![](bg8.png)
resentation of knowledge in information models is
also weak. Furthermore, it was discovered that
knowledge is unequally distributed among employ-
eesin the same department and that access to the in-
formation models is not guaranteed across the depart-
ment. The documentation of knowledge and its acces-
sibility is essential to avoid errors. Also errors in the
processes themselves or potentials for optimizations
can often only be recognized if these have already
been documented in advance. The questionnaire and
the interviews with experts showed that there is po-
tential for development in SME. Among other things,
the modelling of business processes due to variations
was considered complex. Especially the documenta-
tion of these process variants is important to achieve
a holistic representation of the process flows.
As part of the empirical study, expert interviews
were conducted in four SME. Various factors have
been identified that influence the order picking pro-
cess in SME. Together with the companies, these var-
iants of the picking processes were documented. With
the help of adaptive information modeling, an artifact
was created from these process variants as part of the
design science process. Using this artifact, the support
of knowledge management in SME through infor-
mation modeling was demonstrated (RQ2). Specifi-
cally, variants of the picking process were collected
and modelled in four SME. Based on the influencing
factors, the adaptive information model was extended
by configuration terms. Through using element selec-
tion by terms, all process variants found in the com-
panies in Figure 3 can be recreated. The correctness
of the information model and the software tool that
executes the element selection by terms was proven
in the evaluation of this paper.
Overall, adaptive information models can support
SME in documenting their expert knowledge. Espe-
cially in processes with many variation steps, adap-
tive information modeling enables a more compact
option for long-term digitalization of knowledge,
which requires less effort in maintenance and man-
agement. Above all, the possibility of optimizing pro-
cesses on the basis of the documented processes has
great advantages.
ACKNOWLEDGEMENTS
The technology transfer project "Competence Net-
work Intelligent Production Logistics" is funded by
the European Regional Development Fund (ERDF) -
Operational Program "Investment in Growth and Em-
ployment" Bavaria 2014 - 2020.
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