Big data science and cloud computing is changing how engineering driven companies develop highly complex products. Utilizing a novel cloud platform based on hadoop, big data analytics, and applied mathematics tools, the traditional product development cycle can be drastically sped up and used to provide new unique insights into highly complex products improving their final designs. Data science on the cloud can be utilized as a platform to collaborate between disciplinary silo’s within engineering organizations providing new opportunities for applications of advanced machine learning and optimization tools. These tools are demonstrating drastic improvements in aerospace, automotive, and other high-tech industries.
An airplane wing case study will be shown to illustrate the ideas and methods presented. The case study will show how complex engineering disciplines such as aerodynamics and structural analysis can be simultaneously run on the cloud and coupled to not only increase the speed of product development but also used to develop better final product designs. Several tools described in the case study will be shown through a live demonstration.
Joris has a broad engineering background, which he leveraged at Boeing to develop product development software for the 787 airplane. During his time at Boeing Joris’ work resulted in hundreds of millions of dollars in cost savings through improved designs and drastically reducing product development time. More recently after a stint at McKinsey, Joris has decided to utilize his technical and business experience in a new startup venture. The startup is developing a cloud enterprise software platform for engineering product development software.
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