Seeq
Canvas Category 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.
Assembly Line
Seeq and AspenTech accelerate self-service industrial analytics on AWS
With Seeq powered by the wealth of data stored in IP.21 running on AWS, you can clean, perform calculations on, and analyze IP.21 data—including context from relational data sources such as MES, batch, and other applications—to diagnose and predict issues and share findings across the organization. With NRT expert collaboration and deeper insights, Seeq helps organizations advance toward their sustainability and operational excellence goals. By tapping into rich data from IP.21, Seeq helps substantially reduce maintenance costs and minimize downtime. You can set up advanced workflows like ML with data-driven, state-of-the-art methods already proven in critical industries using the Seeq SaaS platform in conjunction with the AWS Cloud. The Seeq SaaS solution is listed on AWS Marketplace, making it easier to procure, deploy, and manage your workload.
📊 Simplify and Accelerate IoT Data-Driven Innovation
Databricks is thrilled to announce strategic partnerships to deliver specialized expertise and unparalleled value to the industry. These partnerships allow companies to simplify access to complex datasets, generate actionable insights and accelerate the time to value with the Lakehouse platform.
Seeq, a global leader in advanced analytics for the process manufacturing industries, delivers self-service, enterprise SaaS solutions to accelerate critical insights and action from historically unused data. Sight Machine enables real-time data-driven operations for manufacturers to achieve breakthrough performance by continuously improving profitability, productivity, and sustainability. Kobai delivers unparalleled semantic capabilities to unify operational and enterprise data and empowers all users to make better decisions and drive operational excellence. Companies across the Fortune 500 leverage Plotly’s powerful interactive analytics and visualization tools to build and scale production-grade data apps quickly and easily.
Predictive Maintenance for Semiconductor Manufacturers with SEEQ powered by AWS
There are challenges in creating predictive maintenance models, such as siloed data, the offline nature of data processing and analytics, and having the necessary domain knowledge to build, implement, and scale models. In this blog, we will explore how using Seeq software on Amazon Web Services can help overcome these challenges.
The combination of AWS and Seeq pairs a secure cloud services platform with advanced analytics innovation. Seeq on AWS can access time series and relational data stored in AWS data services including Amazon Redshift, Amazon DynamoDB, Amazon Simple Storage Service (S3), and Amazon Athena. Once connected, engineers and other technical staff have direct access to all the data in those databases in a live streaming environment, enabling exploration and data analytics without needing to go through the steps to extract data and align timestamps whenever more data is required. As a result, monitoring dashboards and running reports can be set to auto generate and are easily shared among groups or sites. This enables balancing machine downtimes and planning ahead for maintenance without disrupting schedules or compromising yields.
How to Build ML Apps in Seeq with ChatGPT
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.