An integrated systems approach to risk management within a technology driven industry using the design structure matrix and fuzzy logic

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