The automated AI-driven anomaly detection solution developed by Adastra reduced the client’s reliance on production engineers to detect and rectify issues on the foam production lines. Not only can this solution, once productionalized, potentially lower the costs of trained manpower (fewer production engineers needed), but it can also prevent “misses” that sometimes arise due to manual oversight. The alerts issued via LogicApps notified production engineers about possible anomalies early on, and the Power BI dashboard allowed them to view real-time telemetry data and take preventative measures and adjust or recalibrate settings to keep the production line running smoothly, without any issues.
The manufacturing process is complicated and halting the production line due to problems is an expensive, time-consuming undertaking. Adastra’s AI model can quickly learn to catch issues, so that they can be addressed right away, potentially saving tens of thousands of dollars per run. The solution also resulted in an estimated 2-5% reduction in scrap, as it allowed line settings to be changed much faster. Overall, with multiple runs each day, the plant could save upwards of millions of dollars per year.
Upon deployment, this solution will allow the client to manage Edge services from the cloud and analyze data across all 30 plants simultaneously from one place in Azure. By centralizing monitoring, they should be able to eliminate the need for separate production engineers at each plant location and significantly lower overhead costs.
There is scope for future enhancements to this solution once it is productionalized. In addition to the images, a wide array of telemetry data associated with past runs of each foam variant can be reviewed to understand why the anomalies occurred and identify trends. This will enable predictive fixing of the cream line based on trends even before anomalies occur. This will further increase cost savings and reduce production downtime and scrap.
With access to more and more historical data, AI and Machine Learning solutions can be improved over time, increasing their efficiency and impact for the organization. This solution, for instance, was able to significantly reduce the client’s reliance on experienced manpower and consequently, automate the manufacturer’s monitoring and anomaly detection process. Upon productionalization, there will be scope for consolidating the monitoring process for all the client’s plants to one location over the cloud. Automation and adoption of advanced analytics solutions is not only the next logical step for organizations looking to improve quality, and cut down on production costs and wastage, but is also the pathway to creating a factory of the future.