IT-Based Knowledge Management Systems to Support the Design of Product Development Processes - Information Technology and Product Development - page 54

step is conducted simultaneously with Steps 1 and 2. As it employs the social
medium, it requires the use of social tacit knowledge. This results in the
generation of individual knowledge (i.e., individual tacit knowledge is created).
4. Choice of experiment design: This involves identifying appropriate methods
for conducting the experiments (e.g., RDM, RSM). External explicit data is
deployed here, such as participating in training courses and/or reviewing topics.
The result is the generation of individual tacit knowledge (as one interacts with
the explicit data to form their own knowledge of the material).
5. Perform the experiment: This involves executing the experiment and calls for
individual tacit knowledge which has been generated in Steps 1-4. This results
in a greater understanding of the development processes in the organization.
6. Statistical analysis of the data: This step involves analyzing the results of the
experiments. To analyze the data, a knowledge of statistical methods is required
which is often obtained from external sources such as training courses, web-
sites, and books. This results in internal explicit knowledge being generated via
the recording and analysis of experimental data. Furthermore, individual tacit
knowledge is also generated from the interpretation of the results.
7. Conclusions and recommendations: This involves making recommendations
based on the findings from the analysis of the experimental data. When generat-
ing conclusions, the individual has to use the tacit knowledge created in Step 6.
The outcome is the creation of internal explicit knowledge which is disseminated
to the organization through a specific medium.
To enable the above process to occur, a platformmust exist to facilitate it. Nonaka
and Konno (2000) defined “Ba” as a shared place - physical, mental, or virtual - for
knowledge creation. In the following few sections, we describe such a platform for
knowledge creation - specifically, an IT-based KMS that was designed to facilitate
the creation of knowledge in the DOE environment.
3.5 Knowledge Management in DOE
Fahey and Prusak (1998) highlight the importance of experimentation as a method
of encouraging exploration and knowledge creation. However, to date, no research
has been conducted into the development of KMS to support experimentation and
exploit such data. This section will explore issues with regard to the use of KMS
in the DOE context. The following section will then identify how these issues have
been addressed through the implementation of an IT-based KMS in a company.
3.5.1 Need for Knowledge Management to Support DOE
One might question why KMS is required to support the DOE. There are two reasons
for this. First, as mentioned previously, DOE can be resource intensive to execute
in terms of both time and money. Thus, it is important to capture as much of the