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Abstract
Cost Overrun continues to burden aerospace product development projects, and its contributing factors need to be better understood. In line with this effort this paper explores, through multiple case studies within a major aerospace firm, the relationship between the engineering risk management performance and product development projects scope changes driving Cost Overrun, as it evolves over time. Our research provides fresh and novel empirical evidence about of the ability of an ISO31000 based technical risk management process and organization to uncover uncertainty early in PD projects. A novel lean risk management performance metric entitled Surprises is developed, together with a systematic method for data mining technical expressions of uncertainty. We postulate that a reduction of Surprises could help reduce project Cost Overrun. This lean approach to risk management provides for the early detection of new and emerging risks associated labelled as Surprises, leading to scope changes and Cost Overrun, over the entire product development project lifespan. Our analysis shows that there is a positive correlation between the extent of the independent Surprise metric and the PD project Cost Overrun response. This paper explores how the use of a novel lean risk management method supplements conventional ISO31000 based processes, and can be used to improve the value of the risk assessment process through reduced Surprises, effort required and lead time, to ultimately address the major concern about aerospace product development Cost Overrun. Keywords: Risk Assessments Data Mining, Cost Overrun Reduction, Lean Engineering and Product Development Projects
Introduction
PD projects performance dimensions usually include PD project cost, and additional metrics such as adherence to schedule and performance (Holtta-Otto & Magee, 2006). Late discovery of uncertainties and the related Surprises metric in PD projects can yield to significant amounts of non-recurring engineering (NRE) Cost Overruns, negatively impacting organizations credibility and profitability. The reduction of uncertainties is a fundamental tenet of lean PD, and a valuable contribution from engineering (Reinertsen, 2009). Key steps in the risk management process include risk identification, analysis, evaluation, and treatment (ISO, 2009b). With the data rich environment enabled by the risk management process is currently associated a difficulty for risk management practitioners transitioning to a lean organization to distill relevant information from the masses of data generated from multiple workshops over...