After a thorough needs analysis, the i2e team developed an ML-powered algorithm that could better manage disruption alerts. Instead of manually sorting through the comprehensive emails, this model could read the comprehensive emails and parse data to identify alerts that could affect global clinical supply operations at a particular location.
By training the model on historical data, it predicted/forecasted the severity of the issue based on past historical trends.
Once the information is determined it is built into a pre-defined email template and sent to the relevant stakeholders. Our data analysts utilized the consolidated data to build real-time dashboards in Spotfire.