according to the following criteria:
low risk – the value of the degree of risk
from 0 to 0.15;
moderate risk – the value of 0.16 to 0.37;
high risk – the value of 0.38 to 0.75;
critical risk – the value of 0.76 and above.
Implementation of the described methodology is
shown in the example below: Figure 1 (own
elaboration based on: Żmuda, 2016). shows the steps
of data collection in a situation where the company
has completed three projects.
In situation where data were collected for the
three projects, the analysis phase can occur, as it is
schematically presented below in Figure 2 (own
elaboration based on: Żmuda, 2016).
Both the collection and analysis of data for each
of particular phases is carried out similarly to
carrying out these activities for the project, the idea
is presented Figure 3 (own elaboration based on:
Żmuda, 2016).
4 CONCLUSIONS
In the presented paper it has been developed a
methodology for collecting data on completed
projects to allow their subsequent analysis, and also
a methodology of data analysis to identify the key
risks of projects and to provide a valuable
information. Using the developed methodology, in
the future it is planned to create a tool to support the
completion of projects in the form of a spreadsheet.
While continuing work on the field tackled in this
paper, it is recommended to implement the
developed methodology for the data collection and
analysis into a computer application.
While using the developed methodology it
should be borne in mind that phenomena such as risk
and uncertainty are often very dynamic and they
have interdisciplinary nature, thus the degree of
repeatability can vary depending on the nature and
level of innovation and uniqueness of the delivered
project (Gembalska-Kwiecień, 2016). Therefore,
using solutions developed from this paper it should
be taken into account that it is intended to only assist
the decision making process of project manager. It
means that in terms of risk management the project
manager should in the first place follow the logic,
experience gained in the industry and his own
assessment of the situation.
ACKNOWLEDGEMENTS
The article is the result of the registered work with
symbol 13/030/BK_16/0024 entitled "Production
engineering methods and tools for development of
smart specializations" carried out in the Institute of
the Production Engineering, Department of
Organization and Management at Silesian University
of Technology.
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