The global security monitoring team at a pharmaceutical major monitored geopolitical, socio-economic and natural calamities to ensure continuity of varied operations, manufacturing activities, and active clinical trials across geographies. With a complex drug development pipeline and long project plans, it notified dispersed teams of any impending risks that could cause operational delays.

The global security team sends out detailed summary emails of all global events occurring across the world to all the teams within the organization. After which, the business global clinical supply chain team would manually go through these emails, figure out the priority and severity, and send action items emails to the relevant stakeholders within their purview.  However, manually processing these emails took a lot of time and was error prone. They also had historical data on past events and impact emails.

The global supply chain team wanted to eliminate the manual interventions and increase the efficiency of the email alerts process.

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.