Software : Data & Analytics : Asset Performance Management

Website | Blog | Video

Seattle, Washington, United States

VC-C; Insight Partners, Madrona Venture Group

Seeq is founded on the premise that many process manufacturing organizations are DRIP “Data Rich, Information Poor” (DRIP) and the number will increase with new sensor deployments and higher data creation rates driven by the Industrial Internet of Things (IIoT). As a result, the existing need for solutions for process manufacturing companies to derive insight from their data will only become more widespread and important in the future. Seeq’s vision is to address this requirement by closing the gap between advancements in data and computer science - big data and machine learning as examples – and the software available to engineers and plant employees, delivering innovation as features in easy to use, advanced analytics applications. In addition the Seeq vision includes the needs of whole organizations including collaboration, publishing, and IT requirements that span teams, plants, and divisions. Finally Seeq includes the flexibility of on premise or in the cloud and distributed deployments to “future proof” customer investments and accommodate organization strategies for data collection and management.

Recent Posts

Fastest Growing Industrial Companies Grow Over 500%


Flexport and Seeq retain their incredible growth trajectories according to the 2022 Inc. 5000. Robots learn to take instruction in natural language and freight ships self-navigate for weeks with AI.

Assembly Line

Advanced analytics improve process optimization


Topics: Manufacturing Analytics

Organizations: Seeq

With advanced analytics, the engineers collaborated with data scientists to create a model comparing the theoretical and operational valve-flow coefficient of one control valve. Conditions in the algorithm were used to identify periods of valve degradation in addition to past failure events. By reviewing historical data, the SMEs determined the model would supply sufficient notification time to deploy maintenance resources so repairs could be made prior to failure.

Read more at Plant Engineering

How Seeq, a Grantek Partner, Predicts Batch Quality at Life Sciences Manufacturing Facilities


Organizations: Seeq, Grantek

Nothing is more important than protecting patient health. That is why quality is the most critical metric in pharmaceutical manufacturing. During manufacturing of new or existing medicines, drug companies need to test each batch to ensure that the quality consistently meets standards. Predicting the quality of each batch is a challenge for most drug manufacturers. It is a labor-intensive and time-consuming—though necessary—process. Typically, samples are taken and sent to the lab for analysis while the process is actively running. The analysis alone adds several hours to the process time. And, if the lab returns inadequate results, time-consuming—and often expensive—changes need to be made if the batch is recoverable. If not, the manufacturer can lose hundred of thousands to millions for the lost batch.

Using Seeq, the scientists running the processes built a model of process quality based on data from the OSIsoft PI data historian. The manufacturing team uses this model to predict the quality of the in-progress batches. This allows for modifications to be made during the production process before the batch would be lost due to quality issues.

Read more at Grantek Blog

Koch Ag & Energy High Value Digitalization Deployments Leverages AWS


Author: Bill Lydon

Topics: IIoT, predictive maintenance, vibration analysis

Organizations: Koch Industries, AWS, Seeq

This application uses existing plant sensors, Monitron sensors, Amazon Lookout and SeeQ software to implement predictive maintenance on more complex equipment. The goal accomplished was successfully implementing predictive maintenance requires data from thousands of sensors to gain a clear understanding of unique operating conditions and applying machine learning models to achieve highly accurate predictions. In the past modeling equipment behavior and diagnosis issues requiring significant investment in time money inhabiting scaling this capability across all assets.

Read more at Automation

Seeq Announces Expanded Microsoft Azure Machine Learning Support


Organizations: Seeq, Microsoft

Seeq Corporation, a leader in manufacturing and Industrial Internet of Things advanced analytics software, announced today additional integration support for Microsoft Azure Machine Learning. This new Seeq Azure Add-on, announced at Microsoft Ignite 2021, an annual conference for developers and IT professionals hosted by Microsoft, enables process manufacturing organizations to deploy machine learning models from Azure Machine Learning as Add-ons in Seeq Workbench. The result is machine learning algorithms and innovations developed by IT departments can be operationalized so frontline OT employees can enhance their decision making and improve production, sustainability indicators, and business outcomes.

Read more at AutomationWorld

Seeq Accelerates Chemical Industry Success with AWS


Topics: IIoT

Vertical: Chemical

Organizations: Seeq, AWS, Covestro, allnex

Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, today announced agreements with two of the world’s premier chemical companies: Covestro and allnex. These companies have selected Seeq on Amazon Web Services (AWS) as their corporate solution, empowering their employees to improve production and business outcomes.

Read more at Automation

Survey: Data Analytics in the Chemical Industry


Author: Allison Buenemann

Topics: manufacturing analytics

Vertical: Chemical

Organizations: Seeq

Seeq recently conducted a poll of chemical industry professionals—process engineers, mechanical and reliability engineers, production managers, chemists, research professionals, and others—to get their take on the state of data analytics and digitalization. Some of the responses confirmed behaviors we’ve witnessed first-hand in recent years: the challenges of organizational silos and workflow inefficiencies, and a common set of high-value use cases across organizations. Other responses surprised us, read on to see why.

Read more at Seeq