Applying Design Analytics to Understand Engineering Change Request Information
Large complex system development projects take several years to carry out. Such projects involve hundreds of engineers who develop tens of thousands of parts and millions of lines of code. During the course of a project, many design decisions often need to be changed due to the emergence of new information. These changes are often well documented in databases but, due to the complexity of the data, few companies analyze engineering change requests (ECRs) in a comprehensive and structured fashion. ECRs are important and plentiful in the product development process in order to enhance a product.
This thesis sets out to explore the growing need of product developers for data expertise and analysis. Product developers are increasingly looking towards analytics for improvement opportunities within business processes and products. For this reason, we look at the three components necessary to perform data mining and data analytics: exploring and collecting ECR data, collecting domain knowledge towards ECR information needs and applying mathematical tools for solution design and implementation.
Results show two software tools including visuals of ECR text mining and design structure matrix. The tools were evaluated using industrial data showing patterns and improvement for products and process. Results also show a list of engineering information needs towards ECRs. New information derived with data mining and analytics can thus support product developers in making better decisions for new designs/re-designs of processes and products that lead to robust and superior products.
Design Structure Matrix
Engineering Change Request