Abstract
D.Ing.
“Innovation is the act of introducing something new” (Byrd & Brown, 2003).
When companies are competing on the technology “playground” they need to be
innovative. By analysis according to Byrd & Brown (Byrd & Brown, 2003) the “act
of introducing”, relates to risk taking, and the “new” relates to creativity, and
therefore these concepts, creativity and risk taking, in combination, are what
innovation is all about. Risk management has become one of the greatest
challenges of the 21st century, and one of the main components in innovation and
the technology driven industry, intensifying the need for a systematic approach to
managing uncertainties.
During the development and design of complex engineering products, the input
and teamwork of multiple participants from various backgrounds are required
resulting in complex interactions. Risk interactions exist between the functional
and physical elements within such a system and its sub-systems in various
dimensions such as spatial interaction, information interaction etc. The
relationships are of a multi-dimensional complexity that cannot be simplified
using the standard task management tools (Yassine A. A., 2004).
To find a meaningful starting point for the seemingly boundless subject of risk
management the research takes a step back into the basic definition of risk
management and follows an exploratory research methodology to explore each
of the risk management processes (risk assessment, risk identification, risk
analysis, risk evaluation, risk treatment and risk monitoring and review) and how
these processes can be enhanced using the design structure matrix (DSM) and
fuzzy logic thinking.
The approach to risk management within an organisation should be seen as a
holistic approach similar to the total quality management process, providing the
ii
opportunity to incorporated risk management during the design process as a
concurrent task. The risk management model is then developed concurrently
(during the design phase) using product development methodologies such as
conceptual modeling and prototyping, and ultimately the prototype is tested using
a case study.
Finally resulting in a clustered DSM providing a visual representation of the
system risk areas similar to the methodology used in Finite Element Analysis
(FEA).
The research combines alternative system representation and analysis
techniques (Warfield, 2005), in particular the design structure matrix, and fuzzy
logic to quantify the risk management effort neccessary to deal with uncertain
and imprecise interactions between system elements.