Quality assurance

Quality assurance systems allow businesses to prevent defects throughout their processes of delivering goods or services to customers. Building such a system that collects data and identifies potential problems along a pipeline can provide enormous advantages. For example, in digital manufacturing, quality assurance across the assembly line is imperative. Identifying slowdowns and potential failures before they occur rather than after they are detected can help companies reduce the cost of scrap and rework, while improving productivity.

This solution shows how to predict failures using the example of manufacturing pipelines (assembly lines). This is done by leveraging test systems already in place and failure data, specifically looking at returns and functional failures at the end of the assembly line. By combining these with domain knowledge and root cause analysis within a modular design that encapsulates the main processing steps, we provide a generic advanced analytics solution that uses machine learning to predict failures before they happen. Early prediction of future failures allows for less expensive repairs or even discarding, which is usually more cost efficient than going through recall and warranty costs.