Towards big-data analysis of deviation and error reports in product development projects

Í.Ö. Arnarsson, J. Malmqvist, E. Gustavsson, M. Jirstrand. In proceedings of NordDesign 2016, Trondheim, Norway, 10 – 12 August, 2016.


Large complex system development projects, specifically complete truck development project take several years in development. Such projects involve hundreds of engineers who develop tens of thousands of parts and millions lines of codes. During a project, many design decisions often need to be changed due to emergence of new information. The bulk of these changes are discovered late in the development process. It is known that changes late in the development process as very costly and run a risk of delaying the project. These changes are often well documented in databases, but, due to the complexity of the data, few companies analyze engineering change in a comprehensive fashion. This paper proposes that “big data” data (specifically data mining and machine learning) analysis tools can be applied for such analyses, along with the algorithms and process needed for carrying out the analysis. The paper further accounts for experiences gained from testing the approach on a dataset consisting of 4,000 deviation and error reports that were created during a truck development project.


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