Proceedings of the 12th European Conference on Knowledge ...

Eva-Maria Kern et al
In <strong>the</strong> following, a comprehensive approach for implementing knowledge-integrating value stream
analysis is presented and <strong>the</strong> challenges mentioned above are addressed.
5. The knowledge-integrating value stream analysis – an approach
In order to ensure viable and purposeful outcomes, a comprehensive yet straightforward model for
executing a knowledge-oriented value stream analysis has to be defined. This approach has to
contain components for data collection, analysis and visualization on <strong>the</strong> one hand and components
for selection and design <strong>of</strong> knowledge management tools on <strong>the</strong> o<strong>the</strong>r hand.
5.1 General description <strong>of</strong> <strong>the</strong> approach
The approach proposed for integrating knowledge into <strong>the</strong> value stream analysis consists <strong>of</strong> three
layers as depicted in figure 3.
Component (I) – Data collection:
• Structured pre-interviews with
process owners
• General observation
• Detailed observation and questioning
• Semi-structured interviews
Layer 2
Selection mechanism:
• Selection by experts
• Automated assignment
• Implementation <strong>of</strong> best practices
Component (IV) – Knowledge
management processes:
Layer 1
Component (II) – knowledge-specific
preparation:
• Knowledge typology
• Knowledge intensity
Layer 3
Component (III) – visualization:
Figure 3: General approach for conducting a knowledge-integrating value stream analysis
Layer 1 is used for structuredly investigating and representing <strong>the</strong> actual condition <strong>of</strong> a selected
business process based on a knowledge-integrating value stream analysis. This layer contains
components for data collection (I), knowledge-specific preparation (II) and visualization (III).
Component (I) – Data collection: At <strong>the</strong> beginning <strong>of</strong> <strong>the</strong> analysis, data and information about <strong>the</strong>
considered processes and <strong>the</strong> inherent knowledge have to be collected. While input for a
traditional value stream map typically is created by observing <strong>the</strong> relevant process steps and
interviewing <strong>the</strong> employees concerned in an unstructured manner, this is not sufficient to get
transparency about <strong>the</strong> process knowledge. Therefore a structured and comprehensive collection
<strong>of</strong> evidence is necessary to ensure a holistic view on <strong>the</strong> process:
Structured pre-interviews with process owners: Initial interviews have to be conducted
with <strong>the</strong> process owners to roughly map <strong>the</strong> processes, taking into account relevant inand
outputs, material flows, key actions, important decisions and involved persons.
General observation: The processes have to be observed in order to verify <strong>the</strong> general
course <strong>of</strong> <strong>the</strong> process and in order to deepen <strong>the</strong> awareness for process and knowledge
streams.
Detailed observation and questioning: By focused observation <strong>of</strong> <strong>the</strong> employees’ work
and by questioning all activities, <strong>the</strong> process can be detailed in-depths, and <strong>the</strong> patterns
and preconditions <strong>of</strong> process execution can be revealed.
Semi-structured interviews: Subsequent detailed interviews allow for ga<strong>the</strong>ring additional
information and creating meta-data for <strong>the</strong> process, e.g. training time or available media.
The interviews should be conducted with <strong>the</strong> operating personnel as well as <strong>the</strong> process
owners and some selected executives.
Component (II) – Knowledge-specific preparation: Additionally to <strong>the</strong> data and information
prepared for traditional value stream mapping, specific data on <strong>the</strong> resource knowledge have to
experts
required
based on
experience
Component (V) – Knowledge
management methods and tools:
lessons learned
space management
expert circles
learing album
people expert circles
knowledge broker
story telling
wikis visualization
debriefing mentoring
yellow pages
data bases
training on <strong>the</strong> job
folder structure
assistence by key persons
digital planning tools
standardization
technology organization advanced training
ontologies
data bases knowledge maps working instructions
knowledge market places
simplifying processes
…
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Final
knowledge
management
design