Proceedings of the 8th International Conference on Intellectual ...

4.3 Stage 3 - Knowledge Representation
Maksim Maksimovic et al
This stage deals entirely with <strong>the</strong> representation <strong>of</strong> only <strong>the</strong> domain knowledge captured in stage 2. In
order to be able to use KBE, <strong>the</strong> knowledge must be represented in a formal method that will ease <strong>the</strong>
computational use. Examples <strong>of</strong> formal knowledge representation that could be used are object
oriented (e.g. UML) and semantic web.
4.4 Stage 4 - Knowledge Sharing
In this stage <strong>the</strong> captured knowledge from previous projects from stage 2 and also new generated
knowledge from <strong>the</strong> continuous loops <strong>of</strong> <strong>the</strong> LeanKLC are going to be brought toge<strong>the</strong>r and stored in
a database within a clear structure to enable a lean environment. It should be noted that possibly not
all <strong>the</strong> knowledge could be captured and coded into s<strong>of</strong>tware, especially <strong>the</strong> tacit knowledge.
Therefore, <strong>the</strong>re is a need to have a directory <strong>of</strong> those human experts where <strong>the</strong> designer and
engineers could refer to get help. This is done through yellow pages, also known as knowledge
pointers or skills directories.
4.5 Stage 5 - Knowledge Based Engineering
The captured and formal represented domain knowledge model in stage 3 is used to develop a KBE
s<strong>of</strong>tware. The development <strong>of</strong> <strong>the</strong> KBE application is supported by an underlying system architecture
reference model.
4.6 Stage 6 - Dynamic Knowledge Use and Provision
In this stage <strong>the</strong> knowledge should be provided to <strong>the</strong> product development engineers to support <strong>the</strong>
decision taking for a range <strong>of</strong> engineering applications. This could include, for example, <strong>the</strong> provision
<strong>of</strong> <strong>the</strong> most relevant previous project information in order for <strong>the</strong> product development engineers to
initiate a new set <strong>of</strong> product designs and tools. Knowledge provision is mainly driven by easy to use
methods such as design templates, checklists, trade-<strong>of</strong>f curves and A3 problem solving templates.
4.7 Stage 7- Dynamic Knowledge Capturing
Knowledge should be captured dynamically during <strong>the</strong> actual design and development <strong>of</strong> a product. In
this stage, new knowledge is created through new simulation, prototyping and testing <strong>of</strong> <strong>the</strong> product
under development. Stage 7 <strong>of</strong> <strong>the</strong> LeanKLC is <strong>the</strong> most critical one for two main reasons. Firstly, it is
<strong>the</strong> essence in order to keep <strong>the</strong> dynamic dimension <strong>of</strong> <strong>the</strong> LeanKLC running, see Figure 2, by
dynamically capturing <strong>the</strong> knowledge while created. Secondly, <strong>the</strong> dynamic capturing has to be
aligned with <strong>the</strong> tailored tool set and integrated accordingly in <strong>the</strong> product development process.
As soon as new knowledge is acquired <strong>the</strong> LeanKLC loop starts again from stage 3. This can happen
also simultaneously anywhere within <strong>the</strong> defined implementation area regardless time, phase or
function, which means that a continuous loop <strong>of</strong> knowledge creation is in function. Referring back to
<strong>the</strong> lean product development literature, knowledge is regarded as a vital part <strong>of</strong> value creation.
Having established a systematic method <strong>of</strong> knowledge creation equals to a method <strong>of</strong> systematically
contributing to value creation. However, only a well tailored dynamic knowledge environment will
ensure <strong>the</strong> elimination <strong>of</strong> wasteful activities within <strong>the</strong> LeanKLC methodology.
5. Conclusion
The LeanKLC was developed alongside <strong>the</strong> industrial partners within <strong>the</strong> LeanPPD project and
already initiated at an automotive 1 st tier automotive supplier to capture joining domain knowledge to
mitigate it against alternative joining methods <strong>of</strong> laser and adhesive bonding, addressing <strong>the</strong> impact
on manufacturing processes and tooling. The developed methodology addresses both <strong>the</strong> domain
knowledge as well as knowledge from previous projects. In addition it is defining a novel approach <strong>of</strong>
knowledge capturing and provision in dynamic manner. The work is in progress to define <strong>the</strong> detailed
definition <strong>of</strong> <strong>the</strong> LeanKLC, such as <strong>the</strong> IT enabler for modelling and implementation <strong>of</strong> provision <strong>of</strong>
knowledge. More action research is planned within <strong>the</strong> aerospace and ano<strong>the</strong>r automotive company.
6. Acknowledgements
The research presented in this paper has been conducted as part <strong>of</strong> a European project titled ‘Lean
Product and Process Development (LeanPPD)’. The project involves multiple research and industrial
partners from <strong>the</strong> UK, Spain, Germany, Italy and Poland. The project is supported by <strong>the</strong> Commission
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