Seeq
Software : Information Technology : Asset Performance Management
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%
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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.
Breaking Down CB Insights’ Advanced Manufacturing 50 for 2022
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Exponential Industry’s thoughts on CB Insights’ Advanced Manufacturing 50. Data lakes drive MLOps into manufacturing processes. 3D printing companies continue to merge.
Assembly Line
Chevron Phillips Chemical Accelerates Digital Transformation and Cultural Innovation with Seeq
Chevron Phillips Chemical (CPChem) uses advanced analytics software Seeq to make better data-based decisions, Accelerates Digital Transformationenabling a data-driven culture. At the Seeq Conneqt conference, Brent Railey, Manager of Data Science at CPChem, described how Seeq enabled a cultural shift at the company. CPChem was able to use Seeq to digitally transform the organization by improving collaboration and problem-solving to make faster data-based decisions. CPChem’s digital foundation team used the Seeq platform to help them view and understand the value of data and to enable their digital transformation, setting a foundation for CPChem’s digital transformation team.
The software helped create a common framework for interacting with data across CPChem’s sites, and enabled collaboration across the organization. Seeq’s intuitive user design helped make adoption quick and time to insights faster. CPChem was able to solve problems that they could not solve before using Seeq.
Advanced analytics improve process optimization
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.
How Seeq, a Grantek Partner, Predicts Batch Quality at Life Sciences Manufacturing Facilities
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.
Koch Ag & Energy High Value Digitalization Deployments Leverages AWS
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.
Seeq Announces Expanded Microsoft Azure Machine Learning Support
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.
Seeq Accelerates Chemical Industry Success with AWS
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.
Seeq Announces $50 million Series C Funding Round led by Insight Partners
Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, announced today it has closed a $50 million Series C funding round, led by global venture capital and private equity firm Insight Partners. The round includes participation from existing investors Altira Group, Chevron Technology Ventures, Cisco Investments, Saudi Aramco Energy Ventures, and Second Avenue Partners. This round brings Seeq’s total funding since inception to approximately $115 million.
Seeq’s rapid growth is being fueled in part by its partnerships and commitment to cloud-based computing. Seeq is available in the AWS Marketplace and is an AWS Industrial Competency Partner. On Azure, Seeq has been available in the Azure Marketplace since 2019 and was recently recognized as a 2020 Microsoft Energy Partner of the Year Finalist. In addition to cloud partnerships, Seeq connects to an extensive set of automation vendor data storage platforms for on premise engagements including OSIsoft, Siemens, GE, Honeywell, Emerson Automation Solutions, Inductive Automation, AVEVA, AspenTech, Yokogawa, and others.
Survey: Data Analytics in the Chemical Industry
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.