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 costs for 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 assembly line. By combining these with domain knowledge and root cause analysis within a modular design that encapsulates 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 are usually more cost efficient than going through recall and warranty cost.
Cortana Intelligence IT Anomaly Insights solution helps IT departments within large organizations quickly detect and fix issues based on underlying health metrics from IT infrastructure (CPU, Memory, etc.), services (Timeouts, SLA variations, Brownouts, etc.), and other key performance indicators (KPIs) (Order backlog, Login and Payment failures, etc.) in an automated and scalable manner. This solution also offers an easy to 'Try it Now' experience that can be tried with customized data to realize the value offered by the solution. The 'Deploy' experience allows to quickly get started with the solution on Azure by deploying the end to end solution components into your Azure subscription and providing full control for customization as needed.