Dr. Lisa Graham, CEO, Seeq

Dr. Lisa Graham emphasizes the power of Seeq’s growing user community and its role in driving real impact through human-centered AI. Users submit over 188,000 AI prompts and report more than $1B in value created through Seeq.

Case studies from Intel and Energy Transfer demonstrate how Seeq enables productivity gains, energy savings, and automation at scale. Graham reinforces that true change comes from people—not just technology—and previews Seeq’s roadmap, including AI in Teams and executive-focused impact reporting.

Liberty Barney, Data Scientist, Ascend Performance Materials

Distillation tower fouling plagued Ascend’s operations forcing an unplanned 11 day downtime. The team used Seeq coupled with Seeq ML to identify key fouling contributors. This work has allowed Ascend to operationalize an ML model to provide a 60 day lead time in maintenance planning and propose operational optimization to extend run time between cleaning events.

Evan Bean, Digital Manufacturing Engineer, Pfizer

Within the solid dose manufacturing space for small molecule pharmaceuticals, Material-Sparing Tablets (MST) offer a compelling value proposition of comparatively low waste, scalable manufacturability, and low process variability.

A significant roadblock in leveraging latent efficiencies in the MST process train is controlling the significant time and material startup losses associated with first-time-manufactured products. In such products, runtime parameters must be tuned to reach defined product quality attributes where relevant first principle models are often underdefined due to insufficient material knowledge. Thus, it is of interest to deploy a prediction methodology that is both performant and adaptable when operating at a knowledge deficit.

We propose a machine-learning enabled, evolutionary model that learns over the design space of previous equipment operation and improves predictions with the feedback of live process sampling to offer more accurate startup parameters. Successful startups transition the model into a process monitor that performs live calculations of estimated quality attributes and aggregates results as inputs to subsequent models, leveraging Seeq’s time series analysis, forecasting tools, and DataLab engine.

By iterating and crosslinking this methodology over the set of unit operations in the MST train, we create smarter, faster, and more consistent models that reduce startup time and waste on aggregate.

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.