Speaker: Wilfred Mascarenhas, Sr. Director for Data and Analytics – Eli Lilly & Company

Marco Vicentini, Analyst IDS Digital Solutions And Data Engineering, Eli Lilly Italia

Giuseppe Salerno, Sr Associate IDS Digital Solutions And Data Engineering, Eli Lilly Italia

Repeatable and stable manufacturing processes are essential for ensuring a reliable supply. In the realm of pharmaceutical continuous batch processes, maintaining stability amidst the intricate web of multivariate time series variables presents a significant challenge.

This presentation introduces a solution tailored to address this challenge by harnessing the combined capabilities of Seeq and Power BI. The integration facilitates anomaly detection within continuous batch processing, streamlining the diagnostic process to pinpoint and mitigate sources of process variability.

Built on a data-driven foundation, the solution employs time series distance computation between a golden standard series and those generated during production cycles. These data inputs fuel a neural network tasked with classifying cycles as anomalous or within expected parameters.

What distinguishes this solution is its level of abstraction and adaptability, allowing for easy replication across different production processes with very limited configuration effort. Moreover, its operational versatility via Python embedded in Seeq Data Lab promotes scalability across various manufacturing environments.

This presentation not only showcases the effectiveness of the solution in ensuring process stability but also emphasizes its adaptability and scalability, highlighting its potential as a versatile tool for enhancing reliability in pharmaceutical manufacturing.

Speaker: Brad Keigwin, Data Scientist, Bristol Myers Squibb

Speakers: Mike Russ, Head of QCLS,/ Analytical Science and Technology, Genentech

Samir Mukhida, Advanced Analytics Development Engineer, Mettler Toledo

Henny Hampton, Associate Director – Data & Digital Manufacturing Operations, Merck

Overall equipment effectiveness (OEE) is the industry leading metric for quantifying manufacturing productivity. In 2021, Merck began a rollout plan to leverage Seeq as their OEE solution at their Carlow site to automate their manual, excel-based processes for capturing and analyzing production losses. Merck leverages automated OEE calculations in Seeq on top of PI and PAS-X, along with an operator interface for categorizing downtimes in Seeq Data Lab, dashboards in Seeq Organizer, and integration with PowerBI for historical analysis. By standardizing and automating OEE, Merck can optimize productivity and throughput across its manufacturing network.

Jessica Sigurdson, Global Process Engineering Lead, Pfizer | Alex Miller, Digital Infrastructure Lead, Pfizer

At Pfizer, Seeq has allowed a new view on our continuous manufacturing data by turning data into insights. Through connection to the process historians and other data sources, Seeq has helped Pfizer change the way we monitor and analyze a batch. With Seeq, Process Engineers have deployed real time analytics for modeling, profiles to understand and alert operators to new behaviors and a single interface to analyze data across sites. This presentation will review how Seeq was deployed to assist in bringing a new technology online, as well as current implementation plans and future cases.