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

Maksim Maksimovic et al
4. Lean knowledge life cycle methodology
As a result <strong>of</strong> <strong>the</strong> literature review and industrial field study, <strong>the</strong> stages <strong>of</strong> <strong>the</strong> LeanKLC have been
defined as illustrated in Figure 2. For instance, <strong>the</strong> fact that different sources <strong>of</strong> knowledge exist in a
company required a mechanism to identify <strong>the</strong> useful knowledge. Considering that <strong>the</strong> interviewed
engineers, in <strong>the</strong> industrial field study, expressed to rely 71% on average on knowledge from previous
projects when designing new products, outlined <strong>the</strong> importance <strong>of</strong> capturing this knowledge and <strong>the</strong>n
providing it in a preferable format.
Stage 1:
Knowledge
Identification
Stage 2:
Previous Projects and
Domain Knowledge
Capture
Stage 3:
Knowledge
Representation
Knowledge
Creation
Stage 7:
Dynamic Knowledge
Capturing
Static Dimension Dynamic Dimension
Stage 4:
Knowledge
Sharing
Stage 5:
Knowledge Based
Engineering
Stage 6:
Dynamic Knowledge
Use and Provision
Figure 2: Lean Knowledge Life Cycle Stages
The LeanKLC provides knowledge modelling tools and alternative implementation scenarios at <strong>the</strong>
different stages. It is important to understand that knowledge has to be created, captured and
provided dynamically using a tailored set <strong>of</strong> tools. The first implementation <strong>of</strong> <strong>the</strong> LeanKLC considers
one entire loop by following <strong>the</strong> stages 1 to 7. After finishing <strong>the</strong> first loop, <strong>the</strong> company should focus
on repeating <strong>the</strong> stages 3 to 7, which are called <strong>the</strong> “dynamic dimension” where knowledge is
continuously created. The definition <strong>of</strong> each stage as well as <strong>the</strong> modelling tools are presented in <strong>the</strong>
following sections.
4.1 Stage 1- Knowledge Identification
The aim is to localise and identify <strong>the</strong> useful knowledge that a company have and needs. One <strong>of</strong> <strong>the</strong>
tools for identifying <strong>the</strong> useful knowledge in stage 1 is IDEF0. In addition, traditional methods such as
interviews and questionnaires are still common approaches for knowledge identification.
4.2 Stage 2 - Previous Projects and Domain Knowledge Capture
In this stage <strong>the</strong> identified useful knowledge from stage 1 will be captured and structured. It is
important to outline that <strong>the</strong> LeanKLC differs between two types <strong>of</strong> knowledge capturing; which are
<strong>the</strong> capturing <strong>of</strong> a) <strong>the</strong> previous projects knowledge, such as CAD files, lessons learnt and test data,
and b) <strong>the</strong> domain knowledge, which is based on rules, formulas and constraints for a specific domain
such as injection moulding or joining processes. Stage 2 is going to have a passive role after <strong>the</strong> first
implementation loop, because at some point <strong>of</strong> time <strong>the</strong> capturing <strong>of</strong> previous projects should be
completed. Nowadays, Product Data Management (PDM) and Product Lifecycle Management (PLM)
systems are commonly used in companies. In order to address <strong>the</strong> level <strong>of</strong> integration two potential
scenarios are proposed to structure <strong>the</strong> previous projects’ knowledge:
The knowledge capturing will be influenced by <strong>the</strong> current data structure <strong>of</strong> <strong>the</strong> company
database.
The knowledge capturing will influence and guide <strong>the</strong> structure <strong>of</strong> <strong>the</strong> database.
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