Protect Margins. Recover Yield. Shorten Cycle Time. Keep Every Batch On Spec.

Raw material volatility, tariff shifts and energy costs sit outside your control. What happens inside the plant does not. Realize measurable margin improvement with Seeq's human-centric AI platform.

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Specialty Chemical Levers for Margin Improvement

Two-page guide | Yield, batch cycle time, energy intensity, asset reliability

Small improvements in yield, off-spec product, batch cycle time, energy intensity and asset reliability have a measurable impact on profitability. They are also the hardest gains to quantify, because the data that proves them is spread across historian, lab, MES and energy systems.
This guide covers the four operational levers that consistently carry the greatest influence on specialty chemical profitability, and how leading manufacturers are pulling them using data they already have.

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Customers Find Value with Seeq

Seeq is purpose-built to solve the inherent challenges of turning time series data into actionable insights, delivering insights in hours, not months. Seeq is empowering organizations to improve operational performance and accelerate business and ESG outcomes.

Reduce Energy Consumption

Allnex

Allnex reduced greenhouse gas emissions equivalent to 1,095 passenger vehicles driven for one year by using energy modeling in Seeq to inform continuous improvement efforts

Increase Product Consistency

Ascend Performance Materials

Ascend Performance Materials uses near-real-time SPC capabilities of Seeq to reduce monthly quarantined material by two orders of magnitude

Minimize Transition Scrap

CPChem

CPChem uses Seeq to identify, overlay, and optimize product transitions, reducing transition time and off-spec by 60-80% per transition

Reduce Carbon Intensity

Syngenta

Syngenta uses Seeq to measure baseline carbon intensity of operations for monitoring and to identify improvements to make progress towards their 2030 commitment of reducing carbon intensity by...

Optimize Heat Exchanger Maintenance

Covestro

Covestro uses Seeq to build predictive models of heat exchanger fouling for their fleet of heat exchangers enabling optimal maintenance scheduling.

Optimize Filtration Quality

Chevron Oronite

Chevron Oronite uses Seeq combined with advanced Python models to predict filtration quality and provide operator recommendations ensuring each batch is completed to quality specs in the most ...

Predict Quality and Yield

Chemours

Chemours uses Seeq to deploy Python models of product yield enabling continuous improvement of both product quality and model tuning

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From Margin Opportunity to Operational Action

When a batch underperforms, the answer is rarely a single event. A run takes longer than expected. Startup drags. Small process changes accumulate. Energy per batch creeps up. Individually none of it trips an alarm; together it is the difference between a strong quarter and a flat one.
Seeq brings historian, lab, MES and energy data into a single environment so teams can investigate what actually happened, quantify what it cost, and turn the better run into the repeatable one, then reuse that analysis the next time a similar issue appears.

From Margin Pressure to Margin Control

Every plant and portfolio carries its own mix of pressures, so there is rarely a single starting point that applies to every team. Bring a challenge you are already working through, cycle-time variation, yield loss, off-spec production, or why some batches outperform others, and a Seeq expert will help you think through the data, the analysis approach and where to start.

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Protect Margins. Recover Yield. Shorten Cycle Time. Keep Every Batch On Spec.

Specialty Chemical Levers for Margin Improvement

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