Kyle Miller, Automation Manager, Marathon Pipe line

Marathon Pipe Line leverages Seeq to analyze pipeline pressure cycles, critical for predicting pipeline fatigue and ensuring operational integrity. Using Seeq Data Lab, they automate the process of pressure cycle counting, significantly reducing analysis from multiple days to mere minutes. This approach eliminates dependency on third-party tools, improving data reliability and saving approximately $50,000 annually. By quickly assessing cumulative pipeline damage, they enhance safety and operational efficiency.

Joe Menning, Sr. Manufacturing Analytics Engineer, Cargill

Juliana Fidalgo, Automation Scientist, Indorama Ventures

A deeper dive into the Change Management strategies discussed by Amanda Thomas’ on Wednesday’s Main Stage presentation. Discover the effectiveness intentional change management with People at the heart of digital transformation.

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Jonathan Alexander, Manufacturing AI & Advanced Analytics Manager, Albemarle

Pilot purgatory: where AI dreams go to die and budgets go to burn. The good news? You’re not alone. The bad news? It’s costing you big time in lost ROI and wasted potential. In this session, Albemarle spills the secrets behind scaling AI successfully—from their full technology suite including Seeq, Aveva’s PI System, Databricks, Generative AI, SPC, & Machine Learning, paired with the often-overlooked pillars of Change Management, Organizational Buy-In, and a relentless focus on Business Value.

Cara Blankenbicker, Industrial Data Scientist, Syensqo

In the complex world of manufacturing, a process cannot be run optimally if people do not have access to relevant information when they need it. Information has historically been shared using a tapestry of solutions to generate dashboards for operators, monthly spreadsheets for stakeholders and ad-hoc analyses for engineers to troubleshoot processes. Today this can be streamlined into a coherent SEEQ dashboard solution, the product of an ongoing collaboration between site and corporate resources.

Brooke Hill, Data Science Engineer, Pratt Industries

Derek Valentin, Data Science Engineer, Pratt Industries

Pratt Industries, the world’s largest, privately-held 100% recycled paper company, operates six of the most modern and cost-effective paper mills in the country. In the competitive landscape of paper manufacturing, minimizing costs while maintaining product integrity is imperative for maximizing profits and sustainability. Process additives that are introduced for product strength are costly, yet necessary. Over-consumption can lead to unnecessary expenditures thus conscious optimization efforts are vital in reducing costs. High variability in the process means additive requirements can fluctuate, even while producing the same product during different runs. This leads to less uniform process control, making optimization a challenge.

Kyle Denzler, Manufacturing Intelligence Leader, Owens Corning

Faced with limited resources, Owens Corning established a remote monitoring team and empowered the group with technical leadership and  integrated digital tools, including Seeq.  This presentation investigates the effectiveness of this team in saving engineers’ time, enabling insights from data and simplifying the task of data retrieval.

Fariha Imami, Manufacturing Intelligence Project Leader, PQ

At PQ, no sites are alike. Differences in data maturity and resourcing add extra layers of complexity. We didn’t find one recipe that fits all. Instead, we found a new mindset of agility and continuous improvement, enabling us to leverage existing resources and apply our learnings to deliver better results at each iteration.