Paolo Braiuca, Industry Principal, Pharma, Seeq

Michelle Hart, Product Manager, Seeq

In Q1 2025, Seeq launched a new version of its software specifically developed for use in highly regulated environments. The presentation will describe the new Seeq for Pharma Analytics & AI Suite and how it allows a better integration in GMP environment, fulfilling the needs of pharmaceutical organizations in terms of 21 CFR part 11 and the use of advanced analytics in GMP decision-making

Matthew Kishe, Sr. Principal Engineer, Eli Lilly & Co 

Eli Lilly presents a forward-thinking assets qualification monitoring (AQM) by exception initiative. Ensuring assets remain in a “qualified state” is critical for producing quality medicines. But traditional processes were manual, inconsistent, and time-consuming—taking up to 10 hours per month per asset.

Using Seeq Vantage and AI, Eli Lilly built a system that monitors qualified equipment across sites, flagging only exceptions rather than flooding teams with routine data and automatically generating routine reports for submission to the regulatory bodies. This shift empowers engineers to focus on real risks while maintaining a state of control—faster, smarter, and with fewer compliance headaches.

Kevin Louie, Deputy Director, Automation Site Support, Sanofi

Our Environmental Monitoring (EM) system uses vacuum to pull a defined volume of air each sample. The tubing became disconnected at an unknown time. Using SeeQ I defined periods of sampling for flowrate values between rest state and setpoint. I then calculated and trended the durations of these sample periods over a 1-day aggregated period for all sample events. Then we determined: When the tubing was first disconnected based on a change in duration to reach flowrate setpoint What maintenance work was involved that might have led to the disconnect How many batches were impacted to isolate vs which ones were okay to release for sale & distribution.

Nick Contino, Principal Process Control Engineer, Eli Lilly & Company

In recent months, Eli Lilly’s Indianapolis API manufacturing site has gained strong site leadership support for the use of Seeq as a process data analytics tool. One of the biggest benefits, as communicated by frontline engineers using the tool day in and day out, is the ability to quickly access and utilize PI Event Frames to supplement their process data analytics. The Event Frames integration with Seeq allows easy access to ISA-88 batch information (recipe information, batch IDs, phase status, etc.) via Seeq Conditions, Capsules, and Capsule Properties. When coupled with Seeq’s of the shelf analytics capabilities, Event Frames have been used by IAPI to significantly reduce the time “data mining” for deviation investigations. This has led to accelerated time to meaningful data insights and more robust CAPA (Corrective Action & Preventative Action) plans to prevent deviation recurrence.

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.

Andrew Williams, Process Engineer, Just Evotex Biologics

Onboarding new equipment and comparing different equipment of the same type can be time-consuming and error prone. Integrating Seeq with DeltaV, Just Evotec has developed Seeq DataLab templates that streamline this process. These templates have enabled engineers and operators to easily access meaningful views of process data to make informed decisions. With automated asset tree structure, consistent naming and calculation conventions, and ease of access, Just Evotec has been able to put time that would have been spent sorting and organizing data into obtaining valuable process insights.

Alan McMurry, Site Sustainability Lead, Syngenta

Learn how Syngenta are using Seeq to provide data for formal Life-Cycle Analyses, and shifting from occasional retrospective reporting to providing real-time visibility of leading indicators and KPIs for quicker response.

Bryan Butler, Manager, Eli Lilly

Eli Lilly and Company began their journey with Seeq in mid-2020 as a key capability of their overall Digital Plant vision. Since then, Lilly has rolled out Seeq to 12 sites and more than 150 users globally, to support a variety of use cases for operational efficiency, predictive maintenance and CMO collaboration. In this presentation, Lilly will share their deployment and adoption strategy for a successful rollout to empower subject matter experts with data analytics. Included in the presentation will be a deeper dive into some key use cases for increasing manufacturing line capacity.