhaps lies in ondemand expert advising. As mentioned earlier, utilising expert-finding tools may be
a potential method for accelerating knowledge acquisition and distribution. Rather
than spending time converting tacit into explicit knowledge, why not attempt to
connect people to experts faster and easier. The goal is to place every person in the
organisation within one phone call of an expert (Morey, 2001). This means direct
contact with expertise and therefore accelerated on-demand knowledge distribution.
Many companies such as Hewlett-Packard, Microsoft and the National Security
Agency (USA) have successfully implemented expert-finding tools (BecerraFernandez, 2000). It might be argued that avoiding the tacit-to-explicit conversion
there is a danger of loosing essential knowledge if a member of staff leaves the
company. On the other hand, by creating the grounds for easier access to human
expertise we can create a situation where more than one person within the
organisation possesses specific knowledge.
Knowledge Embedding
This is the application stage of the AKDC. Knowledge that has been acquired,
filtered, adapted and distributed to the corresponding RBPs is now ready for trial.
This stage is particularly important because it may result in reducing the restrictions
on the performance of the business processes in question and more importantly
generate new observations that may be applied elsewhere and eventually embedded
in other RBPs. Without taking action, new observations on the effectiveness and
explanatory powers of an operating theory are not generated. New observations are
vital for the learning process to move forward (Morey, 2001). There are numerous
techniques for testing new knowledge. One of the most successful has been
identified as after-action-review, which has been embedded into the AKDC. The
knowledge embedding stage of the AKDC could therefore be identified as the action
part of the after-action-review technique and the knowledge review stage
corresponds to the review part of the same technique.
Knowledge Review
In the final stage of the AKDC the new knowledge that has been embedded into the
RBPs must now be reviewed. This evaluation process helps towards deciding
whether the new practises that have been developed have found a better way to deal
with the restrictions on the business processes involved. Should this not be the case,
further refinement may be necessary. In this case, knowledge flows back to the
knowledge acquisition stage, thus completing the cycle that is demonstrated in
Figure 3. An alternative reason for knowledge to flow back to the knowledge
acquisition stage would be to generate new knowledge, which often means replacing
older knowledge that applies to the same domain.
This stage of the AKDC also provides some essential information for justification of
investment. The apparent inability of traditional models of financial analysis to
justify certain investments has led to a growing number of managers and observers
to call for a moratorium in their use (Gunasekaran et al., 2001). It is absolutely
essential to be able to tackle this kind of justification during the pilot phase of a
developing methodology.
The AKM methodology targets short-term business results but does not ignore the
importance of longer-term benefits. For this reason, this stage has the additional
responsibility of maintaining knowledge clusters. It is widely believed that an
organisation is a distributed knowledge system, which comprises of knowledge
clusters or components (Walsh and Ungson, 1991; Bhatt, 2000). The critical
property of knowledge clusters is that they can be reviewed, revised and
reconfigured (Spender, 1996; Bhatt, 2000). As a result of this continuous review
process, many companies have achieved new competitive advantage through new
product and service developments. Review and revision of knowledge is also
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important because a large part of knowledge, if not used, can be easily forgotten or
ignored (Bhatt, 2000). This continuous review will not happen on every occasion of
the cycle but rather on a periodical basis so that it does not create a resourcerestriction in the acceleration of knowledge development.
Supporting AKM with KM-Technology
Just like other third-generation KM approaches, AKM does not use technology as its
main driver but rather as a support tool. It would be unrealistic to imply that
technology still does not play an important role in successfully applying knowledge
management methodologies. However, it is also important to set the focus of efforts
on business process improvement that is supported and optimised through the use of
KM-specific technologies such as knowledge repositories, information indexing and
retrieval systems, groupware, imaging systems and data warehousing to name a few.
A typical example of a knowledge-sharing system that was successfully
implemented in the service industry is demonstrated by Xerox’s Eureka. A system
developed by Xerox’s researchers to provide an IT system for engineers to share
knowledge tips on servicing machines. Although successful, this type of system
would not be appropriate for use with AKM as it is more suitable to secondgeneration KM, where the focus is on enterprise-wide knowledge sharing and
overall long-term benefit.
A comprehensive KM methodology must also define some guidelines for the KMtechnology to be used. This technology must be able to support a business process
orientated KM approach and also aid in the acceleration of knowledge development.
It has therefore been deemed necessary to review currently available KMtechnology, in order to identify a system for potential use with AKM. Should the
investigation be unsuccessful in identifying such a system, it may be necessary to
build a customised solution. However, this may not be a favourable option due to the
costs and delays that may be incurred.
A preliminary investigation of KM-technology identified numerous solutions that
were available on a commercial basis. Among these, one system seemed to stand out
from the other candidates. EULE, a system developed by the IT Research and
Development department of Swiss Life, focuses on supporting business processes in
a constantly changing environment, very similar to that of Danwood. The key areas
focused upon by EULE are just-in-time knowledge delivery, adaptation and
maintainability. Many of the business processes that are analysed in the AKM
methodology will involve people performing office tasks. Just-in-time knowledge
delivery unifies business process support with the central knowledge management
issues of supplying people with the knowledge they need to do their work properly
(Reimer et al., 2000). The focus of EULE on adaptation revolves around the concept
of being able to deliver knowledge that will be adapted to the requirements of the
novice as well as expert user. Lastly, the maintainability provided by EULE means
that regulations and inferences that may be external or internal to the company are
supported by the system. The above characteristics fit the requirements of AKM in
terms of accelerating knowledge delivery as well as the requirements of the
Danwood business environment, which involves constantly changing service
conditions and regulations. A further evaluation of the EULE system will be
performed in the second stage of this research project. The intention is to provide
further proof of the suitability of the system and whether or not it will need any
modifications for adaptation to AKM.
AKM and the Service Industry
The AKM methodology has been developed in a service-based environment. This is
not to say that the methodology would not be potentially applicable in other
industries, but it is still important to identify why it is specifically suitable for a
service organisation and therefore for Danwood.
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One of the key components for the success of a service organisation is flexibility.
This is especially true in the case of Danwood as part of its business ethos is to
provide as much flexibility as possible to the customer, especially when it is a key
factor in securing large deals. A service offering is actually a package of goods,
facilities and implicit and explicit services. In addition, the process for producing a
service is determined less by the level or sophistication of the equipment used, but
by the degree to which the customer influences the service process (Kellogg and
Nie, 1995). Therefore it is imperative to be able to increase the variety of customer
solutions by adapting to their continuously changing demands. This is an important
factor for securing competitiveness, especially in the current and projected consumer
markets. It is unlikely that the increased variety in customer solutions can be
achieved without knowledge management being a pre-requisite of mass
customisation (Armistead, 1999). Competitive service organisations must be able to
give the customer the feeling that they are in control, even if this is not the case.
This is especially true when an organisation sets its focus on large corporate
customers, like in the case of Danwood where a non-unique sample corporate
contract accounted for 8% of its total annual turnover. The customer can specify
where the service is to be performed, what is to be done and how it is to be done.
The flexibility required to create a unique service package is far more encompassing
than the flexibility that is normally encountered in a product environment. The
service organisation which offers unique service packages should recognise that all
parts of their organisation, including the functions, employees, policies and
procedures, and the structure, need to work together to achieve this flexibility
(Kellogg and Nie, 1995). Accelerated Knowledge Management can provide a
framework for managing all the necessary knowledge required to provide flexible
services as well as aid in the acceleration of change – an essential attribute for
flexibility.
Flexibility in services also leads us on to another important aspect that AKM tackles.
A high level of expertise is required in order to achieve flexibility in the provision of
service, as it is essential when developing, evaluating, applying and administering
innovative changes. Along the service process dimension, the success of the expert
service process relies on the expertise and experience of the service providers and
therefore requires special attention to hiring, training and retention of employees.
Organisational culture is the paramount control mechanism in this process type, as
standard operating procedures may not be effective (Kellogg and Nie, 1995). This is
in line with the notion of expert finding, as recommended by AKM methodology
and also accelerates the rate of knowledge delivery and eventually flexibility.
Further proof of the suitability of AKM to the service industry and to Danwood can
be obtained by analysing the suitability of TOC to the same industry. TOC was
originally developed for a manufacturing environment and therefore the question has
since risen as to whether it would be suitable to a service-based model. The majority
of the literature that was reviewed suggested that it was in fact suitable (e.g.
Motwani et al., 1996a/1996b; Siha, 1999). The main reason given was that the
organisational goals whether in product or service based companies remains the
same in most cases, i.e. maximising profits. Service organisations can be modelled
as systems with measurements comparable to manufacturing. Metrics such as
throughput, operating expense and inventory can be identified in order to measure
progress towards the global organisation’s goal (Motwani et al., 1996b). TOC deals
with restrictive business processes that are responsible for providing constraints to
those organisational goals. Experience shows that most constraints in organisations
are policy or procedural constraints rather than physical. Service may be hindered as
a result of the immediate service provider not being authorised to approve or
perform certain necessary actions. By providing a systematic questioning method to
reveal and clearly describe problematic areas that supposedly are implicitly known
to all, the TOC can be usefully applied not only to manufacturing industry but also
to the service industry (Motwani et al., 1996a). The author’s intentions are not to
underestimate the importance of operating procedures and policies to organisations
as they are crucial in service and manufacturing organisations to guide actions and
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behaviour, and provide solutions to specific problems. However, they are seldom
modified when the external environment changes. Some are so rooted in the
organisation that they are difficult to tackle. The Socratic thinking process proposed
by the TOC handles the inherent resentment to change by using a sequence of
questions leading to self-revelation and creating sense of ownership (Motwani et al.,
1996b). This method of dealing with change is important to the organisation,
especially when considering that change will often occur in a service environment
that requires flexibility. AKM inherits this functionality of TOC as part of its
accelerated change management.
Summary
This paper has described the first stage of the Accelerated Knowledge Management
project that is being carried out at the headquarters of the Danwood Group.
Argumentative action research has been the primary methodology used in this first
stage. An evaluation of KM strategies indicated the need for the development of a
customised KM methodology, which has been derived from third-generation KM
integrated with TOC (Theory of Constraints). The focus of this methodology is on
accelerating knowledge management by focusing on delivering knowledge to
restrictive business processes that hinder organisational performance. Developing
new knowledge has been identified as the primary driver for supporting innovation
and therefore further competitive advantage. This is in opposition to older
generation KM that focused on sharing existing knowledge based on the assumption
that it would be sufficient. For this reason knowledge development is supported in
AKM by using the Accelerated Knowledge Development Cycle (AKDC). It has also
been identified that although IT is not the primary driver of this methodology, it is
still an essential support tool and therefore requires serious consideration as to which
tools would be best suitable to the AKM methodology. Further evaluation of a
candidate system, EULE, will be take place as part of the second phase of this
project. The suitability of the AKM methodology to the service industry and to
Danwood has also been underlined. This therefore provides justification for further
investment in this research project.
Future Research
The primary objective will be to perform a case study that will evaluate the
application of AKM at the Danwood Group in a real-life commercial environment.
The intention will be to develop tools to capture the metrics for the tangible and
intangible values that will result in applying AKM methodology to Danwood.
The evaluation of AKM also intends to highlight any necessary modifications to the
methodology. An evaluation of the methodology compares performance with
objectives and measures the overall efficiency and effectiveness of the enterprise’s
attempts to attain its goals. After the information has been obtained regarding the
success of the knowledge management programme, the necessary modifications will
be made to the strategy (Ndlela and Toit, 2001).
An additional task that has been identified for the second stage of this project is the
development of knowledge measurement. Measurement involves the valuation of
knowledge resources and knowledge processors, including quantitative methods,
qualitative assessment, performance review, and benchmarking. It is a basis for
evaluation of control, coordination, and leadership; for identifying and recognising
value-adding processors and resources; for assessing and comparing the execution of
KM activities; and for evaluating the impacts of an organisation’s conduct of KM on
bottom-line performance. Interestingly, this is an under implemented area, but
organisations that are able to create and use a set of measures that are tied to
financial results to guide their knowledge management activities seem to come out
ahead in the long run (Hiebler, 1996; Holsapple and Singh, 2001). Therefore the
authors have decided to also attempt to develop a knowledge measurement
framework that will aid in evaluating current and future KM research efforts.
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